Glacier Change Detection in GRASS GIS - Revision history

Smitch: /* Calculate NDSI */

2025-01-03T16:08:29Z

Calculate NDSI

OwenKirkham: /* Methods */

2024-12-21T18:08:02Z

Methods

OwenKirkham: /* Landsat 8/9 Imagery */

2024-12-21T17:50:07Z

Landsat 8/9 Imagery

OwenKirkham: /* Landsat 8/9 Imagery */

2024-12-21T17:11:04Z

Landsat 8/9 Imagery

OwenKirkham: /* Landsat 8/9 Imagery */

2024-12-21T17:09:36Z

Landsat 8/9 Imagery

OwenKirkham: /* Introduction */

2024-12-21T17:08:20Z

Introduction

OwenKirkham: /* Conclusion */

2024-12-21T05:13:30Z

Conclusion

OwenKirkham: /* Set Up */

2024-12-20T00:33:47Z

Set Up

OwenKirkham: /* Purpose and Introduction */

2024-12-20T00:19:28Z

Purpose and Introduction

OwenKirkham: /* Purpose */

2024-12-05T23:12:49Z

Purpose

@@ Line 114: / Line 114: @@
 The NDSI formula is "(Green-SWIR)/(Green+SWIR)," where Green is the green band, and SWIR is the SWIR band for an image. For Landsat 8/9 imagery, B3 is the green band, and B6 is the SWIR band.
-In the "Expression" text box, paste this formula: '''(float( raster_B3 ) - float( raster_B6 ) ) /(float( raster_B3 ) + float( raster_B6 ) )'''. The float function allows the output to have decimal places.
+In the "Expression" text box, paste this formula: '''(float( raster_B3 ) - float( raster_B6 ) ) /(float( raster_B3 ) + float( raster_B6 ) )'''. The float function allows the output to have decimal places; without specifying that, the default would be to write integer results since that was the format of the input data layers.
 Then, change raster_B3 and raster_B6 to the appropriate names. Raster names can be input by clicking the raster from the "Insert existing raster map" drop-down menu.

