| 摘 要： |
Accurate fractional vegetation coverage (FVC) detection is beneficial for evaluating the dynamics and development potential of desert ecosystem. However, at present the performance of the related published products in the Qinghai-Tibet Plateau (QTP)'s desert ecosystem has not been verified, and the method for extracting FVC for this region from unmanned aerial vehicle (UAV) RGB images has also not been tested. UAV RGB images were processed via a boosted regression tree model (BRT). 57 ground measurements were collected, and were further paired with vegetation indexes (VIs) from satellite sensors to develop an empirical model that was used to evaluate FVC of QTP's desert ecosystem. The results showed that: (1) BRT effectively enhanced the vegetation information in UAV RGB images and improved the accuracy of FVC ground measurements (the area under the receiver operating characteristic curve (AUC) = 0.95, kappa > 0.95); (2) The relationship between FVC and modified soil-adjusted vegetation index (MSAVI) was most robust in QTP's desert ecosystem, and a power model (FVC = 13.85 x MSAVI(2.07), R-2 = 0.89, RMSE < 0.01) was proposed to derive local FVC within an area of 1.05 x 106 km(2); (3) Although the current published FVC products show spatial consistency in QTP's desert ecosystem, FVC was underestimated by these products by at least 29% compared with the FVC values derived from the power model determined in this study. |
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