Intro to GRASS workshop - Revision history

Smitch: /* Introduction to GRASS GIS (7.6.1) */

2019-10-28T23:56:08Z

Introduction to GRASS GIS (7.6.1)

Smitch: /* Getting Started */

2019-10-28T23:48:31Z

Getting Started

Smitch: /* Getting Started */ fix ]

2019-10-28T23:46:23Z

Getting Started: fix ]

Smitch: /* Getting Started */ clarifications for software links

2019-10-28T23:45:57Z

Getting Started: clarifications for software links

Shenghao: /* Introduction to GRASS GIS (7.6.1) */

2019-10-03T19:47:56Z

Introduction to GRASS GIS (7.6.1)

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Shenghao: /* Analysis example: interpolation (and importing ASCII point data) */

2019-10-03T18:49:47Z

Analysis example: interpolation (and importing ASCII point data)

Shenghao: /* Analysis example: interpolation (and importing ASCII point data) */

2019-10-03T18:43:18Z

Analysis example: interpolation (and importing ASCII point data)

Shenghao: /* Analysis example: vector query, and extracting raster values at vector locations */

2019-10-03T18:40:34Z

Analysis example: vector query, and extracting raster values at vector locations

Shenghao: /* Analysis example: map algebra to calculate NDVI */

2019-10-03T18:15:54Z

Analysis example: map algebra to calculate NDVI

Shenghao: /* Analysis example: map algebra to calculate NDVI */

2019-10-03T18:12:43Z

Analysis example: map algebra to calculate NDVI

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 = Introduction to GRASS GIS (7.6.1) =
-This document was written based on an older version that originally accompanied to a workshop to be given at Carleton University on 17 October 2011, in the Department of Geography and Environmental Studies' main GIS lab (Loeb A200). Anyone is welcome to adapt it for other purposes or to help improve and keep this tutorial up to date (see CC license conditions at the bottom left of the page).
+This document was written based on an older version that originally accompanied a workshop given at Carleton University on 17 October 2011, in the Department of Geography and Environmental Studies' main GIS lab (Loeb A200). Anyone is welcome to adapt it for other purposes or to help improve and keep this tutorial up to date (see CC license conditions at the bottom left of the page).
 The expected audience for this tutorial consists of people that are new to GRASS GIS, but not new to GIS in general - i.e. we do not explain basic GIS concepts, and when the software "behaves" very similarly to other common GIS software, this tutorial does not provide much in the way of detailed instructions (e.g. choosing map symbology).

← Older revision		Revision as of 23:48, 28 October 2019
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	'''In the Define new GRASS Location window, give your project an appropriate name in the Project Location. ''' ~~The~~ ~~general~~ ~~principle~~ of ~~naming~~ is ~~like ArcGIS~~, to ~~avoid~~ ~~spaces~~. The Location Title is no mandatory, choose a name for it if you like. '''Click Next. '''		'''In the Define new GRASS Location window, give your project an appropriate name in the Project Location. ''' You should avoid spaces in this name, but you can create a more friendly version in the Title. The Location Title is not mandatory, choose a name for it if you like. '''Click Next. '''

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 To download the GRASS GIS software:
-[https://grass.osgeo.org/download/software/ Go to this detailed software download page, OR visit specific links below.]
+[https://grass.osgeo.org/download/software/ Go to this detailed software download page], OR visit specific links below.
 * Download binaries: [https://grass.osgeo.org/download/software/ms-windows/#g78x/ WinGRASS 7.6.1 snapshot full installer] or [https://grass.osgeo.org/download/software/linux/#g76x LinuxGRASS 7.6.1 snapshot installer] or [https://grass.osgeo.org/download/software/mac-osx/#g76x|MacOSX-GRASS 7.6.1 installer]

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 == Getting Started ==
-If GRASS is not already installed on a machine for you, getting the software is probably your first step. In this document, the computing environment will use a 64 bit MS Windows 10 OS. The installation part of the tutorial will explain the binaries method.
+If GRASS is not already installed on a machine for you, getting the software is probably your first step. In this document, the computing environment will use a 64 bit MS Windows 10 OS. The installation part of the tutorial will explain the method for installing pre-compiled binaries.
 To download the GRASS GIS software:
-[https://grass.osgeo.org/download/software/ The detailed software download]
+[https://grass.osgeo.org/download/software/ Go to this detailed software download page, OR visit specific links below.]
 * Download binaries: [https://grass.osgeo.org/download/software/ms-windows/#g78x/ WinGRASS 7.6.1 snapshot full installer] or [https://grass.osgeo.org/download/software/linux/#g76x LinuxGRASS 7.6.1 snapshot installer] or [https://grass.osgeo.org/download/software/mac-osx/#g76x|MacOSX-GRASS 7.6.1 installer]
-* Download source code: [https://grass.osgeo.org/download/software Download source code packages] or [https://trac.osgeo.org/grass/wiki/DownloadSource Download source code from GitHub repository]
+* Download source code (optional): [https://grass.osgeo.org/download/software Download source code packages] or [https://trac.osgeo.org/grass/wiki/DownloadSource Download source code from GitHub repository]

