Lab 5: Image Analysis with ArcGIS 10 Unsupervised Classification
|
|
- Phillip Richard
- 5 years ago
- Views:
Transcription
1 Lab 5: Image Analysis with ArcGIS 10 Unsupervised Classification Peter E. Price TerraView 2010 Peter E. Price All rights reserved Revised 03/2011 Revised for Geob 373 by BK Feb 28, V3 The information contained in this document is the exclusive property of Peter E. Price. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by Peter E. Price. The information contained in this document is subject to change without notice. 1
2 As mentioned previously, unsupervised classification uses clustering routines to create the number of classes designated by the analyst. It is up to the analyst to assign meaningful identities to the classes after processing. A typical procedure would be for the analyst to specify a large number of classes and then refine and group them to produce features such as areas of land cover types. The unsupervised classification option provided by ESRI is the Iso Cluster Unsupervised Classification, also known as ISODATA (Iterative Self-Organized Data Analysis Techniques A) (Command 3 in the Classification toolbar dropdown menu). Unsupervised classifications usually do not have unclassified pixels if the parameters are set correctly. As mentioned in Lab 4, Iso Cluster Unsupervised Classification is the only unsupervised classification tool provided. It differs from the Iso Cluster tool in the Spatial Analyst Tools in that it creates an output classified image as well as a signature file. This command, however, does not offer control over the number of iterations used to define the clusters that is found in the Iso Cluster tool in the Spatial Analyst Tools > Multivariate functions as is shown below. You will now perform an unsupervised classification on the HouAirport_TM.tif image. Use the map document (.MXD) that you saved from the supervised classification. Clean the table of contents so that you only retain your base images and aerials, your unfiltered classification, and your best filtered result. Add the 2001 NLCD 1 layer (NLCD_2001.lyr) (downloaded from the Lab 5 web page) to the Table of Contents. Save As a new map document: Lab5.MXD. From the Classification toolbar, select the Iso Cluster Unsupervised Classification drop-down. Input HouAirport_TM.tif as the raster band. Specify the Number of classes as 24, name the output classified raster file HouAir24ISO, a Minimum class size (default, 20), a Sample interval of 1, and an Output signature file for future use. Your window should look like the example 1 NCLD: The USGS Multi-Resolution Land Characteristics Consortium s (MRLCC) National Land Cover Database (NLCD). 2
3 below. Given the small size of the study area, we can set the default Sample interval to 1. When working with larger files it may be necessary to set the sample interval to a greater number if the output contains fewer classes than requested it may be an indication that the sampling interval should be increased. The result should look something like the example below (the colours assigned to each class may differ). Note that the number of classes may differ from the 24 specified, and that some numbers may be skipped (e.g., dropped because that class didn t meet the minimum class size). 3
4 Compare the unsupervised classification with the MLC supervised classification you did earlier, the NLCD layer, the airport aerials and the original TM image. The unsupervised image looks noisy but it contains a lot of valuable information. As the analyst, it is now your job to make a useful classification from the unsupervised classes. Your assignment is to develop a limited set of classes that represent major groups of land cover. If you go to the USGS MRLCC site you can examine the 2001 NLCD classification scheme (one based on the Anderson scheme) A PDF of a report that documents the creation of the 2001 product is available here. If you open the attribute table of the 2001 NLCD layer you can see the correspondence between the class names and numbers: You will begin by assigning real identities to the ISO classes that were created. Use the TM image, aerials, and the NLCD product to help guide your interpretation. A good approach to this is to use your unsupervised classification as the top layer and Swipe it over the other layers. 4
5 An improvement on the plain swipe is to open the classification table of the ISO layer and select a class. This highlights a single class; it can now be compared with the underlying images and classifications. As shown in the example below, first changing the ISO symbology to a gray scale (1) allows the selected class (2) to be highlighted without visual clutter. Use the Identify tool (highlighted in the image below - 3) to view the class values in the underlying layers To improve your efficiency and workflow, first export the ISO classification table, place it in an Excel spreadsheet, and record your observations in the spreadsheet. To export the table, open the classification table and go to the drop down menu. Select the Export option and save the table as a.dbf with an appropriate file name. 5
6 Open the exported table in an Excel spreadsheet you will need to change the file type to All Files (*.*) in order for Excel to recognize the dbf. Save As the file to an.xlsx file. An example spreadsheet is presented below. You should add columns to the file in which to place the NCLD class numbers and the class names from your supervised (MLC) image, as well as a column for the ISO class names. Obviously some judgement will be required in deciding upon an ISO class name, as there will be multiple NCLD classes that fall within a single ISO class, and a single MLC 6
