Practical Manual. Introduction Geo information Science (GRS 10306) B. Kempen, W.TH. ten Haaf (Ed.)

Transcription

1 Laboratory of Geographical Information Science and Remote Sensing Centre for Geo Information Introduction Geo information Science (GRS 10306) Practical Manual B. Kempen, W.TH. ten Haaf (Ed.) September 2010

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3 GENERAL INTRODUCTION This manual contains the software instructions and exercises of the practical part of the course 'Introduction Geo-Information Science' (GRS-10306). The main objectives of this practical course are: 1. to illustrate basic GIS-terms which are connected to data processing by means of exercises; 2. to understand the potential of GIS use in analysis and design processes. After this course the student will be capable: to apply basic GIS concepts in a simulated environment; to carry out data capture, data processing and data presentation; to define the three classes of data handling in GIS; to name and apply specific data handling class functionalities; to explain the way data structure is affecting these operations; to carry out basic display and processing techniques for remote sensing images; to describe the possibilities and limitations of GIS-software; to create data-action models; to solve a spatial problem using GIS and Remote Sensing software based on a data-action model. The practical manual The practical manual contains eleven modules. After this introduction there are ten modules dealing with basic GIS topics using the software package ArcGIS and one module dealing with Remote Sensing topics using the software package Erdas Imagine. Each module is divided into sections that treat different aspects of one topic. Each section contains some background information followed by software instructions and one or more exercises to practice the theory and to test whether you understood the subject. By the end of this practical you will be able to use GIS and Remote Sensing software as tools to help you work on a practical case study. Although some theoretical background is given in each module, this manual is not meant to be a lecture textbook, so for explanations about GIS-concepts we refer to the lectures (PowerPoint presentations are available on the course website) and the lecture book and reader (see below). The practical must be regarded as an extension of the lectures in which GIS and Remote Sensing theory is clarified by means of exercises using the software packages ArcGIS and Erdas Imagine. Theory For the theoretical part of the course 'Introduction Geo-Information Science' (GRS-10306) the following lecture notes are used: Introduction to Geographic Information Systems K.T. Chang, 2009, 5 th edition, McGraw-Hill. Remote Sensing reader J.G.P.W. Clevers (Ed.). PRACTICAL MANUAL GRS GENERAL INTRODUCTION

4 Website More information about the course 'Introduction Geo-Information Science, you can find on the following website: The website contains course information, a course schedule, PowerPoint presentations of the lectures, datasets for the practicals, examination information and marks and a link to the digital versions of the modules including the answers to the exercises. Practical rooms The practical part of the course is scheduled in the practical rooms PC37 or PC38 on the second floor of the GAIA building or in several PC-rooms in Forum. Desks and chairs are adjustable to prevent RSI symptoms. Adjustment instructions for desks and chairs are attached to the doors of the rooms. Food and drinks are prohibited while working behind the computers. Practical room in the Gaia building (PC37). Computer use All computers use the operating system Windows XP professional. You can only log in on the computer if you have a WUR account. You have to work on the same PC during the course, since data is stored on the hard drive of a particular computer and not on your personal (M) drive. Practical work is done individually, in couples of two or groups of four participants. PRACTICAL MANUAL GRS GENERAL INTRODUCTION

5 Printing facilities In GAIA printers are located in the B and C wings. In Forum they are located in the corners of the building. You can only print when you have credit stored on your chipknip card and when it is linked to your WUR account. Software All the software you are working with during this course is already installed on the C-drive of the pc. You will work with the software packages ArcGIS and Erdas Imagine. Installing data Before starting with the first module of the practical, you have to have to install data on your pc. You can access and download data via the website. The data are packed in ZIP files. Click the link Course materials, subsequently click Download practical data and following the instructions on the website. The data are will be automatically stored at the correct location on the D-drive of your pc. Do not change folder structure and names!!! IGI folder structure. The Morning/Afternoon folders contain two folders; one with all ArcGIS related data and one with all Erdas Imagine related data. In the ArcMap documents folder all ArcMap documents are stored. In each module you will work with one or more documents. The datasets you will work with are stored in the Data folder. In the Workspace folder you will store all datasets you will create during the practical so you will not change the original datasets. PRACTICAL MANUAL GRS GENERAL INTRODUCTION

6 PRACTICAL MANUAL GRS GENERAL INTRODUCTION

7 TABLE OF CONTENTS 1. GETTING TO KNOW ARCGIS INTRODUCTION 1-1 WORKING WITH ARCCATALOG 1-2 The ArcCatalog application window 1-2 Catalog tree 1-2 View window 1-2 Menu bar 1-2 Toolbars 1-2 Start the program ArcCatalog and access spatial data 1-3 Examining spatial data 1-3 WORKING WITH ARCMAP 1-7 The ArcMap application window 1-7 Table of contents 1-7 ArcToolbox 1-7 View window 1-8 Menu bar 1-8 Toolbars 1-8 Start the program ArcMap and open an ArcMap document 1-8 Working with data frames, datasets (layers) and tables 1-9 Activate a data frame and turn the visibility of a dataset on and off 1-9 Adding datasets 1-10 Working with layer files 1-10 Zooming in and out 1-12 Viewing a dataset s attribute table 1-13 Symbolizing spatial data 1-14 Single symbol 1-15 Unique values 1-15 Graduated colors/symbols 1-15 USING ARCGIS DESKTOP HELP 1-17 Help topics 1-17 Help for tools, dialog boxes, windows and menu commands 1-17 Online help DATA STORAGE: DIGITIZING AND DATA STRUCTURE INTRODUCTION 2-1 DATA STRUCTURE OF A VECTOR DATASET 2-2 Creating a point dataset 2-2 Adding attributes to point features 2-4 Calculating the area of polygon features 2-6 Creating a new table 2-7 Joining tables 2-8 DATA STRUCTURE OF A RASTER DATASET 2-10 Discrete vs. continuous rasters 2-10 Zone vs. region in raster 2-11 PRACTICAL MANUAL GRS TABLE OF CONTENTS

8 3. MAP PROJECTIONS INTRODUCTION 3-1 From the Earth s surface to a 3D reference surface 3-1 From a 3D reference surface to a 2D map projection plane 3-1 PROJECTING SPATIAL DATASETS 3-3 Defining a projection 3-3 Reprojecting spatial datasets 3-5 PROJECTION OF THE DATA FRAME 3-7 On-the-fly projection 3-7 Map unit and coordinate system of the data frame 3-7 GEOMETRIC DISTORTIONS MAP PRESENTATION INTRODUCTION 4-1 USING THE SYMBOLOGY EDITOR TO SYMBOLIZE DATA 4-2 The symbology editor 4-2 Map types and data scales STEPS TO PRESENT A MAP 4-5 Preparing the map legend (steps 1-2, 4-9) 4-7 Laying out and printing maps (steps 3 & 10-12) 4-9 Creating a layout 4-9 Adding a data frame to a layout 4-9 Adding a legend to a layout 4-10 Adding a north arrow to a layout 4-10 Adding a scale bar to a layout 4-10 Adding text and other graphics to a layout 4-11 Exporting and printing a map 4-11 ADDING GRAPHICS, TEXT AND LABELS TO A MAP 4-13 Adding and modifying graphics 4-13 Adding and modifying text 4-14 Adding and modifying labels QUERIES INTRODUCTION 5-1 TABULAR DATASET QUERIES 5-2 Selecting features by using a query expression 5-2 Sorting attributes 5-3 Refining a query 5-3 SPATIAL DATASET QUERIES 5-5 Setting selectable datasets 5-5 Features selection by cursor 5-5 Feature selection by graphic element 5-7 Feature selection by spatial relationship 5-8 Selecting features by their proximity to other features 5-8 Selecting features that fall within polygon features 5-9 Selecting the nearest features to other features using spatial join 5-9 SAVING YOUR SELECTION INTO A NEW VECTOR DATASET 5-11 PRACTICAL MANUAL GRS TABLE OF CONTENTS

9 RASTER DATA QUERIES 5-12 Selecting raster cells by their value 5-12 Selecting and exporting raster cells using the CON tool TRANSFORMATIONS INTRODUCTION 6-1 PART 1: GEOREFERENCING AN IMAGE 6-2 Overall procedure 6-2 Georeferencing in ArcMap 6-4 Image registration: obtaining control points 6-4 Geometric transformation and resampling 6-7 Validation 6-9 PART 2: DATASET STRUCTURE TRANSFORMATION 6-10 Transforming datasets from vector to raster 6-10 Weight tables to determine the cell value after transformation 6-10 Vector-raster transformation in ArcMap 6-13 Transforming datasets from raster to vector 6-15 Raster-vector transformation in ArcMap 6-15 Changing the cell size of a raster dataset RASTER OPERATIONS INTRODUCTION 7-1 LOCAL OPERATIONS 7-2 Mathematical functions and operators 7-5 Raster overlay 7-7 FOCAL OPERATIONS 7-8 Focal (neighborhood) statistics 7-9 ZONAL OPERATIONS 7-11 Zonal statistics 7-11 Zonal geometry 7-12 GLOBAL OPERATIONS 7-13 Euclidean distance & Buffers 7-13 Creating raster data subsets 7-14 Reclassification of the Euclidean distance raster VECTOR OPERATIONS INTRODUCTION 8-1 FIELD CALCULATIONS 8-2 VECTOR OVERLAY 8-3 Topological overlays 8-4 Intersect 8-4 Union 8-4 Identity 8-4 PRACTICAL MANUAL GRS TABLE OF CONTENTS

10 CALCULATING ATTRIBUTE STATISTICS 8-7 Statistics function 8-7 Summarize function 8-7 BUFFERING VECTOR FEATURES 8-9 CREATING VECTOR DATA SUBSETS SURFACE ANALYSIS INTRODUCTION 9-1 CREATING A DEM FROM POINT OBSERVATIONS 9-2 Spatial interpolation 9-2 Inverse Distance Weighted (IDW) 9-3 Spline 9-4 Spatial interpolation in ArcMap 9-4 ANALYZING SURFACES 9-8 Slope analysis 9-8 Slope gradient calculation in ArcMap 9-8 Slope aspect calculation in ArcMap 9-9 Contour mapping DIGITAL IMAGE PROCESSING INTRODUCTION 10-1 START THE PROGRAM ERDAS IMAGINE 10-2 PART 1: DISPLAYING AN IMAGE DATA FILE 10-3 Display of DN-range (no stretch) 10-3 Display after linear stretch of DN-range minimum... maximum 10-6 Display after linear stretch of DN-range Display after standard deviation stretch 10-7 Display of color composites 10-8 PART 2: SUPERVISED CLASSIFICATION 10-9 Examining land cover types using spectral profiles 10-9 Digitizing training areas & estimation of signatures Collecting signatures Evaluating Signatures Land cover classification Minimum distance classification Maximum likelihood classification Updating a color palette Exporting to ArcGIS file format LGN Database IMAGE SOURCES RELATED INTERNET SITES REFERENCES PRACTICAL MANUAL GRS TABLE OF CONTENTS

11 Introduction Geo-Information Science Practical Manual Module 1 Getting to know ArcGIS

12 1. GETTING TO KNOW ARCGIS INTRODUCTION 1-1 WORKING WITH ARCCATALOG 1-2 The ArcCatalog application window 1-2 Catalog tree 1-2 View window 1-2 Menu bar 1-2 Toolbars 1-3 Start the program ArcCatalog and access spatial data 1-3 Examining spatial data 1-3 WORKING WITH ARCMAP 1-7 The ArcMap application window 1-7 Table of contents 1-7 ArcToolbox 1-8 View window 1-8 Menu bar 1-8 Toolbars 1-8 Start the program ArcMap and open an ArcMap document 1-8 Working with data frames, datasets (layers) and tables 1-9 Activate a data frame and turn the visibility of a dataset on and off 1-9 Adding datasets 1-10 Working with layer files 1-11 Zooming in and out 1-12 Viewing a dataset s attribute table 1-13 Symbolizing spatial data 1-14 Features: Single symbol 1-15 Categories: Unique values 1-15 Quantities: Graduated colors/symbols 1-15 USING ARCGIS DESKTOP HELP 1-17 Help topics 1-17 Help for tools, dialog boxes, windows and menu commands 1-17 Online help 1-17

13 1. GETTING TO KNOW ARCGIS Introduction People have used maps for thousands of years to present and analyze geographic information. The ArcGIS software package is one of the latest extensions to this ancient tradition. The package is developed by the Environmental Systems Research Institute (ESRI), one of the world s leading companies in the field of geographic information system (GIS) technology. ArcGIS is not a GIS program itself. ESRI describes the software package as an integrated collection of (more than fifteen) GIS software products with which the user or company can build a complete GIS. During this course you will get acquainted with three of these ArcGIS software products. These are the desktop applications ArcCatalog, ArcToolbox and ArcMap. You can find more information about the ArcGIS software package on the ESRI website: ArcCatalog The ArcCatalog application helps you to organize and manage all your spatial datasets. It includes tools that allow you to explore, find and view your geographic information in the form of maps or tables. You can replace, rename or delete your spatial datasets and view, create and edit the metadata that is linked to your data. Warning: NEVER use the Windows Explorer for geodata management, e.g. renaming, replacing or deleting your geodata. Instead, use ArcCatalog for these purposes. ArcToolbox The ArcToolbox application offers you a comprehensive set of GIS tools for geoprocessing and analyzing your spatial data. Some common geoprocessing tasks include: Converting data (such as converting a vector to a raster dataset); Overlaying data (by merging or intersecting datasets); Extracting data (by clipping a subset of data or selecting data with certain characteristics); Finding what's nearby (by buffering data or finding points near other features); Managing data (by joining tables, copying datasets, or creating new datasets). ArcToolbox is not a stand-alone GIS application; it is integrated in both ArcCatalog and ArcMap. You can only access ArcToolbox via these two programs. ArcMap ArcMap is the central GIS desktop application of the ArcGIS software package. ArcMap gives you the power to access, visualize, edit and process geographic data stored in various data formats. During this course you will mainly work with data in shapefile format (*.shp), ArcMap s own data type for vector datasets. It is also possible to create your own geographic data in form of maps. Once you have made the map you want, it is easy to add tabular data, such as dbase files and data from database servers, to your map so that you can display, query, process and organize your data geographically. In fact, while ArcMap lets you create great maps to show off your data, you will find the software s true power lies in how easily it enables you to solve simple problems by uncovering and analyzing trends and patterns. In this module: Exploring datasets and metadata in ArcCatalog. Working with data frames, layers and tables in ArcMap. Symbolizing your data: choosing the correct map type. Getting help from ArcGIS extensive help system. ArcMap Document: Intro_AM.mxd Literature: Chang, 2010: Chapter 1 MODULE GETTING TO KNOW ARCGIS

14 Working with ArcCatalog The ArcCatalog application window The ArcCatalog application window (Figure 1) is built up using different components: the Catalog Tree (1), the View Window (2), the Menu bar (3) and an adjustable Toolbar (4) Figure 1. ArcCatalog s application window. Catalog tree (1) The catalog tree works the same as the folder tree in Windows explorer. It allows you to browse through the folders on your computer to find your stored spatial data. Besides the data on your own computer you can also access GIS servers and databases on the web or a network if you have established a connection to these data sources. If you have not, you can add a connection to a GIS server or database with ArcCatalog. View window (2) The view window contains three viewing tabs. The Contents tab lists the items of files or folders such as maps or tables. The view window of Figure 1 shows the content of the folder D:\IGI\Morning\ArcGIS\Data. With the Preview tab you can examine the geographical and tabular data of a selected dataset. You can use the Metadata tab to access the metadata (data about the data) of the selected dataset. Menu bar (3) This bar along the top of the ArcCatalog s window contains ArcCatalog s pull down menus. To choose a function from one of the pull down menu, you can use the mouse or a keyboard shortcut. Some keyboard shortcuts are listed in the menus. MODULE GETTING TO KNOW ARCGIS

15 Toolbars (4) These bars, located beneath the menu bar in the ArcCatalog window, contain buttons that give you quick access to various controls and tools. Note that there are buttons to start ArcMap and ArcToolbox. With the tools of the Geography toolbar you can easily explore geographic datasets in the view window. You can add toolbars via the View pull down menu in the Menu bar. Start the program ArcCatalog and access spatial data INSTRUCTIONS: Start ArcCatalog. Click start, select Programs ArcGIS ArcCatalog. 2. When the ArcCatalog opening banner disappears, you ll see the ArcCatalog application window. 3. Select the folder in the catalog tree that contains your data. Now your datasets are displayed in the view window. Make sure that the Contents tab is selected. Start ArcCatalog and select the folder that contains your spatial data in the catalog tree D:\IGI\...* \ArcGIS\Data (*morning or afternoon). Set the view window setting to Contents. a. Write down the different file types of this folder. b. The shapefile is ArcMap s storage type for vector datasets. In both the catalog tree and the view window you see an icon in front of each dataset name. There are three different icons related to the shapefile storage type (check this). What do these icons represent? Examining spatial data Once you have located your datasets on your computer or a network, you can examine your data with ArcCatalog s preview function. You can view your spatial data in the form of maps and tables that contain the thematic data of your dataset. Besides the viewer functionality, ArcCatalog offers you some tools to analyze and edit the tables. INSTRUCTIONS: 1. Select a dataset in the catalog tree. 2. You can view the dataset by selecting the Preview tab in the view window. 3. At the bottom of the view window a dropdown menu appears. You can examine the selected dataset in either Geography preview (default setting) or Table preview. 4. Click the Preview dropdown arrow, choose Geography preview. A map is drawn in the view window that contains each feature in a vector dataset, each cell in a raster dataset and each triangle in a TIN dataset. When you are viewing data in Geography preview, the Geography toolbar is active. You can explore the map using the buttons on the Geography toolbar. MODULE GETTING TO KNOW ARCGIS

16 5. Choose Table preview from the dropdown menu to view all rows, called records in ArcGIS, and columns, called fields, and the value for each cell, called attribute values, in the selected dataset's table. 6. You can sort a table s records by attribute values in one or more fields, get statistics describing a field s values or delete a field by clicking on the column heading with the right mouse button. 7. Use the Options button in the lower right corner of the view window to locate a specific value in a table or to add a field to the table. Note: you cannot edit table attribute values in ArcCatalog. 2. Select the Netherlands dataset in the catalog tree. Set the view window setting to Preview. A map of the Netherlands appears showing all features of this dataset. Click the Preview dropdown arrow and choose Table view. a. How many records does the table contain? And how many fields? b. Add a field to the table by using the Options button. Enter capital in the Name field of the window. Choose Text from the dropdown menu of the Type field. Change the field length to 20. c. Delete the field you just added. d. Sort the province names (field: PROVNAME) ascending. How many records belong to the province of Noord-Brabant? e. What is the total area of the Netherlands? Hint: use the Statistics function by clicking on the column heading with the right mouse button. f. Which province contains a feature that has a perimeter of meters? Hint: use the Find function by clicking on the Options button. Uncheck search only selected fields. What is the value of the attribute FID of this feature? g. What does the attribute FID represents? Exploring the metadata of a spatial dataset Metadata is critical for sharing data and maps and for searching to see if the resources you need already exist. Metadata describes GIS datasets in the same way a card in a library s card catalog describes a book. Once you've found a dataset with a search, its metadata will help you decide whether it's suitable for your purposes. To make this decision, you may need to know how accurate or current the resource is and if there are any restrictions on how it can be used. Metadata can answer these questions. Any item in ArcCatalog, including folders and file types such as Word documents, can have metadata. Once created, metadata is copied, moved, and deleted along with the item when it is managed with ArcCatalog. MODULE GETTING TO KNOW ARCGIS

17 INSTRUCTIONS: 1. Select a dataset in the catalog tree. 2. You view the metadata belonging to the selected dataset by selecting the Metadata tab in the view window. 3. The metadata document contains three tabs, Description, Spatial and Attributes. You can select the tab you are interested in and browse through the content. 4. You can add or modify metadata by activating the Metadata editor toolbar. Click View in the menu bar and then Toolbar Metadata. The Metadata toolbar will appear in the application window. 5. Click the Edit Metadata button on the metadata editor toolbar. 6. The information in a metadata document is divided into seven sections (1), displayed across the top of the metadata editor. When you click a section title in the editor, several tabs appear that represent the different groups of metadata elements that are defined within the section (2) You can type or edit information in text boxes that are provided for most elements. 3. Select the Netherlands dataset in the catalog tree. Set the view window setting to Metadata. The metadata document belonging to this dataset appears in the view window. a. What kind of information is stored under each of the three tabs of the metadata document? b. Write down the attributes of this dataset. c. What is the data type of the attribute PROVNAAM? d. What are the coordinates of the most South-East part of the Netherlands (lower right corner)? MODULE GETTING TO KNOW ARCGIS

18 4. If you browse through the metadata of the Netherlands dataset you will notice that the metadata document is far from complete. You will now use the metadata editor to create some information that is missing. a. Click on the Description tab in the metadata document. An abstract for this dataset is still required (check this). b. Open the metadata editor. Click on the Identification section and select the General tab. Go to the Abstract text box and delete its red content. Now you can type your abstract, something like: The dataset supplies information about the 12 provinces of The Netherlands. Save your edits. c. Confirm the change in your metadata document. d. Keywords are very useful when you are searching for a dataset in a database on the web. The Netherlands dataset is lacking keywords in the metadata document. Check this. If keywords are present, they can be found under the Description tab. Use the metadata editor to add some keywords. Hint: the metadata element Keywords can be found in the Identification section. Save your edits. e. Again, confirm the change in your metadata document. f. Close ArcCatalog. MODULE GETTING TO KNOW ARCGIS

19 Working with ArcMap The ArcMap application window The ArcMap application window is built up using different components (Figure 2): the Table of Contents (1), ArcToolbox (2), the View Window (3), the Menu bar (4) and an adjustable Toolbar (5). By using the Window menu, you can change the components of the ArcMap application window Figure 2. ArcMap s application window. Table of contents (1) The table of contents (TOC) lists datasets present in an ArcMap session. This is shown in cascade fashion, with the primary slots (for instance Wag_south1, Figure 3) depicting the different Data Frames under which different geographical datasets are organized. Each dataset represents a particular type of geographical features, e.g. land use or roads (Figure 2). ArcMap is only capable of showing one data frame at a time, which has to be active to do so. The active data frame is depicted in bold type (Wag_south1, Figure 2). The properties of a data frame define the context for the data with which you work; these include the coordinate system, measurement units, scale, the drawing order of datasets, and so on. Data frames and the table of contents are the primary ways to interact with geographic data in ArcMap. Figure 3. Detail of the TOC. The dataset Land use is part of data frame Wag_south1. Links to the data frames and datasets of an ArcMap session can be conveniently stored in one file called an ArcMap Document (*.mxd). An ArcMap document is nothing more than a desktop screen of your computer. If you remove, add or change the way the data is presented (other colors in the legend) you don t alter or remove data sets. MODULE GETTING TO KNOW ARCGIS

20 ArcToolbox (2) ArcToolbox can be seen as the power center of ArcGIS, because ArcToolbox contains all the tools to create new geographical datasets and to do the geographical conversions, calculations and spatial analysis, i.e. do your geoprocessing. ArcToolbox can be opened and closed by using the ArcToolbox button in the toolbar. When the ArcToolbox window is initially opened (Figure 4), a list of available toolboxes (1) is displayed in the window. Each toolbox contains toolsets (2), which contain geoprocessing tools (3) that can be run. If you select a tool (e.g. intersect, see figure 4) a userfriendly dialog-driven interface will appear, the dialog box. You can enter the input en output datasets in text boxes as well as the required parameters for the processing tool you selected View window (3) In the view window interactive maps are drawn, which you can explore, query, and analyze. The view window in ArcMap is linked to the table of contents, making it easy to understand and control what is displayed. Note that the datasets in the table of contents are sorted by drawing order. Datasets at the top of the list are drawn on top of datasets lower in the list. Figure 4. The ArcToolbox tree structure. Menu bar (4) This bar along the top of the ArcMap s window contains ArcMap s pull down menus. To choose a function from one of pull down menus, you can use the mouse or a keyboard shortcut. Some keyboard shortcuts are listed in the menus. Toolbars (5) These bars, located beneath the menu bar in the ArcMap window, contain buttons that give you quick access to various controls and tools. The tool remains selected until you choose another one. You can add toolbars via the view pull down menu in the Menu bar Start the program ArcMap and open an ArcMap document An ArcMap Document is a file in which you store the work you do with ArcMap. A document contains or links to all datasets, tables, layouts, and scripts that you use for a particular ArcMap application or set of related applications. In this way, your work is stored in one convenient place. ArcMap documents have the.mxd extension, and are defined as an ESRI ArcMap Document. NOTE: An ArcMap Document itself is not data! It only links to data and presents data in a certain way! INSTRUCTIONS: 1. Start ArcMap. Click start, select Programs ArcGIS ArcMap. 2. When the ArcMap opening banner disappears, you ll see the ArcMap application window which says: Start using ArcMap with. Choose An existing map and select Browse for maps and click Ok. 3. Select the Document file you wish to work with. 4. When you close ArcMap or open a new ArcMap Document, don t forget to save your current ArcMap Document. To stop ArcMap: choose Exit from the File Menu. MODULE GETTING TO KNOW ARCGIS

21 5. a. Start ArcMap. Click An existing map, select Browse for maps, press OK and browse to D:\IGI\...* \ArcGIS\ArcMap documents (*morning or afternoon). Open the ArcMap document called Intro_AM.mxd. Warning: an ArcMap document does NOT store the actual spatial datasets contained in your table of contents. It only stores references to the location of the datasets contained in an ArcMap document, (such reference can for example be: D:\IGI\Morning\ArcGIS\Data\Landuse.shp). When you open a document, ArcMap looks for the datasets on the hard drive of the pc using these references. If it cannot find the dataset, for example when you have moved, renamed or deleted it with ArcCatalog, the dataset cannot be drawn in the view window; it has a broken link. You can immediately tell whether a dataset has a broken data link because it will have a red exclamation mark (!) next to its name in the table of contents and the check box next to the layer will be unavailable. If the data is temporarily unavailable, you can ignore the broken link and display the content of the ArcMap document without the dataset. The dataset will still be part of the map and listed in the table of contents; it simply will not display. However, broken links can be easily repaired. INSTRUCTIONS: Locate the layer with the broken link in the table of contents. 2. Right-click the layer and select Data Repair Data Source. 3. Navigate to the source dataset and click Add. 4. Click OK. 5. ArcMap repairs the link to the dataset. a. Repair the broken link of dataset Soil_types. The source dataset is stored as Soil_types.shp in folder D:\IGI\...* \ArcGIS\Data (*morning or afternoon). Working with data frames, datasets (layers) and tables In the view window datasets of geographic information of a particular area are displayed. Each dataset is a collection of geographic features, such as land use units or roads. Datasets are often referred to as layers in ArcMap. Layers are, in this context, visual representations of spatial datasets. However, layers do not necessarily refer to spatial datasets. In this practical manual, the ESRI term layer is therefore only used when referred to layer files (see section Working with layer files ), otherwise the generic term dataset is used to refer to digitally stored spatial data. All datasets present in an ArcMap document are listed in the table of contents, under one or more data frames. The table of contents also shows the symbols and colors used to draw the features of each dataset. Activate a data frame and turn the visibility of a dataset on and off The order of datasets within the table of contents determines the drawing order; a dataset is drawn on top of those below them. Thus, you have to put the datasets that form the background of your map, e.g. the ocean, at the bottom of the table of contents. Note that for a dataset to be visible in the view MODULE GETTING TO KNOW ARCGIS

