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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: Mapping Waterhyacinth Infestations Using Airborne Hyperspectral Imagery

Authors
item Yang, Chenghai
item Everitt, James

Submitted to: Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment Proceedings
Publication Type: Proceedings
Publication Acceptance Date: July 30, 2007
Publication Date: March 15, 2008
Citation: Yang, C., Everitt, J.H. 2008. Mapping waterhyacinth infestations using airborne hyperspectral imagery. Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment Proceedings.

Interpretive Summary: Waterhyacinth is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. This study evaluated airborne hyperspectral imagery and different image classification techniques for mapping waterhyacinth infestations on Lake Corpus Christi in south Texas. Image analysis and ground verification showed that waterhyacinth could be accurately distinguished from associated woody and herbaceous plant species. These results indicate that airborne hyperspectral imagery in conjunction with image processing techniques can be a useful tool for mapping waterhyacinth infestations.

Technical Abstract: Waterhyacinth [Eichhornia crassipes (Mart.) Solms] is an exotic aquatic weed that often invades and clogs waterways in many tropical and subtropical regions of the world. The objective of this study was to evaluate airborne hyperspectral imagery and different image classification techniques for mapping waterhyacinth infestations on Lake Corpus Christi in south Texas. Hyperspectral imagery with bands in the visible to near-infrared region of the spectrum was acquired from two study sites and Minimum Noise Fraction (MNF) transformation was used to reduce the spectral dimensionality of the imagery. Four classification methods, including minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM), were applied to the MNF-transformed imagery for distinguishing waterhyacinth from associated plant species (waterlettuce, mixed herbaceous species, and mixed woody species) and other cover types (bare soil and water). Accuracy assessment showed that overall accuracy varied from 79% for SAM to 96% for maximum likelihood for site 1 and from 84% for minimum distance to 95% for maximum likelihood for site 2. Producer’s and user’s accuracies for waterhyacinth based on maximum likelihood were 94% and 100%, respectively, for site 1 and 100% and 95% for site 2. These results indicate that airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping waterhyacinth infestations.

Last Modified: 9/20/2014
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