7 class will contain many ISO classes (and vice versa). You will need to identify the dominant class associated with each ISO class something where swiping will be an important contributor to your decision (e.g., you will need to ignore small groups of NCLD and MLC pixels within an ISO class). In order to simplify the comparisons amongst the multiple datasets, use the ISO class names listed below, and follow the equivalences listed between the classes used in the supervised classification (the Maximum Likelihood Classification) and the NLCD 2001 classes (class numbers and names are presented here). The numbers in brackets (##) indicate that that class is present but typically in lower numbers, relative to the other classes. Note, however, that you will find just about every combination of ISO class / MLC class / NLCD class occurs, although the numbers of cells involved should typically be very low. MLC CLASS # MLC CLASS NAME NLCD CLASS # ISO CLASS NAME (DERIVED FROM THE NLCD CLASS NAMES) ISO RECLASS # 1 Dry grass 21, 22, (23) Developed, Open space 1 2 Forest 21, (22), 41, Forest 2 42, 43, (71), 90 4 Parking lot (22), (23), 24 Developed, High intensity 4 5 Building roof (21), 22, 23, Developed, Medium Intensity Green grass 21, 22, 23, (24), 42, (71) Developed, Low intensity 6 The ISO reclass numbers will be used to simplify the 22 ISO classes into 5 classes directly compatible with the MLC classes. I ve also ordered them so that they roughly correspond to the MLC scheme. You should note that the NLCD class names are land-use oriented rather than land-cover oriented (as are the MLC class names). You can see from the incomplete spreadsheet example presented below that, as expected, there are multiple ISO classes associated with a single ISO class name. Merging some ISO classes that have obvious relationships is needed. As shown in the example below, ISO classes 1 and 3 appear to be primarily associated with forest (ISO reclass # 2), classes 4 and 16 correspond primarily to Developed, Open space (ISO reclass # 1), and classes 9 and 10 to Developed, Low intensity (ISO reclass # 6). Once you have completed the table, the 24 ISO classes will need to be reassigned into a smaller set of comprehensive classes using the Reclassify tool. To make the reclassification exercise easier, you should add a column to the spreadsheet wherein you will place the reclassified numbers (ISO Reclass number) as given above (and as shown in the example spreadsheet below). 7
8 Once you have completed your assessment of every ISO class, open the Reclassify tool by going to ArcToolbox > Spatial Analyst Tools > Reclass > Reclassify. This will open the Reclassify menu seen below. The input is the HouAir24ISO layer. If the classes do not appear, be sure the Reclass field is Value and click the Unique button. Enter the new class designations in the New values boxes. Save the completed reclassification to an appropriate file name such as HouAir6ISO. 8
9 If needed, add the reclassified image to the Table of Contents. Set the symbology to display the new classes (match the colour scheme used in your MLC image) and label them appropriately. Your results should look something like the image below. You should clean up the ISO results following the protocol you used in Lab 4 (e.g., Majority Filter, Boundary Clean). Use your skills and the tools you have explored to create a map that represents the major land cover types in this area. Place the map in a Word document with an explanation of your process and the results. Discuss the differences you observe between the NLCD layer, your MLC results, and your ISO classification results. In order to more formally compare your MLC classification to the ISO classification, open the attribute tables of both images (the final images that have been filtered and cleaned) and prepare a summary table similar to the one presented below (include both the raw pixel counts as well as the relative percentages for each class). Class # MLC Class Name MLC Count % ISO Class Name 1 Dry grass Developed, Open 2 Forest Forest 4 Parking lot Developed, High 5 Building roof Developed, Medium 6 Green grass Developed, Low ISO Count % 12
10 A (pseudo) Accuracy Classification A final step in most image classification projects would be an accuracy assessment and the creation of a confusion matrix. As mentioned in class, the proper way to conduct such an assessment would be to collect ground truth in this case, using the higher-resolution aerial photos to provide an assessment of what the land cover actually is. To do this with some statistical rigour, we need to create a number of randomly positioned points throughout the classified image, assign the ISO class name to each point, and then determine through visual inspection at each point what the true land cover is. Once all of the points have been ground truthed, we can create a confusion matrix and calculate the relevant statistics that will tell us how well our classification did. ESRI has provided a number of tools within the Segmentation and Classification toolset (found within the Spatial Analyst set of tools) that automate most of the accuracy assessment / construction of the confusion matrix process. I will lead you through the steps of completing an accuracy assessment, but for the purposes of this lab we will make the assumption that the results of your supervised classification are the truth to which we will compare the results of the ISO unsupervised classification. Step 1) Create the Accuracy Assessment Points: Select your final ISO file (after being filtered and cleaned) as the input raster, provide an appropriate output file name (this will be a point file, it can be either a shapefile or in a geodatabase), and accept the defaults for the other fields (the Target Field should be classified since we want to know what classes in the ISO image are associated with each stratified randomly placed point). You should see a point file containing 500 points scattered about the ISO image. Open the attribute table to see what the file contains. 12