22 window, the data frame that contains the dataset has to be active. The active data frame is highlighted in bold type (Figure 2). INSTRUCTIONS: To activate a data frame, right-click the data frame you wish to activate, and select Activate from to appearing context menu. 2. A dataset can be turned on and off by clicking the check box. 3. To add a data frame click Insert Data Frame in the menu bar. 4. You can rename the inserted data frame. Right-click the data frame and select Properties. The Data Frame Properties window opens. Select the General tab and type in the name of the data frame in the Name text box. The ArcMap document Intro_AM.mxd contains a number of data frames and datasets, which are listed in the table of contents. a. How many data frames does the document Intro_AM.mxd contain? b. How many datasets does data frame Wag_south1 contain? c. Turn the visibility of the datasets contained in the data frame on and off. d. Activate the data frame Wag_south2 to view the dataset LU_raster. e. Insert a new data frame. The inserted data frame is immediately activated. Name this data frame Soil and Land use. Adding datasets INSTRUCTIONS: Activate the data frame you want to add data to. 2. Click the Add Data button, browse to the directory where the dataset is stored. 3. Select the dataset you want to add to your data frame. 4. Click OK. The new layer appears at the top of the table of contents within the active data frame. ArcMap has chosen a default color for this dataset. 5. You can remove layers by right-clicking the layer you want to remove. Select Remove from the context menu. Activate data frame Soil and Land use. a. Add shapefile Landuse.shp, stored in D:\IGI\...* \ArcGIS\Data (*morning or afternoon) to the data frame Soil and Land use. MODULE GETTING TO KNOW ARCGIS

23 Working with layer files You can save datasets as layer files. Layer files do not store spatial datasets; they only reference spatial data contained in shapefiles or rasters stored elsewhere. This means that when you rename or delete a shapefile, the associated layer file cannot be displayed anymore because the stored link to the shapefile is broken. Layers store symbology display and labeling. They define how spatial data is drawn in the view window. For example, a layer might select specific cities from a shapefile, draw them as blue squares, and label them with text stored in a related table. Layers are not spatial datasets!! INSTRUCTIONS: Right-click the dataset in the table of contents you want to save as layer file. Click Save as Layer File from the context menu. Layer files have the.lyr extension. a. Activate the data frame Wag_south1. Add layer Landuse.lyr and dataset Landuse.shp to the data frame. You can see that the land use features of dataset Landuse.shp are displayed in single color. The layer file Landuse.lyr contains an appropriate symbology to display land use types. b. Open the Layer properties window of the land use layer file. Click the Source tab. What is the data source of this layer file? c. What does this mean when you change something in the attribute table of Landuse.lyr? To keep your table of contents organized you can group (thematically) related datasets in one data frame into a group layer. For example, suppose you have two datasets in a data frame, one representing railroads, the other highways. You might choose to group these datasets and name the resulting group layer transportation networks. Once you have grouped related datasets, you can rename the group layer and subsequently save it as a layer file, which you can add to other data frames or ArcMap documents. INSTRUCTIONS: 1. There are two ways to create group layers: Select the layers you want to group by clicking on the layers while holding down the CTRL key. A blue box appears around the layer name when it is selected. Right-click on one of the layers you have selected and choose Group from the context menu that appears. A new group layer appears in the table of contents in which the selected layers are organized. You can also create a group layer by right-clicking the data frame. Select New Group Layer. A new group layer appears in the table of contents. Now you can add layers to your group layer. If the layer you want to add to a group is already present in the table of contents, you can drag and drop it in the group layer. MODULE GETTING TO KNOW ARCGIS

24 2. To save a group layer as layer file right-click on the group layer and select Save as Layer File from the context menu. Layer files have the.lyr extension and can be recognized by the icon. 3. You can rename a group layer. Right-click the group layer and select Properties. The Layer Properties window opens. Select the General tab and type in the name of the group layer in the Name text box. Note that only the name as it appears in the table of contents is changed, NOT the name under which the layer is stored. 4. To ungroup the group layer, right-click the layer name and select ungroup from the context menu. 10. a. Activate the data frame Wag_south1. Group the layers Soil_points and Soil_types. Name the group layer Soil data and save the group layer as a layer file in the workspace directory. Name the layer file soil data. b. Create another group layer in data frame Wag_south1. Use this time the function New Group Layer. Name the layer Infrastructure. c. In the group layer you just created you want to organize datasets that are related to infrastructure. In this case these are the datasets Roads and Trails. These datasets are already present in your data frame so you can drag these layers to the infrastructure group layer. Save the group layer as a group layer file in your workspace. Name the layer file Infrastructure. d. Drag the group layer Soil data to the top of the table of contents. e. What has happened to the other layers in the data frame? Explain your answer. f. Turn off the visibility of the group layer Soil data. What happens with the visibility of the layers that are contained in this group layer? Explain your answer. g. Ungroup the group layers Soil data and Infrastructure. h. Activate the data frame Soils and Land use and add the layer file Soil data. i. Remove data frame Soils and Land use from your ArcMap document. Zooming in and out INSTRUCTIONS: 1. Click the Zoom In button or Zoom Out button on the Tools toolbar. 2. Move the mouse pointer over the map display and click once to zoom around a point. Alternatively, click and drag a rectangle defining the area on which you want to zoom in or out. The view redraws to show you the area of the rectangle you defined. MODULE GETTING TO KNOW ARCGIS

25 3. If you make a mistake, click the Zoom to Previous Extent button to go back to where you were before you zoomed in. 4. Click the Zoom To Full Extent button to zoom to the full spatial extent of all the layers in your data frame. 5. If you want to zoom out, use the 6. Use the Fixed Zoom In or Fixed Zoom Out buttons for stepwise zooming. 7. Click the Pan button to pan around the dataset. 11. a. Zoom in to the ponds in the top-left corner of dataset Landuse and zoom back to full extension. Viewing a dataset s attribute table Attributes describe the characteristics of geographical phenomena in a GIS. For example, attributes of a river might include its name, length and average depth. They are usually stored in a table and are linked to the spatial features of a dataset by a unique identifier. The attribute table is arranged so that each row, called RECORD, represents a feature and each column, called FIELD, represents one attribute. INSTRUCTIONS: Right click a dataset. 2. Click Open Attribute Table to open the attribute table. 3. For quick access to the attribute values of one particular feature, click the Identify tool on the Tools toolbar and click the feature in the view window whose attributes you want to examine. 4. The feature's attribute values are presented in the Identify Results window. Activate data frame Wag_south1. Open the attribute table of dataset Roads. a. 1. What feature type does this table contain? 2. How many attributes does this table contain? Write down the attributes of this table. 3. How many records does this table contain? b. Do the same for the datasets Soil_points and Soil_types. c. Describe in your own words what kind of information a record contains? d. Use the Identify tool to view the attributes of one of the features of the Soil_types dataset. Note that the attributes are displayed of the Top-most layer, which is the default setting. This means that when features from different datasets overlap, only the attribute information of the feature that is on top is displayed. You can change the default setting by clicking the dropdown arrow in Identify Results window. MODULE GETTING TO KNOW ARCGIS

26 13. Activate the data frame Wag_south2. Open the attribute table of dataset LU_raster. a. How many attributes does this table contain? Write down the attributes of this table. b. How many records does this table contain? c. What is the meaning of Value and Count? d. What is the size of one raster cell? Zoom in until you are able to distinguish individual raster cells. Use the Measure button on the Tools toolbar. Check your result by looking at the properties of LU_raster (Right-click the dataset, open the Properties window and select the Source tab). Symbolizing spatial vector data Symbolizing spatial data involves choosing colors and symbols to represent spatial features. The basic colors and patterns of the features can be changed by clicking on the dataset s legend symbol(s) in the table of contents, but when you want to change the map type, you need the symbology editor. The basic options of the Symbology editor (Figure 5) are described in this section. Note that this is a description for symbolizing vector datasets. Raster datasets have limited options for visualization! There are many ways to display the features of a dataset in a map. The choice of a map type depends on the nature of the spatial data (e.g. categorical, classes, quantitative) and what you want to show the user with your map. You can define the map type in the Show field of the symbology editor (box 1, Figure 5). The most important ones are: Features Single symbol Categories Unique values Quantities Graduated color Graduated symbol All the features in the dataset are drawn with the same color and symbol. This is useful when you only need to show where a dataset s features are located. Each unique value (class) of a dataset s attribute is displayed with a different color or symbol. This is the most effective method for displaying categorical data. For example, a land use map shows each unique land use type with one a specific color. The color of the features changes (e.g. from light red to dark red) according to the values of a particular attribute. This is useful when you want to map quantities or for showing data that is ranked. The size of the symbol representing a feature is drawn with changes according to the values of a particular attribute. This map type is the best way to symbolize data that expresses size or magnitude. Graduated symbol is only available for point and line data. MODULE GETTING TO KNOW ARCGIS

27 Figure 5. The symbology editor; where you can change the way your data is displayed. When one of these map types is chosen, which can be done by clicking on it, different sets of options become available. Features: Single symbol In case of the Single symbol map type, one color or pattern is assigned to all features contained in a dataset. Categories: Unique values Click the Value Field dropdown arrow (box 2, Figure 5) to select the attribute you want to display in your map. The central part of the symbology editor (box 4) contains three columns: Symbol, Value, Label and Count. Click a colored rectangle in the Symbol field to change the color. Or use a predefined color scheme by selecting a color ramp (box 3). The Value shows which attribute values are displayed by which color and symbol. In the Label column you can type a description for the features represented by the symbol, which is then legible in the legend next to the symbol in the table of contents. The Count gives the number of features or grid cells present within each symbol class. Within Categories Unique value is one of the options, other more advanced options are Unique values, many fields and Match to symbols in a style. Quantities: Graduated colors & Graduated symbols Click the Value dropdown arrow to select the attribute you want to display. In the Classification field you can define the number of classes, add class breaks and set class ranges. When you use Graduated colors you can choose a pre-defined color ramp to give a graduated color to the features in your map. If you prefer to use Graduated symbols, you can choose a symbol type by clicking the Template button. Once you have chosen the attribute that is to be displayed, you can adjust the symbol size range. The central part of the symbology editor contains three columns: Symbol, Range and Label. The Range defines the range of attribute values that fall within a symbol class. Within Quantities Proportional symbols is a more advanced symbology. MODULE GETTING TO KNOW ARCGIS

28 INSTRUCTIONS (example for map type Unique Values ): Double-click the dataset for which you want to change the symbology 2. Click on the Symbology tab, click Categories in the Show field and select Unique values. 3. Click Value Field dropdown arrow; choose the attribute that you want to display in the map. 4. Click the Add all values button and uncheck the <all other values> check box. 5. If you want to change the labels that will appear next to the symbols in the table of contents, type label text into the Label field. 6. You can choose a pre-defined color ramp, or double-click the rectangular colored box in the Symbol field to set the color of each attribute value manually. 7. Click Apply to redraw the map using your new legend. Activate the data frame Wag_south1. Open the Layer Properties window of dataset Soil_types and click the Symbology tab. a. Which map type is used to display dataset Soil_types? b. Display the attribute Soilname. Which map type did you choose? Explain your answer. c. Display the attribute ph of the Soil_points dataset. d. Which map type did you choose? Explain your answer. e. In which part of the study area are the highest ph values found? 15. Open the Layers properties window of dataset Roads. a. Give every road a different color based on the attribute STREETNAME. b. How many different street names does this dataset contain? c. Open the attribute table of dataset Roads. How many road features does the table contain? When the answer is different from the answer of 15.b, explain the difference. MODULE GETTING TO KNOW ARCGIS

29 Using ArcGIS Desktop Help ArcGIS is equipped with an extensive help system, the ArcGIS Desktop Help, which makes it easy to navigate the scores of topics and to follow the steps provided to guide you through specific tasks. The Help system also includes a glossary of ArcMap and GIS terms. Help topics To browse through the contents of ArcGIS Desktop Help, go to the menu bar click Help ArcGIS Desktop Help (or press the F1 key). Then click the Contents tab. Use the open button to expand the contents of a book; use the Display button to display a Help Topic. To search the Index of ArcGIS Desktop Help, click the Index tab. Type a keyword in the text box and the help system displays a list of index entries for that word. Choose the entry you want, and then click the Display button or double-click the entry to view the Help Topic for that entry. To search ArcGIS Desktop Help for a particular word, use the Search tab. Help for tools, dialog boxes, windows and menu commands A quick way to learn what your software application can do is to get help about the tools, buttons, menu commands, and dialog boxes that appear in the application window. For example, when you position the mouse pointer over a button or menu command, the name of the item pops up in a small box. Simultaneously, a brief description appears in the status bar at the bottom of the application window. You can also access additional help about any command, button, or control in the application. To get help on a command in a pull down menu or button in a toolbar, click the What's This tool on the ArcMap or ArcCatalog standard toolbar, then click the item. To get help on a command in a context menu (menu launched by right-clicking), highlight the command and press Shift + F1. To get help on a control in a dialog box, click the What's This? button at the top of the dialog box and click the control. On some dialog boxes there are also About or Help buttons, both of which provide additional Help information specific to the dialog box. To get help on a window, such as the table of contents or the Identify Results window, click inside the window, then press Shift + F1. Online help You can access additional online help resources provided by ESRI: the GIS dictionary and ESRI s support center. You can find shortcuts to the sites in the Help pull down menu in the menu bar. 16. a. Try to find out what the definition of a join is in ArcMap, using the ArcGIS Desktop Help system. b. Try to find out information about the ArcToolbox button using the What s This? button. MODULE GETTING TO KNOW ARCGIS

30 MODULE GETTING TO KNOW ARCGIS

31 Introduction Geo-Information Science Practical Manual Module 2 Data storage: digitizing and data structure

32 2. DATA STORAGE: DIGITIZING AND DATA STRUCTURE INTRODUCTION 2-1 DATA STRUCTURE OF A VECTOR DATASET Creating a point dataset Adding attributes to point features 2-4 Adding attribute values to point features 2-4 Calculating the area of polygon features 2-6 Creating a new table 2-7 Joining tables 2-8 DATA STRUCTURE OF A RASTER DATASET Discrete vs. continuous rasters Zone vs. region in raster 2-11

33 2. DATA STORAGE: DIGITIZING AND DATA STRUCTURE Introduction This module deals with spatial data storage in GIS. During this course you will mainly use the relational data model as a method of structuring data as collections of tables that are logically associated to each other by shared attributes. The first part of this module deals with data storage in vector which is based on tables. You will create a new vector dataset by digitizing points as a method of (secondary) data capture. Digitizing is the process of using a mouse to automatically store locations of geographic features by converting their map positions to series of x, y coordinates in computer files or database with an associated table. Subsequently, you will view, create and fill tables. In addition you will also join tables. The second part deals with data storage in raster. You will see that spatial data is structured differently in a raster environment. In this module: Creating a new dataset. Adding attributes to tables of vector data. Calculating the area of polygon features. Joining tables. Comparison of the vector and raster data structures. Objectives After having completed this module you will be capable: to create a new dataset using ArcMap; to create and fill a table using ArcMap; describe the different data types; to distinguish the difference between discrete and continuous rasters and between zones and regions; to understand the data structure of vector and rater datasets. ArcMap document: Data storage.mxd Literature: Chang, 2010: Chapter 3: sections 3.1 and 3.3 Chapter 4: sections 4.1, 4.2 (except 4.2.2, 4.2.7), 4.3.1, 4.3.4, 4.5 Chapter 8: all sections, except MODULE DATA STORAGE

34 Data structure of a vector dataset The vector data model is an object-based description of the real world. Geographic phenomena are represented as point-, line- or polygon features. A collection of features of one type with the same attributes and spatial reference (often referred to as feature class by ArcGIS) are stored in a vector dataset: the shapefile. For example the shapefile Landuse.shp contains polygon features that represent land use types. Attribute data associated to features are stored in tables. Each feature has one record entry in the table. Each field (column) describes a particular attribute. In the case of a shapefile the attribute data are saved in a dbase file (.dbf). Important note regarding dataset names! Never use blank spaces in the name of datasets! Use the underscore _ if you want to separate words. Avoid the use of symbols (e.g. +, -) in dataset names and try to limit the length of the name to characters. If you are limited to use of ESRI GIS software 12 is safe, if you also use other GIS software limit the length of the name to 10 characters to avoid interoperability problems. Creating a point dataset If your data consists of features too small to be depicted either as lines or areas, then you should create an ArcMap point dataset. Points represent discrete locations such as wells, shops, telephone booths etc. During the next exercise, you will create a dataset representing soil points you sampled in the southern part of Wageningen. First, you will have to digitize the locations of the points and subsequently fill the attribute table with attribute values. INSTRUCTIONS: 1. Open ArcToolbox, as described in the previous module. 2. Click Data Management Tools Feature Class and double click Create Feature Class (Figure 1). A dialog box opens. Fields marked with a green point have to be filled in before you run the tool. 3. Select a Feature Class Location for the new dataset. 4. Give the new dataset a name in the Feature Class Name field. 5. Select the Geometry Type (point) and press OK (leave the rest of the options on Default). 6. As you can see, a new but still empty point dataset is created. Figure 1. The tool to create a new 7. Click the Editor dropdown arrow in the Editor vector dataset toolbar (Figure 2), click Start Editing and select your workspace in the pop-up window. If the toolbar is not yet open, click View in the menu bar, point to Toolbars and tick Editor. 8. Click OK. 9. In the Editor toolbar, select the Task (create new Feature) and the Target (the file you want to edit). 10. Select the Sketch tool. Now it is possible to draw point features within the new dataset. Make sure the check box in front of the dataset in which you are editing is checked. 11. When you are finished, click the Editor dropdown arrow and click Stop Editing. DON T FORGET TO SAVE YOUR EDITS!!! MODULE DATA STORAGE

35 If you discover that a point is entered at the wrong location, you can move that point. You always can modify point features. 12. Click Start Editing. 13. Use the Pointer tool to select the point you want to move. To select a point you click on the point to be moved. Selection handles will appear around the selected features. 14. Move the selected point by dragging it to its new location. 15. When you are finished moving new points, click Stop Editing and save your changes. Figure 2. The Editor toolbar. Figure 3. Locations of soil point observations (X1 - X9). 1. Open ArcMap document Data storage.mxd. In this exercise you are going to create a new point dataset by digitizing the soil point observations as presented in Figure 3. Activate data frame Wag_south and display the datasets Soil_types and Roads. a. Create a new point dataset. Make sure that the Feature Class Location is your workspace D:\IGI\...* \ArcGIS\workspace (*morning or afternoon) and give the new dataset the name Soil_pts in the Feature Class name text box. b. Digitize the locations of the soil point observations as drawn in Figure 3 in order of profile code: start with point X1 and finish with X9! c. Open the attribute table of the new point dataset. How many records does the attribute table of dataset Soil_pts contain? d. Save your edits. MODULE DATA STORAGE

36 Adding attributes to point features In the previous exercise you digitized the locations of the soil points. No tabular data (attribute data) describing certain characteristics of these points have been added yet. When you create a new vector dataset in ArcMap, an attribute table is automatically created for this dataset. For each digitized point a record is automatically added to the attribute table. Initially the attribute table will contain three fields, called FID, ID and Shape. The FID field contains unique identifiers of the features in the vector dataset. The unique identifier links the thematic (attribute) data to the geometry of a geographic feature. These unique identifiers cannot be changed. In the ID field a user-defined identifier can be stored. The Shape field stores the feature type of the geographic feature (points, lines or polygons). This field is maintained by ArcMap and cannot be edited. You can add new fields to this table at anytime to store additional attribute data for the point features. When you create new fields in an attribute table you must select a data type for that field. The data type determines the kind of data (e.g. text, number) that can be stored in a field. ArcMap supports several data types, but the most important ones are: float, integer, text, and date. Important: NEVER use blank spaces and symbols in attribute names, only letters, numbers and underscores. Attribute names can only be 10 characters long. Adding and deleting fields should be done using ArcToolbox as described below. When adding or deleting fields or rows in a table, make sure that the table itself is NOT open and NOT in edit mode! INSTRUCTIONS: 1. Open ArcToolbox, select Data Management Tools Fields Add field. 2. Select the dataset you want to edit in the Input table field, give the field a Field Name, define the Field (data) type, define the width of the field in the Field Length in case of text, or the Field Precision (number of digits the field can contain) and Field scale (number of decimal places) in case of numeric values. 3. Click OK. 4. If you want to delete a field, select Data Management Tools Field Delete field. An alternative way of adding and deleting fields is using the Options button on the bottom of an attribute table for adding a field and right click in the field with the field name to delete this field. Adding attribute values to point features With the instructions listed before you could add new attributes. However, when you have a look at the table, you will see that the fields are empty. You should now start to add the content of the attribute table. INSTRUCTIONS: 1. Click Start Editing. 2. Open the attribute table, click the cell you want to edit and type the attribute value. To make sure that the right feature is being edited, select the feature (clicking on the left of the row, see the Figure 4) and see in the view window which feature lights up. 3. When finished, stop editing and save your edits. MODULE DATA STORAGE

37 2. a. Write down the meaning of the data types integer, float, text and date. Use the ArcGIS Desktop Help System to find the definitions. Hint: use the keyword 'add field in the Index field of the help. b. You are going to add six attributes to the table of the dataset Soil_pts : Prof_code, ph, Clay, Silt, Sand and Soilcode. Use the field definitions presented in Table 1. c. When you have defined the new fields of attribute table Soil_pts you can enter the attribute values according to Figure 4. When you have entered all attribute values stop editing and save your edits. Then save the ArcMap document. Table 1. Field definitions of the attributes of the digitized point features. Name Data (Field) Type Length Precision Scale Prof_code Text ph Float Clay Short Silt Short Sand Short Soilcode Text Important!! When you have saved a field definition (e.g. data type), it is not possible to change this field definition afterwards!! When you want to change the field definition afterwards, you have to delete the field and add a new field to the attribute table. Figure 4. The attribute table of vector dataset Soil_ points. MODULE DATA STORAGE

38 Calculating the area of polygon features In ArcMap you can calculate the area of the polygon features of a dataset. There are three ways to do this with ArcMap: with ArcToolbox, with the Field Calculator and with the CalculateGeometry option. This section contains instructions to make you acquainted with the last method. First of all you have to add an extra field to the dataset. After adding an extra field to the dataset it s possible to calculate the areas of all polygon features. INSTRUCTIONS (Calculate Geometry): 1. Activate the data frame that contains the dataset you want to calculate the area for. 2. Right-click the dataset you want to calculate the area for and click Open Attribute Table. 3. Click on the Options button in the right bottom corner of the attribute table and choose Add field. 4. Add a field to the attribute table of this dataset. Name the field Area (data type = double, precision = 10, scale = 2). 5. Right-click the field heading for this new area field and click Calculate Geometry. 3. a. Calculate the area of the polygon features of the dataset Soils. MODULE DATA STORAGE

39 Creating a new table Until now you ve seen how to add new fields and attribute values to a dataset s attribute table. Another way to get your data into ArcMap is to create a new, empty table that you can fill in yourself. If your tabular data is stored on the hard drive of your computer yet, creating a new table is a good way to get it into ArcMap. Creating a new table for your data is more flexible than simply adding new attribute information to the attribute table of an existing dataset as you did during the previous exercises. By putting your data in a separate table, you can work with it independently from any particular dataset, and you can join it to any appropriate dataset whenever you want it (see next section). INSTRUCTIONS: 1. Select Data Management Tools Table Create Table. 2. Select the output location (your workspace) and give the table a name in the output table field with extension.dbf. 3. Click OK. The table has now been created, but it doesn t appear in the table of contents. To make it visible, select the Source tab underneath the table of contents. When the table is opened, you can see that it contains two fields: Rowid, OBJECTID and FIELD1. 1. Adding fields to this table works the same as adding fields to the other datasets. You can add fields to your table at any time, so you don t have to add them all at once. 2. You can remove the FIELD1 field and OBJECTID field using the Delete Field option in the ArcToolbox: Data Management Tools Fields Delete Field. Note that a table must always contain two fields. So you can only delete attribute Field1 once you have added a new field to the table. 3. To add data to the table, click Start Editing and open the table. If you edit a table you should select the proper source. In this case the blue highlighted line in the figure below Figure 5. Choosing the correct source when start editing. 4. Add data to the table by clicking any blank cell in the table. 5. When you stop editing, save your changes. MODULE DATA STORAGE

40 Note: you cannot edit the Rowid value. ArcMap automatically assigns an Rowid value to a record once you fill in the first attribute value of your table. You can make further edits to a table at any time to change data values, add or delete fields, and add or delete records. Make sure that the table is not open and in edit mode when you add or delete fields!! 4. a. Create a new table called Landscape.dbf. Save it in your workspace directory. Add two fields to the new table: LU_CODE and LU_DESCRIP. The field definitions for these fields are presented in the table below. b. When you have defined the fields of table Landscape.dbf, you can enter the attribute values. Complete the new table according to Figure 6 and save this the edits. Table 2. Field definitions for the attributes of the landscape table. Name Data (Field) Type Length LU_CODE Text 2 LU_DESCRIP Text 25 Figure 6. Information of landscape stored in a table. Joining tables You can add tabular data to an existing dataset by joining it to its attribute table. When you join a table to a dataset s attribute table, all fields (attributes) from the join table are appended to the attribute table of the dataset. You can use any of these fields to symbolize, label, query, or analyze the dataset s features. A join is based on the values of a similar defined attribute that can be found in both tables. The name of the attribute does not have to be the same in both tables, but the data type and the attribute values have to be the same (Figure 7). INSTRUCTIONS: 1. To make join, go to ArcToolbox and click Data Management Tools Joins Add Join. 2. Select the dataset (Layer name) to which the table will be joined and select the field on which the join is based (Input join field). MODULE DATA STORAGE

41 3. Select the table which is going to be joined in the Join table field and select the Output join field on which the join is based. Click OK. 4. When the table of the dataset to which a table is joined is opened, it can be seen that all fields of the join table are appended into the dataset s attribute table. The fields appear at the right hand side of the table. 5. To remove the join, double click on the dataset name, select the tab Joins & Relates, select the join and click Remove, or Remove all in the Joins field. Figure7. Result of a join based on a common field (LU_CODE). 5. a. Join the fields of the table Landscape to the attribute table of dataset Soil_types by common field LU_CODE. b. Display a map based on the attribute landscape:lu_descrip. c. Remove the join. A second method to establish a link between attribute tables is with the Relate functionality. The difference with Join is that one attribute table is not appended to the other. Relate simply defines a relationship between two tables based on a shared attribute. You can only access related data by working with the attributes of a dataset. Relate is treated in more detail during the following-up course Geo-information Tools (GRS 20806). MODULE DATA STORAGE

