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Wetlands Identification

Where are the wetlands?
This is a Landsat TM scene of rural South Carolina, a forested environment. Can you identify the wetland indicator species Cypress and Tupelo?

IMAGINE Subpixel Classifier did!


Charlestown, South Carolina
Includes material © Space Imaging L.P.

Objective
Researchers from Clemson University were interested in finding a way to exploit multispectral satellite imagery to identify wetlands in an area under pressure of development. If indicator species Cypress and Tupelo can be identified, potential wetland areas which are subject to strict regulations, can be more easily located before expensive problems arise after development begins.

Approach
IMAGINE Subpixel Classifier was used to identify stands of wetland tree species in what are very complex, mixed forest environments. Students from Clemson conducted on-the-ground verification and accuracy assessment of results.

Challenges
· Land cover classifiers can not typically discriminate between different tree species
· On-the-ground survey methods are prohibitively expensive and time consuming
· High-resolution aerial photography was not viable due to the small scale required to identify tree species
· Cypress and Tupelo are often found in very complex forest environments making species identification using airborne or satellite imagery almost impossible

Solution
IMAGINE Subpixel Classifier identified Cypress and Tupelo in this forest environment. Researchers were then able to use this information to map wetland areas quickly and accurately. A detailed field verification study revealed accuracies near 90% for both species.

IMAGINE Subpixel Classifier has the robust discrimination capabilities to identify different species in a natural environment. This coupled with its unique Environmental Correction feature allowed signatures used to process this scene to be successfully applied to other scenes in South Carolina and Georgia.


Includes material © Space Imaging L.P.

 



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External Links
· Leica-Geosystems
· Space Imaging
· DigitalGlobe
· SPOT Image
· ORBIMAGE
· USGS EarthExplorer
· USGS Landsat 7
· GeoCommunity
· Geography Network
· Flight Landata


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