11 Step 2) Update the Accuracy Assessment Points: Select your final MLC image as the Input Raster, the point file you just created in step 1 is the Input Accuracy Assessment Points file, provide an appropriate name for the output file, and change the Target Field to Ground_Truth. Open the attribute table of the output accuracy assessment points file and note that it now contains attributes from both the classified image (the ISO image) and the GrndTruth image (the MLC image). You now need to compute the confusion matrix. Step 3: Create Confusion Matrix: This tool takes the accuracy assessment results and creates a confusion matrix (basically it creates a pivot table [Classified as the rows, GrndTruth as the columns, and the counts as the values] and then calculates a number of statistics from that pivot table). 12
12 You final results should look something like that presented below, although everyone s results will look slightly different (based on the random placement of points, and of the differences in everyone s classifications). Given the order in which we specified the classification and the ground truth, the rows represent the ISO classes and the columns the MLC classes. If you selected the.dbf option you will need to format the cells (removing the decimal places for those cells that are simple counts, and setting the number of decimal points to 5 for those that are statistics) so that your table looks similar to that presented above. Based on the results of your (pseudo) accuracy assessment, which ISO class was the most accurately classified (looking at both the Producer s and the User s Accuracy values), and which was the most poorly classified? What was the Percent Correctly Classified, and what is the Kappa coefficient, for your results? Include a copy of the accuracy assessment in your report. You should provide the proper class names in your table (e.g., row C_1 should be Developed, Open space while column C_1 should be Dry Grass). In your final project you will be required to produce both an unsupervised and a supervised classification of a Landsat scene that you will personally download, and provide similar details / discussion as outlined in your labs. 12
Raster Classification with ArcGIS Desktop. Rebecca Richman Andy Shoemaker
Raster Classification with ArcGIS Desktop Rebecca Richman Andy Shoemaker Raster Classification What is it? - Classifying imagery into different land use/ land cover classes based on the pixel values of
More informationArcGIS Pro: Image Segmentation, Classification, and Machine Learning. Jeff Liedtke and Han Hu
ArcGIS Pro: Image Segmentation, Classification, and Machine Learning Jeff Liedtke and Han Hu Overview of Image Classification in ArcGIS Pro Overview of the classification workflow Classification tools
More informationLab 9. Julia Janicki. Introduction
Lab 9 Julia Janicki Introduction My goal for this project is to map a general land cover in the area of Alexandria in Egypt using supervised classification, specifically the Maximum Likelihood and Support
More informationDigital Image Classification Geography 4354 Remote Sensing
Digital Image Classification Geography 4354 Remote Sensing Lab 11 Dr. James Campbell December 10, 2001 Group #4 Mark Dougherty Paul Bartholomew Akisha Williams Dave Trible Seth McCoy Table of Contents:
More informationData: a collection of numbers or facts that require further processing before they are meaningful
Digital Image Classification Data vs. Information Data: a collection of numbers or facts that require further processing before they are meaningful Information: Derived knowledge from raw data. Something
More informationThis is the general guide for landuse mapping using mid-resolution remote sensing data
This is the general guide for landuse mapping using mid-resolution remote sensing data February 11 2015 This document has been prepared for Training workshop on REDD+ Research Project in Peninsular Malaysia
More informationDIGITAL IMAGE ANALYSIS. Image Classification: Object-based Classification
DIGITAL IMAGE ANALYSIS Image Classification: Object-based Classification Image classification Quantitative analysis used to automate the identification of features Spectral pattern recognition Unsupervised
More informationThe Feature Analyst Extension for ERDAS IMAGINE
The Feature Analyst Extension for ERDAS IMAGINE Automated Feature Extraction Software for GIS Database Maintenance We put the information in GIS SM A Visual Learning Systems, Inc. White Paper September
More informationAerial photography: Principles. Visual interpretation of aerial imagery
Aerial photography: Principles Visual interpretation of aerial imagery Overview Introduction Benefits of aerial imagery Image interpretation Elements Tasks Strategies Keys Accuracy assessment Benefits
More informationFigure 1: Workflow of object-based classification
Technical Specifications Object Analyst Object Analyst is an add-on package for Geomatica that provides tools for segmentation, classification, and feature extraction. Object Analyst includes an all-in-one
More informationClassification (or thematic) accuracy assessment. Lecture 8 March 11, 2005
Classification (or thematic) accuracy assessment Lecture 8 March 11, 2005 Why and how Remote sensing-derived thematic information are becoming increasingly important. Unfortunately, they contain errors.
