基于无人机高光谱影像的植被覆盖度遥感估算模型比较

为了探寻光谱解混模型估算植被覆盖度的精度及适用性，对广东省中山市民众镇义仓村内的一块香蕉林地，利用无人机高光谱数据，比较了3种植被覆盖度估算的经典模型(像元二分模型、Carlson模型和Baret模型)以及目前较为常用的3种光谱解混模型(线性光谱混合模型(Linear Mixed Model, LMM)、后验多项式非线性混合模型(Polynomial Post-nonliner Mixing Model，PPNMM)和考虑光谱变异的正态组分模型(Normal Compositional Model，NCM))估算植被覆盖度的效果. 实验结果表明:像元二分模型高估了植被覆盖度；Carlson模型低估了植被覆盖度；Baret模型在低植被覆盖度区域内高估了植被覆盖度、在高植被覆盖度区域内低估了植被覆盖度；LMM模型在高植被覆盖度区域有较好的估算效果；PPNMM模型在低植被覆盖度出现小幅度高估；NCM模型估算的效果最佳.

The Comparison of Remote Sensing Estimation Models for Fractional Vegetation Cover Based on UAV Hyperspectral Image

The UAV hyperspectral data on a banana forest land in Yicang Village, Minzhong Township, Zhongshan City, Guangdong Province is used to explore the accuracy and applicability of the spectral unmixing model for estimating vegetation coverage. The effects of three classical models (pixel binary model, Carlson model and Baret model) and three spectral unmixing models (LMM model, PPNMM model and NCM model) in estimating vegetation coverage are compared. The experimental results show that the pixel binary model overestimates the vegetation coverage; the Carlson model underestimates the vegetation coverage; the Baret model overestimates the vegetation coverage in the low vegetation coverage area and underestimates the vegetation coverage in the high vegetation co-verage area; the LMM model has good estimation effect in areas with high vegetation coverage; the PPNMM model overestimates slightly in low vegetation coverage; and the NCM model has the best estimation effect.