PaRaVis: An automatic Python']Python graphical package for ensemble analysis of plant beta diversity using remote sensing proxies

The ongoing impacts of climatic changes, coupled with intensified human activities, are leading to a significant loss of plant diversity, prompting urgent calls for comprehensive monitoring of forest ecosystems. This is particularly concerning in regions with the encroachment of human activities into forest zones, and inadequate regulations that primarily view forests as a source of timber without fully accounting for their critical roles in biodiversity. These issues highlight the need for effective geospatial approaches to support conservation strategies and sustained monitoring of plant diversity. The majority of remote sensing studies of plant diversity have focused on alpha-diversity, while beta-diversity plays an important role in providing a comprehensive picture of biodiversity patterns across scales. Among various remote sensing proxies, Rao's Q index stands out as a reliable metric to quantify beta-diversity using multispectral remote sensing indices. However, a holistic approach that can incorporate multiple remote sensing indices and enable comparative analysis using graphical means with computational efficiency is lacking. In response, we developed PaRaVis, an open-source Python-based graphical package for deriving spectral diversity from multispectral remote sensing datasets as a proxy for functional diversity using Rao's Q index. This tool encompasses all the necessary steps for parallelized computation, visualization, and analysis of Rao's index from either single or multiple multispectral indices. It is capable of calculating and visualizing 75 vegetation indices (VIs), from a raster scene, followed by calculating Rao's Q both unidimensionally and multidimensionally in parallel. PaRaVis also offers features for visualizing, analyzing, and comparing Rao's Q outputs using statistical performance diagnostics. It provides a unique means to infer diversity patterns in space and time and is therefore invaluable for researchers or organizations involved with plant diversity monitoring, especially those with limited data analysis or computer programming experience. To demonstrate the tool's effectiveness, we analyzed plant diversity patterns using satellite-derived spectral heterogeneity measures in forest sites within the Hyrcanian Forests of Iran and sites located in Germany. The first case study focused on UNESCO Natural Heritage sites. Our analysis revealed that employing EVI, SR3, and TCI indices as inputs for the multidimensional Rao's Q yielded higher performance in monitoring more heterogeneous forests compared to the unidimensional mode. In the second case study, we utilized field Species Richness and Shannon-Wiener diversity indices to evaluate PaRaVis and our method. We found that using a multitemporal, multi-index approach enhances the results compared to multi-seasonal and classical Rao based on single time.