摘要:植物群落物种多样性的快速无损估测一直是近几十年生态学领域的热点研究问题。相对于大尺度的卫星遥感数据，高光谱遥感数据具有光谱和空间分辨率高的优势。采用ASD HH2便携式高光谱仪，收集浑善达克沙地中部120个样方的高光谱数据，并对样方的alpha多样性指数进行同步测定。对高光谱遥感数据进行预处理，采用相关性分析、主成分分析和经验波段筛选法，从数百个波段中选择敏感波段。采用90个样方的高光谱数据作为训练样本，对筛选的敏感波段进行多元线性逐步回归分析，获得12个回归模型。采用另外30个样方的高光谱数据作为验证样本，对回归模型的拟合效果进行检验。结果发现，采用主成分分析法提取敏感波段的回归模型拟合效果最好，Pielou指数、Shannon-Wiener指数和Simpson指数拟合均达到显著水平。对我国植物物种多样性微尺度的快速评估和高光谱遥感具有一定参考意义，并对未来植物多样性高光谱遥感研究提出了建议。



Abstract:Fast and nondestructive methods for assessing plant community species diversity are topics of great scientific interest and concern to ecologists worldwide. Hyperspectral data, which have the advantage of hundreds of spectral wavebands and multiple spatial scales, have huge potential for assessing plant species diversity. However, identifying the most sensitive wavebands is still a challenge. In this study, we measured the hyperspectral data and plant diversity indices of 120 samples from sandy grasslands in central Hunshandak Sandland, Inner Mongolia, China. Ninety plots were used as training data and thirty plots as validating data. After pre-processing, sensitive wavebands were selected using Pearson's correlation analysis, principle component analysis (PCA), and experienced selection. Multiple linear stepwise regression (MLSR) was conducted based on sensitive wavebands to produce hyperspectral models. The results demonstrated that the regression models based on PCA bands could accurately estimate the Pielou (r=0.65^{* *}), Simpson (r=0.49^{* *}), and Shannon-Wiener indices (r=0.40^*). Communities with different coverages were also used to test the robustness of proposed models based on PCA bands. We propose that the Simpson, Shannon-Wiener, and Pielou indices, widely used as indicators of plant species alpha diversity, can be precisely estimated by hyperspectral indices at a fine scale. Community complexity and coverage can substantially affect the accuracy of estimating plant diversity. First-order derivations of vegetation reflectance can reduce environmental noise, water absorption disturbance, and reflect differences among species, hence greatly improving the estimation accuracy. This study promotes the development of methods in assessing plant community diversity using hyperspectral data. Based on the results of this study, future study topics are also suggested.





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