The potential of satellite greenness to predict plant diversity among wetland types, ecoregions, and disturbance levels

Abstract

The unprecedented global biodiversity loss has massive implications for the capacity of ecosystems to maintain functions critical to human well-being, urgently calling for rapid, scalable, and reproducible strategies for biodiversity monitoring, particularly in threatened ecosystems with difficult field access such as wetlands. Remote sensing indicators of spectral variability and greenness may predict the diversity of plant communities based on their optical diversity; however, most evidence is based on narrowband spectral data or terrestrial ecosystems. We investigate how spectral greenness and heterogeneity from publicly available broadband multi-spectral Landsat satellite imagery explain variation in vegetation diversity across different wetland types, ecoregions, and disturbance levels using 1,138 sites surveyed by U.S. EPA's National Wetland Condition Assessment. We found positive correlations of plant species richness and diversity with indicators of annual maximum spectral greenness and its spatial heterogeneity, explaining up to 43% variation within the global sample, 48% within wetland types or ecoregions, and up to 61% with abiotic covariates. The combined effect of spectral greenness and heterogeneity was stronger than the best-performing model using climatic, topographic, and edaphic factors alone. When compared among major U.S. watersheds and individual states, the fit of diversity-greenness models increased when more wetland types were included within the corresponding region's boundaries, up to 61% at the watershed and 77% at the state level, respectively, for diversity models and up to 73% and 80%, respectively, for richness models. Model outliers were characterized by a significantly greater diversity of nonnative species (P<0.0001), suggesting that changes in model performance and greenness distributions could be used as indicators of shifts in plant community composition, particularly in tidal wetlands making the majority of outliers with significantly lower than predicted diversity. This study represents a first-time national-scale effort to use publicly available remote sensing, climatic, and topographic data to predict plant diversity in wetlands, which tend to be understudied compared to terrestrial ecosystems despite being among the most stressed ecosystems on Earth. Our study suggests that multi-temporal broadband satellite imagery could provide a low-cost assessment of regional and national wetland biodiversity for prioritization of conservation efforts and early detection of biodiversity loss.