@@ Line 73: / Line 73: @@
 === Import Raster Data ===
-Landsat imagery from USGS Earth Explorer will come in a .TAR format when downloaded. Use 7Zip file manager to extract the .TIF files.
+Landsat imagery from USGS Earth Explorer will be downloaded in a TAR format. Use the 7Zip file manager to extract the. TIF files.
 To import the Landsat images to the workspace, go to "File" > "Import raster data" > "Import of common raster formats [r.in.gdal]"
@@ Line 79: / Line 79: @@
 [[File:Add rasters.png]]
-Next, press "Browse" and find the extracted folders for the Landsat imagery. Import bands 3 and 6 for each image (.TIF files ending in "B6" and "B3"). Use a standardized naming convention for all of the images to make the analysis easier. For example, bands 3 and 6 from the 2014 image can be named L8_2014_B3 and L8_2014_B6, respectively.
+Next, press "Browse" and find the extracted folders for the Landsat imagery. For each image, import bands 3 and 6 (TIF files ending in "B6" and "B3"). To make the analysis easier, use a standardized naming convention for all of the images. For example, bands 3 and 6 from the 2014 image can be named L8_2014_B3 and L8_2014_B6, respectively.
 === Import Vector Data ===
 To import the computation region and Otto Glacier outline vector files, go to "File" > "Import vector data" > "Import of common vector formats [v.in.ogr]".
-Using the "Browse" button, find the "CR.shp" and "OttoFiord17N.shp" shapefiles that were downloaded earlier, and import them one at a time.
+Using the "Browse" button, find the "CR.shp" and "OttoFiord17N.shp" shapefiles that were downloaded earlier and import them one at a time.
 [[File:Add rasters 2.PNG]]
@@ Line 90: / Line 90: @@
 === Set Computational Region ===
-GRASS GIS requires a computational region to properly perform calculations on a raster. To set the computational region, "Settings" > "Computational region" > "Set region [g.region]".
+GRASS GIS requires a computational region to perform calculations on a raster properly. To set the computational region, "Settings" > "Computational region" > "Set region [g.region]".
-Find the drop-down menu called "[multiple] Set region to match vector map(s)", and select the "CR" vector layer. Press run.
+Find the drop-down menu called "[multiple] Set regions to match vector map(s)," and select the "CR" vector layer—press run.
 [[File:Set_region_2.PNG]]
@@ Line 98: / Line 98: @@
 === Mask Rasters ===
+We will use a vector mask to mask the Landsat imagery to isolate the glacier tongue from the rest of the image.
-To isolate the glacier tongue from the rest of the image, we will use a vector mask tp mask the Landsat imagery.
-To create a mask, go to "Raster" > "Mask [r.mask]". Then, press on the "Vector" tab and select "OttoFiord17N" from the "Name of vector map to use as a mask" drop-down menu. Press run.
+To create a mask, go to "Raster" > "Mask [r.mask]." Then, press on the "Vector" tab and select "OttoFiord17N" from the "Name of vector map to use as a mask" drop-down menu—press run.
 [[File:Mask.PNG]]
@@ Line 116: / Line 116: @@
 In the "Expression" text box, paste this formula: '''(float( raster_B3 ) - float( raster_B6 ) ) /(float( raster_B3 ) + float( raster_B6 ) )'''. The float function allows the output to have decimal places.
-Then, change raster_B3 and raster_B6 to the appropriate names. Raster names can be input by clicking on the raster from the "Insert existing raster map" drop-down menu.
+Then, change raster_B3 and raster_B6 to the appropriate names. Raster names can be input by clicking the raster from the "Insert existing raster map" drop-down menu.
 Finally, press "Run". The new NDSI raster will be added to the workspace. Repeat this step for every Landsat image in the time series.
@@ Line 124: / Line 124: @@
 === Convert Null values ===
-The rasters contain Null values that will hinder the analysis. To deal with this problem, we will cover the null values to 0.
+The rasters contain Null values that will hinder the analysis. To address this problem, we will set the null values to 0.
 First, go to "Rasters" > "Develop raster map" > "Manage Null values [r.null]"
@@ Line 130: / Line 130: @@
 [[File:Null.png]]
-Then, change the input raster to an NDVI raster in the "Name of raster map" drop-down menu.
+Then, in the "Name of raster map" drop-down menu, change the input raster to an NDVI raster.
 Next, go to the "Modify" tab and enter 0 in the "The value to replace the null value by" box. Press run and repeat for all of the NDSI rasters.
@@ Line 138: / Line 138: @@
 === Reclassify NDSI Rasters ===
-Once we calculate the NDSI rasters, we need to reclassify them to identify the glacier from the rest of the image. The NDSI rasters have values between -1 and 1, with values greater than 0.4 representing snow or ice.
+Once we calculate the NDSI rasters, we must reclassify them to identify the glacier from the rest of the image. The NDSI rasters have values between -1 and 1, with values greater than 0.4 representing snow or ice.
 Open the raster calculator. In the expression box, paste this formula: ```if ( NDSI_raster >= 0.4, 1, if( NDSI_raster < 0.4,0))```, and change the name from "NDSI_raster" to the name of the 2014 raster.
@@ Line 160: / Line 160: @@
 Next, input the name of the 2014 reclassified NDSI raster into the "Name of input raster map" box. Name the output vector layer "Extent2014", and change the "Output feature type" to "area". Press run
-The output of this tool will be a vector of the 2014 NDSI reclassified raster with values of 1 and 0.
+This tool's output will be a vector of the 2014 NDSI reclassified raster with values of 1 and 0.
 [[File:Convert_2.PNG]]
-Then, with the Extent2014 layer displayed on the map, press the select feature's icon, and press on the glacier polygon within the layer. The selection should look like this:
+Then, with the Extent2014 layer displayed on the map, press the select feature's icon and press on the glacier polygon within the layer. The selection should look like this:
 [[File:Convert_3.png]]
@@ Line 172: / Line 172: @@
 === Time Series Change Detection with Landsat 8/9===
-There are two parts to the time series change detection. First, we will visualize the change to the extent of the Otto Glacier Tongue over the 10 year times series. Then, we will calculate the change statistics.
+The time series change detection has two parts. First, we will visualize the extent of the Otto Glacier Tongue's change over the 10-year time series. Then, we will calculate the change statistics.
 Open the raster map calculator, and paste this expression into the expression box: ```2014_NDSI_Reclass@PERMANENT + 2015_NDSI_Reclass@PERMANENT + 2016_NDSI_Reclass@PERMANENT + 2018_NDSI_Reclass@PERMANENT + 2019_NDSI_Reclass@PERMANENT + 2020_NDSI_Reclass@PERMANENT + 2023_NDSI_Reclass@PERMANENT + 2024_NDSI_Reclass@PERMANENT```
-Change the raster names if required, give the output raster a name, and press run
+Change the raster names if required, give the output raster a name, and press run.
 [[File:FinalCalc.PNG]]
-The final output will have values between 1 and 8. A cell value of 1 represents parts of the glacier tongue that disappeared after 2014, a value of two represents parts of the glacier tongue that disappeared after 2015, a value of three represents parts of the glacier that disappeared after 2016, and so on and so forth. Cells that have a maximum value of 8 represent the remaining extent of the glacier.
+The final output will have values between 1 and 8. A cell value of 1 represents parts of the glacier tongue that disappeared after 2014, a value of 2 represents parts of the glacier tongue that disappeared after 2015, a value of three represents parts of the glacier that disappeared after 2016, and so on. Cells with a maximum value of 8 represent the remaining extent of the glacier.
 To better visualize this trend, we can change the symbolization of the output raster.
@@ Line 190: / Line 190: @@
 The second step of the time series detection is to conduct a simple statistical analysis of the rate and magnitude of change in the glacier tongue.
-Go to "Raster" > "Reports and statistics" > "Sum area by raster by raster map and category [r.report]". Select the GlacierChange raster, and check the "area in kilometres under the "Statistics" tab. Press run.
+Go to "Raster" > "Reports and statistics" > "Sum area by raster by raster map and category [r.report]". Select the GlacierChange raster, and check the "area in kilometres under the "Statistics" tab—press run.
 [[File:Stats_3.PNG]]
-The output of this tool is a statistical report that provides you with the total area in square kilometres, cell count, and percent cover of each raster class.
+This tool's output is a statistical report that provides the total area in square kilometres, cell count, and percent cover of each raster class.
 [[File:Stats_2.PNG]]