42 Data structure of a raster dataset Until this moment, this module dealt with spatial data stored in a vector data structure. Another widely used data structure to store geographic information is raster or grid (Figure 8). Raster is a location-based data structure. Space is partitioned into a regular matrix of equally sized cells arranged in rows and columns (left part of figure 8). Each cell is given a value (right part of Figure 8) to correspond to the spatial characteristic in which it is located (e.g. elevation, soil or land use type), as opposed to a vector structure, which associates attributes with geographic objects. Figure 8. The raster data structure. Discrete vs. continuous rasters Depending on the information it represents, a raster dataset may be created out of either integer (whole numbers) or floating point (numbers with decimals) values. In ArcGIS, a raster dataset created out of integer (discrete or non-continuous) values can have an associated rastercell value attribute table. The unique attribute value combinations are saved in this table. Raster datasets created out of floating (nondiscrete or continuous) point values will not have associated tables. Discrete rasters represent geographic features that have definable boundaries, sometimes referred to as categorical or discontinuous data. Examples of discrete terrain object are: lake, forested land, buildings, roads etc. Continuous rasters represent geographic phenomena that vary spatially without discrete steps. Each cell value is a measure of the concentration or level of that location. Continuous geographic phenomena, in general, do not have distinct boundaries like discrete geographic features. A geographic feature, such as a lake, has a real and definable boundary. However a geographic phenomenon, like lake depth, continuously changes. Potentially, each cell in a continuous raster can have a different value. Examples of continuous data include contamination levels, heat from a fire, elevation, or a concentration diminishing from a source. Important: Rasters are always rectangular. Every cell location in a raster has a value assigned to it. When information is insufficient or unavailable for a cell location, the location will be assigned the value of NoData. NoData and 0 are not the same: 0 is a valid value that can be used in geoprocessing whereas NoData is excluded from geoprocessing. MODULE DATA STORAGE

43 INSTRUCTIONS: 1. Display the raster dataset in the view window. The colors are assigned to the raster cells based on the cell value. Each value is symbolized with one color. 2. Click on the Identify tool to identify a cell value. 3. Click on a cell in the view window. 6. Activate the data frame Vector vs. Raster and display the raster dataset LU_raster. a. Explain the meaning of the attributes of the dataset LU_raster as they are displayed in the Identify Results dialog. b. Is the raster dataset LU_raster an example of a discrete or a continuous raster? Why? c. Open the attribute table of LU_raster (right click on dataset). How many different values (i.e. land use classes) does the dataset of LU_raster contain? Zone vs. region in raster Raster cells that share the same value represent the same type of geographic object. Clusters of contiguous raster cells with the same value are called a region. A region represents a discrete geographic object, e.g. a building or a lake. All regions with the same value make up a zone (Figure 9). Zones represent all geographic objects with the same value, e.g. all buildings or lakes. Thus, the zones in thematic raster dataset Land_use are land use types. Note that a region is the raster equivalent of a vector point, line or polygon feature: a discrete object that represents one geographic feature. Figure 9. Raster cells belong to zones and regions. This raster contains five zones. The zone with value 4 is made up of three regions. MODULE DATA STORAGE

44 Of the two GIS data structures discussed in this module (raster and vector), the raster data structure provides the most comprehensive modeling environment and operators for spatial analysis. ArcToolbox contains a comprehensive tool set to perform cell-based (raster) operations. These tools can be found in the Spatial Analyst Tools toolbox and will be discussed in module a. Do you select a zone or region when you select one record in the attribute table of LU_raster? Explain your answer! 8. a. Compare the attribute tables of the datasets LU_raster and Land_use (Figure 10) and write down the main differences in data storage between the two tables. (1) (2) Figure 10. Attribute table in vector (1) and raster (2). MODULE DATA STORAGE

45 Introduction Geo-Information Science Practical Manual Module 3 Map projections

46 3. MAP PROJECTIONS INTRODUCTION 3-1 From the Earth s surface to a 3D reference surface 3-1 From a 3D reference surface to a 2D map projection plane 3-1 PROJECTING SPATIAL DATASETS 3-3 Defining a projection 3-3 Reprojecting spatial datasets 3-5 PROJECTION OF THE DATA FRAME 3-7 On-the-fly projection 3-7 Map unit and coordinate system of the data frame 3-7 GEOMETRIC DISTORTIONS 3-9

47 3. MAP PROJECTIONS Introduction The locations of spatial features on the Earth s surface are described by a three-dimensional coordinate reference system. The spherical reference system that is already in use for more than 200 years is known as the Geographic Reference System that describes locations on the Earth s surface by latitude and longitude (Lo and Yeung, 2002). If you want to produce a map of features on the Earth s surface, you need to transform the spherical surface to a flat map. This transformation from three-dimensional surface onto a two-dimensional map is called projection. Mathematical expressions convert data from the angular, geographical coordinate system of a sphere to a linear, orthogonal projected coordinate system of a flat map. This transformation requires several steps. From the Earth s surface to a 3D reference surface First, a mathematical three-dimensional reference surface, which models the Earth s physical shape, has to be defined: the ellipsoid. The ellipsoid is a smooth surface. The actual surface of the Earth, represented by the mean sea level (geoid), is not smooth. This means that there are always discrepancies between the ellipsoid and the mean sea level surface, the geoid. If you want to establish a 3D reference surface for a particular area (state, country, continent) you have to adjust the ellipsoid for these discrepancies so that the reference surface closely fits the actual surface of the Earth. A datum is an ellipsoid that is adjusted so that it matches the actual shape of the Earth of a particular region as well as possible. The datum serves as the 3D reference surface for calculating the geographic coordinates of a location. From the 19 th until halfway the 20 th century ellipsoids were only fitted to the Earth s shape over a particular country or continent (Snyder, 1987). These ellipsoids were determined by ground measurements. Datums based on these local ellipsoids fit the Earth s surface only in a particular area. Examples include the datum Amersfoort, which closely fits the Earth s surface in the Netherlands and datum NAD27, which fits to the Earth s surface in the North American continent. These datums are based on the Bessel 1841 and Clarke 1866 ellipsoids respectively. Since the space age, satellite-determined ellipsoids have become available that represent the closest fit of the entire surface of the Earth. The most widely used ellipsoid is WGS84. Because this is already the best approximation of the shape of the Earth no adjustment based on the difference between the ellipsoid and actual shape is needed. WGS84 is both ellipsoid and datum. Be aware that although global ellipsoids give a better overall approximation of the shape of the entire Earth than local ellipsoids, they do not generally give the best fit for a particular region (Snyder, 1987). This makes the WGS84 datum less suitable for regional mapping purposes. From a 3D reference surface to a 2D map projection plane By using a set of mathematical functions, the geographic coordinate system of a spherical (3D) reference surface (the datum) can be transformed or projected, to a projected coordinate system of a (2D) projection plane. The outcome of this transformation is a map projection, which can be defined as a systematic arrangement of parallels and meridians on a plane surface (Chang, 2006). Projected coordinate systems describe locations by Cartesian (X,Y) coordinates and linear map units. MODULE MAP PROJECTIONS

48 Map projections allow areas on the surface of the earth (spherical surface) to be represented on a map (flat surface). However, expressing a three-dimensional surface in two dimensions involves distortion of geometric properties shape, area, distance, and direction. No projection can preserve all these properties, although some combinations can be preserved, such as shape and direction (the Mercator projection). As a map maker, you must choose a map projection according to the properties you want to preserve in your map. This clearly depends on the purpose of your map. Remember that ArcGIS has an extensive help system that contains much information about projections and coordinate systems, which can help you to clarify concepts discussed in this module if necessary. In this module: Defining a projection for an unprojected dataset Reprojecting a dataset On-the-fly projection The effect of different map projections on geometric properties Objectives After having completed this module this part you will be capable: to understand the basic theoretical framework of map projections; to project and reproject datasets in ArcMap; to understand the influence of the data frame on map display in ArcMap; to describe the geometric distortions that are associated with different projections. ArcMap document: Map projections.mxd Literature: Chang, 2010: Chapter 2 Coordinate systems (except sections ) MODULE MAP PROJECTIONS

49 Projecting spatial datasets Defining a projection You can use the Define Projection tool when your dataset does not have a projection defined. If your dataset does not have a projection defined, the coordinate system will be listed as Undefined (Figure 1), in the box that displays the Source information of your dataset (Layer properties window). Figure 1. Under the Source tab you find information about the map projection of the dataset. If you define a projection, you can choose between two coordinate systems: Geographic coordinate system Spherical (3D) reference system. Locations described by latitude and longitude. Map units: angular (decimal degrees). Projected coordinate system Planar (2D) reference system. Locations described by an X and Y coordinate. Map units: linear (meters, miles etc.). If your dataset has a geographic coordinate system but no project coordinate system, you can still display it on your map. ArcMap draws the data by simply treating the latitude/longitude coordinates as planar (x,y) coordinates. INSTRUCTIONS: 1. Open ArcToolbox. Click Data Management Tools Projections and Transformations Define projections. 2. Click the Input Dataset or Feature Class dropdown arrow and select the unprojected dataset. 3. Click the button next to the Coordinate system box. The Spatial Reference Properties window opens. Click Select. You can choose between geographic and projected coordinate systems. 4. Browse to the coordinate system you want to assign to the dataset. Click Add. 5. Click Apply and OK. The Spatial Reference Properties window closes. 6. Click OK to run the tool. MODULE MAP PROJECTIONS

50 1. Open ArcMap document Map projections.mxd. Activate data frame Unprojected. a. Open the Layer properties of dataset Netherlands_rd. Click the source tab and confirm this dataset does not have a projection. b. Project this dataset. Choose the projection Rijksdriehoekstelsel, the Dutch reference system. This is a projected reference system. The projection file is located in the National Grids folder. c. Open the Layer properties window of dataset Netherlands_rd. The source information contains now a list with projection parameters (Figure 2). Confirm this dataset references locations using a projected coordinate system. On which geographic coordinate system (datum) is this projected coordinate system based? d. What is the unit of the projected coordinate system? Figure 2. When a dataset has a map projection defined, projection parameters can be found under the source tab. MODULE MAP PROJECTIONS

51 Reprojecting spatial datasets When you collect datasets from different sources to build a GIS application you will often end up with datasets that have different map projections. With the Project tool you can change the map projection of your dataset. The tool creates a new output dataset with the newly defined coordinate system, including datum and ellipsoid. INSTRUCTIONS: 1. Open ArcToolbox. Click Data Management Tools Projections and Transformations Feature Project. 2. Select the Input Dataset or Feature Class you want to reproject. 3. Define name and location of the output dataset (the reprojected dataset). 4. Click the button next to the Output Coordinate system box. The Spatial Reference Properties window opens. Click Select. You can choose between geographic and projected coordinate systems. 5. Browse to the coordinate system you want to assign to the dataset. Click Add. 6. Click Apply and OK. The Spatial Reference Properties window closes. 7. Click the Geographic Transformation dropdown arrow and select a transformation. This is only necessary when there is a datum conversion involved. For example, when the input datum is Amersfoort (in case of the projection Rijksdriehoekstelsel ) and the output datum is WGS84 (in case of the Mercator projection). 8. Click OK to run the tool. 2. In this exercise you will use the Project tool to transform the map projection of dataset Netherlands_rd from Rijksdriehoek ( RD ) to UTM and Mercator. a. Change the map projection from RD to UTM. - Give the new dataset the name Netherlands_utm and save the dataset in your workspace directory. - Use projection file: Projected coordinate systems \ UTM \ WGS84 \ WGS 1984 UTM Zone 31N. - Click the Geographic Transformation dropdown arrow and select: Amersfoort_To_WGS_1984. b. Change the map projection from RD to Mercator. - Give the new dataset the name Netherlands_mercator, save the dataset in your workspace directory. - Use projection file: Projected coordinate systems\ World \ Mercator (world). - Click the Geographic Transformation dropdown arrow and select: Amersfoort_To_WGS_1984. c. Display the three datasets in the view window. Zoom to full extent. Why do the 3 datasets not overlap? Explain your answer. MODULE MAP PROJECTIONS

52 Table 1 gives the length, width and area of The Netherlands for three different projections. d. Explain the large difference in size and area between the Mercator projection and the other two projections. e. Which projection gives the best representation of the length, width and area of the Netherlands? Explain your answer. Table1. Geometric properties of the Netherlands for different map projections. Projection Length (km) Width (km) Area (km 2 ) RD ,749 UTM ,753 Mercator ,367 Figure 3. Some examples of different projections of the Netherlands, resulting in different locations. MODULE MAP PROJECTIONS

53 Projection of the data frame On-the-fly projection ArcGIS uses On-the-fly projection to display datasets that have different projections. This means that dataset projections are automatically displayed as if they have the same projection, so that datasets with different projections can be displayed within one data frame. For example, a country map based on the Mercator projection will overlap a map of the same country with the UTM projection, within a data frame. The user can visually compare and analyze and print maps without having to bother about projections. What determines the common projection? When you add a new data frame to an ArcMap document, the coordinate system of the data frame is not yet defined. The data frame takes automatically the coordinate system of the first dataset added to the data frame. If you choose to add more datasets after the first, then these datasets are displayed as if they have the same coordinate system as the data frame. For example, if the first dataset has the Mercator projection, then all other datasets present in the same data frame, are displayed as if they have the Mercator projection. Even if this dataset has another map projection defined! This means that datasets that represent the same area with different projections, still can overlap. Why did the datasets used in exercise 2 not overlap? The first dataset added to data frame unprojected (exercises 1 and 2) was not projected, which means that the data frame remains unprojected (data frame coordinate system is set to Unknown) so that datasets with different projections are displayed at different locations in the view window. Remember about on-the-fly projection: The common coordinate system is by default the coordinate system of the first dataset that is added to an empty data frame!! Be aware that on-the-fly projection does not change the coordinate system of a dataset!! It only changes the way a dataset is displayed in the view window!! If the datasets to be used in spatial analysis have different coordinate systems, you should convert them to the same coordinate system in order to obtain the most accurate result and to avoid processing errors!! Map unit and coordinate system of the data frame The Data Frame Properties window contains several tabs under which various kinds of properties are stored. These property settings determine how your dataset is displayed and not the properties of the dataset themselves. The most important tabs in the context of map projections are General and Coordinate System. INSTRUCTIONS: 1. Right-click the heading of a data frame to open its properties window. 2. Click the General tab. In the Units frame you can set the map and display units. The map units (units of the coordinate system) are used to calculate geometric properties of the dataset such as area and perimeter of features and to derive the coordinates of a location. Display units are used for visual display. They are shown at the bottom of the view window. They are used if you use the Measure tool to measure distances in the map. 3. Click the Coordinate System tab (Figure 4). The top frame shows the current coordinate system of the data frame. This coordinate system is used to project all datasets present in the data frame. If you want to change the coordinate system of the data frame, select a projection MODULE MAP PROJECTIONS

54 from one of the folders in the bottom frame. Note that the new coordinate system applies to all datasets present in the data frame. Figure 4. The coordinate system of the data frame. 3. Activate data frame Unprojected. Zoom to full extent. Open the data frame properties window. Set the projection of this data frame to Rijksdriehoekstelsel. a. Explain what happened to the orientation of the datasets in the view window. Activate data frame Projected. b. Open the data frame properties window. What is the current coordinate system of this data frame? c. Check if the Display button of the Table of Contents is activated. Drag dataset Netherlands_mercator from data frame Unprojected to the empty data frame. What is the coordinate system of the data frame now? Add datasets World, Alterra and Cities to the data frame. Click OK when a Warning appears. d. Measure the distance between Alterra and Buenos Aires. Change the data frame projection to Robinson (Projected coordinate systems \ World \ Robinson). e. Measure again the distance between Alterra and Buenos Aires. Does the distance differ from the previous measurement? Explain your answer. MODULE MAP PROJECTIONS

55 Geometric distortions When you project a spherical surface on a flat plane, you will create distortions of the geometric properties area, shape, distance and direction. Different projections cause different types of distortions. We can distinguish four projection types: Conformal projections Conformal projections preserve local shape. To preserve individual angles describing the spatial relationships, a conformal projection must show the meridians and parallels intersecting at 90- degree angles on the map. Equivalent projections Equivalent (equal-area) projections preserve the area of displayed features. To do this, the other properties shape, distance and direction are distorted. In equivalent projections, the meridians and parallels may not intersect at right angles. Equidistant projections Equidistant maps preserve the distances between certain points. Distance is not maintained correctly by any projection throughout an entire map. Distance is often only true when measured parallel to the meridians. True-direction projections True-direction maps preserve direction, which means that direction measurements made on the ground are the same as direction measurements made on the map. Azimuthal (planar) projections are always true-direction. Conformal projections, such as the Mercator projection, are also truedirection. Figure 5 shows the effect of different projection types on the shape and size of The Netherlands. Table 2 gives projection details, the length, width and area of The Netherlands for each projection. Figure 5a represents The Netherlands in RD projection. This is a local projection, specially developed to represent The Netherlands on a flat map with minimal distortion. The RD projection is a planar projection which preserves shape and direction. Distance and area are distorted. However, because The Netherlands covers a relatively small area, distortions are very small (e.g. maximum miscalculation of distance is 10 centimeter per kilometer). We assume that the RD projection gives the most realistic estimation of the size and shape of The Netherlands. Figures 5b, 5d and 5f represent The Netherlands using conic projections designed for use on continental (European) scale level. Figures 5c, 5e en 5g represent The Netherlands using cylindrical projections designed for use on global scale level. Figure 5c shows a conformal projection (Mercator), designed for use on global scale level. The shape of the country is preserved. Size distortion is severe compared to the RD projection: the area increase is 268%, the length and width increase with 162%. Figure 5b shows also a conformal projection of The Netherlands, designed for use on continental scale level. The shape is preserved, length, width and area are still distorted but the distortions are less severe compared to the Mercator projection. Figure 5e (equivalent projection on global scale level) shows a deformed country: squeezed in northsouth direction and stretched in East-West direction. The area however, almost equals the RD area. Figure 5d shows The Netherlands using a continental equivalent projection. The area is almost identical to the area of Figure 5e, but shape and distances are better preserved. Figure 5g shows a global, equidistance projection of The Netherlands. The length of the Netherlands is given accurately. Shape and area are distorted. The country appears squeezed in east-west direction and the area is 19.5% smaller than the RD area. Figure 5f shows an equidistance projection on continental scale level. This example shows map projections are scale level dependent. This means that you have to be careful when projecting your dataset. If you working on local scale level, then do not choose a map projection that is designed for use on global scale level. Distortions can be severe. Second choose a map MODULE MAP PROJECTIONS

56 projection that does not distort a geometric property that you might need for your research. For example if you are doing research in which area is important (e.g. quantitative land use changes) make sure the map projection of your datasets is equivalent. a. b. c. d. e. f. g. Figure 5. The Netherlands represented in seven different projections. Table 2. Seven different projections of the Netherlands result in seven different distances and areas. Figure Projection type Projection class Projection Scale Preserved property Length (km) Width (km) Area (km 2 ) 5a True Shape, Azimuthal National direction direction ,749 5b Conformal Conic Continental (Europe) Shape, direction ,810 5c Conformal Cylindrical Global Shape, direction ,357 5d Equivalent Conic Continental (Europe) Area ,752 5e Equivalent Cylindrical Global Area ,751 5f Equidist. Conic Continental (Europe) 5g Equidist. Cylindrical Global Distance (in N- S direction) Distance (in N- S direction) , ,335 MODULE MAP PROJECTIONS

57 In the next exercise you will investigate how different projections of a world map influence distances between cities and the areas of countries. Use the Measure tool to measure distances between cities and the Identify tool to retrieve area information. 4. For this exercise you need data frames Mercator, Equivalent, Equidistance. Each data frame contains a world map with a different projection, the location of the Alterra building and the locations of the world s largest cities. When you retrieve area information make sure you use the F_Area field!!! Activate data frame Mercator. a. Measure the distances between: Alterra Johannesburg and São Paulo Jakarta. Retrieve the areas of the USA, the Dem. Rep. Congo (Zaire) and the (continental part of the )world. Write down your findings in Table 3. Active data frame Equidistant. b. Measure the distances between: Alterra Johannesburg and São Paulo Jakarta. Retrieve the areas of the USA, the Dem. Rep. Congo (Zaire) and the (continental part of the )world. Write down your findings in Table 3. Active data frame Equivalent. c. Measure the distances between: Alterra Johannesburg and São Paulo Jakarta. Retrieve the areas of the USA, the Dem. Rep. Congo (Zaire) and the (continental part of the )world. Write down your findings in Table 3. Table 3.Distance and area comparison using different projections. Mercator Equidistant Equivalent True dist./ area Alterra Johannesburg (km) 8,979 São Paulo Jakarta (km) 15,646 Area USA (km 2 ) 9,631,418 Area Dem. Rep. Congo (km 2 ) 2,345,410 Area World (km 2 ) 148,939, a. The equidistant projection gives the best estimation of the distance between Alterra and Johannesburg. But the distance between São Paulo Jakarta is heavily underestimated. The other two projections give a better estimation. Explain why. MODULE MAP PROJECTIONS

58 b. The equivalent projection gives the best estimation of the areas of the two countries and the world. The Mercator projection is known for its major size distortions (e.g. area USA, Table 3). But the size of the Democratic Republic of the Congo a close approximation of the true size It appears that the distortion is very small. Explain why. c. Try to explain the difference in distance between measured distance (of the Mercator and equivalent projections) and true distance of São Paulo Jakarta. 6. Activate data frame Geometric distortions. a. Indicate the main differences between the projections. b. Uncheck dataset World_eqdist. Click the symbol of dataset World_eqarea, select Hollow from the Symbol Selector. Set Outline Width to 2. Zoom in to the equatorial regions (Northern South America, Central Africa, Indonesia). Write down your findings. MODULE MAP PROJECTIONS

59 Introduction Geo-Information Science Practical Manual Module 4 Map presentation

60 4. MAP PRESENTATION 4-1 INTRODUCTION 4-1 USING THE SYMBOLOGY EDITOR TO SYMBOLIZE DATA 4-2 The symbology editor 4-2 Map types and data scales STEPS TO PRESENT A MAP 4-5 Preparing the map legend (steps 1-2, 4-9) 4-7 Laying out and printing maps (steps 3 & 10-12) 4-9 Creating a layout 4-9 Adding a data frame to a layout 4-9 Adding a legend to a layout 4-10 Adding a north arrow to a layout 4-10 Adding a scale bar to a layout 4-10 Adding a title to a layout 4-10 Adding text and other graphics to a layout 4-11 Exporting and printing a map 4-11 ADDING GRAPHICS, TEXT AND LABELS TO A MAP 4-13 Adding and modifying graphics 4-13 Adding and modifying text 4-14 Adding and modifying labels 4-14

61 4. MAP PRESENTATION Introduction Maps in ArcMap are based on spatial data. Spatial data refer to information about the locations and geographic features on the Earth s surface and the relationships between them, along with attribute information describing what these features represent. You can communicate complex information more effectively using maps than tables or lists, because maps take advantage of our natural abilities to distinguish and interpret colors, patterns and spatial relationships. When you display your data properly on a map you ll see spatial distributions, relationships and trends that you couldn t see before. Your maps will help you make decisions and solve problems. They also help to communicate your information and results more effectively to others. Choosing how to represent your data on a map may well be your most important mapmaking decision. Symbolizing your data involves choosing appropriate colors and symbols to represent features. This also involves grouping or classifying features according to their attributes and attribute values. In this module: Symbolizing your spatial data. Adding text and graphics to a map. Labeling a dataset s features. Graphic symbols and attributes (Bertin). The 12 steps to present a map. Objectives After having completed this module you will be capable: to use the 12 steps to present a map; to symbolize your data: choosing the correct graphic symbols and attributes; to create a complete layout of a map. ArcMap documents: Map presentation1.mxd Map presentation2.mxd Map presentation3.mxd Map presentation4.mxd Literature: Chang, 2010: Chapter 9 Data display and cartography Chapter 10: section 10.2 Map-Based Data Manipulation MODULE MAP PRESENTATION

62 Using the symbology editor to symbolize data Choosing how to represent your data on a map may well be your most important mapmaking decision. Symbolizing your data involves choosing the colors and symbols that will represent features. It also involves grouping or classifying features according to their attribute and attribute values. Symbolization is a powerful tool used to explore, understand and analyze your data. The symbology editor In module 1 the Symbology editor (Figure 1) was discussed. It might be wise to study the section Symbolizing your data again. You use the Symbology tab in the Layer properties window (see Module 1) to control how a dataset is drawn in the view window. When the symbology editor is opened, you can choose for each dataset a map type on basis of the type of information your map represents. ArcMap has various map types to choose from. In some cases like in Figure 1 the symbology editor gives you the option to label items in the legend. In this way the values in the legend are displayed different. For example you could rename the soils with the value Kalkhoudende ooivaaggrond for soilname to Calcareous ooi vague soil if you want to present the map to a English speaking audience. Figure1. The symbology editor; where you can change the way your data is displayed. MODULE MAP PRESENTATION

63 Features: All the features in a dataset are symbolized by a single symbol. Categories: Unique values of one field: displays categories/classes using values of one attribute (attribute = field); Unique values of many fields: displays categories/classes using unique attribute combination values up to 3 attributes; Match to symbol in a style: displays categories/classes by matching attribute values to symbols a symbols list in a style. Quantities: Graduate color: displays quantities using color scale according to an attribute with quantity values; Graduate symbol: displays quantities using a changing symbol size defined by a symbol size attribute; Proportional symbol: displays quantities using a changing symbol size defined by a symbol size attribute to show exact values; Dot density: displays quantities using dot densities according to attribute values. Charts: Pie: displays a pie chart according to multiple attributes for each feature; Bar/Column: displays a bar or column chart according to multiple attributes for each feature; Stacked: displays a stacked chart according to multiple attributes for each feature. Multiple attributes: Quantity by category: Draw quantities for each category/class of one or more attributes. 1. Open ArcMap document Map presentation1.mxd. Activate data frame Wag_south1 and open the Symbology editor of dataset Soil_types : a. Which map type is used to display dataset Soil_types? b. What does this color represent? MODULE MAP PRESENTATION

64 Map types and data scales INSTRUCTIONS: 1. Activate a data frame, check a dataset and open the Symbology editor (in the Layer Properties window). 2. Select a map type, and select which attribute(s) will be used to display the data and the legend from the Values drop down list. When you choose Categories unique values, don t forget to add all values and uncheck all other values. 3. To change the symbol, double-click the symbol in the Symbology editor. The Symbol Selector window appears (Figure 2). 4. The symbolgy selector offers a selection of predefined symbols. These can be altered by clicking on the Preview or on the Properties button. Figure 2. The Symbol Selector. 5. You can also select predefined symbol sets (e.g. for forestry, soils and geology) by clicking on the More Symbols button. Note: it is also possible to change the basic colors by clicking on the symbol in the table of contents. 2. In this exercise you will change the display of a dataset by choosing different map types. a. Create the following legend for dataset Soil_types : Map type: Unique value Value field: Area Is this map type a logical choice to symbolize the attribute area? Explain your answer. b. Change the map type to Graduated color ; choose for Value field Area. Explain why it is not possible to represent the attribute Soilcode with graduated colors. c. Choose an appropriate map type to symbolize the attribute Soilcode. d. Type short names for the soil features in the Label field of the Symbology editor. Set the colors for the different classes individually; choose your own colors and apply the legend to the map. MODULE MAP PRESENTATION

