Filling in the void between forest ecology and remote sensing through monitoring biodiversity variables is of great interest. In this study, we utilized imaging spectroscopy data from the ISRO–NASA Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) India campaign to investigate how the measurements of biodiversity attributes of forests over wide areas can be augmented by synchronous field- and spectral-metrics. Three sites, Shoolpaneshwar Wildlife Sanctuary (SWS), Vansda National Park (VNP), and Mudumalai Tiger Reserve (MTR), spread over a climatic gradient (rainfall and temperature), were selected for this study. Abundant species maps of three sites were produced using a support vector machine (SVM) classifier with a 76–80% overall accuracy. These maps are a valuable input for forest resource management. Convex hull volume (CHV) is computed from the first three principal components of AVIRIS-NG spectra and used as a spectral diversity metric. It was observed that CHV increased with species numbers showing a positive correlation between species and spectral diversity. Additionally, it was observed that the abundant species show higher spectral diversity over species with lesser spread, provisionally revealing their functional diversity. This could be one of the many reasons for their expansive reach through adaptation to local conditions. Higher rainfall at MTR was shown to have a positive impact on species and spectral diversity as compared to SWS and VNP. Redundancy analysis explained 13–24% of the variance in abundant species distribution because of climatic gradient. Trends in spectral CHVs observed across the three sites of this study indicate that species assemblages may have strong local controls, and the patterns of co-occurrence are largely aligned along climatic gradient. Observed changes in species distribution and diversity metrics over climatic gradient can help in assessing these forests’ responses to the projected dynamics of rainfall and temperature in the future.

中文翻译：

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利用光谱多样性度量推断气候梯度上的物种多样性和变异性

通过监测生物多样性变量来填补森林生态学与遥感之间的空白非常令人感兴趣。在这项研究中，我们利用ISRO–NASA机载可见光红外成像光谱仪–印度新一代（AVIRIS-NG）的成像光谱数据，研究了如何通过同步田间和野外观测来增强广域森林的生物多样性属性的测量结果。光谱指标。选择了分布在气候梯度（降雨和温度）上的三个地点，Shoolpaneshwar野生动物保护区（SWS），Vansda国家公园（VNP）和Mudumalai老虎保护区（MTR）。使用支持向量机（SVM）分类器制作了三个地点的丰富物种图，总精度为76-80％。这些地图是森林资源管理的宝贵输入。凸包体积（CHV）由AVIRIS-NG光谱的前三个主要成分计算得出，并用作光谱分集度量。观察到CHV随物种数量增加而增加，表明物种与光谱多样性之间呈正相关。另外，观察到，丰富的物种在具有较小扩散的物种上显示出更高的光谱多样性，暂时揭示了它们的功能多样性。这可能是他们通过适应当地条件而扩大影响范围的众多原因之一。与SWS和VNP相比，MTR较高的降雨对物种和光谱多样性有积极影响。冗余分析解释了由于气候梯度，丰富物种分布中的13–24％的方差。在本研究的三个地点观察到的光谱CHV趋势表明，物种集合可能具有较强的局部控制能力，并且共存模式在很大程度上与气候梯度一致。在气候梯度上观察到的物种分布和多样性指标的变化可以帮助评估这些森林对未来预计的降雨和温度动态的响应。