Difference between revisions of "Glacier Change Detection in GRASS GIS"

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== Purpose ==
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== Introduction ==
   
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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 generic Normalized Difference Snow Index (NDSI) technique. This tutorial is
 +
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]]
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== Introduction ==
  
  +
  +
[[File:Inset.PNG]]
   
 
== Software Download and Instillation ==
  
== Software Download and Instillation ==
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== Data Sources ==
  
== Data Sources ==
   
  +
=== Landsat 8/9 Imagery ===
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This tutorial uses the green and Short-Wave Infrared (SWIR) bands from 8 Landsat 8/9 images. The images were captured in late July/august when the Fiord was mostly free of ice. The Landsat imagery can be downloaded from the [https://earthexplorer.usgs.gov/ USGS Earth Explorer] website. To download the data, create a free student account.
  
  +
This tutorial uses the green and Short-Wave Infrared (SWIR) bands from 8 Landsat 8/9 images. The images were captured in late July/august when the Fiord was mostly free of ice. The Landsat imagery used in this tutorial can be found [https://cmailcarletonca-my.sharepoint.com/:f:/g/personal/owenkirkham_cmail_carleton_ca/EhrUMJnxcLJIoD9bXQdyHAIBEnDZux4TTpVjYXVwhgMnQQ?e=zoPuu1 here]
   
  +
Additional Landsat imagery can be downloaded from the [https://earthexplorer.usgs.gov/ USGS Earth Explorer] website. To download the data, create a free student account.
−
The ID and metadata for the specific images used in this lab can be found in the table below.
  
  +
  +
The ID and metadata for the specific images used in this lab are in the table below.
   
 
{| class="wikitable"
  
{| class="wikitable"
 
|-
  
|-
−
! Landsat Image ID !! Bands Used !! Metadata
 +
! Year !! Landsat Image ID !! Name of images
 
|-
  
|-
  +
| 2014 || LC08_L2SP_060246_20140825_20200911_02_T1 || L8_20140825_B3, L8_20140825_B6
−
| LC08_L2SP_060246_20140825_20200911_02_T1 || Example || 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_046248_20150810_20200908_02_T1 || Example || 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_057001_20160825_20200906_02_T1 || Example || 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_046248_20180802_20200831_02_T1 || Example || Example
  
 
|-
  
|-
  +
| 2019 || LC08_L2SP_048248_20190718_20200827_02_T1 || L8_20190718_B3, L820190718_B6
−
| LC08_L2SP_048248_20190718_20200827_02_T1 || Example || Example
  
 
|-
  
|-
  +
| 2020 || LC08_L2SP_074244_20200811_20200918_02_T1 || L8_20200811_B3, L8_20200811_B6
−
| LC08_L2SP_074244_20200811_20200918_02_T1 || Example || 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
−
| LC09_L2SP_046248_20220821_20230401_02_T1 || Example || Example
  
 
|-
  
|-
  +
| 2023 || LC08_L2SP_067245_20230819_20230826_02_T1 || L8_20230819_B3, L8_20230819_B6
−
| LC08_L2SP_067245_20230819_20230826_02_T1 || Example || 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
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| LC09_L2SP_052247_20240820_20240821_02_T1 || Example || Example
  
 
|}
  
|}
  +
  +
=== Supplementary Vector Data ===
  +
  +
The supplementary vector data for this tutorial can be found [https://cmailcarletonca-my.sharepoint.com/:f:/g/personal/owenkirkham_cmail_carleton_ca/EncvJfEXf2FAvG2Nwe99GAsB3JgUQUueMe6U9OoXCwK6bg?e=OGhQF2 here]. The vector data includes a polygon computational region and a polygon outline of the Otto Glacier.
   
 
== Methods ==
  
== Methods ==
   
−
=== Set Up ===
 +
=== Set Up Project===
   
  +
Open GRASS GIS
   
  +
Press the "Create new project" button [[File:Projectbutton.PNG]]
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=== Import Data ===
  
   
  +
Give the project a relevant name, such as ChangeDetection or GlacierChangeDetection. Press "Next"
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Landsat imagery from USGS Earth Explorer will come in a .TAR format when downloaded. Use 7Zip file manager to extract the .TIF files.
 
