Remote Sensing & GIS (Bio/Env384 A): 10 November 2015 GIS Database Query

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1 Remote Sensing & GIS (Bio/Env384 A): 10 November 2015 GIS Database Query One primary purpose of establishing any GIS database is to provide the possibility of querying that database, i.e., of asking questions that draw on existing data and produce new information as a result of those queries. For this exercise, we will use two raster images that contain all the data needed to produce the required derivative map as well as the numerical value for the number of hectares suitable for a given purpose. 1.) Log on to your computer. 2.) Gain access to the necessary files by doing the following: Your four images are located on the University of Portland campus network at Academic / Environmental Science / Env384 / Idrisi_Files / Idrisi_DatabaseQuery_F. Right-click on it and then press the Control and C keys simultaneously to copy the folder. Locate your personal folder on the RS-GIS disk drive and double-click on it to open it. Press the Control and V keys simultaneously to paste the folder into your personal folder. One purpose of doing this is to prevent accidental changes to the original copies of the files. A second purpose of doing this is to keep your personal output files in your own folder. 3.) Launch Idrisi Selva. 4.) Single-click on the Environment icon (it looks like a blue globe with folders in front of it) at the far left on the toolbar. Click on the Projects tab. Leave the Default project, but remove all other projects. While not strictly necessary, this will keep clutter to a minimum. They can be easily restored later. Establish a new project by right-clicking anywhere below the listing for the Default project. You can use the Insert (Ins) button to accomplish the same thing. Navigate to your personal folder on the RS-GIS drive and then to Idrisi_DatabaseQuery_F. 5.) Single-click on the Display Launcher icon (the miniature world map) second from the left on the toolbar. Single-click on the ellipsis button to bring up the list of files to choose from. Any files that you have previously saved to Idrisi_Output_F will appear. There should not be any such files at the start of this exercise. If the file names are not already displayed, then single-click on Idrisi_DatabaseQuery_F to display the list of files contained in the folder. Insure that the following two files are available: drelief (a digital terrain model) dsoils (a map of different soil types) Double-click on the file named drelief to insert it into the edit field. Toward the lower right, check the box for the IDRISI Default Quantitative palette. Toward the upper right, check the box for Autoscale, Equal Intervals. This should be the default. Toward the lower left, check the box for Legend. This should be the default. Toward the lower left, check the box for Title. This should be the default. Click the OK button on the Display Launcher dialog box. You will see a very colorful scene with the title Dirol Plain, Mauritania: Relief (DTM) Use Cursor Inquiry Mode (the toolbar icon near the center that looks like a question mark with an arrow pointing toward the lower left) to inspect the numerical value of numerous pixels in the scene. Can you tell whether the pixel data are byte or real format data? If necessary, check the Metadata listing (lower left of your computer screen) to confirm your conclusion about Data Type. Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 1 of 12