More informationUsing ArcGIS for Landcover Classification. Presented by CORE GIS May 8, 2012
Using ArcGIS for Landcover Classification Presented by CORE GIS May 8, 2012 How to use ArcGIS for Image Classification 1. Find and download the right data 2. Have a look at the data (true color/false color)
More informationIntroduction to digital image classification
Introduction to digital image classification Dr. Norman Kerle, Wan Bakx MSc a.o. INTERNATIONAL INSTITUTE FOR GEO-INFORMATION SCIENCE AND EARTH OBSERVATION Purpose of lecture Main lecture topics Review
More informationSTUDENT PAGES GIS Tutorial Treasure in the Treasure State
STUDENT PAGES GIS Tutorial Treasure in the Treasure State Copyright 2015 Bear Trust International GIS Tutorial 1 Exercise 1: Make a Hand Drawn Map of the School Yard and Playground Your teacher will provide
More information(Refer Slide Time: 0:51)
Introduction to Remote Sensing Dr. Arun K Saraf Department of Earth Sciences Indian Institute of Technology Roorkee Lecture 16 Image Classification Techniques Hello everyone welcome to 16th lecture in
More informationLab #4 Introduction to Image Processing II and Map Accuracy Assessment
FOR 324 Natural Resources Information Systems Lab #4 Introduction to Image Processing II and Map Accuracy Assessment (Adapted from the Idrisi Tutorial, Introduction Image Processing Exercises, Exercise
More informationINTRODUCTION TO GIS WORKSHOP EXERCISE
111 Mulford Hall, College of Natural Resources, UC Berkeley (510) 643-4539 INTRODUCTION TO GIS WORKSHOP EXERCISE This exercise is a survey of some GIS and spatial analysis tools for ecological and natural
More informationRASTER ANALYSIS S H A W N L. P E N M A N E A R T H D A T A A N A LY S I S C E N T E R U N I V E R S I T Y O F N E W M E X I C O
RASTER ANALYSIS S H A W N L. P E N M A N E A R T H D A T A A N A LY S I S C E N T E R U N I V E R S I T Y O F N E W M E X I C O TOPICS COVERED Spatial Analyst basics Raster / Vector conversion Raster data
More informationMaking Yield Contour Maps Using John Deere Data
Making Yield Contour Maps Using John Deere Data Exporting the Yield Data Using JDOffice 1. Data Format On Hard Drive 2. Start program JD Office. a. From the PC Card menu on the left of the screen choose
More informationGEOG 487 Lesson 7: Step- by- Step Activity
GEOG 487 Lesson 7: Step- by- Step Activity Part I: Review the Relevant Data Layers and Organize the Map Document In Part I, we will review the data and organize the map document for analysis. 1. Unzip
More informationExporting ArcScene to 3D Web Scenes. Documents. An Esri White Paper November 2013
Exporting ArcScene to 3D Web Scenes Documents An Esri White Paper November 2013 Copyright 2013 Esri All rights reserved. Printed in the United States of America. The information contained in this document
More informationCombine Yield Data From Combine to Contour Map Ag Leader
Combine Yield Data From Combine to Contour Map Ag Leader Exporting the Yield Data Using SMS Program 1. Data format On Hard Drive. 2. Start program SMS Basic. a. In the File menu choose Open. b. Click on
More informationLab 12: Sampling and Interpolation
Lab 12: Sampling and Interpolation What You ll Learn: -Systematic and random sampling -Majority filtering -Stratified sampling -A few basic interpolation methods Videos that show how to copy/paste data
More informationGIS Fundamentals: Supplementary Lessons with ArcGIS Pro
Station Analysis (parts 1 & 2) What You ll Learn: - Practice various skills using ArcMap. - Combining parcels, land use, impervious surface, and elevation data to calculate suitabilities for various uses
More informationRaster Data Model & Analysis
Topics: 1. Understanding Raster Data 2. Adding and displaying raster data in ArcMap 3. Converting between floating-point raster and integer raster 4. Converting Vector data to Raster 5. Querying Raster
More informationImage Classification. RS Image Classification. Present by: Dr.Weerakaset Suanpaga
Image Classification Present by: Dr.Weerakaset Suanpaga D.Eng(RS&GIS) 6.1 Concept of Classification Objectives of Classification Advantages of Multi-Spectral data for Classification Variation of Multi-Spectra
More informationEx. 4: Locational Editing of The BARC
Ex. 4: Locational Editing of The BARC Using the BARC for BAER Support Document Updated: April 2010 These exercises are written for ArcGIS 9.x. Some steps may vary slightly if you are working in ArcGIS
More informationIntroduction to LiDAR
Introduction to LiDAR Our goals here are to introduce you to LiDAR data. LiDAR data is becoming common, provides ground, building, and vegetation heights at high accuracy and detail, and is available statewide.