65 12 steps to present a map Creating and presenting a map is not as easy as it may seem. This section presents a guideline to help you when making a map presentation. The guideline is referred to as the 12 steps to present a map. First you will practice this on paper, checking a present map. Once you have done this you will apply it in ArcMap. The 12 steps are: 1. What purpose does the map serve? 2. What user group is aimed at? 3. What is the title of the map going to be? 4. What spatial components are to be displayed in the legend? 5. What is the hierarchy between the components? 6. What is the data scale of these components? 7. How many classes does each component contain? 8. Which graphic symbols can be used? 9. Which graphic attributes have to be included? 10. Add the North arrow 11. Add the Scale bar or map reference grid, 12. Add the data source, text, and graphics. Data scale (Step 6) Attribute values of features are either qualitative or quantitative. Data scales are: nominal: ordinal: interval: ratio: Qualitative attribute values are different in nature, without one aspect being more important than another (e.g. forms of land use). Qualitative attribute values are different from each other, but there is one single way to order them, as some are more important/intense than others (e.g. importance of roads). Quantitative attribute values are different and can be ordered. The distance between individual measurements can be determined (e.g. Celsius temperature scale). Quantitative attribute values are different and can be ordered. Distances between individual measurements can be determined and these individual measurements can be related to each other. They also have an absolute zero (e.g. number of visitors). Graphic symbols (Step 8) Spatial data represented in a map always refers to real world phenomena. These can be heights, measured at specific points, traffic intensities measured along a route network, or numbers of inhabitants living in an area. We distinguish point, line and area (polygon) features that refer to point, line and area locations. Graphic attributes (Step 9) All the differences imaginable between symbols can be summarized as being cases of six graphical variables (Bertin, 1983, Figure 3): size (grey) value grain / texture color hue orientation shape MODULE MAP PRESENTATION

66 Figure 3. Three graphic symbols and six graphic attributes. Graphic attributes cannot be used randomly. Each one is perceived in a different way by the map user and they vary in their perception properties. For example, a difference in color, orientation or shape will be interpreted as a difference in properties or quality. But a difference in size or (grey) value will be interpreted as a difference in the amount or quantity. The choice of any particular graphic attribute thus depends on the data scale of the attribute that is to be presented in the map. Figure 4 lists the graphic attributes that most suitably represent the various perception properties and shows the relationship between data scale and graphic attributes. DATA SCALE Figure 4. Relation between data scale and graphic attributes (Bertin). MODULE MAP PRESENTATION

67 Preparing the map legend (steps 1-2, 4-9) Before you start to create a map of your spatial data, you should think about the purpose and the user group of your map. For example, topographic maps used by the military differ from the topographic maps used by civilians because the military is interested in other kind of topographic information (e.g. width of roads) than civilians (road hierarchy). Furthermore, a topographic map is very useful for example a land use planner but not for a package courier. The topographic map is not primarily focused on roads, it contains too much distracting information to be useful for the courier. A map designed for children in primary school that shows the different types of land forms of The Netherlands should be more simple (in detail as well as terminology) than a land form map used by an environmental scientist. The way you present your spatial data in a map should reflect the purpose of the map and the corresponding user group. Data view vs. Layout view During the next exercises you will practice with the 12 steps to present a map approach with an existing map, made in ArcMap. ArcMap provides two ways to view a map: Data view Layout view Each view lets you look at and interact with the map in a specific way. When you want quick look your datasets, choose data view. You have been working in data view until now. Data view is an all-purpose view for exploring, displaying, and querying your data. This view excludes all the map elements on the layout, such as titles, north arrows, and scale bars, and lets you focus on your datasets destined for a single map data frame. When you're preparing your map to hang on the wall, put in a report, or publish on the web, you'll want to work with the layout view. In layout view, you'll see a virtual page upon which you can place and arrange map elements. You can do almost everything you can in data view, plus design your final map. INSTRUCTIONS: 1. You can switch between data view and layout view in the View menu in the menu bar. 2. Or use the two buttons in the lower left corner of the view window to switch between the two views. 3. Open ArcMap document Map presentation2.mxd. This document contains a soil map of the Wageningen South area, called Soil map Wageningen south. Set the view window to layout view. a. Give an example of the purpose and user group of this soil map. Based on the purpose and user group of your map, you decide which spatial components (datasets) your map (and thus your legend) should contain, e.g. roads, land use, soil types, and the hierarchy of the components in the legend. The legend of a soil map should begin with the soil types and not with secondary information as build-up areas or roads. The hierarchy in your legend differs from the hierarchy in your Table of Contents (TOC). The latter is used to draw the features in the correct order. As an example the soil map might include build-up area. This will probably be drawn on top of your soil map (highest element in your TOC). In your legend you should mention it as one of the last items. MODULE MAP PRESENTATION

68 Next, you determine the data scale of these components. This is important because the data scale determines the symbology (graphic symbol and attribute) that is appropriate to represent the spatial component(s). Continue with the number of categories/classes each component should contain. Should you distinguish highways from secondary roads, or not? If yes, the road component values highway and secondary road are separate classes thus different symbols. If not, the road component values highway and secondary roads form one class thus one symbol. The number of component classes that is appropriate depends clearly on the purpose of your map. The last step is to decide how the spatial components are presented in the map. Choose the appropriate graphic symbols and attributes according to the data scale of the components. 4. In this exercise you will practice the 12 steps to present a map approach with a map made in ArcMap. Make sure the view window of ArcMap document Map presentation2.mxd is set to layout view. If you want to look at a larger (full screen) version of the map, go the menu bar and select: File Print Preview. A soil map of the study area Wageningen South is displayed with legend. a. Repair the link of the missing information about Clay% (Data Source is file soil_points.shp). b. Describe in Table 1 the steps 4 to 9 how this map has been created. c. What is your opinion about the chosen graphic attributes according to figures 3 and 4? Table 1. Steps 4 to 9 of the 12 steps to present a map approach. Step 4 Spatial components Step 5 Component hierarchy Step 6 Data scale Step 7 Component classes Step 8 Graphic symbols Step 9 Graphic attributes When you have finished this exercise, close the ArcMap document without saving. MODULE MAP PRESENTATION

69 Laying out and printing maps (steps 3 & 10-12) During steps 4 to 9 you have determined which spatial components (datasets) you want to present on your map along with the components hierarchy, attribute scale, classes and symbology. Your map however, is not ready yet to print. It still lacks some important map elements: title, scale bar, legend, north arrow, descriptive text and maybe some graphics. If you want to add these elements, you have to create a layout. A layout lets you assemble the spatial components and map elements you want to represent in your map, arrange them to get the design you want, and then print it when it is ready. Layouts make it easy to produce presentation quality maps with ArcMap. You can place more than one data frame onto your layout along with any of the charts and tables in your ArcMap document. If you change your mind later, you can add, remove, resize and move each element in a layout as required. This section now continues with a description how to create a layout and how to add various map elements to it and is concluded with an exercise in which you will create a layout yourself. The ArcGIS Desktop Help system contains also an elaborate description of the map layout. Use the keywords layout, map elements or legend in the search index. Creating a layout INSTRUCTIONS: 1. Open a document and check the datasets you want to present in a map. 2. Set the view window to layout view. 3. You will find all necessary buttons to add map elements to your map in the Insert pull down menu in the menu bar (figure 5). Note that these buttons are only active when working in layout view. When the layout view is activated, it is possible to change its settings. It is possible to change the size and location of the visualized datasets. By right clicking on the layout view, it is also possible to alter the type of layout and the page settings. Adding a data frame to a layout Figure 5. Map feature buttons. The central element of the layout is the data frame. This is a frame that presents your datasets. You can have more than one data frame in your layout. Simple maps usually have only a single data frame. Sometimes you want to show changes in time, for example landuse in the Netherlands in 1980, 1990 and If this is the case, you can add 3 data frames to the map. Each frame has the appropriate land use period. ArcMap uses all data frames present in the table of contents in the layout. If you have two data frames in the table of contents, you will get automatically two data frames in your layout!! Warning: it is not possible to simply delete a data frame from the layout. When you do this, you also delete the corresponding dataset from the table of contents, and at the same time from the entire map document!!! If you do not want to have multiple data frames in your layout, you should rearrange the datasets in the table of contents into one data frame and then delete the other frames. It is possible to alter the properties of the frames in layout view: right-click the frame and select Properties. You can for example change the border of the frame, set a scale or add a reference grid to your map. MODULE MAP PRESENTATION

70 Adding a legend to a layout A legend tells a map user what the symbols on the map, used to represent features, mean. A legend in a layout is associated with the legend already used in the table of contents. When you change the symbology of a dataset (its appearance) it automatically changes the legend in the layout. INSTRUCTIONS: 1. In the menu bar select: Insert Legend (this is only possible when you are in layout view). 2. It the appearing window, select the dataset(s) you want to add to the legend. You can also change the order of the datasets in the legend (think about hierarchy!!) with the up and down arrows. Click next. 3. In the following windows some options appear to customize your legend. For now, just click next and finish. Of course, you are free to play around with the options if you wish! 4. The legend is placed in the layout, where it is possible to move it around and change its size. 5. If you are not satisfied with the result you can always modify your legend. Right-click the legend in the layout and select Properties from the menu that appears. The Legend Properties window opens in which you can adjust some legend settings. It is always possible to change the name of one of the layers by clicking on it in the table of contents. Adding a Title to a layout A title gives the user of the map guidance in the use and content of a map. INSTRUCTIONS: 1. In the menu bar select: Insert Legend (this is only possible when you are in layout view). 2. In the Textbox you can define your title 3. Click OK and move the title to an appropriate place within the layout. Adding a north arrow to a layout North arrows indicate the orientation of the map. INSTRUCTIONS: 1. In the menu bar select: Insert North Arrow (this is only possible when you are in layout view). 2. In the North Arrow Selector menu you can choose the north arrow style. 3. Click the Properties button and select the North Arrow tab to set the calibration angle. You can also change the size, style and font of the arrow. 4. Click OK and move the north arrow to an appropriate place within the layout. Adding a scale bar to a layout A scale bar provides a visual indication of the size of features and distance between features on the map. A scale bar is a line or bar divided into parts and labeled with its ground length, usually in multiples of map units such as tens of kilometers or hundreds of miles. If the map is enlarged or reduced, the scale bar remains correct. Add a scale bar after the datasets and a legend are added to the layout. Note: Before you add the scale bar to your layout, you have to check if your data frame has a coordinate system (see Module 3) and if the map units of the data frame are set to meters!!! To check this, go to the menu bar and select: View Data Frame Properties General. MODULE MAP PRESENTATION

71 INSTRUCTIONS: 1. In the menu bar select: Insert Scale Bar (this is only possible when you are in layout view). 2. Choose the scale bar you want to insert and click OK. 3. The Scale bar is added to the layout where you can change its size and move it. Make sure it is not too small! 4. Double click on the scale bar, to open the Properties window. 5. In this window the Scale and Units and the Numbers and Marks tabs are important. 6. In the Scale and Units tab it is possible to set the Division value (not to small!), number of divisions, number of subdivisions and what the scale bar should do when it is being resized (don t use the adjust division value, because the division value should be a round number, like 1km, 500 m etc.). 7. Choose the scale bar units. 8. In the Number and Marks tab it is possible to define how often a numerical value appears on the scale bar, and where this is placed in relation to the scale bar. 9. Click apply and OK. Adding text and other graphics to a layout INSTRUCTIONS: 1. You can use the Text tool to add text for titles and other descriptive text (e.g. the source) and you can use the drawing tools to add graphics such as boxes, circles and arrows anywhere on your layout. 2. The drawing tools are in a dropdown list in the Draw tool bar 3. You always can change the properties of a frame: right-click the frame in the layout and select properties. You can t change the font size directly. If you wan t to change it you should enlarge the text box. Exporting and printing a map If you want to use your map in a report you want to keep the quality of the map and not use a screendump of your map. INSTRUCTIONS: 1. In the menu bar select: export map 2. In the following dialog box you can specify the file type (e.g..bmp,.jpg,.png), dpi and the output location and name of your map. 3. Click Ok MODULE MAP PRESENTATION

72 5. Open the ArcMap document Map presentation3.mxd. For the inhabitants of the Hinkeloord area a map should be created that gives information about nature in this area. You are free to choose information out of the available datasets. a. Have a look at the different datasets and write down the 12 steps to present the map. 1. Purpose 2. User group 3. Title Step 4 Spatial components Step 5 Component hierarchy Step 6 Data scale Step 7 Component classes Step 8 Graphic symbols Step 9 Graphic attributes 10 North Arrow 11 Scale bar or map reference grid 12 Source b. Create the map using ArcMap according to the 12 steps to present a map guideline. c. Before finishing your map / layout perform the exercises 6 9. Store the map as a.jpg file with the name Nature map Hinkeloord. Discuss your map together with the table above with one of the supervisors. MODULE MAP PRESENTATION

73 Adding graphics, text and labels to a map Using ArcMap s drawing tools, you can draw graphics like circles, boxes and lines on your map to draw attention to particular features or highlight important areas. You can also add additional information by typing text (also referred to as annotation) directly onto your map. Adding and modifying graphics INSTRUCTIONS: 1. Open a document, click the New Graphic dropdown arrow in ArcMap s Draw toolbar and select the type of graphic. 2. Position the cursor where you want the graphic to start, hold down the left mouse and drag to where you want the graphic to end, then release the button. For the polygon type, click once to start, and the click once for each corner. Finish by double-clicking. 3. Adding a line or a curve, click where you want the line to start, click each point along the line and double-click where you want the line to end. 4. To add a circle, click the circle tool, position the cursor where you want the center of the circle to be. 5. To change the appearance of a graphic, select a graphic in a view with the Pointer tool. When a graphic is selected, selection handles appear around it. To change the individual vertices, click on the edit vertices tool. 6. To change the appearance of the graphic, double-click it in the view window. 7. The Graphic Properties window appears. Click the Symbology tab and choose how you want the selected graphic to look. Note: to make a graphic transparent, open the Symbology Selector. Click on Fill Color and choose No Color. 6. Open ArcMap document Map presentation1.mxd. Activate data frame Wagsouth2. a. Change the color of one of the soil polygons with the Symbol selector. Create a polygon with a red outline and a green raster fill. b. Change the color of one of the soil polygons with the Symbol selector. Create a light gray polygon without a border (outline). c. Draw 4 points using the Draw toolbar and display each with a different size and color. 7. In the map you ll find a well and some locations with rare vegetation near the river Rhine. a. Draw a circle around the well. Keep the circle within the borders of the feature. Make this area recognizable by changing the outline, color, etc. b. Add an irregularly shaped graphic around the locations with rare vegetation. Make it recognizable on the map by changing the appearance of this graphic. MODULE MAP PRESENTATION

74 Adding and modifying text INSTRUCTIONS: Open a document, activate a data frame and display a dataset. 2. Click the Text tool in the Draw toolbar, click the new text button and click in the view window where you want the text to go. 3. In the dialog box that appears you can type in the text you want to add and press enter. By clicking on the Text tool again or double-clicking on the text with the Pointer tool you can change the text. 4. To change the font, size, style or color of the text, double click on the text field, and select Change Symbol. Continue with the results of exercise 7: a. Add the following text to the graphics in data frame Wag_south2 : Well, Nature Reserve. b. Change the size and style of the text and drag it to another position if it s necessary. c. Change the color of the text to red. Save the changes. Adding and modifying labels You can label a dataset s features with text showing the values from any attribute/field in that dataset s attribute table. When you label features, the labels are attached to the dataset they belong to, so they will only appear on your map when that theme is displayed. ArcMap offers a wide range of options to modify the labels according to your wishes. INSTRUCTIONS: 1. Display the dataset you wish to label. 2. Open the Layer Properties window of that dataset and select the Labels tab (Figure 6). Figure 6. Label editor. MODULE MAP PRESENTATION

75 3. Check the Label features in this layer box and select in the Text String dropdown list which field from the attribute table you want to use for labeling. In the Text Symbol, Other options and the Pre-defined you can modify the font, size, color, style and placement. Click apply. All features will be labeled. 4. You can also add individual labels to features. Click on the Text dropdown arrow and select the label function. The Label Tool Options window opens in which you can select two straight forward options. 5. Click on the feature in the view to appoint a label. 9. Activate date frame Wag_south1 again. a. Label the features of dataset Soil_types based on the field Soilcode. Set the label properties to: Verdana, 18, Bold Italic, red. b. What will happen to the labels when you turn the Soil_types dataset off? Explain your answer. c. Turn the labels off. You can uncheck the Label features in this layer box or right-click the dataset in the table of contents and uncheck the Label features option. d. Add a label to the most northern soil feature using the label function in the draw toolbar. e. What will happen to this label when you turn the Soil_types dataset off? And when you deactivate the data frame? Explain your answers. Save the changes. MODULE MAP PRESENTATION

76 MODULE MAP PRESENTATION

77 Introduction Geo-Information Science Practical Manual Module 5 Queries

78 5. QUERIES INTRODUCTION 5-1 TABULAR DATASET QUERIES Selecting features by using a query expression Sorting attributes 5-3 Refining a query 5-3 SPATIAL DATASET QUERIES Setting selectable datasets Features selection by cursor 5-5 Feature selection by graphic element 5-7 Feature selection by spatial relationship 5-8 Selecting features by their proximity to other features 5-8 Selecting features that fall within polygon features 5-9 Selecting the nearest features to other features using spatial join 5-9 SAVING YOUR SELECTION INTO A NEW VECTOR DATASET 5-11 RASTER DATA QUERIES Selecting raster cells by their value Selecting and exporting raster cells using the CON tool 5-12

79 5. QUERIES Introduction When you want to set up a GIS application to solve a spatial problem, you start with defining a conceptual model of reality and collecting spatial data. The next logical step involves spatial and tabular operations using the data you collected. You need to analyze, combine and integrate your datasets, the so-called data handling, to extract the spatial information you need to solve your spatial problem. The query (abbreviation of question and reply ) is the first data handling class that will be discussed during this ArcGIS practical. Queries are processes that extract information from a GIS by making selections from tabular and spatial datasets. These selections do not change the geometry and attribute information of the datasets. These processes do not change the thematic and geometric meaning of the original data. With queries you can select a set out of the whole dataset. You can discover and analyze new spatial relationships and solve problems when you start asking questions such as: Where is...?, Where's the closest? What's inside? What intersects? ArcMap provides a number of tools to help you find answers to these types of questions. Queries can be divided into tabular and spatial dataset queries. The structure of a query depends on the query language used. ArcGIS uses SQL (Standard Query Language). The basic syntax of SQL is: Select <features/records> From <dataset> Where <condition> ; Example of a tabular dataset query: Where are the soil points located with ph > 6? Select Point features From Soil_points Where ph-value > 6 Example of a spatial dataset query: How many soil points are located within 500 m1 from the river Rhine? Select Point features From Soil_points Where within a distance of 500 m1 from the river Rhine In this module: Querying a vector dataset: tabular dataset queries: selecting features by using a query expression; selecting features based on spatial relationships; saving selected features into a new vector dataset. Querying a raster dataset: selecting raster cells by using a query expression; selecting raster cells with ArcMap s Con tool. Objectives After having completed this module you will be capable: to describe the difference between tabular dataset and spatial dataset queries; to perform various queries with ArcMap tools; to create new datasets containing selected features or raster cells. ArcMap document: Queries.mxd Literature: Chang, 2010: Chapter MODULE QUERIES

80 Tabular dataset queries Tabular dataset queries retrieve a set of table records. Your query condition is based on attributes and their values. When the tabular records are related to features both table records and associated features are selected. Selecting features by using a query expression A query expression is a precise definition of what you want to select. Building a query expression is a powerful way to select features because an expression can include multiple attributes and operators. The expression contains logical operators: Boolean (AND, OR etc.) and relational (=,<, >, <> etc.). The output of the query expression is therefore TRUE or FALSE. The features for which the query condition is TRUE are selected; the features for which the condition is FALSE are not selected. When you select features, they are highlighted. Features remain highlighted until you make a different selection, or when you deselect them. When you open the attribute table you will see that the feature table records for the features you selected are also highlighted. INSTRUCTIONS: 1. Open the attribute table of the dataset you want to query. 2. Go to Options Select By Attributes. You can also click Selection in the menu bar and click Select by Attributes. 3. The Select by Attributes window appears (Figure 1). 4. In the Method field the selection type can be defined. 5. Double click the attribute to define on which attribute the selection is based. This attribute appears in the text field. 6. Specify the condition in this text field by using the operators (=, >, <, etc.). 7. Type a value into the expression, or click on the Get Unique Values to retrieve all values of the selected attribute. 8. Now you have built a single query. To build a compound query you have to Figure 1. The Select by Attributes window. use the And or Or operators. If you want to build a compound query repeat steps 5 to 7. Use the And operator when both expressions must be true. Use the Or operator when at least one expression must be true. 9. Click Apply to make the selection. 10. Click the Clear Selection button to clear the selection. This button is available in the attribute table under the button Options. To find out how many features fulfill the conditions in your expression, or to review the attributes of the selected features, open the attribute table. The number of records that are selected is displayed at the bottom of the attribute table!! MODULE QUERIES

81 1. Open ArcMap document Queries.mxd and activate data frame Wag_south. a. Select all features of dataset Soil_types, which have an Area that is larger than m 2, by using the query builder. Make sure that the method used is: Create a new Selection. How many records are selected? b. Select all features of dataset Soil_types which have an Area that is larger than m 2 AND which are classified as: kalkhoudende poldervaaggrond. How many records are selected? Clear the selected set of features so none are selected. c. Write the query of exercise 1b down according to the formal structure of a SQL query. Select: From: Where: Sorting attributes By sorting an attribute table you can list the features in order of importance. INSTRUCTIONS: 1. In the table, right-click the header of the field you wish to sort the attribute values for. 2. Select the Sort Ascending button to sort the attribute values, smallest values first or in alphabetical order. 3. Select the Sort Descending button to sort the attribute values, highest values first or in opposed alphabetical order. 2. a. Use the Sort Descending button to order the attribute values of field polygon_co of the dataset Soil_types, the highest value first, the lowest last. Refining a query Until now we have seen how to create a new selection set of features based on a certain query condition. Once you have selected a set from a dataset, you can refine the selection. For example, you can reduce the set or expand the number of features. ArcMap offers three possibilities to refine your selection. Add to current Selection Adds the features selected in your query to the existing set of selected features. Use this option to expand your selection. MODULE QUERIES

82 Remove from current Selection Removes the features selected in your query from the existing set of selected features. Use this option to reduce your selection. Select from current Selection This applies for queries using the already selected features. Therefore you select features from your current selection. Use this option to reduce your selection to progressively focus in on those features you want. INSTRUCTIONS: 1. To refine a query click the Method dropdown arrow in the Select by Attributes window and select one of the options. 2. Proceed with the query as usual. 3. Select features from dataset Soil_points that have a clay % of more than 10%. a. How many features are selected? b. The clay % of point X9 appears to contain a measurement error. Remove that point feature from the selection. Clear the selection.. MODULE QUERIES

83 Spatial dataset queries Spatial data queries select a set by working directly with features without using attributes and query expressions. This means that features are selected based on their distance to a certain location or based on their spatial relationship to other features. In this section, three spatial data query methods are discussed: selection by cursor, by graphic and on basis of a spatial relationship. Features selection by cursor INSTRUCTIONS: Selecting features by cursor 1. Activate the data frame and check the dataset(s) from which you want to select certain features. 2. Click the Select Feature tool and click the feature(s) you want to select. Hold SHIFT to select multiple features or to drag a box around them. Features that fall partly or wholly inside the box are selected. The outline of the selected features turns light blue to indicate they are selected. 3. Open the attribute table. 4. Click the Show: Selected button to display only the records of the selected features. Refining the selection by cursor To remove one feature from the selection and to add one other feature you need the following procedure: 1. Holding SHIFT, use the Select Feature tool to click the feature you wish to remove from the selection, and then click the feature you wish to add to the selection. 2. Open the attribute table, click the Show: Selected button to display the records of the selected features. Click the All button to display all the records of the attribute table again. 4. a. Display only dataset Soil_types. Select the soil feature with soil name Kalkloze poldervaaggrond. Open the attribute table and display only the records of the selected features. b. Write this query down according to the formal structure format of a SQL query. S(elect): F(rom): W(here): c. Remove the most southern soil feature from the selection and add the most northern one to the selection. Afterwards display the selected records in the table. d. Clear the selection. MODULE QUERIES

84 Setting selectable datasets When using the Select Feature or Select by Graphic (see next section) tools, you can only select features from a dataset when it is displayed in the view window. If several datasets are displayed at the same time (e.g. soil types, land use, roads) and you select a feature from one of these datasets, ArcMap will also select features at the same location from the other datasets underneath. For example, suppose you have two datasets displayed in the view window: soil types and land use, with the land use dataset on top of the soil types dataset. If you select a land use feature with the Select Feature tool inconvenient., you will also select the underlying soil feature at that location. This can be To overcome this inconvenience you can specify the datasets you want to select from with the selectable layers setting. INSTRUCTIONS: Click the Selection tab at the bottom of the table of contents. Now the table of contents lists all layers in your data frame. You can only select features, using the cursor or a graphic, from datasets that are checked. Uncheck the datasets you do not want to be able to select from. Now these datasets can be drawn in the view window but you do not select features from these datasets when you use the Select Feature or Select by Graphic tools. A dataset turns bold in this list when a set of features is currently selected from it. The number of selected features is also shown between brackets. 5. a. Display the datasets Land_use and Soil_types. Select the mathematics building (Figure 2) with the Select Feature tool. Mahematics Building Figure 2. Location of the Mathematics Building. Now you see that besides the mathematics building also the soil feature in which the building is located is selected (turn off and on the Land_use dataset). b. Click the Selection tab at the bottom of the table of contents. The datasets in the data frame appears are listed here. You can see that there is one feature selected in both datasets layers. Uncheck the box of the dataset Soil_types and click the Display tab at the bottom of the view window. c. Select the mathematics building again with the Select Feature tool. Now you see that only the mathematics building is selected in the Land_use dataset and not the soil feature on which this building is located. MODULE QUERIES

85 Feature selection by graphic element This method uses a graphic element like a circle, a box, a line or a polygon to select features that fall inside or that intersect the graphic element (Chang, 2006). For example, with the Circle tool you can draw a circle with a given radius in your view window, and subsequently select all features that fall inside or that intersect the circle, from the selectable datasets. INSTRUCTIONS: 1. Make sure that the Draw toolbar is activated. Select from the menu bar: View Toolbars Draw. 2. Click the New Graphic dropdown arrow and click a graphic tool, such as the circle tool. 3. Move the cursor to a location where you want the circle to be centered, hold down the left mouse button and drag out to define the circle. You can set the diameter of the circle by rightclicking the graphic. Select Properties from the pulldown menu and click the Size and Position tab. Here you can adjust the with and height of the circle. With anchor point (see Figure 3) you can define the fixed point of the circle. By default this is the lower left corner, but since you want to keep the circle in the center you should use the middle as the anchor point Figure 3. Choosing Anchor Point. 4. Optionally, click the Fill Color dropdown arrow and click No Color to make the graphic. When the features are selected, you can see the selected features under the graphic. 5. To select features with the graphic go to the menu bar and click: Selection Select by Graphics. 6. The features that fall within or intersecting the graphic will be highlighted to show they are selected. 7. Now that you ve found the features, you can look at their attributes by opening the attribute table. 8. You can use the Pointer tool to move the drawn shaped around the map or resize them by dragging one of the handles. To delete a graphic, select it and choose Delete from the Edit menu in the menu bar (or press Delete ). 6. a. Draw two circles centering on soil points X1 and X2, with a radius of 400 m. What are the soil codes of the selected soil features? Delete the two circles. MODULE QUERIES

