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

Cuicui Ji,Jinying Wang,Bin Sun,Xiaosong Li,Guoxiong Zheng,Zhihai Gao

doi:10.3390/rs8100800

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.

Highlights

Accurate and timely information about vegetation cover is fundamental for monitoring desertification [1,2,3] and assessing the impacts of land management strategies [4] in the Otindag SandyLand, one of the four largest sandy lands in China
Because the acquiring time corresponds to the maximum vegetation growing season, fields were dominated by green vegetation resulting in high fpv
We identified some particular cases of large photosynthetic vegetation (PV) underestimation for grassland sites when the observation was made before grass cutting, but the GF-1 wide-field view (WFV) image was acquired after grass cutting

Summary

Introduction

Accurate and timely information about vegetation cover is fundamental for monitoring desertification [1,2,3] and assessing the impacts of land management strategies [4] in the Otindag SandyLand, one of the four largest sandy lands in China. 2016, 8, 800 indices, which exploit the difference between visible and near-infrared (NIR) reflectance caused by the presence of chlorophyll, such as the normalized difference vegetation index (NDVI) [11] and the enhanced vegetation index (EVI) [12], have been widely utilized for assessing vegetation cover and dynamics. These indices are only sensitive to the amount of photosynthetic vegetation (PV), as well as its turgidity and greenness [13]. Simultaneously acquiring the fractional cover of PV and NPV in the Otindag Sandy Land would provide new insights for desertification monitoring and land management

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Conclusion