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 [[File:IDWWW.JPG]]
-[[File:IDW RESULT.JPG|500PX]]
+[[File:IDW RESULT.JPG|500px]]
+''Notice any difference between two outputs? That is because Inverse distance weighted (IDW) interpolation makes the assumption that spatial features that are closer to each other are more likely to be related than those that are farther apart. In IDW interpolation, any estimated value is measured base on its surrounding values. The nearer value to your prediction location have the greater impact on how the value is estimated.''
 I think you'll find that clearly we need more data to make a sensible map of housing prices, but this exercise at least introduced you to interpolations in GRASS.  There are many more algorithms available, and the GEOM4008 class will be exploring this in detail in their next assignment.

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 Now in the Map Display window, you can see all the big parks stand out with the default black and grey coloration.
-[[File:Bigparks Display.JPG]]
+[[File:Bigpark newcolor.JPG]]
 * ''' In the layer manager, on the Map layers pane, select the park layer, right click on it, and choose to set the computational region to the extent of this file''' (to avoid wasting time processing the entire region of the NDVI layer)
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 [[File:Bigparks Attribute.JPG]]
+By click on the title "NDVI_average" we can check the average NDVI value smallest value first. We can tell that most of the big parks have high NDVI value (>0.4) which means dense vegetation coverage, which meets our exception.
+However, we also find a few big parks with low NDVI value. So let's explore the reason behind it.
+We want to check which parks have low NDVI value. To do so, ''' double click on the row''', this will highlight the row you chose. Use your mouse to drag and zoom to the park to visually check for the reason. We will show two example here.
+First, by checking the name of the park with lowest NDVI value (0.088) we know it is the Lansdowne Park Complex, which we know is more of a commercial and recreational center than a typical park with high vegetation coverage.
+[[File:Lansdowne.jpg]]
+''Map of Lansdowne Park Complex. Source: City of Ottawa''
+Then, let's look at the big park with the second low NDVI value - the Dick Bell Park.
+Below is an image of its relative location and the NDVI map of the region. We can tell the reason for low NDVI is likely due to the large water body in the park.
+[[File:Comparsion D.jpg|800px]]
 == Analysis example: interpolation (and importing ASCII point data) ==

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 As a result, you will have your map display showing something similar to this: which tells us the vegetation density visually.
+* White color represents water body
+* Light brown color represents bare lands with little vegetation
+* Darker green color represents denser vegetation coverage
+* Lighter green color represents sparser vegetation coverage
 [[File:NDVI DISPLAY.JPG]]

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 * '''From the Raster menu in the Layer Manager, choose Raster map calculator/Raster map calculator
 * In the resulting window '''enter NDVI as the name of the new raster to create, then use the other buttons to create a formula of the form: float( Landsat5.4@PERMANENT -  Landsat5.3@PERMANENT ) / float( Landsat5.4@PERMANENT + Landsat5.3@PERMANENT )''' (if you use the dropdown menu to insert the layer names, it will add "@PERMANENT" to indicate that these maps are in the PERMANENT mapset - that's fine, but is not needed so you can left them out for simplicity)
+''In the formula above, the float() function was used to force r.mapcalc to evaluate the addition and subtraction operations using floating point numbers; since all the input layers were integer maps, it would have otherwise defaulted to using integers for the output map, which leads to a pretty boring 3 category map because NDVI typically varies between 0-1 (with some exceptions).''
 [[File:Calculator.JPG]]
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 * '''Press the Run button to execute the formula'''
+You will get an output of NDVI as a result. However, by just looking at the map display, it is not making clear sense of where should you expect vegetation coverage. Let's fix that by ''' right click on the NDVI layer in the layer manager, and choose Set color table. In the new window, go to Define tab and choose ndvi as your name of color table. Press Run.'''
-''In the formula above, the float() function was used to force r.mapcalc to evaluate the addition and subtraction operations using floating point numbers; since all the input layers were integer maps, it would have otherwise defaulted to using integers for the output map, which leads to a pretty boring 3 category map because NDVI typically varies between 0-1 (with some exceptions).''
+[[File:NDVI Setting.jpg]]
+As a result, you will have your map display showing something similar to this: which tells us the vegetation density visually.
+[[File:NDVI DISPLAY.JPG]]
 == Analysis example: vector query, and extracting raster values at vector locations ==