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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: Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed

Authors
item Yang, Chenghai
item Everitt, James

Submitted to: International Journal of Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: April 16, 2009
Publication Date: October 20, 2010
Citation: Yang, C., Everitt, J.H. 2010. Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed. International Journal of Remote Sensing. 31(20):5423-5438.

Interpretive Summary: Broom snakeweed is a perennial shrub widely distributed across western North America and is considered undesirable and troublesome due to its toxicity to livestock and its ability to reduce forage production. This study evaluated airborne hyperspectral imagery and compared it with aerial photography and multispectral digital imagery for mapping broom snakeweed infestations. Image analysis and accuracy assessment showed that broom snakeweed could be accurately distinguished from associated woody and herbaceous plant species using any of the three types of the imagery. These findings should be of interest to rangeland resource managers in mapping and monitoring broom snakeweed populations.

Technical Abstract: Broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby] is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial color-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image, which were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracy for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) was 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.

Last Modified: 10/25/2014
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