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United States Department of Agriculture

Agricultural Research Service

Research Project: AERIAL APPLICATION RESEARCH FOR EFFICIENT CROP PRODUCTION Title: Evaluating airborne hyperspectral imagery for mapping saltcedar infestations in west Texas

Authors
item Yang, Chenghai
item Everitt, James -
item Fletcher, Reginald

Submitted to: Journal of Applied Remote Sensing (JARS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 26, 2013
Publication Date: May 29, 2013
Citation: Yang, C., Everitt, J.H., Fletcher, R.S. 2013. Evaluating airborne hyperspectral imagery for mapping saltcedar infestations in west Texas. Journal of Applied Remote Sensing (JARS). 7(1):073556 (May 29, 2013). doi: 10.1117/1.JRS.7.073556.

Interpretive Summary: The Rio Grande basin of west Texas contains by far the largest infestation of saltcedar in Texas. This study evaluated airborne hyperspectral imagery and different classification techniques for mapping saltcedar infestations. Accuracy assessment showed that the support vector machine classification technique produced the best classification results for identifying saltcedar. These results indicate that airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping saltcedar infestations.

Technical Abstract: The Rio Grande of west Texas contains by far the largest infestation of saltcedar (Tamarix spp.) in Texas. The objective of this study was to evaluate airborne hyperspectral imagery and different classification techniques for mapping saltcedar infestations. Hyperspectral imagery with 102 usable bands covering a spectral range of 475-845 nm was acquired from two sites along the Rio Grande in west Texas in December 2003 and 2004 when saltcedar was undergoing color change. The imagery was transformed using minimum noise fraction (MNF) and then classified using four classifiers: maximum likelihood, spectral angle mapper (SAM), mixture tuned matched filtering (MTMF), and support vector machine (SVM). Accuracy assessment showed that overall accuracy varied from 75% to 86% in 2003 and from 80% to 90% in 2004 for site 1 and from 60% to 76% in 2003 and from 77% to 91% in 2004 for site 2. The SVM classifier produced the highest overall accuracy as well as the best user’s and producer’s accuracies for saltcedar among the four classifiers. The imagery taken in early December 2004 provided better classification results than that in mid-December 2003. These results indicate that airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping saltcedar infestations.

Last Modified: 7/22/2014
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