86 Feature selection by spatial relationship This query method selects features based on their spatial relationships to one or more source features. Features to be selected may be in the same dataset as the source features or they are in different datasets (Chang, 2006). Selecting features by their proximity to other features The Select By Location functionality can be used to select features (target features) that are within a specified distance to user-defined source features in the same or a different dataset. Examples include finding a forest (target feature) adjacent to a city border (source feature) and finding a pub (target feature) within 200 meters from a train station (source feature). When you use the Select by Location functionality, select first the source features with a tabular data query before you start to use the tool. INSTRUCTIONS: 1. Select the source feature(s). 2. Click in the menu bar: Selection Select by Location. 3. In the window that appears (Figure 4) certain choices have to be made. The basic idea is that the sentence that can be read from top to bottom in the window, defines the selection you want to make. For instance, you want to select the land use features that are adjacent to a certain forest feature. First select the forest feature of your interest. Then open the Select by Location tool and create the selection statement which will read (see Figure 3): I want to: Select features from the following Layer(s): Landuse that: Are within a distance of the features in this layer: Landuse. Figure4. The Select by Location window. 4. Make sure the Use selected features is checked. 5. Specify the distance within which to search for features. For example when you set the distance to 200 meters, all features will be selected that are located within 200 meters from the source feature(s). If you want to select features that are adjacent to other features like in the example above, set the distance to 0! 6. Click Apply. Note: If you would follow these instructions you would end up with a selection of land use features adjacent to a certain forest feature AND the forest feature itself! The Select by Location dialog also allows the user to refine a query. Click the I want to: dropdown arrow and choose one of the options offered. MODULE QUERIES

87 7. a. Select the features from the dataset Soil_types adjacent to features with soil name kalkloze poldervaaggrond. What will be the source feature of this selection? How many features are selected? b. You used the spatial relationship Are within a distance of but you can also use another spatial relationship to make same selection. Which one? Check it by comparing both results. c. Select the features from the dataset Land_use that are within 250 meters from the soil feature with soil name kalkloze poldervaaggrond. How many features of the land_use dataset are selected? Clear the selection! Selecting features that fall within polygon features In many situations, you might be interested in the question: Which features fall within particular polygon features? You can use the Select by Location dialog, as described in the previous exercise, to answer this question. INSTRUCTIONS: 1. Select the source features. 2. Repeat the steps as described in the previous instructions but use a different spatial relationship. 8. a. Write down the codes of the soil points (dataset Soil_points ) that fall completely within the soil features classified as kalkhoudende ooivaaggrond. Clear the selection! Selecting the nearest features to other features using spatial join So far you have seen how to select features that are within a specified distance from or adjacent to or that fall within other features. However, in many situations you are interested in finding out which feature in one dataset is nearest to a feature in another dataset. In ArcMap these queries are performed by joining the attribute tables of the two datasets together in a procedure called spatial join. A spatial join is a join the attribute tables of two datasets using the Shape fields in these tables to join them (Figure 4). A spatial join creates a new dataset, which contains both sets of attributes (like a normal join) and the new attribute distance. This is can be the distance from a line feature to the nearest point feature or vice versa. MODULE QUERIES

88 When you use a spatial join to find the nearest feature relative to another feature, you can: find the nearest point features to point features in a different dataset; find the nearest point features to line features in a different dataset; find the nearest line features to point features a different dataset. Spatial join shape 1 2 Point point Table 1. shape 3 4 poly line poly line poly line Table 2. shape 1 2 distance 3 4 poly line poly line Etc. New table after 'spatial join'. Figure 5. Spatial join based on the shape-field of both tables. In the new table a distance field is added in which the distance from each polylines to its nearest point are given. INSTRUCTIONS: The procedure, you have to follow to find the nearest line feature to a point feature using spatial join, is the following: 1. Right-click the dataset to which you want to join attributes, click Joins and Relates Join. 2. Select Join data from another layer based on spatial location from the first dropdown list. 3. Click the layer dropdown arrow and click the name of the dataset whose attributes you want to join. 4. Click Each line will be given all the attributes of the point that is closest to it, and a distance field showing how close that line is. 5. Define the name and output location of the created new dataset. Make sure you save the dataset in your Workspace. 6. Click OK. 7. A new dataset is added to the map. Note: Whenever a new dataset is added, do not forget to save the ArcMap document! 9. a. What is the code of the nearest soil point to the road named Veerweg? To answer this question you will have to create a spatial join between datasets roads and Soil_points. b. Write down the distance between the Veerweg and its nearest soil point. MODULE QUERIES

89 Saving your selection into a new vector dataset You have seen that there are many possibilities to make a selection. In many applications you might want to use this set of selected data for further analysis. So it can be convenient to store this set of data as a new dataset (shapefile). Note that by doing this, there is no change in geometry and attribute information. INSTRUCTIONS: 1. Select features of your interest. 2. Right-click the dataset with the selected features and click Data Export Data. 3. A dialog box appears. Click the Export dropdown arrow and choose if you want to export the selected features or the complete dataset. 4. Choose the coordinate system. 5. Specify the name and output location (workspace) of the new dataset. 6. Click OK. 10. a. Select the forest features ( C2 = 3) from the dataset Land_use. b. Export the selected features to a new dataset. MODULE QUERIES

90 Raster data queries Raster data queries differ from vector data queries. Vector queries allow you to select individual features from a dataset. But you cannot select individual raster cells. When you query a raster, you use a query expression. When you use the attribute value in the query expression, you will select a zone of the raster dataset (for zones, see module 2). You selection will contain all raster cells with the same value. The vector equivalent of this type of selection is the selection of a class (a class is formed by all features with the same attribute value, e.g. all buildings in a land use dataset). Selecting raster cells by their cell value You can also use the Select by Attributes dialog in a raster environment. INSTRUCTIONS: 1. Open the attribute table of a raster dataset. 2. Click Options Select by Attributes. 3. Formulate your query. Cells with the selected values will be highlighted in the view. Unfortunately you cannot export the selection to a new raster dataset. With the Select by Attributes functionality you can only visualize your selection. To create a new dataset from a subset you have to use the CON tool, which is located in the Spatial Analyst toolbox. INSTRUCTIONS to activate the Spatial Analyst : 1. Activate the extension Spatial Analyst by choosing Extensions of the Tools menu. 2. Check the extension Spatial Analyst. This extension allows you to use the Spatial Analyst tools for geoprocessing of raster datasets! Selecting and exporting raster cells using the CON tool Whit the Con tool you can select and export raster cells based on a conditional if/else evaluation. This means that a user-defined query expression is evaluated for each cell of the input raster. The output is a new raster dataset. If the evaluated cell values of the input raster are TRUE, then user-defined value(s) are assigned to these cells in the output raster. User-defined values can in this case be the original cell value, a constant or the value from another raster dataset. The cell values that are evaluated as FALSE can be set to a different set of user-defined values. For example, if the cell value in the input raster is greater than 10, then return 1, otherwise return 100. A selection is where the geometry and attribute information do not change. This is the case where the original cell values are saved in the output raster. If other values are saved in the output raster, then not only a selection takes place, but also an operation because the thematic (attribute) data of the raster changes. A great advantage of the use of the Con tool is that you can retain the original cell value in the output raster! In module 7 you will see that you can also make selections with the Raster Calculator functionality but then you will loose the original values. A disadvantage of the Con tool is that you can only use it for discrete rasters. For raster with a floating data type you can not use the con tool. MODULE QUERIES

91 INSTRUCTIONS: 1. Open ArcToolbox and click: Spatial Analyst Tools Conditional Con. 2. Select the Input conditional raster. This is the raster that contains the cell values that will be evaluated by the query condition. 3. Select the Input true raster or constant value. This is the raster or constant holding the value that will be assigned to the cell as output value if the condition is true. You retain the original value if you choose here the same raster as you chose as input raster! 4. Select the Input false raster or constant value if you want to give an output value to the raster cells for which the condition is false. If you do not select a raster or constant value, the value of NoData is assigned to cells for which the condition is false. This is the default setting. 5. Type a logical query condition in the Expression box. 6. Click OK. 11. Activate data frame Raster and display dataset LU_raster. a. Select all cells for which value = 5. How many cells are selected? b. Clear the selection. Select only those land use classes which cell count is higher than Which classes are selected? c. Create a new raster dataset that contains all raster cells from LU_raster classified as forest (value=3). Retain the original cell value. Save the new dataset in your workspace under a logical name. d. Create a new raster dataset that contains all raster cells from LU_raster classified as buildings (value=1) and roads (value=2). Assign a constant value to the output raster cells. Save the new dataset in your workspace under a logical name MODULE QUERIES

92 MODULE QUERIES

93 Introduction Geo-Information Science Practical Manual Module 6 Transformations

94 6. TRANSFORMATIONS 6-1 INTRODUCTION 6-1 PART 1: GEOREFERENCING AN IMAGE 6-2 Overall procedure Georeferencing in ArcMap Geometric transformation and resampling Image registration: obtaining control points Validation PART 2: DATASET STRUCTURE TRANSFORMATION 6-10 Transforming datasets from vector to raster Weight tables to determine the cell value after transformation Vector-raster transformation in ArcMap Transforming datasets from raster to vector Raster-vector transformation in ArcMap Changing the cell size of a raster dataset

95 6. TRANSFORMATIONS Introduction The second data handling class comprises Transformations. Transformations can be divided into three groups: Projection transformation: the mathematical conversion of a map from one projected coordinate system to another. Georeferencing: the geometric transformation from digitizer units or image coordinates to a projected coordinate system using a set of control points. Data structure transformation: conversion from one data structure into another, for example vector to raster. The first transformation group was discussed in module 3 Map projections : you have projected and reprojected vector datasets. In this module the other two groups of transformations are treated. Part 1 of this module discusses georeferencing. You will perform an image-to-map transformation, which means that you reference image coordinates to map projected coordinates. The second part deals with data structure transformation: vector data to raster data or raster data to vector data conversions. In this module: Image registration and geometric transformation of an image file. Resampling raster values. Assessing positional accuracy by RMS error: validation of the georeferenced raster. Vector-raster transformations. Raster-vector transformations Objectives After having completed this module you will be capable: to interpret and explain the meaning of the terms: image registration, image rectification, georeferencing, (ground) control points, validation, spatial resolution and spatial accuracy within the context of geometric transformation of raster data; to enumerate which actions and data are required to carry out an image rectification; to argue your choices for methods of image rectification and resampling method; to reason the spatial accuracy obtained. to define decision-rules that determine which attribute is stored after data structure transformation; to perform data structure transformations in ArcMap using ArcToolbox. ArcMap documents: Transformations part1.mxd Transformations part2.mxd Literature: Chang, 2010: Chapter 4: section 4.5 Data conversion and integration Chapter 6 Geometric transformations (except and 6.3) MODULE TRANSFORMATIONS

96 PART 1: Georeferencing an image A common question posed to us by students and professionals is: Why cannot these two maps or images be properly overlaid since they refer to the same area? Many of you will at a later stage of your study or during your professional career be presented with images (e.g. satellite data, aerial/digital photographs or scanned data) or digitized maps on which information is depicted but do not have a map projection coordinate system. If the image or map is spatially referenced to a known map projection, it can be mathematically (re)projected to another coordinate system (see module 3). However, this part of the ArcGIS practical concerns the recurring problem where (1) coordinates of a newly digitized map are represented by digitizer units from a point of reference on a sheet of paper or (2) coordinates of an image are represented by column and row pixel indices. You have to georeference the digitized map or image to a map projected coordinate system in order to use this data in GIS analysis (Figure 1). Figure 1. An image of the Droevendaal Experimental Farm (left) has to be georeferenced in order to align it with a topographical map in a GIS. Overall procedure The georeferencing of raster data is often referred to as image rectification and can be divided into three steps: Image registration Image registration refers to the process of the identification of corresponding points in an input image and a reference dataset in a known map projection. These points are called (ground) control points. Geometric transformation The control points are used to determine the coefficients for two (polynomial) transformation functions that describes the relationship between image coordinates and map coordinates (Figure 2). x = f1( X, Y ) y = f 2 ( X, Y ) with (x,y) = input image coordinates (column, row) (X,Y) = map coordinates f 1, f 2 = transformation equations Figure 2. Transformation functions are defined on basis of the control points. MODULE TRANSFORMATIONS

97 Once the coefficients for these transformation functions are determined, the image coordinates for any set of map projected coordinates can be estimated (Lillesand et al., 2004). A common transformation is the 1 st order (linear) Affine transformation (Figure 3). x = b Affine transformation y = b with b 01 b 22 = linear transformation coefficients (x,y) = input image coordinates (column, row) (X,Y) = map coordinates b + b 1 1 X + b X + b 2 22 Y Y Figure 3. Transformation equation coefficients are determined on basis of the control points. While you might think each cell in an image is transformed to its new location in a projected raster dataset, the process actually works in reverse. During georeferencing, a matrix of "empty" cells is computed using the map coordinates. Then, each empty cell of the output raster is given the value of the corresponding cell (determined by the transformation function) or cells in the input image, based on a process called resampling. Resampling The two most common resampling techniques are nearest neighbor assignment and bilinear interpolation. These techniques assign a value to each empty cell by examining the cell values in the unreferenced raster dataset (the input image). A third common resampling technique is cubic convolution. This technique and the effects of the different techniques on the output raster are discussed in more detail in the course Remote Sensing (GRS-20306). Figure 4 gives a schematic overview of the georeferencing process. Transformation functions Reference (a) Image Output raster (b) Input raster + + Figure 4. Schematic overview of the image rectification procedure. (a) Image registration: identification of control points in the image and reference datasets. (b) Using a polynomial transformation function the cell centres in the output raster are mapped to locations in the input raster coordinate system. Next, the cell value is determined using a resampling method: the nearest neighbour method assigns the value of the nearest cell centre (marked by the dashed circle around the cell); bilinear interpolation involves computing a distance weighted average of the four nearest neighbours (marked by circles around cell centers) in the input grid. MODULE TRANSFORMATIONS

98 Georeferencing in ArcMap In this module you will georeference an image of the Droevendaal Experimental Farm from image coordinates to projected map coordinates. 1. Open ArcMap document Transformations part1.mxd. Activate data frame Scanned image. This data frame contains an image of the Droevendaal Experimental Farm. a. What is a nominal data scale? How do you know that the values of raster layer Scanned map are on nominal scale? b. What is the unit of the coordinates of the scanned image coordinates (meters or pixels)? How many rows and columns does the image have? c. Add vector layer Top10vct.lyr to the data frame. This layer can be found in the data folder. Image registration: obtaining control points You start the georeferencing by obtaining coordinates of control points. These are points where both image and map coordinates can be identified (Chang, 2006). INSTRUCTIONS: 1. Open the Georeferencing toolbar. Click View in the menu bar, point to Toolbars and click Georeferencing. 1 2 Figure 5. The Georeferencing toolbar. 2. In the table of contents, right-click the referenced dataset (in this case the Top10vct ) and click Zoom to Layer. 3. Click the Georeferencing dropdown arrow in the Georeferencing toolbar, click Fit to display. Make sure the layer box is set to scanmap. 4. Now both layers are displayed in the view window. MODULE TRANSFORMATIONS

99 5. Click the Control points tool (box 1, Figure 5). The pointer on the display changes into a crosshair. 6. To add a control point, click the mouse pointer on a location in the raster dataset which can be easily recognized in the reference dataset (e.g. a crossing of roads, Figure 6). You will need to zoom in considerably to obtain accurate x, y positions. When collecting control points, first click the source (unreferenced raster) dataset, then click the coinciding point in the referenced dataset. Figure 6. Collecting control points. A detail of the image and reference datasets showing a control point that links an identical locations in image (top) and reference (bottom) datasets. 7. Collect sufficient control points to solve the transformation equations: 1 st order polynomial (affine): minimal 3 control points; 2 nd order polynomial: minimal 6 control points; 3 rd order polynomial: minimal 10 control points. However, it is wise to collect more than the required minimum. With more control points coefficient estimations are improved which results in smaller measurement errors and a higher accuracy!! 8. The image and map coordinates of the control points are stored in the Link Table (Figure 7). Click the View Link Table button (box 2, Figure 5). The table shows the x and y coordinates of the control points and the residuals (errors). Make sure the Auto Adjust box is checked. 9. To delete a control point, select it and click the delete button on the upper right hand of the dialog box. Figure 7. The Link Table with 12 control points. The RMS error is for these 12 points The map shows the distribution of the control points. MODULE TRANSFORMATIONS

100 2. a. What are control points and where do you need them for? b. What are residuals and how are they calculated? Start collecting control points. Answer exercises c and d when you have collected 2 and 4 points, respectively. Collect 12 control points in total. Note that during the collection, the scanned map is adjusted (based on the transformation function) to match the reference dataset after each collected point. Make sure your points are well distributed among the map. c. When you have collected 2 points, open the Link Table. Are there already any residuals calculated. Why (not)? d. Open the table again when you have collected 4 points. What is the Total RMS error (Figure 7)? e. What Total RMS error did you achieve after the collection of the 12 control points? A Total RMS error between 2 and 4 is acceptable. Save the set of control points (open the Link Table and click Save). Place the file in your workspace! f. Replace control points with high residuals if necessary. What happens with the Total RMS error if such control points are removed? g. What does a low RMS value indicate; does it imply an accurate registration and hence good rectification results? Why (not)? MODULE TRANSFORMATIONS

101 Geometric transformation and resampling Until now you have registered the input image with the reference dataset by obtaining twelve control points. These points were used to fit a transformation function (i.e. determine the coefficients) to the coordinate values of the control points. For each point the residual was calculated which is the difference between fitted coordinate value and the true coordinate value of that point. Now you will perform the geometric transformation and resampling of the input image. In ArcGIS these two steps are integrated into one operation! INSTRUCTIONS: 1. First select the transformation function. Click the Georeferencing dropdown arrow in the toolbar, point to Transformation and select a transformation polynomial. 2. Click the Georeferencing dropdown arrow and click Rectify. A dialog box opens (Figure 8). 3. Set the Cell Size for the output raster dataset. 4. Select a Resample Type. 5. And specify the name and location of the output raster. 6. Set format to GRID 7. Click Save. Figure 8. Enter the rectification settings. 3. Rectify the input image of the experimental farm. Use a 1 st order polynomial (Affine) transformation function. Use both the Nearest Neighbor and the Bilinear Interpolation resampling types. Name the output rasters farm_1st_nn and farm_1st_bl, respectively. Save the output rasters in your workspace. Change the cell size to 2 meters and set the format to GRID. a. What is the meaning of the Nearest Neighbor and Bilinear resampling type options? Activate data frame Referenced image and add both output rasters to it. Change the symbology of the two raster datasets. Open the Symbology editor and import farm.lyr b. Which of the two is correct and why? MODULE TRANSFORMATIONS

102 4. Add layer top10vct.lyr to data frame Referenced image. a. What pattern in the displacements between the rectified images and the top10vct layer can be observed? Hint: zoom in. 5. Activate data frame Scanned image. Repeat the rectification using a 2 nd order transformation function. Name the output raster farm_2nd. Choose the appropriate resampling type. a. Open the Link Table. What Total RMS error have you achieved now? Activate data frame Referenced image and add dataset farm_2nd. b. Did the spatial match between the rectified image and the reference layer top10vct improve? c. How does this result relate to your answer to exercise 5a? Is there any reason to use a 2 nd order (quadratic) transformation function? MODULE TRANSFORMATIONS

103 Validation A low RMS error should not be confused with an accurate rectification. For example, using the minimum number of control points required by the used transformation function, zero RMS should be given. Nevertheless, the transformation may contain substantial errors due to a poorly entered control point. In fact, the RMS only indicates how well the transformation function could be calibrated to the control points. A validation with an independent set of reference points allows checking the positional accuracy obtained. Using a precision instrument (Real Time Kinematic GPS) the locations of corner points of specific experimental plots inside the farm were measured (with centimeter accuracy). These locations were subsequently projected to the Dutch Grid (RD reference system). Figure 9 identifies 15 of the measured locations (north-east corners). Their coordinates (Dutch Grid) are stored in the MS Excel file Validate.xls. 6. Validate the georeferenced images. Follow the following procedure: Figure 9. Fifteen validation locations, the north-east corners of experimental plots. 1. Open the Excel workbook Validate.xls. This file can be found in folder D:\IGI\...* \ArcGIS\data\georeferencing (*morning or afternoon). 2. Measure the 15 coordinates of the northeast corner of the validation plots in the map farm_2nd. Use the identify tool and make sure the correct layer (i.e. Farm_2nd) is selected in the identify from box. The coordinates are given in the Location box 3. Fill in the coordinates in the columns X_image and Y_image of the Excel sheet. 4. The RMS E(rror) of the residuals is automatically computed. 5. Repeat the measurements and calculations with the raster dataset Farm_1st_nn. a. What overall RMS error did you achieve for the 1 st and 2 nd order polynominal transformations (give unit)? b. How does this compare to your answer(s) to exercises 5a and if applicable 2d? c. Which factors contribute to the positional accuracy achieved? MODULE TRANSFORMATIONS

104 Part 2: Dataset structure transformation The second part of this module deals with the transformation of one dataset structure to another dataset structure: vector data to raster data conversion (called rasterization) and raster data to vector data conversion (called vectorization). Transforming datasets from vector to raster In vector-vector and raster-raster transformations the geometric elements of the datasets remain the same. In a vector-raster transformation you are confronted with a change in geometric elements. Point, line or polygon features are converted into raster cells. Weight tables to determine the cell value after transformation Transforming vector geometric elements into raster cells force the user to formulate decision rules concerning the attribute value that has to be stored in a specific raster cell. For example, when two line elements intersect in the area of a raster cell which line value will be labeled to the cell? 7. a. Draw a situation showing that it is necessary to make decisions about the value labeling of raster cells, when transforming: 1. point features to raster cells; 2. polygon features to raster cells. Sketch 1: Sketch 2: In most GIS systems, the user is able to influence the outcome of a decision in case of ambiguity (multiple vector features in one raster cell). A common used approach to define a decision rule is by means of a weight table. These weights are used to resolve cases when a single cell contains more than one vector element. In this case the vector elements with the highest value in the weight value will be assigned to this cell. MODULE TRANSFORMATIONS

105 8. The following wells are stored in a vector data-set (Figure 10): 1. wells with good drinking-water quality 2. wells with reasonable drinking-water quality; the water is suitable for livestock consumption 3. wells with poor drinking-water quality; this water will create illness but it is not fatal to humans or livestock 4. wells with toxic chemicals; this water is fatal for humans and livestock As a remark, in this example it is important to know which the wells with toxic drinking water are. For further analysis, the point elements have to be converted to raster cells. a. Which data scale do the descriptions of the wells have? b. Write down the relation between the above mentioned descriptions about wells by giving it a value and a weight. The weight value has to ensure that the worst water quality is represented in a raster cell after transformation. c. During the vector-raster transformation, the coordinates which describe the position of a geometric element disappear. Describe in your own words what the reason is that this happens. Use your lecture book to support your answer. d. In the figure below, a raster is draped on the points of the wells described earlier. Fill in the empty raster at the right with the code number of the well which is assigned to the raster cell according to your weight table given. Figure 10. Wells described in a vector structure as point elements, with a raster along those points (left) and an empty raster (right). MODULE TRANSFORMATIONS

106 9. In the next example line objects have to be transformed into a raster environment (Figure 11). The line objects represent different types of roads. The road with the highest traffic capacity is the most important for further analysis, so it has the highest weight value. a. Fill in the empty raster presented below with codes for the different road types according to their traffic intensity. Figure 11. Roads described in a vector structure as line elements, with a raster along those lines (left) and an empty raster (right). MODULE TRANSFORMATIONS

107 10. In the next example polygon objects have to be transformed into a raster environment (Figure 12). More than one polygon object is able to share the area of a raster cell. Many GIS systems determine which partial area is the largest in the cell. The value of this area object will be stored in the raster cell. This means that very small polygons, which are situated completely within a raster cell, are lost. These areas could be very important to the user. Once again a weight table is used as a rule of decision to make sure that this information is not lost. In the example presented below, heather areas play the most important role for future analysis. a. Fill the empty raster with a code number, make sure that all heather areas maintain in the final result. Figure 12. Land use described in a vector structure as polygon elements, with a raster draped on the polygons and an empty raster. Vector-raster transformation in ArcMap In ArcMap, any type of feature dataset created from any type of source file can be converted to a raster dataset. Only the selected features in a vector dataset will be converted to raster. If the vector dataset does not contain a selected set, then all features will be converted to a raster dataset. All GIS programs have default decision rules settings. The most common rules are now discussed. When you convert polygons to raster cells, cells are given the value of the polygon with the largest area within the raster cell. During transformations of line features, cells are given the value of the line feature that is found within each cell. When more than one line feature intersects a raster cell, the first line feature value that is encountered during processing is given to the cell. Cells that are not intersected by a line feature are given the value of NoData. When you convert point features, cells are given the value of the point that is found within each cell. If more than point is found in a cell, then the cell is given the value of the point it first encounters when processing. Cells that do not contain a point feature are given the value of NoData. It is important to realize that all raster cells get a value after the transformation from vector. This can either be a value based on the vector feature or the NoData value if the raster cell does not intersect with a vector feature. There will be no empty cells in a raster. MODULE TRANSFORMATIONS

108 INSTRUCTIONS: 1. Activate the data frame that contains the vector dataset you want to convert to raster 2. Open ArcToolbox; select Conversion Tools to Raster Feature to Raster 3. In the dialog box, define the input dataset, the field which will be used to assign the values to the raster, the output dataset (select a proper location) and the cell size. Click OK. 11. Activate the data frame Wag_south of ArcMap document Transformations part2.mxd. Convert the features of dataset Soil_types into raster cells. The cell size of the new raster dataset Soilraster1 has to be 100 m. For cell values choose field Soilcode. Store dataset Soilraster1 in your workspace directory. a. Overlay Soil_types with Soilraster1. Explain why some parts of the polygon features are not given a cell value based on the soil code (e.g. along the borders of the river Rhine). b. Which value has the highest number of cells? What is the total number of raster cells? c. Select the Identify tool and click in the Rhine. Are these cells as empty as they appear in the map? Or do they also have a value? d. Give the cells with the value of NoData a color. Open the Layer properties (double-click the layer). In the lower right corner of the Symbology tab there is the Display NoData as dropdown list. Click on the arrow and select a color. Explain what happens. e. How is the raster extent (the raster borders) determined? f. The total number of raster cells you calculated earlier, is the number of raster cells that have a value based on the attribute Soil code. Calculate the total number of raster cells again, taking into account the cells with value NoData. Hint: you can find the number of rows and columns of the raster in the Layer properties. Open the layer properties window and select the Source tab. Convert the features of dataset Soil_types to raster cells again, but now the cell size of the new raster dataset Soilraster2 has to be 10 m. g. Which value has the highest number of cells? What is its total number of raster cells (including the NoData cells)? h. Which of the two vector-raster transformations gives the best results? Explain your answer. MODULE TRANSFORMATIONS