More informationObtaining Submerged Aquatic Vegetation Coverage from Satellite Imagery and Confusion Matrix Analysis
Obtaining Submerged Aquatic Vegetation Coverage from Satellite Imagery and Confusion Matrix Analysis Brian Madore April 7, 2015 This document shows the procedure for obtaining a submerged aquatic vegetation
More informationii. From the Tools menu choose Multi-Extract
Created By: Grant J. Firl Advisors: Paul Evangelista, Jim Graham Date: November 2010 Software: ArcGIS v9.2, TUGZip v3.5 Lesson 8: Mosaicking and Clipping Landsat Data The purpose of this tutorial is to
More informationLab 10: Raster Analyses
Lab 10: Raster Analyses What You ll Learn: Spatial analysis and modeling with raster data. You will estimate the access costs for all points on a landscape, based on slope and distance to roads. You ll
More informationMAG Demographic Map Viewer Training
Exercise 1 In this exercise you will create a map showing the percentage of Hispanic population of each block group, showing eight data breaks using equal intervals, an orange and purple color scheme,
More informationGEO 465/565 Lab 6: Modeling Landslide Susceptibility
1 GEO 465/565 Lab 6: Modeling Landslide Susceptibility This lab will give you more practice in understanding and building a GIS analysis model. Recall from class lecture that a GIS analysis model is a
More informationObject Based Image Analysis: Introduction to ecognition
Object Based Image Analysis: Introduction to ecognition ecognition Developer 9.0 Description: We will be using ecognition and a simple image to introduce students to the concepts of Object Based Image
More informationUsing ArcScan for ArcGIS
ArcGIS 9 Using ArcScan for ArcGIS Copyright 00 005 ESRI All rights reserved. Printed in the United States of America. The information contained in this document is the exclusive property of ESRI. This
More informationSelect the Parks within Forest Acres
Select the Parks within Forest Acres TASKS RESULT 1. Add the county boundary, municipalities and parks (pts.) layers to your map 2. Save your map (.mxd) to your desktop Your map should look something like
More informationUsing Imagery for Intelligence Analysis
2013 Esri International User Conference July 8 12, 2013 San Diego, California Technical Workshop Using Imagery for Intelligence Analysis Renee Bernstein Natalie Campos Esri UC2013. Technical Workshop.
More informationSetting up a 3D Environment for the City of Portland
Setting up a 3D Environment for the City of Portland www.learn.arcgis.com 380 New York Street Redlands, California 92373 8100 USA Copyright 2018 Esri All rights reserved. Printed in the United States of
More informationLab 3: Digitizing in ArcGIS Pro
Lab 3: Digitizing in ArcGIS Pro What You ll Learn: In this Lab you ll be introduced to basic digitizing techniques using ArcGIS Pro. You should read Chapter 4 in the GIS Fundamentals textbook before starting
More informationModule 7 Raster operations
Introduction Geo-Information Science Practical Manual Module 7 Raster operations 7. INTRODUCTION 7-1 LOCAL OPERATIONS 7-2 Mathematical functions and operators 7-5 Raster overlay 7-7 FOCAL OPERATIONS 7-8
More informationGeographical Information Systems Institute. Center for Geographic Analysis, Harvard University. LAB EXERCISE 1: Basic Mapping in ArcMap
Harvard University Introduction to ArcMap Geographical Information Systems Institute Center for Geographic Analysis, Harvard University LAB EXERCISE 1: Basic Mapping in ArcMap Individual files (lab instructions,
More informationGIS Exercise 10 March 30, 2018 The USGS NCGMP09v11 tools
GIS Exercise 10 March 30, 2018 The USGS NCGMP09v11 tools As a result of the collaboration between ESRI (the manufacturer of ArcGIS) and USGS, ESRI released its Geologic Mapping Template (GMT) in 2009 which
More informationGEO 465/565 - Lab 7 Working with GTOPO30 Data in ArcGIS 9
GEO 465/565 - Lab 7 Working with GTOPO30 Data in ArcGIS 9 This lab explains how work with a Global 30-Arc-Second (GTOPO30) digital elevation model (DEM) from the U.S. Geological Survey. This dataset can
More informationGlacier Mapping and Monitoring
Glacier Mapping and Monitoring Exercises Tobias Bolch Universität Zürich TU Dresden tobias.bolch@geo.uzh.ch Exercise 1: Visualizing multi-spectral images with Erdas Imagine 2011 a) View raster data: Open
More informationIntroduction to LiDAR
Introduction to LiDAR Our goals here are to introduce you to LiDAR data, to show you how to download it for an area of interest, and to better understand the data and uses through some simple manipulations.
More informationRASTER ANALYSIS GIS Analysis Winter 2016
RASTER ANALYSIS GIS Analysis Winter 2016 Raster Data The Basics Raster Data Format Matrix of cells (pixels) organized into rows and columns (grid); each cell contains a value representing information.
