Detection of Plant Species Beta-Diversity in Hunshandak Sandy Grasslands Using Hyperspectral Data
PENG Yu1, 2*, TAO Zi-ye2, XU Zi-yan2, BAI Lan2
1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
2. College of Life and Environmental Sciences, Minzu University of China, Beijing 100081, China
摘要： 近年来，光谱分析应用于植物多样性的估算引起了全球生物多样性学界的广泛关注。基于光谱异质性假说（SVH），大量案例研究应用光谱指数估算了森林、草原等的植物物种alpha多样性，但是beta多样性的研究尚缺乏。在我国浑善达克沙地中部调查270个直径为0.8 m的植物群落样方，测量植物物种beta多样性，并采集样方高光谱数据（375～1 025 nm）。中随机抽取样方数据165个作为模型训练数据，105个作为模型验证数据。beta多样性指数选用Bray-Curtis index (BC), Sörensen index (S) 和Jaccard index (J)。基于物种特征波段，开发了164个高光谱指数估算物种beta多样性指数。采用Pearson相关性分析对开发的高光谱指数进行初步筛选，然后比较不同植物群落盖度和群落复杂性条件下高光谱指数的稳定性，进一步筛选。结果表明，400～1 000 nm光谱反射率一阶导数的相似性指数和欧氏距离指数，以及760～800 nm之间的相似性指数，能够较好地估算植物物种beta多样性。其中，物种BC指数与高光谱欧氏距离指数表现最为一致，二者都考虑了物种组成数量的差异，物种S和J指数拟合效果较差。本研究对于促进高光谱应用于植物物种多样性估算具有推进作用。
关键词：高光谱数据；beta多样性；植物多样性；沙地草地；微尺度
Abstract：Spectral analysis has been increasingly applied to estimate plant species diversity through the world, especially for biodiversity field. Although spectral variability hypothesis (SVH) has been widely proved in estimating plant alpha diversity for tropical, temperate, and sub-tropical forests, meadow, steppe and grasslands, however, the performance on beta diversity is still lack. In this study, we measured the hyperspectral reflectances and plant species diversity indices of 270 plots at a fine scale (0.8 meter) in central Hunshandak sandy grasslands of Inner Mongolia, China. 195 plots were used as training data and 75 plots as validating data. Bray-Curtis dissimilarity index (BC), Sörensen index (S) and Jaccard index (J) were calculated to indicate actual beta diversity. Based on spectral biological features of different plant species, 164 hyperspectral indices were developed and used to assess plant species beta diversity. Pearson’s correlation analysis and multiple linear stepwise regression were conducted based on sensitive wavebands to produce hyperspectral models. The hyperspectral indices which high Pearson’s correlation coefficients will be remained for further tested. Communities with different coverages and richness were also used to test the robustness of proposed models. By comparing the stability of hyperspectral indices under different communities, the indices with high stability is remained for validation by 75 plots. Results demonstrated that BC, Euclidean distances of first-order derivation values between 400～1 000 nm, and BC of 760～800 nm could accurately estimate species beta diversity. BC can be accurately estimated by hyperspectral indices, since they were both calculated as parameters of the distance between plots. The Jaccard and Sörensen indices were hardly estimated, it is hard to find the suitable wavebands or other parameters in spectral data to replace the “common reflectance” between pairwise plots. This study promotes the development of methods in assessing plant species beta-diversity using hyperspectral data.
Key words：Hyperspectral indices; Beta-diversity; Plant diversity; Sandy grasslands; Fine scale
彭 羽，陶子叶，许子妍，白 岚. 应用高光谱数据估算植物物种beta多样性[J]. 光谱学与光谱分析, 2020, 40(07): 2016-2022.
PENG Yu, TAO Zi-ye, XU Zi-yan, BAI Lan. Detection of Plant Species Beta-Diversity in Hunshandak Sandy Grasslands Using Hyperspectral Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(07): 2016-2022.
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