109 Transforming datasets from raster to vector The procedure of a raster-vector transformation is the following: 1. Determine the raster cells that form a feature; 2. Determine the edges between the features when necessary. If point features are the final result, this step is not needed. 12. Determine the individual features in Figure 13. Give each new feature a new identifier number in the empty raster. a. How many new area objects have you determined? Number of objects with value 1: Number of objects with value 2: Figure 13. Land use described in a raster structure (left) and an empty raster (right). Raster-vector transformation in ArcMap If the raster dataset does not contain selected cells, then all the cells will be converted to a vector feature type. In this module only the raster to polygon feature transformation is discussed. The output polygon feature dataset will contain a field called Gridcode. This field will hold the value of the raster cells used to create the polygon. When the simplify polygons option is used, the polygons in the output feature dataset are smoothed using a cluster tolerance. The cluster tolerance is found in the General Settings in the analysis Environments. If no value is given a program default cluster value is used. INSTRUCTIONS: 1. Activate the data frame that contains the raster dataset you want to convert to vector. 2. Open ArcToolbox; select Conversion Tools From Raster Raster to polygon. 3. In the dialog box, define the input dataset, the field which will be used to assign the values to the vector dataset, the output dataset (select a proper location). Click OK. MODULE TRANSFORMATIONS

110 13. Convert raster dataset Soilraster1 into the polygon feature dataset Soilvector1. Dataset Soilvector1 has to be stored in your workspace directory. Choose the field Soilcode to be used in the transformation. a. What are the field names of the attribute table Soilvector1? b. What are the differences between the attribute table of dataset Soilvector1 and the attribute table of the original dataset Soil_types? c. Where are the biggest discrepancies located in comparison to the original dataset Soil_types? d. Write down, in your own words, the raster-vector conversion process. 14. Convert raster dataset Soilraster2 into the polygon feature dataset Soilvector2. Dataset Soilvector2 has to be stored in your workspace directory. a. What are the major differences between datasets Soilvector1 and Soilvector2, and between datasets Soilvector2 and Soil_types? Explain these differences. b. Where are the biggest discrepancies between datasets Soilvector2 and Soil_types? Changing the cell size of a raster dataset If you want to compare rasters, they have to have the same cell size. However there are some pitfalls when changing the raster cell size. 1. If you decrease the cell size (e.g. from 30 m to 10 m) the spatial accuracy does not increase, since the source data had a cellsize of 30 x 30 m.(see Figure 14a) 2. If you increase the cell size you will encounter similar problems as in Part I of this module. You will have to use a resampling technique (nearest neighbor, bilinear interpolation). (See Figure 14b) 3. In both cases you can also encounter geometric shift of the data if your two cellsizes do not fit in each other. If you go from 30 x 30 m cells to 10x10 m cells, exactly 9 news cells with similar values will be created at the original postion of one 30x30 cell. However if you go from 30x30 cells to 20x20 cells your new cells boundaries won t coincide with your old cell boundaries. In these cases the resampling method again is of importance. (See Figure 14c) MODULE TRANSFORMATIONS

111 z a b ? c 1 2 1? 2??? 2 1 2? 1 Figure 14. Different resampling options. (a) going to a smaller cell size, without changing the geometric meaning, (b) going to a larger cell size, (c) going to a smaller cell size and changing the geometric meaning. MODULE TRANSFORMATIONS

112 INSTRUCTIONS 1. Open ArcToolbox, go to Data Management Tools Raster Raster Processing Resample 2. Select your input raster 3. Define a name for the output raster 4. Define the new Cell Size 5. Select a resampling technique (default is nearest neighbour) 6. Click OK 15. a. Convert soilraster1 to a dataset soilraster3 this time with a cell size of 50m with the nearest neighbor resampling technique. Is the new raster dataset geometrically different and more accurate? b. Convert soilraster1 to a dataset soilraster4, this time with a cell size of 30m with the nearest neighbor resampling technique. Is the new raster dataset geometrically different and more accurate? c. Should you use the bilinear resampling technique for transforming the soilraster dataset? Explain your answer. d. Suppose you have to compare to land use rasters, one from 1950 (with a cell size of 50m) and one from 2000 (30m) which cell size and resampling technique will you use for your analysis? MODULE TRANSFORMATIONS

113 Introduction Geo-Information Science Practical Manual Module 7 Raster operations

114 7. RASTER OPERATIONS INTRODUCTION 7-1 LOCAL OPERATIONS 7-2 Mathematical functions and operators 7-5 Raster overlay 7-7 FOCAL OPERATIONS 7-8 Focal (neighborhood) statistics 7-9 ZONAL OPERATIONS 7-11 Zonal statistics 7-11 Zonal geometry 7-11 GLOBAL OPERATIONS 7-13 Euclidean distance & Buffers 7-13 Creating raster data subsets 7-14 Reclassification of the Euclidean distance raster 7-16

115 7. RASTER OPERATIONS Introduction The first two data handling classes, queries and transformations, were discussed in modules 5 and 6. This module and the next two modules deal with the third data handling class: operations. This module focuses on operations in raster environment, so called cell-based analysis. Raster operations are powerful tools in spatial modeling. ArcGIS offers the user a comprehensive toolset for cell-based GIS operations: ArcGIS Spatial Analyst. These operations can be divided into five types: local, focal, zonal and global operations and operations that perform a specific application (e.g. hydrologic runoff analysis functions and cost or resistance functions). The specific application operation type will not be discussed in this module. Cell-based operations use Map Algebra (Tomlin, 1990). Map Algebra is a computational language that models the surface of the earth as a multitude of independent, coincident dataset layers using operators or functions to one or more (coinciding) raster dataset layers (Bruns and Egenhofer, 1997); see examples below. The operations are performed on individual cell value(s) of the input layer(s) (Heywood et al., 2002). Map Algebra output is always a new raster layer. Note that in order to use Map Algebra with more than one input raster dataset, the geometry (cellsize, extent, orientation) of the input raster datasets the geometry must match. In this module the following GIS tools and operations are used: The Spatial Analyst Toolbox Local operations with the raster calculator and the combine function Focal operations: focal (neighborhood) statistics Zonal operations: zonal statistics and tabulate areas Global operations: Euclidean Distance (Buffers) Objectives After having completed this module this part you will be capable: to give definitions of the different groups of operations; to describe the possibilities of each group of operations; to perform various operations with ArcGIS using the Spatial Analyst Toolbox. ArcMap document: Raster operations.mxd Literature: Chang, 2010: Chapter 12 Raster Data Analysis (except ) MODULE RASTER OPERATIONS

116 Local operations Local operations compute a raster ouput dataset where the output value at each location (cell) is a function of the input value(s) at spatially coinciding location(s) in the input raster(s) (figure 1). Figure 1. Local operations affect only one cell. We will discuss four types of local operations in this module. There are more operations available in ArcGIS. You can find them in the Local toolset of the Spatial Analyst Tools in ArcToolbox. Feel free to explore them yourself if you desire. It is adivised to explore ArcGIS Desktop Help to gain more information about these operations. When you perform local operations you should realize that: if a cell in an input raster dataset has the value of NODATA, the value of the cell at the same location in the output raster is always NODATA (Figures 2-6)!! the cell values of the input raster dataset(s) are not added to the ouput table as attributes, except when using the combine function. You can imagine that this is not always desireable. More background information about how the value of NoData is treated during operations is given in the course Geo-information Tools (GRS 20806). 1. The first type of local operations calculate output values for each location as a function of the cell values in a single raster dataset, using mathematical functions. For example, the output value is the base e of the input value (figure 2). Mathematical functions (f) have the syntax: output raster = f(input). They are applied to the values in a single input raster. There are four groups of mathematical functions: Logarithmic; Arithmetic (add, substract, divide, multiply); Trigonometric (sin, cos, tan); Powers. These functions can be carried out with the following ArcGIS tools: Spatial Analyst toolbar: Raster Calculator. Spatial Analyst toolbox: Math toolset. Figure 2.The logarithmic function Exp (base e). Syntax: ouput raster = Exp(Inlayer1). MODULE RASTER OPERATIONS

117 2. The second type of local operations calculate output values for each location as a function of cell values at spatially coinciding locations in two or more raster datasets, using arithmethic (mathematical) or logical operators. For example, the average of two values (Figure 3) or value input raster 1 > value input raster 2. These operations have the the syntax: output raster = (input1 opr input2). The opr refers to an operator that operates on two or more variables, the input raster datasets. Arithmetic operators. Arithmetic operators allow for the addition, subtraction, multiplication, and division of two rasters, numbers, or a combination of the two. Spatial Analyst toolbar: Raster Calculator. Figure 3. Using arithmetic operators to calculate the cell average of two raster datasets. Syntax: ouput raster dataset = (Indataset1 + Indataset 2) / 2. Logical operators. Logical operators evaluate the values of an input rasters using a conditional statement. Logical operators can be Boolean (AND, OR, etc.) or Relational (<, >, <>, etc.). One conditional statement can contain one or multiple Boolean and relational operators. The outcome of a logical operation is always TRUE or FALSE. Spatial Analyst toolbox: Math Logical. The logical toolset contains tools for Boolean end relational evaluation. The logical tools use one operator and have the syntax: output raster = (input1 logical opr input2). Figure 4. The Boolean AND tool finds cells that have non-zero values in both input raster datasets. Syntax: ouput raster dataset = (Indataset1 & Indataset 2). Spatial Analyst toolbar: Raster Calculator. With the raster calculator you can combine multiple Boolean and relational operators within one conditional statement. For example output raster dataset = (soil type = 1 AND elevation >=40). MODULE RASTER OPERATIONS

118 3. Operations that calculate summary statistics for each location as a function of cell values at coinciding locations in two or more raster datasets using cell statistic functions (Figure 5). Spatial Analyst toolbox: Local Cell statistics Cell statistic functions have the syntax: output = f(input1, input2). You can calculate ten statistics: 1. Majority 2. Maximum 3. Mean 4. Median 5. Minimum 6. Minority 7. Range 8. Standard Deviation 9. Sum 10. Variety Figure 5. Calculating the mean statistic for three inpu raster datasets. Syntax: outgrid = mean(ingrid1, ingrid2, ingrid3). 4. The fourth type of local operations is the raster overlay. In an overlay the cell values of two or more input raster datasets are combined into one output raster dataset using the combine function of Spatial Analyst. The Combine function assigns a new unique value to each unique combination of values at each location. The original Value items, or the alternative field values if specified, are added to the output rasters' attribute table: one for each input raster (Figure 6). Spatial Analyst toolbox: Local Combine Figure 6. Overlay of two input raster datasets. Syntax: outgrid = combine(ingrid1, ingrid2, ingrid3). MODULE RASTER OPERATIONS

119 1. Open ArcMap document Raster operations.mxd. First activate the Spatial Analyst extension. Click Tools in the menu bar, click Extensions and check Spatial Analyst. This extension allows you to use the Spatial Analyst tools for geoprocessing of raster datasets! a. Are local operations geometric (spatial) or thematic (non-spatial) operations? Explain your answer. b. What geometric criteria should be fulfilled in order to execute local operations that involve more than one dataset? Mathematical functions and operators The Raster Calculator of ArcMap can create simple mathematical expressions with a single function or complex mathematical expressions with many operators and functions. The output raster dataset is automatically named Calculation followed by a unique number. This name can be changed after the creation, by clicking in the name field of the raster dataset. Warning: the output raster dataset of the Raster Calculator is a TEMPORARY raster dataset, which is stored in the TEMP folder on your harddisk and NOT in your workspace. This raster dataset will be lost when you restart your ArcMap document. If you are satisfied with the output result of your operation, you can make the raster dataset permanent. You can do this by right-clicking the raster dataset and selecting Data Make Permanent. Give the raster dataset an appropriate name and save the file in your workspace folder. Make sure the type is set to ESRI GRID. INSTRUCTIONS: 1. Open the Spatial Analyst toolbar: in the menu bar select: View Toolbars Spatial Analyst. 2. Click the Spatial Analyst dropdown arrow, click Raster Calculator. 3. The raster calculator dialog box aids in the creation of an expression that produces a new output raster dataset. The expression can be based on a single raster dataset or multiple raster datasets. 4. There are seven sections in the Raster Calculator dialog box (Figure 7). 1. The layers box lists the available raster datasets in the active data frame; 2. Arithmetic operators; 3. A keypad of numbers; 4. Relational operators; 5. Boolean operators; 6. Mathematical functions; 7. The expression box. 5. Type your expression in the expression box or create your expression by clicking the operator buttons. You can insert raster datasets in your expression by double-clicking a raster dataset in the layers list. 6. To access recently entered expressions, right-click in the expression box and click Recent Expressions. Then copy and paste the expression into the expression box. 7. Click Evaluate. MODULE RASTER OPERATIONS

120 8. The output raster dataset is added to the active data frame. 9. Make the output raster dataset permanent; store it in your workspace Figure 7. The Raster Calculator. 2. Activate data frame Local. The dataset dem (digital elevation model) contains elevation values, expressed in meters. Create a new raster where elevation is expressed in centimeters. a. Write down the created expression which you formulated in the Raster Calculator dialog box. b. What is the name of this new raster dataset? Make the raster dataset permanent. Save it in your workspace folder and change the name to something more appropriate. 3. You can also select raster cells with the Raster Calculator using relational and Boolean operators in the expression. Note: when you press the = button in the raster calculator, a double equal to = = appears in the expression. This is normal raster calculator syntax. a. Create an expression in the raster calculator with which you select buildings (Lu_raster value 1) located on sites that have an elevation of more than 30 meters. Write down the expression. b. What is the meaning of the values 0 and 1 in the output raster dataset? MODULE RASTER OPERATIONS

121 You have been asked by the municipality to select potential housing sites. These sites must fulfill one of the following conditions: (1) The sites must be located on a holtpodzolgrond (soil code gy30, value 4) but it is not allowed to clear forest. (2) Sites with another soil type are also suitable if the elevation is at least 9 meters. c. Write down the expression you created for this selection. d. What is the total area of the potential housing sites? e. You can consider the expression you used as a query: you made a selection. However, there is a difference between this selection and the queries you made in module 5. What is this difference? Raster overlay The output rasters you created during the previous exercise were binary: a raster cell did or did not fulfill the condition you stated in the expression. The attribute table of the output raster contains only the values 0 and 1. The original values of the soil code or land use code were lost which can be inconvenient if you want to do further analysis. A raster overlay by using the combine function avoids this problem. The original values of the input rasters are kept in the attribute table of the output raster. INSTRUCTIONS: 1. Open ArcToolbox. Click Spatial Analyst Tools Local Combine. 2. Click the dropdown arrow of the Input rasters box; click the rasters you want to combine. 3. In the Output raster box you specify the name and location of the output raster. 4. Click OK. Note: the values in the output attribute table are integer. Values that contain decimals (such as elevation values) will be rounded!! 4. Overlay the rasters soil_raster, lu_raster and dem using the combine function. a. Select from the attribute table pasture areas (value 5) on soils with soil code Rn95C (value 3). What is the elevation range of the selected raster cells? b. What part (in %) of the soils with soil code Rn95C is covered by pasture? MODULE RASTER OPERATIONS

122 Focal operations Focal (neighborhood) operations create an output raster dataset in which the output value at each location is a function of the input value at that same location and the values of the cells in a specified neighborhood around the location (Figure 8). A neighborhood can be a rectangle, a circle, an annulus (a doughnut shape), and a wedge in any direction. The size of the neighborhood is userdefined. Figure 8. Focal operations affect the source cell and a user-defined neighborhood. Two important types of focal operations are focal statistics and focal filters. The focal filters are beyond the scope of this course. This subject is treated in the course Remote Sensing (GRS 20306). However, feel free to consult ArcGIS Desktop Help for more information about filters. In this module we will focus on the focal stastics, also referred to as neighborhood statistics. How does a focal operator work? We know that focal operators use the value of a source cell and the values of the cells in a user-defined neighborhood around the source cell to calculate a new cell value for the output raster cell at the same location as the source cell. Suppose we define a rectangular neighborhood focus of 5x5 raster cells (Figure 9) and a focal function, for example the neighborhood statistic focal sum. You can consider the neighborhood focus as a window that moves over your raster dataset layer. In this example the moving window is located at the top left corner of the raster dataset layer (Neighborhood A, figure 9). Figure 9. Processing by 5x5 raster cells neighborhood. MODULE RASTER OPERATIONS

123 The central cell in the window is the source or processing cell. A new value is calculated for each processing cell based on the chosen function. The newly calculated cell value is stored in the output raster, at the same location. Then the window moves one cell further to the right (neighborhood B, Figure 9). The processing cell has also moved one cell further to the right (Figure 9). The calculation is repeated for this new processing cell. The result is again stored in the output raster dataset. Then the window moves again one cell further to the right.and so on, until it reaches the last cell of the input raster dataset layer. Figure 10 illustrates the calculation of a neighborhood statistic focal sum. The upper part of Figure 10 shows the function for a 3x3 rectangular neighborhood. The processing cell has value 2. The same cell in the output raster will get the value of the sum of the processing cell value plus the values of the neighboring cells; in this case the sum equals 21. So a value of 21 is assigned to the cell in the output raster at the same location as the processing cell in the input raster. The lower part of Figure 10 shows the result for an entire raster dataset. Figure 10. Illustration of the calculation of the neighborhood statistic focal sum. If a cell with the value of NoData is present in the neighborhood, it will be ignored in the processing, on contrary with NoData cells in local operations. However, if the entire neighborhood consists of cells of NoData, the output cell value will be NoData!! It is beyond the scope of this course to explain why ArcMap treats the value of NoData different during focal operations than during local operations. The course Geo-information Tools (GRS 20806) will elaborate on the treatment of NoData values during processing. Focal (neighborhood) statistics INSTRUCTIONS: 1. Open ArcToolbox. Click the Spatial Analyst Tools Neighborhood Focal Statistics. 2. Choose the Input raster dataset you want to calculate a statistic for. MODULE RASTER OPERATIONS

124 3. Specify the name and location of the output raster in the Output raster box. 4. Click the Neighborhood dropdown arrow to select a neighborhood shape. The available neighborhoods are Annulus, Circle, Rectangle and Wedge. 5. Specify height and width of the neighborhood. Set Units to Cell. 6. From the Statistic type dropdown list, choose a statistic. The available statistics are Minimum, Maximum, Range, Sum, Mean, Standard Deviation, Variety, Majority, Minority and Median. 7. Click OK. 5. Activate data frame Focal. a. Calculate for raster dataset dem the neighborhood statistic mean. Use the following setting: - Name the raster dataset focal_mean. Save it in your workspace. - Use a rectangular neighborhood. - The neighborhood size: 9x9 cells. b. Choose the same symbology for the output raster as the elevation raster. Explain the effects of this focal mean operation. c. Calculate the neighborhood statistic maximum for raster dataset lu_raster. Use the same neighborhood settings described above. d. Do the calculated values have any meaning? Use the word data scale or measurement scale in your answer. MODULE RASTER OPERATIONS

125 Zonal operations A zone is where all cells in a raster have the same value, regardless of whether or not they are contiguous. A zone represents one thematic class, for example buildings of thematic raster dataset land use. Zonal functions are similar to focal functions except that the definition of the neighborhood in a zonal function is the configuration of the zones of the input dataset and not a specified neighborhood shape. ArcMap offers a variety of zonal operations which can be found in Spatial Analyst toolbox (Zonal toolset) of ArcToolbox. In this module we will discuss two zonal operations: Zonal Statistics and Zonal Area. Zonal statistics The Zonal Statistics function calculates statistics for each zone (see Module 2 for a definition fo Zone ) defined by a zone dataset, based on values from another dataset (the value raster) found within a zone (Figure 11). This could be average population density per zone of pollution or most common vegetation type per zone of elevation. The output of this operation can be presented in a (dbf) table or as raster dataset. During this course only tabular output is created. The zone dataset can be vector or raster data. The value raster must be a raster dataset. With an integer input value raster the statistics are: area, minimum, maximum, range, mean, standard deviation, sum, variety, majority, minority, and median. If the input values are of floating-point data type, the zonal calculations for majority, median, minority and variety are not available Zonal mean a b c Figure 11. The zone dataset (a) defines zones, in this case there are two zones. Zones can for example be land use types. The value raster (b) contains the input values used in calculating the output for each zone. These can for example be elevation values. The zonal function mean would return output (c). The average elevation value is calculated for each zone (check this). All cells in the output raster that belong to the same zone receive the same value, in this case the average elevation within each zone. Note that in this example raster output is given. In the following exercises tabular output is created. INSTRUCTIONS: 1. Open ArcToolbox. Click Spatial Analyst Tools Zonal Zonal Statistics as Table. 2. Click the Input raster or feature zone data dropdown arrow and choose the vector or raster dataset that defines the zones. Statistics will be calculated for each zone of this raster dataset, based on the information in the value raster (step 4). 3. Click the Zone field dropdown arrow and choose the field containing the attribute values which define the zones. 4. Click the Value Raster dropdown arrow and choose the input raster. This raster supplies the values that are used to calculate statistics for each zone of the zone dataset (step 2). 5. Specify the name and location (your workspace) of the output table. 6. Click OK. 7. To view the table, click the Source tab at the bottom of the table of contents. MODULE RASTER OPERATIONS

126 6. Activate data frame Zonal. Calculate elevation statistics for every soil zone (soil type). Use SOILCODE as Zone field. Chart the mean statistic. a. What does a record in the statistic table represent? b. What is the average elevation of the gy30 (value 4) soil? c. What data scale or measurement scale should data have to calculate statistics? Calculate landuse statistics for every soil zone. Use again SOILCODE as Zone field. d. What does a record in the statistic table represent? e. Which of the attributes of the statistic table gives relevant information? Explain your answer. Zonal geometry The Zonal Geometry tool calculates several geometric properties of zones, including the area. The output is in form of a (dbf) table. INSTRUCTIONS: Open ArcToolbox. Click Spatial Analyst Tools Zonal Zonal Geometry as Table. 2. Click the Input raster or feature zone data dropdown arrow and choose the dataset that contains the zones you want to calculate the areas for. 3. Click the Zone field dropdown arrow and choose the field that contains the values of the zones. 4. Specify name and location of the output table. 5. Specify the processing cell size (optional) and click OK. 6. Pay attention to data management. The output tables often provide more information than you are interested in. Delete the fields that contain information that is not directly relevant for your application (see Module 2 how to delete fields)! 7. You can join the attribute table that contains the area values to the original raster dataset. Calculate the areas of the land use zones ( Lu_raster ) by using Zonal Geometry as Table and join the output table to original raster dataset. a. On which field do you have to base the join? b. What is the area covered with buildings (value 1)? c. What % of the buildings (value 1) is located on soil Holtpodzolgrond (value 4)? Hint: first use the combine function to overlay the land use raster with the soil raster. MODULE RASTER OPERATIONS

127 Global operations Global functions compute an output raster dataset in which the output value at each cell location is potentially a function of the cells combined from the various input raster datasets. There are two main groups of global functions: the Euclidean and cost (or weighted distance) functions. In this module we will only treat the Euclidean distance function. The other Euclidean functions (allocation and direction) and the cost functions are beyond the scope of this course. These functions will be treated in the followup course Geo-Information Tools (GRS 20806). According map algebra, Euclidean distance is calculated from the center of the source cells to the center of each of the surrounding cells (Figure 12) that contains the value of NoData. Each cell in the output raster contains a distance value which is the distance to its nearest source cell. Source cells are given the value of zero. Be aware that the output raster is a continuous dataset with floating-point distance values. Figure 12. Calculating the Euclidean distance from a raster cell to its nearest source cell. In this module we focus on the use the Euclidean distance function for proximity analysis. Proximity analysis is used to determine the proximity of spatial features within a dataset layer or between two dataset layers. A widely applied proximity function in GIS analysis is the buffer. Buffers can be applied to both raster and vector data. The output dataset that contains the buffer zone can also be raster or vector. Euclidean distance & Buffers When you create buffer zones you select raster cells based on a specified distance from the source cells. The principle of buffering is simple. 1. Select the cells or features that will function as source. 2. Calculate the Euclidean distance to the source cells. 3. Select the raster cells based on a user-defined distance criterion. For example, if you want to create a buffer zone of 50 meters around a road, select the raster cells that have a distance value 50 meters. Note that if you calculate the Euclidean distance to vector features, a transformation must take place because the Euclidean distance function can only be applied to raster cells. However, you do NOT have to do this yourself. The transformation is done automatically by ArcMap during the process of Euclidean distance calculation!!! INSTRUCTIONS: Part 1: Euclidean distance calculation 1. Display the dataset that contains the source cells or source features. 2. Select the zone(s) or feature(s) of interest. If you do not select particular feature or zone, all will be used for as sources for Euclidean distance calculation. MODULE RASTER OPERATIONS

128 3. Open ArcToolbox. Click Spatial Analyst Tools Distance Euclidean distance. 4. Select the Input raster or feature source data. This is the dataset that contains the source cells or features!! 5. Specify name and location in the Output distance raster box. 6. A Maximum distance can be specified, this is however not required. 7. Define Output cell size. Leave the Output direction raster box empty. This output cellsize does not only determine the cell size of the new raster dataset. If features from a vector dataset are used as input they are transformed to a raster dataset with the defined cell size. 8. Click OK. A raster that contains the Euclidean distances to the source cells or features is calculated. Part 2: Creating buffer zones from the Euclidean distance raster 9. Select Raster Calculator from the Spatial Analyst dropdownmenu 10. Create a query expression to select a range of distance values, which defines the buffer zone. 11. Click OK. 8. Activate data frame Global. Create a new dataset Dist_roads that contains Euclidean distance values to all road features. Do not specify a maximum distance and choose 10 m as output cell size. a. How many distance classes are distinguished in the new raster dataset Dist_roads? The number of classes is determined by an ArcMap default setting. Change the symbology with the Symbology editor. Choose map type Stretched and a color ramp to visualize the Euclidean distance raster. b. Does the Euclidean distance raster contain discrete or continuous data? Explain your answer. Use the Raster Calculator to create a buffer zone of 100 meters around the roads. c. Calculate the the area of the buffer zone? Creating raster data subsets In the previous exercise you saw that the output raster is a rectangle. The extent (borders) of the rectangle is determined by the extent of the input raster: the upper border of the output coincides with the top coordinate of the input raster, the lower border with the bottom coordinate of the input raster, etc. Datasets can, however, have different extents. When you build a GIS application it is convenient to work with datasets that have the same extent. You can control how the extent of the output raster is determined with setting the Output Extent and Mask. These two processing settings allow the user to take a geometric subset from a raster dataset. Note that the output extent and mask settings have effect on all raster operations. However, they are frequently used during buffer and reclass operations. We will therefore discuss these settings in this context. During the course Geo-information Tools (GRS 20806) the mask and output extent are discussed in more detail. MODULE RASTER OPERATIONS

