Data from: Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

采用机载成像光谱学（AIS）的遥感技术可反演生态系统的基础光学特性。然而，此类光学特性在植物物种分布预测中的应用价值仍未明确。本研究以法瑞两国的阿尔卑斯草原为研究对象，评估AIS数据能否为基于地形变量的植物分布预测模型增添附加价值。我们采用高光谱与高空间分辨率反射率数据拟合统计模型，并测试了四类对叶片叶绿素含量、叶片含水量及叶面积指数敏感的光学指数。研究结果显示，AIS数据在阿尔卑斯植物物种分布预测中仅具备中等程度的辅助预测价值。与预期相悖的是，不同物种分布模型（Species Distribution Models，SDMs）之间的性能差异，与其对应物种的本地丰度或系统发育/功能相似性并无关联。此外，研究发现物种的光谱特征在一定程度上具有位点特异性。本研究还讨论了基于AIS的物种分布模型当前存在的局限，重点探讨了其尺度适配问题与AIS数据的信息含量相关议题。