Will remote sensing shape the next generation of species distribution models?

By Kate S. He, Bethany A. Bradley, Anna F. Cord, Duccio Rocchini, Mao-Ning Tuanmu, Sebastian Schmidtlein, Woody Turner, Martin Wegmann, Nathalie Pettorelli. 2015

349 citations recorded by the source corpus.

Two prominent limitations of species distribution models (SDMs) are spatial biases in existing occurrence data and a lack of spatially explicit predictor variables to fully capture habitat characteristics of species. Can existing and emerging remote sensing technologies meet these challenges and improve future SDMs? We believe so. Novel products derived from multispectral and hyperspectral sensors, as well as future Light Detection and Ranging (LiDAR) and RADAR missions, may play a key role in improving model performance. In this perspective piece, we demonstrate how modern sensors onboard satellites, planes and unmanned aerial vehicles are revolutionizing the way we can detect and monitor both plant and animal species in terrestrial and aquatic ecosystems as well as allowing the emergence of novel predictor variables appropriate for species distribution modeling. We hope this interdisciplinary perspective will motivate ecologists, remote sensing experts and modelers to work together for developing a more refined SDM framework in the near future.

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/ee268cbc4c3142d3ca0501610878e018265f8a0f

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Citations in the corpus

Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives (2016, background): “Species distribution models (SDM) The quantification of species-environment relationships using pecies distribution models (SDMs), however, is the most effecive way to analyze information gathered from species point data ollections such as Map of Life (www.mol.org) or GBIF (Global Bioiversity Inform”