129 INSTRUCTIONS (Output Extent): Click the Tools in the menu bar. Click Options Geoprocessing Environments General settings. 2. Click the Output Extent dropdown arrow and select an extent. Note that this is an ArcMap setting. It does not change the extent of datasets that have already been created. Only datasets that are created after you changed the output extent setting, will have the specified extent. Set the Output extent to Same as Layer lu_raster. a. Create a new dataset Dist_extent that contains Euclidean distance values to all road features. Do not specify a maximum distance and choose 10 m as output cell size. b. Give the NoData values of dataset lu_raster a color. You can see that the extent of the distance raster coincides with the extent of dataset lu_raster. You created a distance raster that has the same extent as dataset Lu_raster. However, the land use dataset has an irregular shape. Parts of the distance raster fall outside the borders of the Lu_raster dataset. If you want your distance raster to have a specific spatial area, for example, the same area as the Lu_raster dataset, you have to set the Mask. The mask identifies those locations within the output extent that will be included during geoprocessing. This can be an area, like in this case, but it can also be a selection of cells or features. Cells that fall outside the mask will be assigned the value of NoData. INSTRUCTIONS (Mask): Click the Tools in the menu bar. Click Options Geoprocessing Environments Raster analysis settings. 2. Click the Mask dropdown arrow and select a dataset that will be used as mask. 3. You can also set the output cell size. This cell size is automatically chosen for all raster output datasets. Set the Mask to lu_raster. a. Create a new dataset Dist_mask that contains Euclidean distance values to all road features. Do not specify a maximum distance and choose 10 m as output cell size. b. Display the Dist_mask dataset in the view window and compare it with the dataset Dist_extent to see the result of a mask. MODULE RASTER OPERATIONS

130 Reclassification of the Euclidean distance raster Reclassifying data simply means replacing cell values with new cell values. There are many reasons why you might want to reclassify your data. Some of the most common reasons are: to replace values based on new information; to group certain values together; to reclassify values to a common scale and; to set specific values to NoData or to assign a value to cells with NoData. There are several approaches to reclassify your data; by individual values, by ranges, by intervals or area, or through an alternative value. You can reclassify the continuous Euclidean distance raster to a discrete raster. INSTRUCTIONS: 1. Open ArcToolbox. Click Spatial Analyst Tools Reclass Reclassify. 2. Click the Input raster dropdown arrow and click the input raster. 3. Click the Reclass field dropdown arrow and click the field that contains the values you want to reclasify. 4. You can reclassify manually: set ranges for the Old values and assign a New value to each range, or you can use reclassification options under Classify button. 5. Click the Classify button. 6. Click the Method dropdown arrow and select a method: Equal interval: set the number of classes you want, ArcMap determines the interval size. Defined interval: set the interval size, ArcMap determines the number of output classes. 7. Specify a name for the output raster dataset. 8. Click OK. Be aware that you lose the original distance values during reclassification. They are replaced by a new value. 11. a. Reclassify raster dataset Dist_mask into distance classes of 50 meters. Name the dataset Recl_dist and save the dataset in your workspace. b. How many classes are distinguished? MODULE RASTER OPERATIONS

131 Introduction Geo-Information Science Practical Manual Module 8 Vector operations

132 8. VECTOR OPERATIONS INTRODUCTION 8-1 FIELD CALCULATIONS 8-2 VECTOR OVERLAY 8-3 Topological overlays 8-4 Intersect 8-4 Union 8-4 Identity 8-4 CALCULATING ATTRIBUTE STATISTICS 8-7 Statistics function 8-7 Summarize function 8-7 BUFFERING VECTOR FEATURES 8-9 CREATING VECTOR DATA SUBSETS 8-11

133 8. VECTOR OPERATIONS Introduction In the previous module, operations with raster data were discussed. Although vector operations are not divided into local, focal, zonal and global operations, there are vector counterparts of the raster local, focal and zonal operations. Focal operations do not exist in vector environment. Raster operations are cell-based; the raster cell is the smallest geometric object to which operations are applied. The smallest geometric object to which vector operations are applied is a point, line or polygon feature. Vector operation can therefore be considered feature-based. All ArcGIS tools for raster operations are conveniently stored in the Spatial Analyst toolbox. The vector counterpart of this toolbox is the Analysis toolbox. The Analysis toolbox provides a powerful set of tools to perform various geoprocessing operations for all types of vector data. With these tools, you can perform overlays, create buffers, calculate statistics, perform proximity analysis, and much more. When you need to solve a spatial or statistical problem, you should always look in the Analysis toolbox. As with raster datasets, vector datasets should always be projected in the same reference system in order to apply geoprocessing when multiple datasets are involved!! In this module: Recalculating attribute values with the field calculator. Vector overlays: the union, intersect and identity functions. Deriving attribute statistics. Vector buffers. Objectives After having completed this module you will be capable: to calculate new attribute values with the field calculator and field statistics; to describe and apply the most important vector overlay types; to create vector buffer zones around features. ArcMap document: Vector operations.mxd Literature: Chang, 2010: Chapter 11 Vector Data Analysis (except , , 11.4) MODULE VECTOR OPERATIONS

134 Field calculations Entering values with the keyboard is not the only way you can edit tables. In some cases, you might want to perform a mathematical calculation to set a field value for a single record or even all records. With the raster calculator you were able to perform simple and more complex calculations on raster values. With vector data you can use the ArcMap field calculator to perform simple as well as advanced calculations on any selected record. The simplest type of expression assigns a single value to every cell in the active field. More complex expressions use arithmetic and logical functions to modify values found in other fields of the table. Note that field calculations and vector overlays (next section) are vector counterpart of the local raster operations. Local operations are performed on single raster cells, the smallest geometric object. Field calculations and overlays are performed on single features, the smallest geometric object in a vector environment. INSTRUCTIONS: 1. Click the Editor dropdown arrow in the Editor toolbar and click Start Editing. 2. Open the attribute table of the dataset you want to do calculations for. 3. Click on the header of the field you wish to calculate. Note that the field turns blue. 4. Right click on the Field s name and select Field Calculator. The Field Calculator dialog box appears (Figure 1). 5. Type the expression for the calculation in the input area of the dialog box. You can use the Field and Functions items and the operator Input area buttons to create the expression. Note that the expression NewField = is already stated. 6. Click OK to perform the calculation. ArcMap displays the results in the attribute table. The calculation applies to the selected set of Figure 1. The Field Calculator. records. If no records are selected, the calculation applies to all records. 7. You can do the calculations for a new field but you can also replace the attribute values of an existing field by new values. Note: Adding and deleting fields should be done using ArcToolbox, as described in module 2. Whenever problems (errors) occur, close ArcMap and re-open it. 1. Open ArcMap document Vector operations.mxd. Activate data frame Wag_south. In the attribute table of dataset Soil_points, the values of attribute Clay% and Silt% are stored. During the texture analysis of the soil samples something went wrong. The clay percentage of all the soil points is 2 % too low, and the silt percentage is 2 % too high. a. Correct these clay and silt fractions for this soil sample stored in the attribute table Soil_points. b. The ph-value of point X9 is measured 0.1 too high. Correct this! Suppose you want to analyze the Sand% / Clay% - ratio (SC-ratio) of the soil samples. c. Describe how to implement this in ArcMap and write down the soil sample with the highest and the soil sample with the lowest ratio-value. (hint: use field setting: Data type float, Precision: 4, Scale: 2). MODULE VECTOR OPERATIONS

135 Vector overlay Very often the user wants to know information combinations from two or more datasets: for example, he wants to know where a given kind of land use occurs on a particular kind of soil. The soil information is encoded in one dataset, the land use in another dataset. The classical analogue method of solution is to lay transparent copies of each map on a light table and to trace the corresponding boundaries of the required areas. This is an out-of-date, time-consuming and inaccurate solution; so one of the first requirements of many geographical information systems is to overlay (integrate) different datasets with geometric features along with their attribute information. This is definitely one of the key GIS analysis functions. Using GIS it is possible to take two different thematic map layers of the same area and overlay them one on top of the other to form a new layer (Heywood et al., 2002). Map layers and layer refers to vector datasets in this reference of Heywood. Be aware that for any overlay, the datasets must have the same geographic projection and coordinate system!! So, an overlay can be considered as the process of stacking digital representations of various spatial datasets on top of each other so that each position in the area covered can be analyzed in terms of these datasets (Burrough, 1986). From the references of Heywood and Burrough two types of overlays are distinguished: 1. Optical overlays; 2. Topological overlays. In optical overlays, spatial datasets are visually superimposed; there is NO integration of geometry and attribute data involved. Thus, strictly speaking, optical overlays are not operations. Optical overlays can be used to query; selections based on spatial relationships (this was discussed in module 5). In a topological overlay, the geometry and attribute data of two datasets are integrated. Where lines or polygon borders cross each other, new intersection points are calculated. By definition, the topological overlay always creates a new dataset. Figure 2.Example of a topological overlay (Bernhardsen, 1992). Figure 2 shows the results of a topological overlay of two thematic datasets. In this example, the output dataset contains 8 polygon features. The number of polygons of the final map is not only dependent on the numbers of polygons in initial datasets, but also dependent on the form of the boundaries. The more complicated the boundaries in the source datasets, the more polygons features in the output datasets. MODULE VECTOR OPERATIONS

136 The new attribute table contains a new identifier attribute. The attributes of the source datasets, including their identifier attribute, are joined to the new output table. Note that this is the vector counterpart of the raster combine function. Topological overlays With topological overlays you integrate geometry and attribute data spatial datasets. In this way you create new spatial data that gives you new information. ArcGIS offers a variety of overlay tools (the Analysis toolset in ArcToolbox). We will discuss three overlays. Consult the ArcGIS Desktop Help system for more background information about the different types of overlays. Intersect The Intersect function computes a geometric intersection of the input features and a tabular join of the attribute tables. Features or portions of features which overlap in all dataset layers will be written to the output dataset (Figure 3). Note that this is a different intersect than the optical intersect (Select by Location) you used in exercise 3. Figure 3. The Intersect overlay. Union Union is a topological overlay of two or more polygon spatial datasets that preserves the features that fall within the spatial extent of either input dataset; that is, all features from both datasets are retained and extracted into a new polygon dataset and a tabular join of the attribute tables is computed (Figure 4). Figure 4. The Union overlay. Identity Identity is a topological overlay that computes the geometric intersection of two datasets. The output dataset preserves all the features of the first dataset plus those portions of the second dataset that overlap the first and a tabular join of the attribute tables is computed. For example, a road passing through two counties would be split into two arc features, each with the attributes of the road and the county it passes through (Figure 5). Figure 5. The Identity overlay. MODULE VECTOR OPERATIONS

137 2. a. Figure 6 illustrates an intersect overlay of two datasets. Write down the attribute table of the output dataset that results from this overlay. Input features Intersect features D E A B C F Attribute table: FID Attr1 Attr2 A U1 X1 B U2 X1 C U3 X1 Attribute table: FID Attr3 Attr4 D Z1 Y3 E Z2 Y3 F Z3 Y3 Figure 6. An intersect overlay of two datasets. INSTRUCTIONS: 1. Display the datasets you want to intersect. 2. Select the features you want to intersect. If no features are selected, all features will be used in the intersection. 3. In ArcToolbox select: Analysis Tools Overlay Intersect. 4. A dialog box opens (Figure 7). Choose the input features; define the name and location of the output dataset. 5. Click OK (leave the other options on default). 6. In the attribute table of the new dataset you have to recalculate the areas of the new polygons!! Figure 7. The Intersect dialog box. The union and identity overlays can also be found in the Overlay toolset of the Analysis toolbox. MODULE VECTOR OPERATIONS

138 3. Intersect dataset Land_use with the soil feature(s) classified as gy30. Save the output dataset in your workspace. a. How many polygons features does the output dataset contain? b. The attribute table of the output dataset contains two 'area' fields. Explain the meaning of both fields. c. Recalculate the area of the features of the intersect output dataset. What is the total area of the of the output dataset? d. Calculate the total residential (C2=1) area within the intersected area. MODULE VECTOR OPERATIONS

139 Calculating attribute statistics In this section the statistics and summarize functions are discussed. These functions are the vector counterparts of the zonal raster operations zonal statistics and zonal area. Zonal operations are performed on zones: all raster cells with the same value. The vector functions treated in this section are performed on attribute class level: all features with the same attribute value! For example all features with land use type forest are one class of the attribute land use. An attribute class is the vector equivalent of the raster zone. Statistics function When exploring the contents of a dataset you can derive statistics describing the attribute values in numeric fields. You'll see how many values the column has, as well as the sum, minimum, mean, maximum, and standard deviation of those values. A histogram is also provided showing how the field's values are distributed. Statistics are calculated for all numeric fields in the table. Be aware that only statistics calculated for ratio and interval (quantitative) data are meaningful!! INSTRUCTIONS: Open the attribute table of the dataset from which you have selected features. 2. Right-click the header of the field you wish to calculate statistics for. 3. Click Statistics. The window that appears shows the sum, mean, maximum, minimum, range, variance and standard deviation of the attribute which you have chosen (or which you have selected from the drop down menu). The count shows how many features are currently selected. You can only calculate statistics of fields that contain numeric data. 4. To see a description of another field's values, click the Field dropdown arrow and select another field. 5. If you want to calculate statistics for a subset of your data, make a selection first!! Select the residential features (C2=1) from the Land_use dataset. a. Write down the values of statistics Sum, Count, Mean, Maximum, Minimum and Standard Deviation of the Area field. b. How many residential features does the Land_use dataset contain? c. What is the meaning of the statistic Sum? Summarize function Sometimes the attribute information you have about map features is not organized the way you want for instance, you have population data by municipality when you want it by province. By summarizing the data in a table, you can derive various summary statistics including the count, average, minimum, and maximum value and get exactly the information you want. You can do this using the Summarize function. ArcMap creates a new table containing one record for each unique value (class) of the selected field (e.g. C2 ), along with statistics summarizing any of the other fields (e.g. Area ) in the attribute table. You can then join this table of summary statistics to the MODULE VECTOR OPERATIONS

140 attribute table of the dataset so you can symbolize, label, or query the dataset's features based on their values for the summary statistics. Note that this has some similarities to the zonal statistics function (module 7). With zonal statistics you could calculate for example the average elevation of each soil type. This function used two raster datasets as input: one dataset contained the zones (soil_types); the other datasets contained the values from which the statistics were derived (elevation). The summarize function does basically the same. Instead of two datasets, summarize uses two fields of an attribute table as input. One field functions as zone, the values in the other field are summarized for each zone. In tabular statistics the zone attribute is often referred to as case field or grouping attribute. In ArcGIS it is referred to as case field. INSTRUCTIONS: Open the attribute table of the dataset for which you want to summarize statistics. 2. Right-click the heading of the field you want to summarize and click Summarize (this function can also be found in ArcToolbox: Analysis Tools Statistics Summary statistics). 3. In the dialog box that appears: select the field to summarize. Summary statistics are calculated for each unique attribute value that this field contains (for example each land use zone of field C2 ); choose one or more summary statistics for the fields you want to include in the output table; specify the name and location of the output table. 4. It is possible to only use the selected features, by checking the box Summarize on the selected records only. 5. To view the table, click the Source tab at the bottom of the table of contents. 6. Click OK. 7. Click Yes. Summarize the statistics minimum, maximum, sum and average of the field area for the seven land use types (zones) that are present in the Wageningen South study area. a. What is the total residential (C2 = 1) area? b. Write this statistical query down according to the formal structure format of a query. So, select attributes S from database F, which fulfill the condition W (see module 5). S: F: W: c. Join the summary statistics table to the Land_use attribute table. Where is the largest building located? If you don t remember how to join tables, consult module 2 again. d. Select buildings that are larger than the average? Write down the number of selected polygons. e. What data scale must the zone field have? And the field that contains the values that are used to calculate the statistics? MODULE VECTOR OPERATIONS

141 Buffering vector features You can create buffer zones around the point, line and polygon input features. A buffer polygon is created to a specified distance around the input feature(s), (Figure 8). Figure 8. Buffering two polygon features with a vector buffer as output. INSTRUCTIONS: (buffer) 1. Make sure the map units are defined in your dataframe and/or dataset. 2. Select the features you want to buffer. 3. Open ArcToolbox, click Analysis Tools Proximity Buffer. 4. Specify the input features from the dropdown list or browse to the dataset. 5. Define the output feature class (save the dataset in your workspace). 6. Fill in the buffer distance (or choose a field which provides buffer distances). 7. Optional you can choose to dissolve the buffers (Dissolve: all), the result will then be one single feature instead of one buffer feature for every input feature. 8. To calculate the area of a vector buffer: see module 2 (page 2-6). Figure 9. Buffering in multiple rings INSTRUCTIONS: (Multiple ring buffer) 1. Select the features you want to buffer 2. If you want to create a buffer that consists of multiple zones. Select in ArcToolbox: Analysis Tools Proximity Multiple Ring Buffer. You can specify the number and the distance of the ring zones in the dialog box. Add the breaks of the classes, as given in Figure 10 So if you want a buffering 0-50, , , add 50, 100, 150, 200. MODULE VECTOR OPERATIONS

142 Figure 10. Input screen for multiple ring buffer 6. Create a buffer zone of 500 meters around the Gen. Foulkesweg. Use a query to make sure you select all features with street name Gen. Foulkesweg. Set Dissolve to all to dissolve the borders between the buffers around the different features. a. What is the area of the buffer zone you created? b. Is the river Rhine located within the zone of 500 m around the Gen. Foulkesweg? 7. Create a vector buffer zone of 400 meters, divided into 4 sub zones of 100 m each, around soil point X7. a. How many other soil points are located within 400 m of this soil point? b. Calculate the areas of the different sub zones of this multiple ring buffer. MODULE VECTOR OPERATIONS

143 Create a raster buffer zone of 400 meters, divided into 4 sub zones of 100 m each, around soil point X7. Step 1 Calculate the Euclidean distance from soil profile point X7. Set Maximum distance to 400, Output cell size 5 meters. Set your extent to Lu_raster if you don t get a full circular distance raster Step 2 Reclassify the output distance raster to 4 buffer zones of 100 meters. c. Compare the result with the multiple ring vector buffers you just created. What is noticeable? d. Calculate the areas of the four raster buffer zones (see module 7 how to do this). Join the output table to the attribute table of the buffer dataset. Compare these areas with the areas of the vector buffer rings. Creating vector data subsets In module 7 the raster analysis extent and analysis mask were mentioned as methods to take a geometric subset of a raster dataset. The vector equivalent is the clip function, which can be found in the Extract toolset of the Analysis toolbox. You can use clip to cut out a piece of one feature class using one or more of the features in another dataset as a "cookie cutter" (Figure 9). Figure 11. The Clip function. INSTRUCTIONS: 1. Select the features which define your clip feature 2. Open ArcToolbox, click Analysis Tools Extract Clip 3. In the next dialog screen select your input 4. Select your clip features 5. In the box output feature class you can specify the name and location of the output. 6. Press OK 8. Activate data frame Create subset. This data frame contains the Land_use dataset and a dataset with the postal code zones of Wageningen. a. Select the feature with postal code Take a subset from the Land_use dataset that contains the area that has postal code Other interesting tools can be found in the Data Management toolbox: Merge; can be found in the General toolset. Dissolve and Eliminate; both can be found in the Generalization toolset. Consult the ArcGIS Desktop Help to learn more about these tools. MODULE VECTOR OPERATIONS

144 MODULE VECTOR OPERATIONS

145 Introduction Geo-Information Science Practical Manual Module 9 Surface analysis

146 9. SURFACE ANALYSIS INTRODUCTION 9-1 CREATING A DEM FROM POINT OBSERVATIONS Spatial interpolation Inverse Distance Weighted (IDW) 9-3 Spline 9-4 Spatial interpolation in ArcMap 9-4 ANALYZING SURFACES Slope analysis Slope gradient calculation in ArcMap 9-8 Slope aspect calculation in ArcMap 9-9 Contour mapping 9-10

147 9. SURFACE ANALYSIS Introduction This last module of the ArcGIS part of the course Introduction to Geo-information Science focuses on operations involving surfaces. A surface can be defined as a geographic phenomenon represented as a set of continuous data, for example rainfall, temperature, ice thickness of a glacier or organic matter content of a soil. A continually varying surface can be represented by isolines (contour lines), and these contours can be effectively regarded as sets of closed and nested polygons. Although contours are very suitable presentation form of a continually varying surface, they are not particularly suitable for numerical analysis (or modelling). So other data formats were developed in order to be able to represent and to use effectively in spatial analysis where a continuous phenomenon is involved. A surface model is an approximation of a surface. Surface models are stored and displayed as rasters or TINs (Triangular Irregular Networks; vector format). Because a surface contains an infinite number of points, it is impossible to measure and record the value at every point. A surface model approximates a surface by taking a sample of the values at different points on the surface and interpolating the values between these points. A widely used surface model in GIS is the Digital Terrain Model (DTM). This is a digital representation of the continuous variation of topography over space. A well known example of a DTM is the Digital Elevation Model (DEM): a digital model of a topographic surface using information on elevation of the land s surface. Other examples of DTMs are models of slope or aspect. Note that these terrain models are derived from a DEM. Digital elevation models have many uses. Among the most important are the following: 1. For hydrological analysis. 2. Three-dimensional display of landforms for landscape design and planning. 3. For planning routes of roads, location of dams, etc. 4. For statistical analysis and comparison of different kinds of terrain. 5. For computing slope maps, aspect maps, and slope profiles that can be used to prepare shaded relief maps, assist geomorphological studies, or estimate erosion and run-off. 6. Provide data for image simulation models of landscapes and landscape processes. ArcMap s Spatial Analyst offers the user a wide range of functions to analyze surfaces in the Surface toolset. In this module: Interpolation of height points. Deriving slope and aspect from a DEM. Objectives After having completed this module you will be capable: to understand different interpolation methods; to apply an interpolation method to derive a elevation surface from observation points; to derive other terrain models from a DEM. ArcMap document: Surface analysis.mxd Literature: Chang, 2010: Chapter 13 Terrain Mapping and Analysis (except , 13.4) Chapter 15 Spatial interpolation sections 15.1, , (except formulas) MODULE SURFACE ANALYSIS

148 Creating a DEM from point observations A common data structure of a DEM is a point elevation raster. These height points form a mesh of square areas. The DEM is obtained through different sources of height measurements. These sources used to be stereoscopic measurements from aerial photographs using digital stereo-plotters or stereo image correlation using aerial photographs or digital images, land surveying measurements and maps with spot heights and / or contours. Nowadays DEMs are frequently obtained by digital aerial or satellite images rather than from a direct survey. Powerful techniques include radar interferometry (RADARSAT 1, Shuttle Radar Topography Mission) and airborne laser altimetry ( The Dutch AHN (Actual Height Model) was derived from laser altimetry measurements. Note that the contour data or any other sampled elevation datasets (by GPS or ground survey) are not DEMs. A DEM implies that elevation is available continuously at each location in the study area. Measured elevation data are often irregularly spaced point observations. By means of an interpolation model a regular spaced elevation surface is computed. Spatial interpolation Visiting every location in a study area to measure a phenomenon is usually nearly impossible and very expensive. Instead, you can measure the phenomenon at strategically dispersed sample locations, and predicted values can be assigned to all other locations by means of an interpolation technique (Figure 1). Input points can be either randomly or regularly spaced or based on some sampling scheme. Spatial interpolation functions create a continuous (or prediction) surface from observation point values (Figure 1). They make predictions from observed values for all locations in a raster dataset, whether a measurement has been taken at the location or not Figure 1. Observation points (dark dots) with interpolated surface DEM and from the DEM computed contour lines. Why interpolation? The assumption that makes interpolation a viable option is that spatially distributed objects are spatially correlated; in other words, things that are close together tend to have similar characteristics: the values of points close to sampled points are more likely to be similar than those that are farther apart. This is the basis of spatial interpolation. There are a variety of ways to derive a prediction for each location; each method is referred to as a model. With each model, there are different assumptions made of the data, and certain models are more applicable for specific data, for example, one model may account for local variation better than another. Each model produces predictions using different calculations. Based on how the sample points are distributed, each interpolation method will compute a different result. No matter which interpolation method is used, the more input points and the greater their distribution, the more reliable the end result. MODULE SURFACE ANALYSIS

149 Spatial interpolation functions can be divided into two groups: 1. Deterministic models: Inversed Distance Weighted (IDW) and Spline; These functions assign values to locations based on the surrounding measured values (IDW) and on specified mathematical formulas that determine the smoothness of the resulting surface (Spline). 2. Geostatistical models: Kriging. Kriging models use the statistical relationship (autocorrelation) among the observation points. In this module we focus on the deterministic functions. Kriging is beyond the scope of this course. Feel free to consult ArcGIS Desktop Help to learn more about Kriging. Note: the distribution of the observation points is very important when applying an interpolation. Depending upon the interpolation when making a DEM, observation points must be well distributed over the whole surface according to the relief of the terrain. Sharp changes in height over a short distance requires more observation points than relatively flat areas. Inverse Distance Weighted (IDW) Inverse Distance Weighted (IDW) is a method of interpolation that estimates cell values by averaging the values of observation data points in the neighborhood of each processing cell. This method assumes that the variable being mapped decreases in influence with distance from its sampled location. The closer a point is to the center of the cell being estimated, the more influence, or weight, it has in the averaging process. Figure 2: Inverse distance weighted interpolation method (IDW): the values of points closer to the observation point (points in the circle) are more similar to the value of the observation point than the points further away. IDW allows you to control the influence of the observation points upon the interpolated values, based upon their weight and their distance from the output point: The power parameter in the IDW interpolation controls the significance of the surrounding points upon the interpolated value. Power is the exponent of distance. A higher power results in less influence from distant points: distant points receive lower weights. However, lower powers tend to treat all the sample points equally resulting in a smoother surface. When the power is 0 there is no decrease in weight with distance which means that all observation points get the same weight. The prediction will be the mean of the measured values. A specified number of points or all points within a specified radius can be used to determine the output value for each location. The IDW interpolation method can be accessed using the Spatial Analyst toolbar or the IDW tool in the Interpolation toolset of the Spatial Analyst toolbox. MODULE SURFACE ANALYSIS

