automated change detection in grass gis

Automated Change Detection in GRASS

GISEXPLORING THE APPLICATIONS AND UTILITY OF GRASS

GIS

JACOB CHILA

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

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

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

Why GRASS? Available to anyone, for free.

Comparison to commercial software

Applications

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 annuallyMine

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

http://earthexplorer.usgs.gov/

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

i.atcorrCan 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 requirethis 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)

Parameter File17 - Geometrical conditions associated with Landsat 88 11 18.5030759012 -114.43339 50.29982 - The month, day (decimal hours) and Long/Lat of image centre2 - Atmospheric mode1 - Aerosols model0 - Visibility [km]0.112 - Aerosol model concentration-1.5278 - Mean target elevation above sea level-1000 - Sensor height, -1000 is a preset for satellites117 - 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

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.

Corrected LC8 Scene

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 IndexTasseled Cap Transformation Principle Component Analysis

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)

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

Tasseled Cap Transformation (TCT)

Transforms the original bands into a set of four bands designed to target aspects used to assess vegetation health:

BrightnessGreennessWetnessand Haze

The Greenness band was the focus of this study since it will produce the most contrast between bare soil and vegetation

TCTThis represents the change detected through the Tasseled Cap Greenness band differencing between 2001 and 2014.

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 compositeswhich 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

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

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

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

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

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.

Questions?

NDVI

The change from 2001 to 2002

NDVI


TCT


PCA


automated change detection in grass gis

Education

free gis software suite

atmospheric mode

atmospheric distortion

subsets atcorr

aerosols model

data integrity

data continuity

geospatial data