2 What part(s) of the scene have the highest elevations? What part(s) of the scene have the lowest elevations? Is there anything unusual about any part(s) of the scene? 6.) Some areas in this scene near the Senegal River flood every year during a very brief rainy season. Farmers plant their crops (mostly sorghum) immediately after the flood waters recede (an approach known as recessional agriculture ) in order to benefit from the retained soil moisture. This was a real project by which the local residents were seriously considering building a small dam on the North side of the Senegal River to hold the flood waters longer than usual, allowing more water to soak into and be retained by the soil. The normal maximum flood stage (i.e., maximum elevation of the flood) is 9.0 meters. In addition to sufficient soil moisture, the recessional sorghum farmers need the soil type that is most efficient at retaining water, and the only such soil type is clay. 7.) Single-click on the Display Launcher icon. Single-click on the ellipsis button to bring up the list of files to choose from. If necessary, single-click on Idrisi_DatabaseQuery_F to display the list of files contained in the folder (presuming the list is not already displayed). Double-click on the file named dsoils to insert it into the edit field. Toward the lower right, check the box for the second palette, i.e., IDRISI Default Qualitative. Toward the upper right, un-check the box for Autoscale. This should be the default. Toward the bottom left, check the box for Legend. This should be the default. Toward the bottom left, check the box for Title. This should be the default. Use Cursor Inquiry Mode to inspect the numerical value of numerous pixels in the scene. Do you notice any difference between these pixel values and the pixel values for the drelief layer? Is there anything else different about the display of pixel values? 8.) In determining whether or not to build the dam, the decision makers need to have some quantitative idea about how much sorghum yields will increase. That, in turn, depends almost exclusively on how many hectares of land will benefit from the trapped flood waters. Our task is to determine all areas suitable for recessional sorghum agriculture. Specifically, we need to find all regions (i.e., all pixels) which are: Located in the normal flood zone AND Located on clay soils Notice that this is a logical statement rather than a mathematical statement. Thus, at least in the latest stages of completing our task, we will need to use logical operators specifically, the logical AND operator. Naturally enough, how we approach our task is determined by the data layers we start with and the derivative map layer we end with. Why do we need the two layers to achieve our goal? We need a digital terrain model (DTM) layer because (a) the flood waters reach some particular height each year and (b) any dam that is constructed will also reach some particular height. We need a soil layer because soil type is crucial to sorghum yield using recessional sorghum agriculture. 9.) Our ultimate goal is a derivative map layer that shows all the best areas for growing sorghum using trapped soil moisture. We will call this final derivative map layer BestForSorg. One relatively obvious strategy for accomplishing our goal is as follows: Use the digital elevation model together with our knowledge of flood stage to generate a derivative layer that separates all pixels into two categories: one below maximum flood stage and the other above maximum flood stage. We will call this intermediate derivative map layer _9m. - This process is called single-attribute query because only one input layer is involved. Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 2 of 12

3 - This derivative map will be Boolean (named after George Boole), i.e., a data type that has only zeroes (0 = unacceptable) and ones (1 = acceptable). Use the soils map together with our knowledge that clay is the best soil type to retain water and then generate a derivative layer that separates all pixels into two categories: one with non-clay soils and the other with clay soils. We will call this intermediate derivative map layer Soil_Clay. - This is another example of a single-attribute query. - This derivative map will also be Boolean (non-clay = 0 and clay = 1). Combine the Flood_LT9m and Soil_Clay derivative map layers into the final derivative map layer BestForSorg using the logical AND operator. - This process is called multiple-attribute query because more than one input layer is involved. - This derivative map will also be Boolean. Acceptable pixels will be added up and then multiplied by the amount of ground area covered by each image pixel to give us what we ultimately need: the total area that would benefit from construction of the proposed dam. 10.) At this point, we need to know which of Idrisi Selva s many capabilities will help us get from what we have (drelief and dsoils) to what we need, the number of suitable hectares. The first helpful Idrisi Selva module is Reclass. Go to the Idrisi Selva help function and look up Reclass (or page 7 of this document). The Reclass module lets us search a raster file for specific numerical values and then reclassify each numerical value as we desire. In particular, we will want to assign either one of two values: either zeroes or ones. Working with drelief, we will reclassify the scene such that: - all pixels with numerical values greater than or equal to 9.0 meters elevation will be classified as 0 (i.e., not to be affected by the proposed dam) and - all pixels with numerical values less than 9.0 meters elevation will be classified as 1 (i.e., affected by the proposed dam). Working with dsoils, we will reclassify the scene such that: - all pixels with the numerical code for soil types other than simple clay soils will be classified as 0 (i.e., not acceptable for recessional sorghum agriculture), and - all pixels with the numerical code for clay soils will be classified as 1 (i.e., acceptable for recessional sorghum agriculture). That numerical code is. The second helpful Idrisi Selva module is Overlay. Go to the Idrisi Selva help function and look up Overlay (or page 9 of this document). The basic purpose of this module is to simplify several processes that involve simple mathematical operations on pixel data layers. The capability used most often is multiplication on Boolean (zeroes and ones) data layers, which yields unacceptable and acceptable areas for specified purposes. The third helpful Idrisi Selva module is Area. Go to the Idrisi Selva help function and look up Area (or page 11 of this document). The basic purpose of this module is to calculate the area covered by a selected set of pixels. The fourth helpful Idrisi Selva module is Group. Go to the Idrisi Selva help function and look up Group (or page 12 of this document). The basic purpose of this module is to assign a label number to contiguous groups of identically valued pixels. To do this, Idrisi Selva inspects neighboring pixels to determine whether or not they have the same numerical value as the center pixel presently being inspected. One question that must be addressed is the definition of neighboring pixels (will neighbors only be left, right, up and down, or will they also include the diagonal pixels? 11.) Before proceeding, we need to set some Idrisi Selva preferences so that the program will automatically do certain operations for us. Proceed as follows: From the File menu, select User Preferences. This will show a dialog box with three tabs. Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 3 of 12