150 Spline The Spline interpolation method is a general purpose interpolation method that fits a 2-dimensional minimum-curvature surface through the input points. A spline passes exactly through the input data points, to minimise certain aspects of curvature. Conceptually, it is like bending a sheet of rubber to pass through the points, while minimising the total curvature of the surface. It fits a mathematical function to a specified number of nearest input points while passing through the sample points. This method is best for generating gently varying surfaces such as elevation, water table heights, or pollution concentrations. There are two Spline methods: Regularized and Tension. The Regularized method creates a smooth, gradually changing surface with values that may lie outside the sample data range. The Tension method controls the stiffness of the surface according to the character of the modeled phenomenon. It creates a less smooth surface with values more closely constrained by the sample data range. Further control of the output surface is accomplished through two additional parameters: weight and number of points. Consult ArcGIS Desktop Help to learn how these two parameters influence Spline interpolation (use keyword Spline interpolation, subsequently choose the described subentry). Figure 3: Spline interpolation method. In Figure 3, Spline estimates the value of the selected cell at 23. Spline tries to fit a curve using the selected subset of samples, in this case 6 samples. The curve would start at one of the cells with a value of 10, start up to a cell with a 20, continue up or overshooting, then come back down to another 20 and back down to a 10. The estimate cell, 23, may have been on the upswing of the curve. Spatial interpolation in ArcMap The interpolation methods in ArcGIS can only use point observations as input. Height profiles, terrain structure lines such as a river drainage pattern and contour lines cannot be used. INSTRUCTIONS: 1. Open ArcToolbox. Click Spatial Analyst Tools Interpolation IDW / Spline. 2. Choose an input point dataset that contains elevation values. 3. Choose the Z value field (field that contains elevation values). 4. Choose name and location of the output raster dataset. 5. Specify the parameters for the chosen interpolation method in parameter type-in boxes. Inverse Distance Weighted: o Specify the cell size o o Specify the power. Choose a Variable or a Fixed Radius interpolation. If you choose Variable, enter the number of input points and/or the maximum distance. If you choose Fixed, enter the distance to search for points and/or the minimum number of points. MODULE SURFACE ANALYSIS

151 Spline interpolation: o Choose cell size o Choose the Regularized or Tension method. o Enter a weight factor. o Specify the number of points to use per region. 6. Click OK. 1. Open the ArcMap document Surface analysis.mxd. a. How many point observation points are stored in dataset Height_observations? b. Is this a vector or raster dataset vector or raster structured? Explain your answer. 2. In this exercise you have to interpolate the elevation point observations using different interpolation methods, types and parameter settings. a. Set the Output Extent to Same as Layer lu_raster and set the Mask to lu_raster (see Module 7 how to set the output extent and mask). b. Create 5 digital elevation models by interpolation of the elevation points. Use the interpolation parameters as given in Table 1. c. Display the DEMs you created. The DEMs must be displayed with the same symbology in order to compare. Open the Symbology editor, right-click the box that contains the ranges and labels, choose Load class breaks from the menu and select Elevation_classification.xml from the data folder. d. Fill in Table 2. You cannot open de attribute table of the DEM (because this is a continuous raster dataset). You must use dataset statistics to retrieve minimum, maximum and average elevation. These statistics can be found under the Source tab in the Layer properties window. e. How is it possible to get lower and higher elevation values after interpolation than the minimum and maximum values contained in dataset Height_observations? Table 1. Interpolation parameters. Name Interpolation method Type No. of neighbouring points Radius Power / Weight Cell size IDW_4_5 IDW Variable IDW_4_25 IDW Variable IDW_12_5 IDW Variable IDW_50_5 IDW Fixed radius Spline_4_5 Spline Tension MODULE SURFACE ANALYSIS

152 Table 2. DEM statistics. Name Minimum Elevation Maximum Elevation Average Elevation IDW_4_5 IDW_4_25 IDW_12_5 IDW_50_5 Spline_4_5 3. In this exercise you have to compare the results of exercise 2. a. Describe in your own words how the Variable IDW-interpolation method works. b. What is the effect of cell size when using IDW as an interpolation method? (compare IDW_4_5 with IDW_4_25) c. What is the effect of increasing the number of nearest points when using IDW as an interpolation method? (compare IDW_4_5 with IDW_12_5) d. Describe in your own words the differences between the Fixed Radius IDW-interpolation method and the Variable IDW-interpolation method. (compare IDW_4_5 with IDW_50_5) e. Write down the effect of a 50 meter radius on the spatial coverage of the interpolation. What could you do to improve the coverage of the interpolation IDW_50_5. MODULE SURFACE ANALYSIS

153 f. Write down the advantages of using the Fixed Radius IDW-interpolation method. g. For which type of terrain is it better to use the Spline interpolation method instead of IDW? Zoom in to different locations of the Wageningen South area and compare IDW_4_5 with Spline_4_5 h. Write down what the differences are between the different interpolation methods IDW and Spline. Use interpolation Spline_4_5 for the next exercises. Remove the other created datasets from the data frame! IDW and Spline interpolations are basic methods. The more advanced interpolation method Topo to Raster is discussed in the follow-up course Geo-information Tools (GRS 20806). MODULE SURFACE ANALYSIS

154 Analyzing surfaces Before digital elevation models were available, geomorphologists applied a wide variety of qualitative and semi-quantitative techniques to describe and compare terrain. Quantitative analysis was difficult due to the amount of work collecting data, either in the field or from aerial photographs. Once a DEM is produced, several standard computations allow the production of digital terrain models showing slope and other terrain features. When analyzing surfaces, you perform a specified calculation that results in different representations of a surface or that derives patterns not readily apparent in the original surface, using a continuous raster dataset. With surface-analysis operations, you can derive additional information by producing new data and identifying patterns in existing surfaces. Slope analysis Slope is defined by a plane tangent to the surface as modelled by the DEM at any given point and comprises two components: Gradient: the maximum rate of change of elevation from each cell to its neighbours; Aspect: the compass direction of this maximum rate of change. Slope gradient calculation in ArcMap The Slope function calculates the maximum rate of change between each cell and its neighbors, for example, the steepest downhill descent for the cell (the maximum change in elevation over the distance between the cell and its eight neighbors). Every cell in the output raster has a slope value. The slope gradient can be calculated in percentage or degrees. INSTRUCTIONS: Open ArcToolbox. Click Spatial Analyst Tools Surface Slope. 2. Select the Input raster. 3. Specify a name and location for the output raster. 4. Specify whether you want the output slope raster in degrees or in percentages. 5. Enter the Z-factor if the horizontal unit differs from the vertical unit. For example when the horizontal unit is meter and the vertical unit centimeter, then the Z-factor is Click OK. Derive the slope gradient from the DEM you have created. Improve the display; change the number of classes to 15. a. What type of raster operation (module 7) is the calculation of slope gradient? Explain your answer. b. Write down the maximum, minimum and mean of the slope gradient dataset. Maximum: Minimum: Mean: MODULE SURFACE ANALYSIS

155 Slope aspect calculation in ArcMap Aspect identifies the steepest downslope direction from each cell to its neighbors. It can be thought of as slope direction or the compass direction a hill faces. Aspect is usually measured in degrees from the north: 0 is north; 90 degrees is east. INSTRUCTIONS: 1. Open ArcToolbox. Click Spatial Analyst Tools Surface Aspect. 2. Select the Input raster. 3. Specify a name and location for the output raster. 4. Click OK. Cells with a zero slope (flat) receive the value -1. The slope aspect in the output raster is represented in 8 cardinal directions, e.g. East [ degrees], Southeast [ degrees]. 5. Derive the slope aspect from the DEM. a. What is the slope aspect of the Rhine facing side of the Wageningen hill? b. Add layer landuse.lyr to the data frame. What is the average slope aspect of the feature with FID 445? MODULE SURFACE ANALYSIS

156 Contour mapping Contours are lines, also referred to as isolines that connect points of equal value, such as elevation, temperature, precipitation, pollution, or atmospheric pressure. The distribution of the lines shows how values change across a surface. Where there is little change in a value, the lines are spaced farther apart. Where the values rise or fall rapidly, the polylines are closer together. Elevation contour lines are derived from a DEM. You can access the contour function in ArcToolbox and in the Spatial Analyst toolbar. The latter is the most convenient for mapping contour lines. The contour function does not connect cell centres; it interpolates a line that represents the most likely location with the same height. Since the lines are smoothed, an idealistic representation of the surface is produced. You can also create an individual contour line by clicking the Contour tool, and then selecting a location in the view. The function traces the contour of the value that the chosen point represents. INSTRUCTIONS: 1. Open ArcToolbox. Click Spatial Analyst Tools Surface Contour. 2. Select the Input raster. 3. Specify a name and location for the Output polylines features. 4. Type a Contour interval. 5. Type a Base contour. Contours are generated above and below the base contour. 6. Optionally, type a value for the Z factor. 7. Click OK. Smaller contour intervals create more contours. The optimum contour interval is a matter of the realistic elevation change in the DEM, the size of the area covered by the DEM, the analyses to follow, and the desired aesthetics of the contour line dataset. 6. Derive 3 contour datasets from the DEM. Use the following contour parameter combinations. Contour dataset Contour interval Base contour a. Which contour parameter combination do you prefer? Explain why? b. What is the meaning of the Base contour? MODULE SURFACE ANALYSIS

157 Introduction Geo-Information Science Practical Manual Module 10 Digital image processing

158 11. DIGITAL IMAGE PROCESSING 10-1 INTRODUCTION 10-1 START THE PROGRAM ERDAS IMAGINE 10-2 PART 1: DISPLAYING AN IMAGE DATA FILE 10-3 Display of DN-range (no stretch) Display after linear stretch of DN-range minimum... maximum Display after linear stretch of DN-range Display after standard deviation stretch Display of color composites PART 2: SUPERVISED CLASSIFICATION 10-9 Examining land cover types using spectral profiles Digitizing training areas & estimation of signatures Collecting signatures Evaluating Signatures Land cover classification Minimum distance classification Maximum likelihood classification Updating a color palette Exporting to ArcGIS file format LGN Database IMAGE SOURCES RELATED INTERNET SITES 10-17

159 10. DIGITAL IMAGE PROCESSING Introduction The aim of the exercises in this module is to acquire a first experience in understanding remote sensing data by handling multi-spectral image data with the GIS and Remote Sensing package Leica Erdas Imagine for Windows. For the exercises, we will use image data of Wageningen and its surroundings (Figure 5). This is a subset of a much larger scene taken by the remote sensing satellite Landsat-5 TM (Thematic Mapper) on 11 July A spatial subset of the entire scene (185x185 km 2 ), with seven spectral bands is available (Table 1). Table 1. The seven spectral bands of the Landsat-5 TM sensor. TM band Spectral band Color name µm blue µm green µm red µm near-infrared µm mid-infrared µm thermal-infrared µm mid-infrared The image covers an area of km 2 and consist of 510 columns 510 rows. Each pixel represents an area of m 2. The sensor of band 6 observes pixels with a size of m 2. In this module you will practice with different image processing techniques including different display methods, the use of color composites and supervised classification. The results of a digital image classification can be used as input in a GIS. In this module: An introduction to the software package Erdas Imagine. Displaying an image data file: stretching and color composites. Selecting training sites for classification. Collecting spectral signatures of training sites. Three supervised classification methods. Objectives After having completed this module you will be capable: to understand the principle behind various image display techniques; to perform a supervised classification with Erdas Imagine; to describe the differences between three supervised classification methods. Erdas Imagine Images: Wag95.img, Meris_wag.img, Quickbird_ _rd.img Literature: Remote Sensing reader, Jan Clevers (Ed.) MODULE DIGITAL IMAGE PROCESSING

160 Start the program Erdas Imagine INSTRUCTIONS: 1. Start the Erdas Imagine package. Click start, select Programs ERDAS Geospatial Imaging 9.3 ERDAS IMAGINE 9.3. NOTE: The first time you start Erdas Image the program might give some errors, Ignore them and start the program again. 2. Click Session in the main menu bar (Figure 1), click preferences. 3. Set Default data directory: to: D:\IGI\...* \Erdas_imagine\data (*morning or afternoon). 4. Set Default output directory to: D:\IGI\...* \Erdas_imagine\workspace (*morning or afternoon). 1. Set your default Data directory and Output/Workspace directory On top of the Erdas Imagine window you see the main menu bar (Figure 1). Clicking one of the items of the menu bar gives a pull down menu with a number of options. Figure 1. The Erdas Imagine menu bar. Just below the menu bar you see the viewer (Figure 2). The menu and icons in the viewer can be used to open an image and applying basic viewer functions. If you move the cursor over the icons, you see a short indication of the function in the lower left corner of the viewer. Figure 2. The Erdas Imagine Viewer, where images are displayed.. MODULE DIGITAL IMAGE PROCESSING

161 For questions about the tools you will use, you are encouraged to press the context sensitive help button in the dialogue box of the selected tool. In the dialogue boxes often default settings are given. In general they are used; if not, then you will be notified. PART 1: Displaying an IMAGE data file In the Erdas package an image data file is usually stored in the unsigned 8-bit (or 1 byte) data type. This means that integer values from 0 to 255 can be stored. Pixel values are often called DN-values (Digital Number), being simply a value without a unit. They represent a distinct level of electromagnetic radiation received by the sensor. Speaking in terms of attribute scales, this type of data belongs to the ratio category. The image data file names have the extension.img which is accompanied by a.rrd file where the so-called pyramid layers are stored. These are used for fast zooming and panning in the image. In order to get familiar with image processing and remote sensing data we start with displaying and processing of one image data file. We use the image data of a Landsat-5 TM recording of band 4 (see Table 1) during this exercise. This band contains spectral information of a near-infrared band: µm. You will find that different image stretching techniques of the same image data file produce different pictures on the screen. The following cases will be investigated: display of DN-range ; display of DN-range minimum... maximum; display after linear stretch of DN-range ; display after linear stretch with saturation; displaying color composites. Keep the resulting pictures on the screen to notice the differences!! Display of DN-range (no stretch) INSTRUCTIONS: 1. Click in the viewer menu bar either File Open Raster layer or click the Open layer button. 2. Select image wag95.img. DO NOT OPEN THE IMAGE YET!!! 3. Click the Raster Options tab (Figure 3), click the Display as dropdown arrow and select Gray Scale, select Layer 4, and switch on the No Stretch option. 4. Press the OK button to open the image. Figure 3. The Raster Options tab. This way, the Grey Scale Palette produces a picture on the screen with 256 grey tones. The range in grey tones is a linear scale from black (DN-value 0) to white (DN-value 255); each DN-value of the image will basically have its own grey value (Figure 4). MODULE DIGITAL IMAGE PROCESSING

162 Figure 4. Principle of no stretch of image values (DN) into display levels. Since not all DN-values from 0 up to 255 are present in the original image, not all grey tones are used in the picture. Although you will recognize Wageningen and surroundings, the picture can be made brighter. But first you examine the DN distribution of the image. In order to examine the DN distribution of an image, display the histogram. INSTRUCTIONS: Click the ImageInfo button in the standard toolbar or click Utility Layer info. 2. The ImageInfo window opens, showing file, layer, statistics and map information. 3. Select layer Click the Histogram tab or the histogram button in the toolbar of the ImageInfo window. 5. If the cursor is placed inside the histogram, three vertical lines are displayed showing the minimum, maximum and mean values. a. What is plotted at the horizontal axis and what at the vertical axis? b. Write down the values for minimum, maximum, mean and standard deviation for band 4. When an image is displayed on the screen, the DN-values (File Pixel values) are translated to a grey tone (Lookup Table (LUT) -value). In case of an image displayed without stretch, the DN-value is the same as the LUT-value (Figure 4). You can view these values with Inquire cursor (click or Utility Inquire cursor). It is important to zoom in to a level where you can distinguish the individual pixels. 3. a. Check the DN-values and LUT-values of water, grass, forest and heath land. You can find the location of these objects in Figure 5. Write down the values in Table 2 in the no stretch columns. b. Which cover types has a DN-value of less than 30 in band 4? Explain this in terms of absorption/reflectance. c. Which two factors determine the grey tone of a pixel on the screen? d. Where is the origin of the column/row coordinate system? MODULE DIGITAL IMAGE PROCESSING

163 Table 2. DN-values of land use types using different display techniques. Land cover type Water Grass Forest Heath land DN-value (no stretch) Exercise 3 LUT-value (no stretch) Exercise 3 DN-value (linear stretch) Exercise 4 LUT-value (linear stretch) Exercise 4 Figure 5. Selected training fields in the Landsat TM band 5 scene of 11 July 1995 of the area around Wageningen. MODULE DIGITAL IMAGE PROCESSING

164 Display after linear stretch of DN-range minimum... maximum Within Erdas Imagine an option is available to stretch the original DN-values of the image for display. The minimum DN-value of the image will be presented by the minimum grey tone (black) on the screen; the maximum DN-value will get the maximum grey tone (white) if the grey tone palette with 256 levels is used (Figure 6). Figure 6. Principle of linear stretch of image values (DN) into display levels. The linear relationship between DN-value and LUT-value is in this example: LUT = 2.60*DN-156. INSTRUCTIONS: 1. To apply a linear stretch to your image with the instructions mentioned on page 10-3, click in the viewer menu bar: Raster Data scaling. 2. Make sure Linear selected in selected in the Binning field. 3. Replace the values for Min and Max with the minimum and maximum DN-values from the image info. 4. Click OK. 4. Open a new viewer (click the viewer button in the main menu bar), but don t close the viewer where you showed your image with no stretch. In this new viewer display the same image, but now with min..max linear stretch. a. Check the DN-values (FILE PIXEL) and LUT-values of the four land cover types again. Add these values to Table 2 in the linear stretch columns. b. Can you explain the changes in LUT-values? Explain why some land cover types get a higher LUT value, while other land cover types get a lower LUT-value. Display after linear stretch of DN-range Linear stretch of the minimum and maximum DN-values does improve the image somewhat compared to the image without stretch but contrast is still relatively low. The histogram shows that the majority of the DN-values are distributed between 40 and 90. You can gain more contrast in your image by emphasizing this DN-range on your screen. 5. Open a new viewer (click the viewer button in the main menu bar), but don t close the viewers where you showed your image with no stretch and with linear stretch DN-range min..max Use the data scaling function to apply a linear stretch of the DN-range from 40 to 90. a. Which land cover types can you distinguish now with more grey tones in a smaller DN-range? b. Investigate the DN and LUT-values of the four land cover types. Explain the linear stretch principle. MODULE DIGITAL IMAGE PROCESSING

165 Display after standard deviation stretch Standard Deviation Stretch is based on the idea that image stretching for display in the DN-range of the minimum up to the maximum value may not give a good picture because the minimum and/or maximum value may be unfortunate extreme(s). When using this function, results in a linear stretch between -2 and +2 standard deviation from the average. In practice, this means that from both sides of the histogram 2.5% of the observations are skipped. As a result, single observations with very low or high values are ignored during the stretching. Standard Deviation Stretch is the default stretch function used in Erdas Imagine. INSTRUCTIONS: 1. Open a new viewer, select band 4 for a display in grey scale, but do not switch the no stretch button on this time. The image will now be opened with Standard Deviation Stretch. 2. This stretch function can also be assessed through the menu bar. Click Raster Contrast Standard Deviation Stretch. 3. You can use Tile Viewers to put the viewers easily in one screen. Click in the viewer menu bar: View Tile Viewers. 6. Open a new viewer (click the viewer button in the main menu bar), but don t close the viewers where you showed your image with no stretch and with linear stretch DN-range min..max. a. Compare layer 4 of wag95.img with the three other different stretching options. Which stretch function gives in your opinion the best picture? Display layer 3 of wag95.img according to your previous answer in a new viewer. b. Which grey tone has grassland (see e.g. the meadows near the river) in band 3; is this different from band 4? In what way? Explain the difference (remember the typical spectral signature of green vegetation). 7. Landsat-5 TM band 6 contains the thermal-infrared image data. Display layer 6 of wag95.img. You can re-open the image, or change the band which is displayed with a. Why is the image of band 6 so coarse? b. Which cover type has a relative low temperature and which one has a relatively high temperature? Close all viewers. MODULE DIGITAL IMAGE PROCESSING

166 Display of color composites Color composites of remote sensing data can be very helpful during investigation and interpretation in the field or for presentation purposes. Color composites have three spectral bands displayed simultaneously. INSTRUCTIONS: 1. Open a viewer. 2. Add a raster layer to the viewer. Click the Raster Options tab. 3. Display as: True Color (even if you want to display False or Pseudo colors). 4. Attach bands to the Red, Green and Blue colors. 5. Click OK. 6. When a color composite is opened, you can always change the band combination. Click Raster Band Combinations. 7. Change the spectral bands for the three channels. If the Auto Apply box is ticked, band changes appear immediately on screen. Note: the terminology used by Erdas Imagine may be confusing. The fact that you use the option true color in the selection menu does not mean that you display a true-color image. This depends on the spectral bands you attach to the Red, Green and Blue band respectively. 8. Open three color composites of image wag95.img with band combinations as described in Table 3. a. Why are the composites called true, false or pseudo color? b. Check the colors for the cover types water, forest and bare soil in each composite. Write your findings down in Table 4. c. Which band combination or color composite shows the largest contrast between the different land cover types? Why? Close all viewers. Table 3. Band combinations of three types of color composite for Landsat TM5.. Red Green Blue True Color False Color Pseudo Color e.g. 4 e.g. 5 e.g. 3 Table 4. Land cover colors in each composite. Water Forest Bare soil True Color False Color Pseudo Color MODULE DIGITAL IMAGE PROCESSING

167 PART 2: Supervised classification Supervised classification is one of the techniques to transform remote sensing data into useful thematic information that could be used as input to a geographic information system. As a preparation for supervised classification, one decides beforehand which cover types must be classified and one selects proper training areas. These training areas are known cover types, based on field visits or general knowledge of parts of the area. Since we assume that you have some knowledge of the area around Wageningen, you will make several classifications without extensive fieldwork. Statistical characteristics of the spectral data of the selected training areas are set down in signature files. These signature files are then used by the classification method to derive the class boundaries for each cover type in the feature space. The actual classification of all pixels is performed in this feature space. The following activities will be executed: examining spectral profiles; digitizing training areas; estimation of signatures; classifications; updating a color palette (optional exercise) 9. a. Give a description of a 2-dimensional feature space. b. What is plotted on the axes of the feature space? Examining land cover types using spectral profiles You will start the classification procedure by examining the spectral profiles of several land cover types. INSTRUCTIONS: Open the spectral profile tool. Click in the viewer menu bar: Raster Profile Tools. 2. Click Spectral and click OK. The Spectral Profile window opens. 3. Click to activate the inquire tool 4. Click with the inquire cursor a land cover type in the image. The spectral profile of this pixel will be drawn in the graph. The line represents the value of the selected pixel for each band (Figure 7). 5. To display wavelength on the x-axis click Edit Use Sensor Attributes. Click the Sensor type dropdown arrow and select landsattm. Try to locate a few different land cover types (water, forest, agricultural land, and town) and show their spectral profiles in the graph. a. Which two bands show the largest difference in pixel value between water and vegetation? MODULE DIGITAL IMAGE PROCESSING

168 Figure 7. Spectral profiles of three land cover types. Digitizing training areas & estimation of signatures During the first phase of the classification process you choose a band combination that shows a clear discrimination between most land cover types in order to digitize training fields of the cover types you are going to classify: grass; bare soil; deciduous forest (Dutch: loofbos); pine forest (Dutch: naaldbos); heather; maize; town; water. Representative examples of these cover types are shown in figure 5. You will use user-defined polygons in the image for the selection of training samples Note: The training areas are in general small areas with at least 25 pixels. These areas should be chosen as pure (homogenous) as possible, so if you digitize e.g. a training site of water in the river, do not include the river borders! Collecting signatures INSTRUCTIONS: 1. Open a new viewer and display your most expressive composite (see your answer to exercise 8c) and zoom in to get a more detailed look at the picture during digitizing. 2. Click in the main menu bar and click Signature Editor... A new window will be opened, move it so the area with the training fields can be seen clearly. 3. Click in the viewer menu bar AOI Tools Click the AOI Tool palette button to create a polygon. Draw a polygon in one of the training areas (see figure 5). Digitize polygon points by clicking the LMB (Left Mouse button) and finish it by double clicking the LMB. MODULE DIGITAL IMAGE PROCESSING

169 5. Click in the Signature Editor the button to add the signature of the digitized training area to the signature list. 6. Give this signature a name according to the land cover (e.g. Water, Beets, Town, etc.). Notice that the color assigned to this class is the same as the color inside the AOI in the picture in default display (R=4; G=3; B=2). You can change the color combination if you wish. For instance town as red, pine forest as dark green, water as blue, etc. 11. Digitize the 8 training areas (7 indicated in figure 5 and the class town) according to the steps described above, and add the signatures to the signature list. Save the signature file in the workspace folder located in the Erdas Imagine folder. Name the signature file wag95_your_name.sig. Evaluating Signatures Before you perform a classification you need to study the signatures to get an accurate idea about the position and size of the classes in the feature space. You can present the results of the signature computation in a mean plot or histogram. You can compare the signatures of the different cover types; see if they are well separated. If not, then perhaps you did not choose the correct training area or it is a matter of different growth conditions or a registration error is made during field visit at the time of image recording. This way you can also get an idea if it is useful to perform the classification with all available bands. For this exercise you need a viewer with the source image wag95.img and the Signature Editor with wag95_your_name.sig. Mark the signature you want to investigate by clicking the row in the column with the > mark. In the ERDAS IMAGINE package the signatures can be studied in different ways. Add statistical data INSTRUCTIONS: 1. Click in the Signature Editor window View Columns, the Viewer Signature Columns window opens. 2. Select all rows except red, green and blue, click Statistics and click min, max and mean in the Column Statistics window. 3. Click Apply in the View Signature Columns window; close this window and the Column Statistics window. 4. If you move the slide bar in the Signature Editor window to the right and you will see that all statistical values appear. 12. a. Which spectral bands show the clearest (spectral) distinction between land use classes? MODULE DIGITAL IMAGE PROCESSING

170 Show the mean value(s) in a graph INSTRUCTIONS: 1. Click in the Signature Editor window View Mean Plots..., the Signature Mean Plot window opens. Depending on the option you choose you can display either the marked signature or selected signatures or all signatures. You can select more than one signature by keeping the shift key down during selection in the signature editor. Show histograms INSTRUCTIONS: 1. Click in the Signature Editor window View Histograms..., the Histogram Plot Control Panel opens and simultaneously the histogram of the first band of the marked signature appears. 2. Select the classes you want to display in a histogram in the Signature Editor if you want to visualize multiple classes in one plot. 3. The chosen options in the Histogram Plot Control Panel are activated when you click the Plot... button. 13. a. Check the separability of the classes in all spectral bands by examining the histograms. b. Which bands can be used to differentiate between deciduous and pine forest? c. Which land use classes will be hard to distinguish? d. What is the consequence of poorly distinguishable spectral signatures during classification? c. Suppose you could only use three spectral bands for land use classification. Which three bands would you choose? MODULE DIGITAL IMAGE PROCESSING

171 Land cover classification For classification of a remote sensing image, the ERDAS IMAGINE package is equipped with parametric and non-parametric decision rules. The difference between these decision rules will be treated in more detail during the course Remote Sensing (GRS 20306). For the classifications in this module, you will use the parametric decision rules Minimum distance and Maximum likelihood. INSTRUCTIONS: 1. Click in the main menu bar and click Supervised Classification. The Supervised Classification window opens (Figure 8). 2. Select the Input Raster File, this is the image you want to classify. 3. Select the Input Signature File, this is the file in which you stored the spectral signatures of the training areas. It can be found under your ERDAS workspace directory. 4. Give the output image a name in the Classified File box. 5. Select classification decision rules: Non-parametric Rule, Overlap Rule, Unclassified Rule and Parametric Rule. 6. Click OK. Figure 8. The supervised classification window where you name the output files and set the decision rules. 14. a. Open the Supervised Classification window. Which Parametric Rules are available? MODULE DIGITAL IMAGE PROCESSING