  +
  +
[[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]]
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  +
Finally, press "Finish"
  +
  +
=== Import Raster Data ===
  +
  +
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]"
  
To import the Landsat images to the workspace, go to "File" > "Import raster data" > "Import of common raster formats [r.in.gdal]"
Line 53: Line 79:
 
[[File:Add rasters.png]]
  
[[File:Add rasters.png]]
   
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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.
   
 
[[File:Add rasters 2.PNG]]
  
[[File:Add rasters 2.PNG]]
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  +
=== Set Computational 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]".
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  +
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]]
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  +
=== 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 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]]
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  +
   
 
=== Calculate NDSI ===
  
=== Calculate NDSI ===
  +
  +
Use the "Raster map calculator" to calculate the NDSI rasters for each year. To find the raster calculator, go to "Raster" > "Raster map calculator" > "Raster map calculator [r.mapcalc]".
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  +
[[File:Raster_calc1.png]]
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  +
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.
  +
  +
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.
  +
  +
[[File:Raster_calc_4.PNG]]
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  +
=== Convert Null values ===
  +
  +
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]"
  +
  +
[[File:Null.png]]
  +
  +
Then, in the "Name of raster map" drop-down menu, change the input raster to an NDVI raster.
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  +
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.
  +
  +
[[File:Null_2.PNG]]
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  +
=== Reclassify NDSI Rasters ===
  +
  +
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.
  +
  +
Press run and repeat with the other 7 NDSI rasters.
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  +
This formula tells the raster calculator to reclassify the raster values as 1 if they have an NDSI value greater than 0.4 and 0 if they have a value less than 0.4. Cells with a value of 1 represent the glacier, and cells with a value of 0 represent everything else
  +
  +
[[File:Reclass2.PNG]]
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  +
=== Converting Raster to Vector ===
  +
  +
Before starting the time series analysis, we need to create a new vector mask that describes the maximum extent of the glacier. This will help eliminate noise created by remnant sea ice in the Fiord that was incorrectly identified as the glacier in the NDVI raster.
  +
  +
For this step, we will assume that the 2014 reclassified NDSI raster describes the maximum extent of the glacier within the 10 years of imagery in the time series.
  +
  +
First, go to "Raster" > "Map type conversions" > "Raster to vector [r.to.vect]"
  +
  +
[[File:Convert.png]]
  +
  +
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
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  +
This tool's output will be a vector of the 2014 NDSI reclassified raster with values of 1 and 0.
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  +
[[File:Convert_2.PNG]]
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  +
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]]
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  +
Finally, press the "Create new map" button to create a new layer. Delete the previous vector mask from the workspace and create a new mask using this Extent2014_selection layer.
   
 
=== Time Series Change Detection with Landsat 8/9===
  
=== Time Series Change Detection with Landsat 8/9===
   
  +
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.
−
=== Change Detection with Sentinel 2 ===
  
  +
  +
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.
  +
  +
[[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 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.
  +
  +
Right-click on the layer in the layers pane and go to "Set colour table". Then, press the defined tab and locate the "gyr" colour table under the "Name of colour table" drop-down menu. Check the invert box and press run. This resulting layer will look like this:
  +
  +
[[File:Final_output1.PNG]]
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  +
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.
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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]]
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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.
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[[File:Stats_2.PNG]]
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The glacier tongue lost 0.68 km2 of ice between 2014 and 2015, 0.56 km2 between 2015 and 2016, 0.62 km2 between 2016 and 2018, 0.44 km2 between 2018 and 2019, 0.72 km2 between 2019 and 2020, 1.44 km2 between 2022 and 2023, and 3.17 km2 between 2023 and 2024. In total, the glacier lost 7.63 km2 or 11% of the glacier tongue area between 2014 and 2024
   
 
== 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.

Latest revision as of 13:08, 21 December 2024


Introduction

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.

