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

Remote Sensing in Ecology and Conservation

Kate S. He⁷  Bethany A. Bradley²  Anna F. Cord⁶  Duccio Rocchini⁸  Mao-Ning Tuanmu⁴  Sebastian Schmidtlein³  Woody Turner⁵  Martin Wegmann¹ 

[1] Department of Remote Sensing, University of Wuerzburg, Wurzburg, Germany;Department of Environmental Conservation, University of Massachusetts, Amherst, Massachusetts;Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany;Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut;Earth Science Division, NASA, Washington, District of Columbia;Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany;Department of Biological Sciences, Murray State University, Murray, Kentucky;GIS and Remote Sensing Unit, Department of Biodiversity and Molecular Ecology, Research and Innovation Center, Fondazione Edmund Mach, S. Michele all'Adige, Trento, Italy

关键词: Ecological niche modeling;    habitat suitability modeling;    hyperspectral and multispectral data;    LiDAR and RADAR metrics;    predictor and response variables;    spatial and temporal resolution;   

DOI  :  10.1002/rse2.7

来源: Wiley

【 摘 要 】

Abstract

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.

【 授权许可】

CC BY-NC-ND   
© 2015 The Authors Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London.

Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.