4 Under the System Settings tab, make sure that - the box is checked for Automatically display the output of analytical models. Under the Display Settings tab, make sure that - the Default qualitative palette is set to QUAL256 (single-click on the ellipsis button and search in the c:\program files\idrisi Selva\symbols folder); - the Default quantitative palette is set to IDRIS256; - the box is checked for Automatically show title; - the box is checked for Automatically show legend. Click the OK button to have your selections take effect. 12.) Our first step will be as follows: Select the Reclass module (the fifth-from-last button near the right end of the toolbar). Type of file to reclassify: Image Classification type: User defined reclass Input file: Single click the ellipsis button and double click on drelief Output file: Type in the name Flood_LT9m Assign a new value of: 1 (acceptable for our intended purposes) To all values from: 0.0 (the lowest possible elevation value in drelief) To just less than: 9.0 (the height of the proposed dam) Assign a new value of: 0 (not acceptable for our intended purposes) To all values from: 9.0 (again, the height of the proposed dam) To just less than: (much higher than the highest elevation in drelief) Note that every possible elevation value is explicitly given a value to make absolutely sure we don t get any surprises. Click the OK button to execute the procedure. Accept Integer as the output data type. Inspect the file named Flood_LT9m, which should automatically display when the procedure is finished. Does this image make sense visually (i.e., does it look like the areas in color are the ones below 9.0 meters elevation and the areas in black are the ones above 9.0 meters elevation)? If so, proceed; if not, try it again taking special care to avoid any mistakes. 13.) Our second step will be as follows: Select the Reclass module (the fifth-from-last button near the right end of the toolbar). Type of file to reclassify: Image Classification type: User defined reclass Input file: Single click the ellipsis button and double click on dsoils Output file: Type in the name Soil_Clay Inspect the dsoils image and determine which code number represents clay soils. Note this number for inclusion below. Assign a new value of: 0 (not clay soils) To all values from: 0 To just less than: The numerical code for clay soils Assign a new value of: 1 (clay soils) To all values from: The numerical code for clay soils To just less than: The numerical code for clay soils + 1 Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 4 of 12