@@ Line 24: / Line 24: @@
 {| class="wikitable"
 |-
-! Landsat Image ID !! Name
+! Year !! Landsat Image ID !! Name of images
 |-
-| LC08_L2SP_060246_20140825_20200911_02_T1 || Example
+| 2014 || LC08_L2SP_060246_20140825_20200911_02_T1 || L8_20140825_B3, L8_20140825_B6
 |-
-| LC08_L2SP_046248_20150810_20200908_02_T1 || Example
+| 2015 || LC08_L2SP_046248_20150810_20200908_02_T1 || LC08_L2SP_046248_20150810_20200908_02_T1_SR_B3, LC08_L2SP_046248_20150810_20200908_02_T1_SR_B6
 |-
-| LC08_L2SP_057001_20160825_20200906_02_T1 || Example
+| 2016 || LC08_L2SP_057001_20160825_20200906_02_T1 || LC08_L2SP_057001_20160825_20200906_02_T1_SR_B3, LC08_L2SP_057001_20160825_20200906_02_T1_SR_B6
 |-
-| LC08_L2SP_046248_20180802_20200831_02_T1 || Example
+| 2018 || LC08_L2SP_046248_20180802_20200831_02_T1 || LC08_L2SP_046248_20180802_20200831_02_T1_SR_B3, LC08_L2SP_046248_20180802_20200831_02_T1_SR_B6
 |-
-| LC08_L2SP_048248_20190718_20200827_02_T1 || Example
+| 2019 || LC08_L2SP_048248_20190718_20200827_02_T1 || L8_20190718_B3, L820190718_B6
 |-
-| LC08_L2SP_074244_20200811_20200918_02_T1 || Example
+| 2020 || LC08_L2SP_074244_20200811_20200918_02_T1 || L8_20200811_B3, L8_20200811_B6
 |-
-| LC09_L2SP_046248_20220821_20230401_02_T1 || Example
+| 2022 || LC09_L2SP_046248_20220821_20230401_02_T1 || LC09_L2SP_046248_20220821_20230401_02_T1_SR_B3, LC09_L2SP_046248_20220821_20230401_02_T1_SR_B6
 |-
-| LC08_L2SP_067245_20230819_20230826_02_T1 || Example
+| 2023 || LC08_L2SP_067245_20230819_20230826_02_T1 || L8_20230819_B3, L8_20230819_B6
 |-
-| LC09_L2SP_052247_20240820_20240821_02_T1 || Example
+| 2024 || LC09_L2SP_052247_20240820_20240821_02_T1 || LC09_L2SP_052247_20240820_20240821_02_T1_SR_B3, LC09_L2SP_052247_20240820_20240821_02_T1_SR_B6
 |}