More informationGeomatica II Course guide
Course guide Geomatica Version 2017 SP4 2017 PCI Geomatics Enterprises, Inc. All rights reserved. COPYRIGHT NOTICE Software copyrighted by PCI Geomatics Enterprises, Inc., 90 Allstate Parkway, Suite 501
More informationJoining data from an Excel spreadsheet
Geographic Information for Vector Surveillance Day 3 of a 3 day course with Malaria examples Getting your own data into QGIS Learning objectives be able to join data from an Excel spreadsheet to a shapefile
More informationA Vector Agent-Based Unsupervised Image Classification for High Spatial Resolution Satellite Imagery
A Vector Agent-Based Unsupervised Image Classification for High Spatial Resolution Satellite Imagery K. Borna 1, A. B. Moore 2, P. Sirguey 3 School of Surveying University of Otago PO Box 56, Dunedin,
More informationClassifying. Stuart Green Earthobservation.wordpress.com MERMS 12 - L4
Classifying Stuart Green Earthobservation.wordpress.com Stuart.Green@Teagasc.ie MERMS 12 - L4 Classifying Replacing the digital numbers in each pixel (that tell us the average spectral properties of everything
More informationGST 105: Introduction to Remote Sensing Lab 6: Supervised Classification
GST 105: Introduction to Remote Sensing Lab 6: Supervised Classification Objective Perform a Supervised classification Document Version: 2014-08-08 (Beta) Author: Richard : Smith, Ph.D. Texas A&M University
More informationMasking Lidar Cliff-Edge Artifacts
Masking Lidar Cliff-Edge Artifacts Methods 6/12/2014 Authors: Abigail Schaaf is a Remote Sensing Specialist at RedCastle Resources, Inc., working on site at the Remote Sensing Applications Center in Salt
More informationRemote Sensing & Photogrammetry W4. Beata Hejmanowska Building C4, room 212, phone:
Remote Sensing & Photogrammetry W4 Beata Hejmanowska Building C4, room 212, phone: +4812 617 22 72 605 061 510 galia@agh.edu.pl 1 General procedures in image classification Conventional multispectral classification
More informationGraded Project. Microsoft Excel
Graded Project Microsoft Excel INTRODUCTION 1 PROJECT SCENARIO 1 CREATING THE WORKSHEET 2 GRAPHING YOUR RESULTS 4 INSPECTING YOUR COMPLETED FILE 6 PREPARING YOUR FILE FOR SUBMISSION 6 Contents iii Microsoft
More informationRaster Suitability Analysis: Siting a Wind Farm Facility North Of Beijing, China
Raster Suitability Analysis: Siting a Wind Farm Facility North Of Beijing, China Written by Gabriel Holbrow and Barbara Parmenter, revised on10/22/2018 for 10.6.1 Raster Suitability Analysis: Siting a
More informationPriming the Pump Stage II
Priming the Pump Stage II Modeling and mapping concentration with fire response networks By Mike Price, Entrada/San Juan, Inc. The article Priming the Pump Preparing data for concentration modeling with
More informationLORI COLLINS, RESEARCH ASSOCIATE PROFESSOR CONTRIBUTIONS BY: RICHARD MCKENZIE AND GARRETT SPEED, DHHC USF L IBRARIES
LORI COLLINS, RESEARCH ASSOCIATE PROFESSOR CONTRIBUTIONS BY: RICHARD MCKENZIE AND GARRETT SPEED, DHHC USF L IBRARIES AERIAL AND TERRESTRIAL SURVEY WORKFLOWS Workflow from project planning applications
More informationMap Algebra Exercise (Beginner) ArcView 9
Map Algebra Exercise (Beginner) ArcView 9 1.0 INTRODUCTION The location of the data set is eastern Africa, more specifically in Nakuru District in Kenya (see Figure 1a). The Great Rift Valley runs through
More informationClassify Multi-Spectral Data Classify Geologic Terrains on Venus Apply Multi-Variate Statistics
Classify Multi-Spectral Data Classify Geologic Terrains on Venus Apply Multi-Variate Statistics Operations What Do I Need? Classify Merge Combine Cross Scan Score Warp Respace Cover Subscene Rotate Translators
More informationIntroduction to GIS & Mapping: ArcGIS Desktop
Introduction to GIS & Mapping: ArcGIS Desktop Your task in this exercise is to determine the best place to build a mixed use facility in Hudson County, NJ. In order to revitalize the community and take
More informationSpectral Classification
Spectral Classification Spectral Classification Supervised versus Unsupervised Classification n Unsupervised Classes are determined by the computer. Also referred to as clustering n Supervised Classes
More informationRASTER ANALYSIS GIS Analysis Fall 2013
RASTER ANALYSIS GIS Analysis Fall 2013 Raster Data The Basics Raster Data Format Matrix of cells (pixels) organized into rows and columns (grid); each cell contains a value representing information. What
More informationLab 10: Raster Analyses
Lab 10: Raster Analyses What You ll Learn: Spatial analysis and modeling with raster data. You will estimate the access costs for all points on a landscape, based on slope and distance to roads. You ll
More informationSubmerged Aquatic Vegetation Mapping using Object-Based Image Analysis with Lidar and RGB Imagery
Submerged Aquatic Vegetation Mapping using Object-Based Image Analysis with Lidar and RGB Imagery Victoria Price Version 1, April 16 2015 Submerged Aquatic Vegetation Mapping using Object-Based Image Analysis
More informationSoil texture: based on percentage of sand in the soil, partially determines the rate of percolation of water into the groundwater.
Overview: In this week's lab you will identify areas within Webster Township that are most vulnerable to surface and groundwater contamination by conducting a risk analysis with raster data. You will create
More informationHow to Create Metadata in ArcGIS 10.0
How to Create Metadata in ArcGIS 10.0 March 2012 Table of Contents Introduction... 1 Getting Started... 2 Software Requirements... 2 Configure ArcGIS Desktop to View FGDC Metadata... 2 Other Thoughts...
