Enhancing Vegetation Biodiversity Estimates Based on Environmental Similarity Across Scales: An Open-Source Strategy

Abstract:

Understanding the spatial distribution of vegetation diversity is crucial for ecological, conservation, and sustainability-related studies. However, continental to global scale plant diversity mapping based on occurrence data is biased by the sampling strategy. The increasing complex patterns of vegetation diversity across scales have recently been recognized, but understanding is limited. Patches of biodiversity can reflect areas where patterns of species diversity are mechanistically related to each other as represented by environmental variables. In this study, an open-source strategy is proposed to enhance the vegetation biodiversity prediction based on the Third Law of Geography. The National Ecological Observatory Network (NEON) is the first open-source continental-scale ecological observatory that combines in-situ field observations with airborne remote sensing. The intersection of instrumentation and ecosystem provides essential future opportunities for NEON to lead new types of biodiversity surveys to answer key ecological questions. The large amount of NEON field data that can be combined with site-wide coverage of Lidar and hyperspectral remote sensing provides a framework for uniquely testing the spatial distribution of biodiversity patterns based on the Third Law of Geography. Here, we apply the Third Law of Geography to NEON Terrestrial Observational Sampling (TOS) and Airborne Observation Platform (AOP) datasets to map vegetation biodiversity and predict uncertainty in the Eastern US. We assume that in a specific climate eco-region, the similarity of environmental variables (e.g., topography) related to vegetation biodiversity in different locations positively correlates to similarities in the occurrence and magnitude of biodiversity indicators, such as Simpson’s Index, which we calculate from NEON TOS-derived biodiversity matrix from field measurements. The incorporation of open-source data through the NEON portal, enables our effort to test predictive modeling for regional upscaling effort highly transferrable and repeatable. Moreover, hyperspectral and Lidar mosaics at the NEON site level is a scaling bridge that allows us to develop upscaling techniques by relating high resolution airborne remote sensing variables to coarser satellite Earth observations.