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.

利用机载成像光谱学（airborne imaging spectroscopy, AIS）开展的遥感技术可获取生态系统的基础光学特性，但其相关参数在预测植物物种分布方面的价值仍未明确。本研究旨在评估此类数据能否为法瑞阿尔卑斯草原的植物分布预测提供优于地形变量的附加价值。我们采用高光谱与高空间分辨率反射率数据构建统计模型，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。研究发现，AIS数据在预测阿尔卑斯植物物种分布方面仅具备中等程度的附加价值。与预期相悖的是，不同植物物种分布模型（species distribution models, SDMs）间的性能差异与其本地丰度或系统发育/功能相似性并无关联。此外，物种的光谱特征被发现存在一定的位点特异性。最后，我们讨论了基于AIS的物种分布模型现存局限，重点探讨了尺度问题与AIS数据的信息含量问题。