More informationChoropleth Mapping with GIS
Choropleth Mapping with GIS In this lab you will be making 4 choropleth maps of the data you downloaded and processed last week. You will make your maps in ArcGIS using three different methods of classing
More informationAPPLICATION OF SOFTMAX REGRESSION AND ITS VALIDATION FOR SPECTRAL-BASED LAND COVER MAPPING
APPLICATION OF SOFTMAX REGRESSION AND ITS VALIDATION FOR SPECTRAL-BASED LAND COVER MAPPING J. Wolfe a, X. Jin a, T. Bahr b, N. Holzer b, * a Harris Corporation, Broomfield, Colorado, U.S.A. (jwolfe05,
More informationUsing ArcGIS. Tracking Analyst. GIS by ESRI
Using ArcGIS Tracking Analyst GIS by ESRI Copyright 2002 ESRI All Rights Reserved Printed in the United States of America The information contained in this document is the exclusive property of ESRI This
More informationStream network delineation and scaling issues with high resolution data
Stream network delineation and scaling issues with high resolution data Roman DiBiase, Arizona State University, May 1, 2008 Abstract: In this tutorial, we will go through the process of extracting a stream
More informationRaster: The Other GIS Data
Raster_The_Other_GIS_Data.Docx Page 1 of 11 Raster: The Other GIS Data Objectives Understand the raster format and how it is used to model continuous geographic phenomena. Understand how projections &
More informationArcScan for ArcGIS Tutorial
ArcGIS 9 ArcScan for ArcGIS Tutorial Copyright 00 008 ESRI All rights reserved. Printed in the United States of America. The information contained in this document is the exclusive property of ESRI. This
More informationUsing GIS to Site Minimal Excavation Helicopter Landings
Using GIS to Site Minimal Excavation Helicopter Landings The objective of this analysis is to develop a suitability map for aid in locating helicopter landings in mountainous terrain. The tutorial uses
More informationSharing GIS Content Using ArcGIS STUDENT EDITION
Sharing GIS Content Using ArcGIS STUDENT EDITION Copyright 2018 Esri All rights reserved. Course version 1.1. Version release date April 2018. Printed in the United States of America. The information contained
More informationCOMBINING HIGH SPATIAL RESOLUTION OPTICAL AND LIDAR DATA FOR OBJECT-BASED IMAGE CLASSIFICATION
COMBINING HIGH SPATIAL RESOLUTION OPTICAL AND LIDAR DATA FOR OBJECT-BASED IMAGE CLASSIFICATION Ruonan Li 1, Tianyi Zhang 1, Ruozheng Geng 1, Leiguang Wang 2, * 1 School of Forestry, Southwest Forestry
More informationMODULE 1 BASIC LIDAR TECHNIQUES
MODULE SCENARIO One of the first tasks a geographic information systems (GIS) department using lidar data should perform is to check the quality of the data delivered by the data provider. The department
More informationIntroducing ArcScan for ArcGIS
Introducing ArcScan for ArcGIS An ESRI White Paper August 2003 ESRI 380 New York St., Redlands, CA 92373-8100, USA TEL 909-793-2853 FAX 909-793-5953 E-MAIL info@esri.com WEB www.esri.com Copyright 2003
More informationMAPLOGIC CORPORATION. GIS Software Solutions. Getting Started. With MapLogic Layout Manager
MAPLOGIC CORPORATION GIS Software Solutions Getting Started With MapLogic Layout Manager Getting Started with MapLogic Layout Manager 2011 MapLogic Corporation All Rights Reserved 330 West Canton Ave.,
More informationChapter 7. A Quick Tour of ArcGIS Pro
Chapter 7 A Quick Tour of ArcGIS Pro Skills you will learn: This tutorial is intended to get you going using ArcGIS Pro, a new desktop application that is part of ArcGIS Desktop. A separate tutorial gives
More informationUSING CCCR S AERIAL PHOTOGRAPHY IN YOUR OWN GIS
USING CCCR S AERIAL PHOTOGRAPHY IN YOUR OWN GIS Background: In 2006, the Centre for Catchment and Coastal Research purchased 40 cm resolution aerial photography for the whole of Wales. This product was
More informationArcScan. for ArcGIS. GIS by ESRI
ArcScan for ArcGIS GIS by ESRI Copyright 2002 ESRI All rights reserved Printed in the United States of America The information contained in this document is the exclusive property of ESRI This work is
More informationGST 105: Introduction to Remote Sensing Lab 5: Unsupervised Classification
GST 105: Introduction to Remote Sensing Lab 5: Unsupervised Classification Objective Perform an unsupervised classification Document Version: 2014-07-15 (Beta) Author: Richard : Smith, Ph.D. Texas A&M
More informationLab 10: Raster Analyses
Lab 10: Raster Analyses What You ll Learn: Spatial analysis and modeling with raster data. You will estimate the access costs for all points on a landscape, based on slope and distance to roads. You ll
More informationConservation Applications of LiDAR. Terrain Analysis. Workshop Exercises
Conservation Applications of LiDAR Terrain Analysis Workshop Exercises 2012 These exercises are part of the Conservation Applications of LiDAR project a series of hands on workshops designed to help Minnesota
More informationGGR 375 QGIS Tutorial
GGR 375 QGIS Tutorial With text taken from: Sherman, Gary E. Shuffling Quantum GIS into the Open Source GIS Stack. Free and Open Source Software for Geospatial (FOSS4G) Conference. 2007. Available online
More informationLab 12: Sampling and Interpolation
Lab 12: Sampling and Interpolation What You ll Learn: -Systematic and random sampling -Majority filtering -Stratified sampling -A few basic interpolation methods Data for the exercise are in the L12 subdirectory.
