Predicting Vegetation Attributes with Neural Networks and Sentinel-1 & 2

Abstract

Evidence suggests that plant traits, plant functional diversity, and species diversity are linked to ecosystem functions to different extents. However, these relationships are sometimes inconsistent because of the presence of environmental gradients (e.g. climate, topography, land use) and scale mismatches between sampling units and landscape processes. Relationships between satellite data and vegetation parameters seem to be also case-specific, which hinders the creation of generalizable models. We have built predictive models of structural parameters and species composition across a broad range of climatic and topoedaphic conditions and management practices across grasslands and forests in Germany. For that, we use Sentinel multitemporal imagery and neural networks. Our models manage to explain 50% of the data variability for structural parameters, show high stability, and can generalize well across environmental gradients and sites. We also found that prediction models of biodiversity parameters show lower predictive capabilities. Spatially continuous models of grassland and forest attributes provide vital information on ecosystem functions at landscape scale. Thus, they can contribute to studying the feedback mechanisms between biodiversity, ecosystem functions, and land management at the scales to which ecological processes occur.