Open AccessWill remote sensing shape the next generation of species distribution models?
Author(s) -
He Kate S.,
Bradley Bethany A.,
Cord Anna F.,
Rocchini Duccio,
Tuanmu MaoNing,
Schmidtlein Sebastian,
Turner Woody,
Wegmann Martin,
Pettorelli Nathalie
Publication year - 2015
Publication title -
remote sensing in ecology and conservation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.191
H-Index - 21
ISSN - 2056-3485
DOI - 10.1002/rse2.7
Subject(s) - remote sensing , hyperspectral imaging , computer science , lidar , multispectral image , key (lock) , citizen science , perspective (graphical) , radar , data science , systems engineering , environmental science , artificial intelligence , geography , engineering , telecommunications , biology , botany , computer security
Two prominent limitations of species distribution models ( SDM s) 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 SDM s? 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.