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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING AND GIS FOR DETECTING AND MAPPING INVASIVE WEEDS IN RIPARIAN AND WETLAND ECOSYSTEMS Title: Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast

Authors
item Yang, Chenghai
item Everitt, James
item Fletcher, Reginald
item Jensen, Ryan - INDIANA STATE UNIV,IND
item Mausel, Paul - INDIANA STATE UNIV,IND

Submitted to: Photogrammetric Engineering and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: June 1, 2008
Publication Date: April 1, 2009
Citation: Yang, C., Everitt, J.H., Fletcher, R.S., Jensen, R.R., Mausel, P.W. 2009. Evaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas Gulf Coast. Photogrammetric Engineering and Remote Sensing. 75(4):425-435.

Interpretive Summary: Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study examined airborne hyperspectral imagery and image compression and classification techniques for mapping black mangrove populations along the south Texas Gulf coast. Image analysis and accuracy assessment showed that black mangrove could be detected and mapped with at least 91% accuracy. These results indicate that airborne hyperspectral imagery combined with image processing and classification techniques can be effective for monitoring and mapping black mangrove distributions in coastal environments.

Technical Abstract: Mangrove wetlands are economically and ecologically important ecosystems and accurate assessment of these wetlands with remote sensing can assist in their management and conservation. This study was conducted to evaluate airborne AISA+ hyperspectral imagery and image transformation and classification techniques for mapping black mangrove [Avicennia germinans (L.) L.] populations on the south Texas Gulf coast. AISA+ hyperspectral imagery was acquired from two study sites and both minimum noise fraction (MNF) and inverse MNF transforms were performed. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the noise-reduced hyperspectral imagery and to the band-reduced MNF imagery for distinguishing black mangrove from associated plant species and other cover types. Accuracy assessment showed that overall accuracy varied from 84% to 95% for site 1 and from 69% to 91% for site 2 among the eight classifications for each site. The MNF images provided similar or better classification results compared with the hyperspectral images among the four classifiers. Kappa analysis showed that there were no significant differences among the four classifiers with the MNF imagery, though maximum likelihood provided excellent overall and class accuracies for both sites. Producer’s and user’s accuracies for black mangrove were 91% and 94%, respectively, for site 1 and both 91% for site 2 based on maximum likelihood applied to the MNF imagery. These results indicate that airborne hyperspectral imagery combined with image transformation and classification techniques can be a useful tool for monitoring and mapping black mangrove distributions in coastal environments.

Last Modified: 4/24/2014
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