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Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments

Okin, Gregory S. and Roberts, Dar A. and Murray, Bruce and Okin, William J. (2001) Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sensing of Environment, 77 (2). pp. 212-225. ISSN 0034-4257. https://resolver.caltech.edu/CaltechAUTHORS:20140409-163214214

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Abstract

Hyperspectral remote sensing is a promising tool for the analysis of vegetation and soils in remote sensing imagery. The purpose of this study is to ascertain how well hyperspectral remote sensing data can retrieve vegetation cover, vegetation type, and soil type in areas of low vegetation cover. We use multiple endmember spectral mixture analysis (MESMA), high-quality field spectra, and AVIRIS data to determine how well full-range spectral mixture analysis (SMA) techniques can retrieve vegetation and soil information. Using simulated AVIRIS-derived reflectance spectra, we find that, in areas of low vegetation cover, MESMA is not able to provide reliable retrievals of vegetation type when covers are less than at least 30%. Overestimations of vegetation are likely, but vegetation cover in many circumstances can be estimated reliably. Soil type retrievals are more than 90% reliable in discriminating dark-armored desert soils from blown sands. This simulation comprises a best-case scenario in which many typical problems with remote sensing in areas of low cover or desert areas are minimized. Our results have broad implications for the applicability of full-range SMA techniques in analysis of data from current and planned hyperspectral sensors. Several phenomena contribute to the unreliability of vegetation retrievals. Spectrally indeterminate vegetation types, characterized by low spectral contrast, are difficult to model correctly even at relatively high covers. Combinations of soil and vegetation spectra have the potential of generating mixtures that resemble an unmixed spectrum from different material, further confounding vegetation cover and soil type retrievals. Intraspecies spectral variability and nonlinear mixing produce uncertainties in spectral endmembers much larger than that only due to instrumental noise modeled here. Having established limits on linear spectral unmixing in areas of low cover through spectral simulations, we evaluate AVIRIS-derived reflectance data from the Mojave Desert, California. We show that MESMA is capable of mapping soil surface types even when vegetation type cannot be reasonable retrieved.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1016/S0034-4257(01)00207-3DOIArticle
http://www.sciencedirect.com/science/article/pii/S0034425701002073PublisherArticle
Additional Information:© 2001 Elsevier Science Inc. Received 21 March 2000, Revised 15 August 2000, Accepted 15 August 2000, Available online 8 August 2001. The author will gladly make the data used in this study available for investigators wishing to evaluate other methods for vegetation mapping in an arid shrubland.
Subject Keywords:spectral mixture analysis; AVIRIS data; multispectral images; green vegetation; desert shrub; soil; leaf; reflectance; cover; variability
Issue or Number:2
Record Number:CaltechAUTHORS:20140409-163214214
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20140409-163214214
Official Citation:Okin, G., D. Roberts, et al. (2001). "Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments." Remote sensing of environment 77(2): 212-225.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:44840
Collection:CaltechAUTHORS
Deposited By: David McCaslin
Deposited On:10 Apr 2014 14:49
Last Modified:03 Oct 2019 06:23

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