5 Assign a new value of: 0 (not clay soils) To all values from: The numerical code for clay soils + 1 To just less than: 9999 Note that every possible numerical code value (even if it is ridiculously low or high) is explicitly given a value to make absolutely sure we don t get any surprises. Click the OK button to execute the procedure. Inspect the resulting file named Soil_Clay. If it appears black, then go to the Composer window, click on the button labeled Layer Properties, select the Qual256 palette, and click on the Apply button. Does this image make sense visually (i.e., does it look like the areas in color are clay soils and the areas in black are not clay soils)? If so, proceed; if not, try it again taking special care to avoid any mistakes. 14.) Our third step will be as follows: Select the Overlay module (the fourth-from-last button near the right end of the toolbar). First image: Single click the ellipsis button and double click on Flood_LT9m Second image: Single click the ellipsis button and double click on Soil_Clay Output image: Type in the name BestForSorg Overlay options: First * Second (because only 1 * 1 will be acceptable) Output documentation: Title: Dirol Plain, Mauritania: Best Sorghum Value Units: Boolean Inspect the resulting file named BestForSorg. Does this image make sense visually (i.e., does it look like the areas in color are fit both criteria and the areas in black do not fit both criteria)? If so, proceed; if not, try it again taking special care to avoid any mistakes. 15.) Our fourth step will be as follows: Select the Area module (from the GIS Analysis / Database Query menu). Output format: Tabular Input image: Single click the ellipsis button and double click on BestForSorg Calculate area as: Hectares Title: Leave blank since no image is being generated How many hectares would benefit from the proposed dam? 16.) We may decide that we also want to calculate the area of each individual polygon (i.e., cluster of pixels) that represents an area that would benefit from the proposed dam. This is why we might want to use the Group module in Idrisi Selva. Select Group from the Analysis / Context Operators menu. Input image: Single click the ellipsis button and double click on BestForSorg Output image: Type in the name Groups Select the check box to include diagonals. Title: Dirol Plain, Mauritania: Pixel groups Click the OK button to execute the procedure. Inspect the resulting file named Groups. Does this image make sense visually? Which polygon ID numbers correspond to the three polygons best suited to recessional sorghum agriculture? Run the Area module on Groups to determine the area of all the polygons, and note the areas of the three polygons that represent the areas best suited to recessional sorghum agriculture. Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 5 of 12

6 Do these add up to the same number that you got for the total area? If so, proceed; if not, try it again taking special care to avoid any mistakes. 17.) When working with computer programs, it is often the case that we can get from what we have to what we need in more than one way. That is almost always the case with GIS software, and it is certainly the case here. Note what we have already done: Reclassified the drelief scene. Reclassified the dsoils scene. Combined the two into our final derivative image. We can also use the Image Calculator do very quickly get to the same goal. Launch Image Calculator (the sixth-from-last button near the right end of the toolbar). Operation type: Logical expression Output file name: bestcalc Expression to process: Single-click the ( button Single-click the Insert image button and double-click on drelief Single-click the < button Type in number 9.0 (the elevation of the proposed dam) Single-click the ) button Single-click the AND button Single-click the ( button Single-click the Insert image button and double-click on dsoils Single-click the = button Type in number 2 (the code number for clay soils) Single-click the ) button Single-click the Process Expression button Inspect the resulting file named bestcalc. Does this image make sense visually (i.e., does it look like exactly like BestForSorg)? Run the Area module on bestcalc. Does this yield the same area as that calculated for BestForSorg? If so, you are finished; if not, try it again taking special care to avoid any mistakes. 18.) Homework assignment 1: Assume that financial constraints may limit the dam height to 8.0 meters instead of 9.0 meters. How many hectares would benefit from the proposed dam under these new conditions? 19.) Homework assignment 2: Assume that all three kinds of clay soils would be acceptable and the dam height would still be 9.0 meters. How many hectares would benefit from the proposed dam under these new conditions? Put the final results of both homework assignments on the E:/ drive on the Master computer. Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 6 of 12