OttoGlacier.PNG

Inset.PNG

Software Download and Instillation

This tutorial is demonstrated in Windows using GRASS GIS version 8.4.0. The latest version of GRASS GIS can be downloaded here

Data Sources

Landsat 8/9 Imagery

This tutorial uses the green and Short-Wave Infrared (SWIR) bands from 8 Landsat 8/9 images. The images were captured in late July/august when the Fiord was mostly free of ice. The Landsat imagery used in this tutorial can be found here

Additional Landsat imagery can be downloaded from the USGS Earth Explorer website. To download the data, create a free student account.

The ID and metadata for the specific images used in this lab are in the table below.

Year Landsat Image ID Name of images
2014 LC08_L2SP_060246_20140825_20200911_02_T1 L8_20140825_B3, L8_20140825_B6
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
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
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
2019 LC08_L2SP_048248_20190718_20200827_02_T1 L8_20190718_B3, L820190718_B6
2020 LC08_L2SP_074244_20200811_20200918_02_T1 L8_20200811_B3, L8_20200811_B6
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
2023 LC08_L2SP_067245_20230819_20230826_02_T1 L8_20230819_B3, L8_20230819_B6
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

Supplementary Vector Data

The supplementary vector data for this tutorial can be found here. The vector data includes a polygon computational region and a polygon outline of the Otto Glacier.

Methods

Set Up Project

Open GRASS GIS

Press the "Create new project" button Projectbutton.PNG

Give the project a relevant name, such as ChangeDetection or GlacierChangeDetection. Press "Next"

Newproject.PNG

Then, select "Select CRS from a list by EPSG or description." Press "Next"

Project2.PNG

Next, type "17N" into the search bar and scroll down until you find the "WGS 84 / UTM zone 17N" projection. Press "Next"

Project3.PNG

Finally, press "Finish"

Import Raster Data

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]"

Add rasters.png

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.

Add rasters 2.PNG

Set Computational 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 regions to match vector map(s)," and select the "CR" vector layer—press run.

Set region 2.PNG

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 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.

Mask.PNG


Calculate NDSI

Use the "Raster map calculator" to calculate the NDSI rasters for each year. To find the raster calculator, go to "Raster" > "Raster map calculator" > "Raster map calculator [r.mapcalc]".

Raster calc1.png

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.

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.

Raster calc 4.PNG

Convert Null values

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]"

Null.png

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.

Null 2.PNG

Reclassify NDSI Rasters

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.

Press run and repeat with the other 7 NDSI rasters.

This formula tells the raster calculator to reclassify the raster values as 1 if they have an NDSI value greater than 0.4 and 0 if they have a value less than 0.4. Cells with a value of 1 represent the glacier, and cells with a value of 0 represent everything else

Reclass2.PNG

Converting Raster to Vector

Before starting the time series analysis, we need to create a new vector mask that describes the maximum extent of the glacier. This will help eliminate noise created by remnant sea ice in the Fiord that was incorrectly identified as the glacier in the NDVI raster.

For this step, we will assume that the 2014 reclassified NDSI raster describes the maximum extent of the glacier within the 10 years of imagery in the time series.

First, go to "Raster" > "Map type conversions" > "Raster to vector [r.to.vect]"

Convert.png

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

This tool's output will be a vector of the 2014 NDSI reclassified raster with values of 1 and 0.

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:

Convert 3.png

Finally, press the "Create new map" button to create a new layer. Delete the previous vector mask from the workspace and create a new mask using this Extent2014_selection layer.

Time Series Change Detection with Landsat 8/9

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.

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 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.

Right-click on the layer in the layers pane and go to "Set colour table". Then, press the defined tab and locate the "gyr" colour table under the "Name of colour table" drop-down menu. Check the invert box and press run. This resulting layer will look like this:

Final output1.PNG

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.

Stats 3.PNG

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.

Stats 2.PNG

The glacier tongue lost 0.68 km2 of ice between 2014 and 2015, 0.56 km2 between 2015 and 2016, 0.62 km2 between 2016 and 2018, 0.44 km2 between 2018 and 2019, 0.72 km2 between 2019 and 2020, 1.44 km2 between 2022 and 2023, and 3.17 km2 between 2023 and 2024. In total, the glacier lost 7.63 km2 or 11% of the glacier tongue area between 2014 and 2024

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.

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