Mapping multiple dimensions of forest diversity using spaceborne spectroscopy

Observing biodiversity across space and time is essential for advancing and verifying conservation efforts toward global biodiversity and sustainability goals. Spaceborne imaging spectroscopy has emerged as a revolutionary tool for quantifying and tracking forest diversity, yet its application at large spatial scales remains a central challenge. We develop a framework to map multiple dimensions of forest community composition and diversity by integrating imaging spectroscopy from two spaceborne sensors (DESIS and EMIT) with taxonomic, phylogenetic, and functional trait datasets, and 43,155 forest inventory plots across the Eastern United States. We find that spectral dissimilarity among forest communities is positively correlated with β-diversity matrices of compositional dissimilarity. We then show that imaging spectroscopy can be used to predict ordination axes of β-diversity and to map multiple dimensions of forest diversity at high spatial resolution (30 or 60 m). Predicted β-diversity axes can be used to model forest attributes, including forest types, plant lineages, and community plant traits. On average, β-diversity axes explain more than 48% of the variance—outperforming climatic and topographic predictors—and enable accurate mapping of 95 forest attributes. Our framework shows that spaceborne imaging spectroscopy, when combined with inventory data, allows indirect yet comprehensive observation of forest diversity attributes across broad spatial extents. This integrative approach sets the stage for scalable forest monitoring in support of global biodiversity conservation and forthcoming satellite missions.