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Remote Sens. 2016, 8(10), 800; doi:10.3390/rs8100800

Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data

1
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3
Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Academic Editors: James Campbell, Richard Gloaguen and Prasad S. Thenkabail
Received: 18 April 2016 / Revised: 7 September 2016 / Accepted: 20 September 2016 / Published: 27 September 2016
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Abstract

Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) are important ground cover types for desertification monitoring and land management. Hyperspectral remote sensing has been proven effective for separating NPV from bare soil, but few studies determined fractional cover of PV (fpv) and NPV (fnpv) using multispectral information. The purpose of this study is to evaluate several spectral unmixing approaches for retrieval of fpv and fnpv in the Otindag Sandy Land using GF-1 wide-field view (WFV) data. To deal with endmember variability, pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, and Automated Monte Carlo Unmixing Analysis, AutoMCU) endmember selection approaches were applied. Observed fractional cover data from 104 field sites were used for comparison. For fpv, all methods show statistically significant correlations with observed data, among which AutoMCU had the highest performance (R2 = 0.49, RMSE = 0.17), followed by MESMA (R2 = 0.48, RMSE = 0.21), and SMA (R2 = 0.47, RMSE = 0.27). For fnpv, MESMA had the lowest performance (R2 = 0.11, RMSE = 0.24) because of coupling effects of the NPV and bare soil endmembers, SMA overestimates fnpv (R2 = 0.41, RMSE = 0.20), but is significantly correlated with observed data, and AutoMCU provides the most accurate predictions of fnpv (R2 = 0.49, RMSE = 0.09). Thus, the AutoMCU approach is proven to be more effective than SMA and MESMA, and GF-1 WFV data are capable of distinguishing NPV from bare soil in the Otindag Sandy Land. View Full-Text
Keywords: non-photosynthetic vegetation; fractional cover; multispectral data; endmember variability; bare soil; GF-1 WFV non-photosynthetic vegetation; fractional cover; multispectral data; endmember variability; bare soil; GF-1 WFV
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Li, X.; Zheng, G.; Wang, J.; Ji, C.; Sun, B.; Gao, Z. Comparison of Methods for Estimating Fractional Cover of Photosynthetic and Non-Photosynthetic Vegetation in the Otindag Sandy Land Using GF-1 Wide-Field View Data. Remote Sens. 2016, 8, 800.

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