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Title: Using High Resolution Satellite Imagery to Map Black Mangrove on the Texas Gulf Coast

Author
item Everitt, James
item Yang, Chenghai
item SRIHARAN, S - VSU, PETERSBURG, VA
item JUDD, F - UTPA, EDINBURG, TX

Submitted to: Journal of Coastal Research
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/28/2007
Publication Date: 11/25/2008
Citation: Everitt, J.H., Yang, C., Sriharan, S., Judd, F.W. 2008. Using high resolution satellite imagery to map black mangrove on the Texas Gulf Coast. Journal of Coastal Research. 24:1582-1586.

Interpretive Summary: Mangrove communities are an important component in coastal areas of the tropics and subtropics, where they prevent shore erosion and provide fisheries and wildlife habitat. Black mangrove occurs along the central and lower South Texas Gulf Coast. A study was conducted along the extreme southern Texas Gulf Coast to evaluate QuickBird false color satellite imagery in conjunction with computer image analysis for detecting and mapping black mangrove communities. Accuracy assessments performed on computer classified maps on two subset images had producer’s and user’s accuracies ranging from 60.9% to 100%. These results should be of interest to coastal resource managers interested in mapping the extent of black mangrove.

Technical Abstract: QuickBird false color satellite imagery was evaluated for distinguishing black mangrove [Avicennia germinans (L.) L.] populations on the south Texas Gulf Coast. The imagery had three bands (green, red, and near-infrared) and contained 11-bit data. Two subsets of the satellite image were extracted and used as test sites. Supervised and unsupervised image analysis techniques were used to classify the imagery. For the supervised classification of site 1, black mangrove had a producer’s accuracy of 82.1% and a user’s accuracy of 95.8%; whereas for the unsupervised classification, black mangrove had a producer’s accuracy of 100% and a user’s accuracy of 60.9%. In the supervised classification of site 2, black mangrove had a producer’s accuracy of 91.7% and a user’s accuracy of 100%; whereas in the unsupervised classification, black mangrove had a producer’s accuracy of 100% and a user’s accuracy of 85.7%. These results indicate that QuickBird imagery combined with image analysis techniques can be used successfully to distinguish and map black mangrove along the south Texas Gulf Coast.