Submitted to: Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: April 30, 2008
Publication Date: October 25, 2008
Citation: Everitt, J.H., Yang, C., Sriharan, S. 2008. Using airborne and satellite imagery to distinguish and map black mangrove. Geoscience and Remote Sensing Symposium Proceedings. CDROM.
Interpretive Summary: Mangrove communities are an important vegetation component of coastal areas of the tropics and subtropics where they prevent shore erosion and provide wildlife habitat. Black mangrove occurs along the lower south Texas gulf coast. A study was conducted on the lower Texas gulf coast to evaluate aerial color-infrared photography and digital imagery, and QuickBird high resolution satellite imagery in conjunction with image analysis techniques for detecting and mapping black mangrove communities. Accuracy assessments performed on computer-- classified maps from all three types of imagery had user’s and producer’s accuracies ranging from 78.6% to 100%. Our findings indicate that all three types of imagery can be used successfully for distinguishing and quantifying the extent of black mangrove along the Texas gulf coast. The results should be of interest to coastal zone resource managers.
This paper reports the results of studies evaluating color-infrared (CIR) aerial photography, CIR aerial true digital imagery, and high resolution QuickBird multispectral satellite imagery for distinguishing and mapping black mangrove [Avicennia germinans (L.) L.] populations along the lower Texas gulf coast. Unsupervised image analysis techniques were used to classify the imagery. Accuracy assessments were performed on the computer-classified maps of the imagery. Accuracy assessments performed on classified maps of photographic and digital images of the same study site had both producer’s and user’s accuracies of 100% for black mangrove. In an accuracy assessment performed on a classified map of a digital image only of a second study site, black mangrove had a producer’s accuracy of 78.6% and a user’s accuracy of 100%. In a classification of the satellite image, black mangrove had a producer’s accuracy of 100% and a user’s accuracy of 85.7%. These findings indicate that all three image types combined with unsupervised image analysis techniques can be used successfully to distinguish and quantify the extent of black mangrove along the Texas gulf coast.