Abstract
Linear spectral mixture analysis (SMA) is commonly used to infer fractional vegetation cover (FVC), especially for pixel dichotomy models. However, several sources of uncertainty including normalized difference vegetation index (NDVI) saturation and selection of endmembers inhibit the effectiveness of SMA for the estimation of FVC. In this study, Moderate-resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI) remote sensing data for the early growing season and in situ measurement of spectral reflectance are used to determine the value of endmembers including VIsoil and VIveg, with equally weighted RVI and NDVI measures used in combination to minimize the inherent biases in pure NDVI-based FVC. Their ability to improve estimates of grassland FVC is analyzed at different resolutions. These are shown to improve FVC estimates over NDVI-based SMA models using fixed values for the endmembers. Grassland FVC changes for Inner Mongolia, China from 2000 to 2013 are then monitored using the MODIS data. The results show that changes in most grassland areas are not significant, but in parts of Hulunbeier, south Tongliao, middle Xilin Gol and Erdos, grassland FVC has increased significantly.
Highlights
The ratio of the vertical projected area of vegetation to the total ground area, termed fractional vegetation cover (FVC), is a commonly used indicator for evaluating and monitoring vegetation degradation and desertification [1]
Normalized Difference Vegetation Index (NDVI) and RVI were calculated from canopy spectrum, with the red and near infrared band resampled based on 250 m Moderate-resolution Imaging Spectroradiometer (MODIS) data bands
The NDVI-based pixel dichotomy model has been commonly used for retrieval of grassland FVC
Summary
The ratio of the vertical projected area of vegetation to the total ground area, termed fractional vegetation cover (FVC), is a commonly used indicator for evaluating and monitoring vegetation degradation and desertification [1]. SMA has often been used to estimate FVC from multi-spectral remote sensing data [2,14,15,16,17,18]. Several approaches have been used for the retrieval of image endmembers including the use of two-dimensional feature space plots [31] and the identification of pure pixels with reference to field data or higher-resolution remote sensing data [32]. In most cases, these approaches assume a constant endmember signature (e.g., for bare soil or for full vegetation cover) even with large changes in the land surface. Through the use of remote sensing data in the early growing season and in situ measurement of spectral reflectance
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