More informationProcedure for Development of Crop Mask for Major Seasonal Crops in Punjab & Sindh Provinces of Pakistan
[Type text] Procedure for Development of Crop Mask for Major Seasonal Crops in Punjab & Sindh Provinces of Pakistan Preface Agriculture sector contributes around 21% to GDP annually. Major contributing
More informationHow to Set Workspace Environments for Project Work
How to Set Workspace Environments for Project Work An ESRI Short Tutorial January 2006 ESRI 380 New York St., Redlands, CA 92373-8100, USA TEL 909-793-2853 FAX 909-793-5953 E-MAIL info@esri.com WEB www.esri.com
More informationFeature Analyst Quick Start Guide
Feature Analyst Quick Start Guide Change Detection Change Detection allows you to identify changes in images over time. By automating the process, it speeds up a acquisition of data from image archives.
More informationThe Reference Library Generating Low Confidence Polygons
GeoCue Support Team In the new ASPRS Positional Accuracy Standards for Digital Geospatial Data, low confidence areas within LIDAR data are defined to be where the bare earth model might not meet the overall
More informationSession 3: Cartography in ArcGIS. Mapping population data
Exercise 3: Cartography in ArcGIS Mapping population data Background GIS is well known for its ability to produce high quality maps. ArcGIS provides useful tools that allow you to do this. It is important
More informationData Import and Quality Control in Geochemistry for ArcGIS
Data Import and Quality Control in Geochemistry for ArcGIS This Data Import and Quality Control in Geochemistry for ArcGIS How-To Guide will demonstrate how to create a new geochemistry project, import
More informationRaster Suitability Analysis: Siting a Wind Farm Facility North Of Beijing, China
Raster Suitability Analysis: Siting a Wind Farm Facility North Of Beijing, China Written by Gabriel Holbrow and Barbara Parmenter, revised by Carolyn Talmadge 11/2/2015 INTRODUCTION... 1 PREPROCESSED DATA
More informationWatershed Sciences 4930 & 6920 GEOGRAPHIC INFORMATION SYSTEMS
Watershed Sciences 4930 & 6920 GEOGRAPHIC INFORMATION SYSTEMS WATS 4930/6920 WHERE WE RE GOING WATS 6915 welcome to tag along for any, all or none WEEK FIVE Lecture VECTOR ANALYSES Joe Wheaton HOUSEKEEPING
More informationRESEARCH ON THE VISUALIZATION SYSTEM AND APPLICATIONS OF UNCERTAINTY IN REMOTE SENSING DATA CLASSIFICATION
RESEARCH ON THE VISUALIZATION SYSTEM AND APPLICATIONS OF UNCERTAINTY IN REMOTE SENSING DATA CLASSIFICATION YanLiu a, Yanchen Bo b a National Geomatics Center of China, no1. Baishengcun,Zhizhuyuan, Haidian
More informationGEOG 487 Lesson 4: Step-by-Step Activity
GEOG 487 Lesson 4: Step-by-Step Activity Part I: Visually Explore Trends In Part I, we will explore several tools and technique to make it easier to visually interpret patterns in your data using ArcGIS.
More informationGraded Project. Microsoft Excel
Graded Project Microsoft Excel INTRODUCTION 1 PROJECT SCENARIO 2 CREATING THE WORKSHEET 2 GRAPHING YOUR RESULTS 4 INSPECTING YOUR COMPLETED FILE 6 PREPARING YOUR FILE FOR SUBMISSION 6 Contents iii Microsoft
More informationLAB 1: Introduction to ArcGIS 8
LAB 1: Introduction to ArcGIS 8 Outline Introduction Purpose Lab Basics o About the Computers o About the software o Additional information Data ArcGIS Applications o Starting ArcGIS o o o Conclusion To
More informationArcGIS 9. Using ArcGIS Tracking Analyst
ArcGIS 9 Using ArcGIS Tracking Analyst Copyright (c) 2004-2005 TASC, Inc. and its licensor(s). All Rights Reserved. Printed in the United States of America. The information contained in this document is
More informationENVI. Get the Information You Need from Imagery.
Visual Information Solutions ENVI. Get the Information You Need from Imagery. ENVI is the premier software solution to quickly, easily, and accurately extract information from geospatial imagery. Easy
More information