7 RECLASS RECLASS classifies or reclassifies the pixel values stored in images, the feature ID values of vector files or the second column values of attribute values files into new integer categories. Classification or reclassification is by equal intervals division of the data range, or by the application of userdefined limits. Related Modules: ASSIGN, PCLASS, OVERLAY User-defined reclass RECLASS Operation 1. Specify the type of file to reclassify, either an image, vector, or a values file. 2. For classification type, indicate user-defined reclass. 3. Specify the name of the input file and a name for the resulting output file. 4. Enter the reclass parameters in the grid, the new class value and the range of old values to be assigned to this new class. Click into the upper left cell of the grid and enter the first new value. Tab or click into the next cell and enter the lower limit for the range of old values to be given the new value. Tab or click into the third cell on that line and enter the upper limit. As soon as a value is entered into the first grid cell, a second row will appear in the grid. Fill in each row until all new classes have been defined. You alternately have the option of using an existing.rcl file with the reclass parameters. Select the Use.RCL button to launch the Pick List. The choice of old values is important. The lower limit is inclusive while the upper limit is exclusive. Values outside the data range in the image are permitted and are used to ensure that all old data values are reclassified. For example, suppose old data values ranging from are to be classified into two new classes, the first containing all old values less than 30 and the second containing all old values greater than or equal to 30. Ranges from 11 - < 30 for the first and 30 - < 51 for the second will not work, since 51 is not included in the second class. Ranges from for the first, and 30 - < 52 for the second will work. The new value, lower limit and upper limit values may be real or integer. The order of class definition is not important. A blank row will always appear at the bottom of the grid. It is not necessary to remove it. Use the Remove line button to remove one line in the grid. Use the Clear grid button to clear the entire grid. Any original values not covered by the specified ranges will retain their original values (except that these may be rounded to integer, see Note 4). An.RCL file may be created in any text editor. See Note 2 for the.rcl file structure. 5. If desired, save the reclassification assignments to an ASCII file with an.rcl extension by clicking the Save as.rcl file button. 6. Select Output documentation to enter a title and value units. Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 7 of 12

8 7. Click OK. Equal interval reclass 1. Specify the type of file to reclass, either an image, vector, or a values file. 2. For classification type, indicate equal interval reclass. 3. Specify the name of the input file and a name for the resulting output file. 4. Set the new minimum and maximum values for the classification if desired. It is common to change the minimum and maximum in order to derive classes that start and end at some even value. For example, a data set with a minimum of 2 and a maximum of 57 might be changed to 0 and Indicate whether to use class width or number of classes and enter that value. 6. Click OK. RECLASS Notes 1. Any values not specified in a set of reclassification ranges will be left unaltered. For example, if the image contains values from 1 to 5 and you indicate that those from 2 to those just less than 5 should be reclassified to a 3, the final image will contain values of 1, 3 and 5. (Note, however, that original real values may be rounded to integers in the output image -- see Note 4.) 2. The format for the.rcl file is a set of lines with 3 numbers per line separated by spaces. The numbers represent the new value, the old from value, and the old to just less than value. See RECLASS Command File Structure for more information. The.RCL file may be created with Edit or may be saved from the RECLASS dialog. Note that if you are using the RECLASS dialog just to create an.rcl file, it is not necessary to fill in input and output filenames. Just fill in the classification grid and choose Save as.rcl file. 3. As an alternative to RECLASS, ASSIGN can be used as a reclassifier whenever the input image is integer. Unlike RECLASS, ASSIGN forces unmentioned values to take a new value of When RECLASS is run interactively from the dialog box, the reclassification parameters entered into the dialog box are written to a RECLASS Command File (see note 2) called idrtemp.rcl that is stored in the working folder. 5. If the IDRTEMP.RCL file has been set to read-only, RECLASS will not be able to open it and will not run. This can sometimes happen when data, along with a file called idrtemp.rcl are copied from a CD. Use your operating system explorer to remove the read-only attribute or delete the file (a new one will be constructed when you run RECLASS). 6. When real values are rounded to integer, "banker's rule" rounding is used. If the value is exactly 0.5, it rounds to the nearest even number Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 8 of 12

