The document describes using GRASS GIS to detect land cover change over 13 years at a mining site in British Columbia. Atmospherically corrected Landsat images from 2001-2014 were analyzed using image differencing of NDVI, TCT, and PCA outputs. Thresholding identified significant change areas. NDVI detected over 2300 ha of change, while TCT and PCA detected over 2000 ha. The open source and automated nature of GRASS GIS makes it suitable for replicable change detection.

1. Automated Change
Detection in GRASS GIS
EXPLORING THE APPLICATIONS AND UTILITY OF GRASS GIS
JACOB CHILA

2. Presentation Contents
 Objective
 Why GRASS GIS
 Target Site
 Data
 Atmospheric Correction
 Composites and Subsets
 Image Differencing
 Normalized Difference Vegetation Index
 Tasseled Cap Transformation
 Principle Component Analysis
 The Change
 Recommendations
 Acknowledgments
 Questions

3. Objective
To create an automated, easily replicated, accurate and free method to track landcover change
by scripting in GRASS GIS.
Compare the products of Open Source software to those created in commercial software
packages

4. GRASS GIS
Geographic Resources Analysis Support System (GRASS)
Created by the US Army Corp of Engineers
GRASS is a free GIS software suite used extensively for examining and managing geospatial data

5. Why GRASS?
Available to anyone, for free.
Comparison to commercial software
Applications

6. Fording River Mine
South Eastern British Columbia
One of the largest coking coal reserves in Canada
263.8 million tonnes in reserves
8.34 million tonnes annually
Mine

7. Data
Continuing the ‘free’ theme -- http://earthexplorer.usgs.gov/
The scenes (Path 42, Row 25) cover 13 years of mining activity from 2001 to 2014 and collected
by three different Landsat sensors; 5, 7, and 8.
Each image was collected in August of that year to maximize the data continuity

8. Atmospheric Correction
In order to maintain data integrity and produce accurate, usable, products, the images must be
corrected for atmospheric distortion.
GRASS GIS includes an atmospheric Correction module; i.atcorr
The 6S Algorithm – Second Simulation of a Satellite Signal in the Solar Spectrum

9. i.atcorr
Can be applied to all Landsat satellites as well as several other common platforms including IKONOS,
RapidEye, and Modis, as well as Aerial Photography
The script command:
Flags indicate options within the software,
for ETM+ images taken before 1999 the flag
‘a’ must be used, and ‘b’ for ETM+ images
after 1999. None of the other sensors require
this flag.
The algorithm requires several parameters to be written to a parameter file, ‘pfile’
grass.run_command('i.atcorr',
flags='a',
input=line,
elevation='elev_int',
parameters=pfile,
output=oname,
overwrite=True)
grass.run_command('i.atcorr',
input=line,
elevation='elev_int',
parameters=pfile,
output=oname,
overwrite=True)

10. Parameter File
17 - Geometrical conditions associated with Landsat 8
8 11 18.5030759012 -114.43339 50.29982 - The month, day (decimal hours) and Long/Lat of image centre
2 - Atmospheric mode
1 - Aerosols model
0 - Visibility [km]
0.112 - Aerosol model concentration
-1.5278 - Mean target elevation above sea level
-1000 - Sensor height, -1000 is a preset for satellites
117 - The band number associated with Landsat 8 Band 3
The atmospheric mode was set to ‘Midlatitude Summer’
The aerosol model uses the pre defined ‘continental model’
Aerosol model concentration was used instead of a visibility map

11. Composites and Subsets
Atcorr must be run on the entire scene because corrections must
acknowledge the entire histogram. To reduce processing time and
memory requirements, a subset was created for each scene from
a near infrared composite.

12. Corrected LC8 Scene

13. Change Detection – Image Differencing
Image Differencing is one of the simplest forms of change detection, and as such, is one of the
easiest to understand. By subtracting the pixel values of the more recent image from the older
image, areas of change can be easily highlighted.
Three classification methods were chosen for this analysis:
Normalized Difference Vegetation Index
Tasseled Cap Transformation
Principle Component Analysis

14. Normalized Difference Vegetation Index (NDVI)
Focuses on the presence and health of vegetation it is calculated using the red and near infrared
bands by dividing the difference by the sum of the two bands. The output has a range of -1 to 1.
Creates a stark contrast between a healthy
forest canopy (represented by high values)
and the exposed soil (characterized by low
values)

15. NDVI
This map shows the cumulative
change over the whole dataset.
Subtracting the NDVI scene from
2014 from the 2001 scene.

16. Tasseled Cap Transformation (TCT)
Transforms the original bands into a set of four bands designed to target aspects used to assess
vegetation health:
Brightness
Greenness
Wetness
and Haze
The Greenness band was the focus of this
study since it will produce the most contrast
between bare soil and vegetation

17. TCT
This represents the change
detected through the Tasseled
Cap Greenness band
differencing between 2001 and
2014.

18. Principle Component Analysis (PCA)
This method transforms the dataset into a series of bands, Principle Components (PC), which
account for the most variance possible. Each subsequent PC accounts for the maximum
remaining variance. A composite of these PCs creates an image defined solely by the amount of
change present in the image.
Performed on near infrared composites
which already enhance the difference
between soil and vegetation. The composites
chosen for image differencing are created
with the fifth, fourth, and third Principle
Components.
Left: 5,4,3
Above: 3,2,1

19. PCA
Using the third, fourth, and fifth
Principle Components, this map
is created by differencing the
PCA composites.

20. The Change
Creating a change mask by setting a threshold for each image differencing method allows the
user to focus on areas which underwent significant change. These thresholds were set based on
where change was known to have occurred.
The amount of change was calculated based on the number of pixels found in the image once
the thresholds have been applied

21. The Change
By using the amount of pixels in each change class, the amount of change was calculated two
ways. The absolute change was calculated from everything above the threshold. The change was
also calculated separately for classes which cover at least two hectares.
NDVI TCT PCA
Total 2388.608 2141.049 2353.261
More than 2 ha 2351.425 2117.373 2121.599

22. To do..
Object Based Image Analysis: Quite possible in GRASS, time restrictions meant this portion of
the project had to be abandoned, but significant progress was made.
Add i.landsat.acca, Automated Cloud-Cover Assessment, not used because it does not work for
Landsat 8 scenes.
Add interactive g.region command to prompt users for subset

23. GRASS GIS community
The GRASS Mailing list was important to the success of this project and I would like to thank
Micha Silver, Vaclav Petras, and Nikos Alexandris for their help.

24. Questions?

25. NDVI
The change from 2001 to 2002

26. NDVI
The change from 2002 to 2006

27. NDVI
The change from 2006 to 2009

28. NDVI
The change from 2009 to 2010

29. NDVI
The change from 2010 to 2011

30. NDVI
The change from 2011 to 2014

31. TCT
The change from 2001 to 2002

32. TCT
The change from 2002 to 2006

33. TCT
The change from 2006 to 2009

34. TCT
The change from 2009 to 2010

35. TCT
The change from 2010 to 2011

36. TCT
The change from 2011 to 2014

37. PCA
The change from 2001 to 2002

38. PCA
The change from 2002 to 2006

39. PCA
The change from 2006 to 2009

40. PCA
The change from 2009 to 2010

41. PCA
The change from 2010 to 2011

42. PCA
The change from 2011 to 2014