Samenvatting
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.