9 OVERLAY OVERLAY produces a new image from the data of two input images. New values result from applying one of the nine possible operations to the two input images, referred to as the first and second images during program operation. Related Modules: RECLASS, PCLASS, SCALAR, TRANSFORM OVERLAY Operation 1. Enter the names of the first and second images as well as a name for the output image. 2. Select an overlay option: First + Second: Corresponding pixels from the two images are added. First - Second: Pixels from the second image are subtracted from those of the first. First x Second: Corresponding pixels from the two images are multiplied. First / Second: Pixels from the first image are divided by corresponding pixels from the second. First - Second / First + Second: Divides the subtraction of the two images by their addition. Commonly used in the derivation of vegetation indices from remotely sensed data. First to the power of the Second: Raises pixels in the first image to the power of corresponding pixels in the second. Minimum: Output pixels represent the minimum of those in corresponding positions on the first and second image. Maximum: Output pixels represent the maximum of those in corresponding positions on the first and second image. First covers Second except where zero: Covers pixels of the second image with those of the first, except where the first image has values of zero. In these cells, the value of the second image shows through. Pay careful attention to the relationship between these two images in the overlay option you choose. For example, image 1/image 2 does not yield the same result as image 2/image 1. Image order does not affect functions that are reflexive, i.e., addition and multiplication. It does affect all of the other overlay functions. 3. If the ratio option is chosen, select an option for how to handle division by zero if this occurs in your data. The choices are: Change analysis: 0/0=0; <0/0=-1e37 (machine negative infinity); >0/0=+1e37 (machine infinity). Division by zero yields zero: any_number/0=0. Division by zero yields error: any_number/0=error message. 4. Choose the Output Documentation button to enter a title and value units. Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 9 of 12

10 5. Click OK. OVERLAY Notes 1. When both images are Boolean (i.e., they both contain only zeros and ones), various logic operations (Boolean algebra) can be undertaken. For example: multiply = AND maximum or cover = OR. 2. OVERLAY uses standard mixed arithmetic rules in determining the data type of the result. Thus any operation involving a real number input image will result in a real number output image. Dividing two integer images will lead to a real number output. All other operations involving integers will lead to an integer result. 3. OVERLAY creates a byte output file only if the input files are in byte format and the lowest possible minimum and the highest possible maximum fall with the range of If it is determined that an output file predicted to have non-byte values completely conforms to byte limits, a message is displayed indicating that compaction to byte format is possible. 4. The maximum and minimum operations record the values but do not record information about which image provided the recorded value. To create such an image, use MDCHOICE Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 10 of 12

11 AREA AREA measures the areas associated with each integer category in an image and creates an output in the form of a new image, where each pixel takes on the area of the category to which it originally belonged, an attribute values file listing integer categories and their areas, or a summary table. Related Modules: PERIM, GROUP AREA Operation 1. Select whether you would like to create an image, attribute values file or tabular output type. 2. Indicate the name of the input image. 3. If you chose to create an image or attribute values file, enter a name for the output file. 4. Choose the measurement units for the area calculation: cells, hectares, acres, square meters, square feet, square kilometers, or square miles. AREA undertakes the proper conversion to these units based on the unit type and the reference system parameters listed in the image documentation file. 5. Click OK. AREA Notes 1. AREA will correctly adjust for differences in the longitudinal length of cells when a lat/long reference system is used. 2. For cases other than reporting the number of cells, AREA uses the reference units stated in the image documentation file as the basis for any conversion calculations. 3. In order to calculate the areas of individual features that share the same identifier, run the module GROUP before running AREA Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 11 of 12

12 GROUP GROUP determines contiguous groupings of identically valued integer cells in an image. Cells belonging to the same contiguous grouping are given a unique identifier, numbered consecutively in the order found. GROUP Operation 1. Enter the input and output filenames. 2. Indicate whether you wish to include diagonals in defining groups. If you include diagonal links, cells will be considered to belong to the same group if they have the same value and they touch in any of the eight possible directions, n, e, s, w, nw, ne, sw, se. If diagonal links are not included, pixels will be considered to be part of the same group only if they have the same value and touch in one of the cardinal directions, n, e, s, w. The results can be remarkably different. In general you want to include diagonal links unless you are trying to uncover polygons separated by lines of a single pixel width. 3. Specify an initial group value. 4. Choose whether to ignore a background value. 5. If you choose to ignore a background, specify its value to be excluded from the groupings. 6. Click OK. GROUP Notes 1. GROUP operates on byte and integer data type images only. However, if the number of groups exceeds the integer range of 32,767, the output image will be real data type. 2. Group numbering starts with the cell in the top left corner of the image. The numbering starts with the value specified for the initial group value Remote Sensing & GIS (Env/Bio 384 A): 10 November 2015 GIS Database Query, page 12 of 12