@@ Line 24: / Line 24: @@
 {| class="wikitable"
 |-
-! Landsat Image ID !! Bands Used !!
+! Landsat Image ID !! Name
 |-
-| LC08_L2SP_060246_20140825_20200911_02_T1 || Example ||
+| LC08_L2SP_060246_20140825_20200911_02_T1 || Example
 |-
-| LC08_L2SP_046248_20150810_20200908_02_T1 || Example ||
+| LC08_L2SP_046248_20150810_20200908_02_T1 || Example
 |-
-| LC08_L2SP_057001_20160825_20200906_02_T1 || Example ||
+| LC08_L2SP_057001_20160825_20200906_02_T1 || Example
 |-
-| LC08_L2SP_046248_20180802_20200831_02_T1 || Example ||
+| LC08_L2SP_046248_20180802_20200831_02_T1 || Example
 |-
-| LC08_L2SP_048248_20190718_20200827_02_T1 || Example ||
+| LC08_L2SP_048248_20190718_20200827_02_T1 || Example
 |-
-| LC08_L2SP_074244_20200811_20200918_02_T1 || Example ||
+| LC08_L2SP_074244_20200811_20200918_02_T1 || Example
 |-
-| LC09_L2SP_046248_20220821_20230401_02_T1 || Example ||
+| LC09_L2SP_046248_20220821_20230401_02_T1 || Example
 |-
-| LC08_L2SP_067245_20230819_20230826_02_T1 || Example ||
+| LC08_L2SP_067245_20230819_20230826_02_T1 || Example
 |-
-| LC09_L2SP_052247_20240820_20240821_02_T1 || Example ||
+| LC09_L2SP_052247_20240820_20240821_02_T1 || Example
 |}

@@ Line 24: / Line 24: @@
 {| class="wikitable"
 |-
-! Landsat Image ID !! Bands Used !! Metadata
+! Landsat Image ID !! Bands Used !!
 |-
-| LC08_L2SP_060246_20140825_20200911_02_T1 || Example || Example
+| LC08_L2SP_060246_20140825_20200911_02_T1 || Example ||
 |-
-| LC08_L2SP_046248_20150810_20200908_02_T1 || Example || Example
+| LC08_L2SP_046248_20150810_20200908_02_T1 || Example ||
 |-
-| LC08_L2SP_057001_20160825_20200906_02_T1 || Example || Example
+| LC08_L2SP_057001_20160825_20200906_02_T1 || Example ||
 |-
-| LC08_L2SP_046248_20180802_20200831_02_T1 || Example || Example
+| LC08_L2SP_046248_20180802_20200831_02_T1 || Example ||
 |-
-| LC08_L2SP_048248_20190718_20200827_02_T1 || Example || Example
+| LC08_L2SP_048248_20190718_20200827_02_T1 || Example ||
 |-
-| LC08_L2SP_074244_20200811_20200918_02_T1 || Example || Example
+| LC08_L2SP_074244_20200811_20200918_02_T1 || Example ||
 |-
-| LC09_L2SP_046248_20220821_20230401_02_T1 || Example || Example
+| LC09_L2SP_046248_20220821_20230401_02_T1 || Example ||
 |-
-| LC08_L2SP_067245_20230819_20230826_02_T1 || Example || Example
+| LC08_L2SP_067245_20230819_20230826_02_T1 || Example ||
 |-
-| LC09_L2SP_052247_20240820_20240821_02_T1 || Example || Example
+| LC09_L2SP_052247_20240820_20240821_02_T1 || Example ||
 |}

