1 November 2007 Evaluating airborne hyperspectral imagery for mapping waterhyacinth infestations
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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. Kappa analysis showed that maximum likelihood was significantly better than the other three methods and that there were no significant differences in overall classifications among the other three methods. 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.
Chenghai Yang, Chenghai Yang, James H. Everitt, James H. Everitt, } "Evaluating airborne hyperspectral imagery for mapping waterhyacinth infestations," Journal of Applied Remote Sensing 1(1), 013546 (1 November 2007). https://doi.org/10.1117/1.2821827 . Submission:

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