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Journal of Applied Remote Sensing

Estimating wetland vegetation abundance from Landsat-8 operational land imager imagery: a comparison between linear spectral mixture analysis and multinomial logit modeling methods
Author(s): Min Zhang; Zhaoning Gong; Wenji Zhao; Ruiliang Pu; Ke Liu
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Paper Abstract

Mapping vegetation abundance by using remote sensing data is an efficient means for detecting changes of an eco-environment. With Landsat-8 operational land imager (OLI) imagery acquired on July 31, 2013, both linear spectral mixture analysis (LSMA) and multinomial logit model (MNLM) methods were applied to estimate and assess the vegetation abundance in the Wild Duck Lake Wetland in Beijing, China. To improve mapping vegetation abundance and increase the number of endmembers in spectral mixture analysis, normalized difference vegetation index was extracted from OLI imagery along with the seven reflective bands of OLI data for estimating the vegetation abundance. Five endmembers were selected, which include terrestrial plants, aquatic plants, bare soil, high albedo, and low albedo. The vegetation abundance mapping results from Landsat OLI data were finally evaluated by utilizing a WorldView-2 multispectral imagery. Similar spatial patterns of vegetation abundance produced by both fully constrained LSMA algorithm and MNLM methods were observed: higher vegetation abundance levels were distributed in agricultural and riparian areas while lower levels in urban/built-up areas. The experimental results also indicate that the MNLM model outperformed the LSMA algorithm with smaller root mean square error (0.0152 versus 0.0252) and higher coefficient of determination (0.7856 versus 0.7214) as the MNLM model could handle the nonlinear reflection phenomenon better than the LSMA with mixed pixels.

Paper Details

Date Published: 20 January 2016
PDF: 13 pages
J. Appl. Rem. Sens. 10(1) 015005 doi: 10.1117/1.JRS.10.015005
Published in: Journal of Applied Remote Sensing Volume 10, Issue 1
Show Author Affiliations
Min Zhang, Capital Normal Univ. (China)
Zhaoning Gong, Capital Normal Univ. (China)
Univ. of South Florida (United States)
Wenji Zhao, Capital Normal Univ. (China)
Ruiliang Pu, Univ. of South Florida (United States)
Ke Liu, Satellite Surveying and Mapping Application Ctr., NASG (China)

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