@@ Line 3: / Line 3: @@
 == Introduction ==
-This tutorial will use Grass GIS to perform a change detection analysis on Landsat and sentinel imagery of the Otto Glacier, a marine-terminating glacier located on Ellesmere Island in Nunavut. Ellesmere Island's mean annual air temperature has increased by 3.6°C between 1948 and 2016, reducing ice coverage by 3.68% in the last decade. The tutorial demonstrated how to visualize and quantify recent changes in the Otto glacier while demonstrating key GIS concepts such as the Normalized Difference Snow Index (NDSI), raster math, raster clipping, and raster visualization within Grass GIS.
+This tutorial will use Grass GIS to perform a change detection analysis on Landsat imagery of the Otto Glacier, a marine-terminating glacier located on Ellesmere Island in Nunavut. Ellesmere Island's mean annual air temperature has increased by 3.6°C between 1948 and 2016, reducing ice coverage by 3.68% in the last decade (White & Copland, 2018). The tutorial demonstrated how to visualize and quantify recent changes in the Otto glacier while demonstrating key GIS concepts such as the Normalized Difference Snow Index (NDSI), raster math, raster clipping, and raster visualization within Grass GIS.
 [[File:OttoGlacier.PNG]]

← Older revision		Revision as of 05:13, 21 December 2024
Line 201:		Line 201:

	== Conclusion ==		== Conclusion ==

			In conclusion, the tutorial teaches users about the impacts of climate change in the Arctic through an informative demonstration of GRASS GIS and universal raster calculator-based raster analysis. Some key concepts demonstrated in this tutorial were raster math, NDSI, raster masking, raster reclassification, and statistical analysis of rasters.

@@ Line 51: / Line 51: @@
 == Methods ==
-=== Set Up ===
+=== Set Up Project===
+Open GRASS GIS
+Press the "Create new project" button [[File:Projectbutton.PNG]]
+Give the project a relevant name, such as ChangeDetection or GlacierChangeDetection. Press "Next"
+[[File:Newproject.PNG]]
+Then, select "Select CRS from a list by EPSG or description." Press "Next"
+[[File:Project2.PNG]]
+Next, type "17N" into the search bar and scroll down until you find the "WGS 84 / UTM zone 17N" projection. Press "Next"
+[[File:Project3.PNG]]
+Finally, press "Finish"
 === Import Raster Data ===

← Older revision		Revision as of 00:19, 20 December 2024
Line 1:		Line 1:


	== ~~Purpose and~~ Introduction ==		== Introduction ==

	This tutorial will use Grass GIS to perform a change detection analysis on Landsat and sentinel imagery of the Otto Glacier, a marine-terminating glacier located on Ellesmere Island in Nunavut. Ellesmere Island's mean annual air temperature has increased by 3.6°C between 1948 and 2016, reducing ice coverage by 3.68% in the last decade. The tutorial demonstrated how to visualize and quantify recent changes in the Otto glacier while demonstrating key GIS concepts such as the Normalized Difference Snow Index (NDSI), raster math, raster clipping, and raster visualization within Grass GIS.		This tutorial will use Grass GIS to perform a change detection analysis on Landsat and sentinel imagery of the Otto Glacier, a marine-terminating glacier located on Ellesmere Island in Nunavut. Ellesmere Island's mean annual air temperature has increased by 3.6°C between 1948 and 2016, reducing ice coverage by 3.68% in the last decade. The tutorial demonstrated how to visualize and quantify recent changes in the Otto glacier while demonstrating key GIS concepts such as the Normalized Difference Snow Index (NDSI), raster math, raster clipping, and raster visualization within Grass GIS.

@@ Line 1: / Line 1: @@
-== Purpose ==
+== Purpose and Introduction ==
-The purpose of this tutorial is to visualize and assess the change in the extent of a marine-terminating glacier in the Canadian Arctic over time. To assess the change in extent, this tutorial will demonstrate how to classify the snow/ice cover in Landsat 8/9 images using a universal Normalized Difference Snow Index (NDSI) technique.
+This tutorial will use Grass GIS to perform a change detection analysis on Landsat and sentinel imagery of the Otto Glacier, a marine-terminating glacier located on Ellesmere Island in Nunavut. Ellesmere Island's mean annual air temperature has increased by 3.6°C between 1948 and 2016, reducing ice coverage by 3.68% in the last decade. The tutorial demonstrated how to visualize and quantify recent changes in the Otto glacier while demonstrating key GIS concepts such as the Normalized Difference Snow Index (NDSI), raster math, raster clipping, and raster visualization within Grass GIS.
+[[File:OttoGlacier.PNG]]
+[[File:Inset.PNG]]
 == Software Download and Instillation ==