(untitled) (Zoological Records and Reviews, 2025)
Remote sensing technologies have transformed the capacity to study faunal distributions, population dynamics, habitat associations, and behavioural ecology at spatial and temporal scales previously unachievable through conventional ground-based survey methods. This review synthesises advances in remote sensing applications for faunal studies from 202 primary studies published 2012-2025, evaluating six major platform categories: satellite optical and multispectral imagery, synthetic aperture radar (SAR), light detection and ranging (LiDAR), unmanned aerial vehicles (UAVs/drones), thermal infrared imaging, and hyperspectral sensors. Applications evaluated include: species distribution modelling using satellite-derived habitat variables, direct animal detection and counting from aerial and satellite imagery, habitat quality assessment for wildlife management, movement corridor identification, and marine mammal and seabird colony monitoring. UAVs demonstrate the highest versatility across faunal application contexts (composite utility score 2.64/3.0), with thermal infrared UAV surveys achieving detection rates of 84-96% for medium-to-large mammals compared to conventional ground counts. Satellite-based species distribution modelling has been validated for 284 European vertebrate species, with mean AUC 0.82 for habitat-specialist species. LiDAR-derived structural habitat variables explain 42-68% of variance in bird species richness across forest systems. Machine learning integration – particularly convolutional neural networks for automated species detection from aerial imagery – has reduced manual image analysis time by 78-94% while maintaining detection accuracy. A practical framework for remote sensing platform and method selection for European faunal monitoring applications is presented.