Applications of remote sensing in faunal studies (Zoological Records and Reviews, 2025)
Remote sensing technologies have transformed the capacity to study faunal distributions, population dynamics, habitatassociations, and behavioural ecology at spatial and temporal scales previously unachievable through conventionalground-based survey methods. This review synthesises advances in remote sensing applications for faunal studies from202 primary studies published 2012-2025, evaluating six major platform categories: satellite optical and multispectralimagery, 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 usingsatellite-derived habitat variables, direct animal detection and counting from aerial and satellite imagery, habitat qualityassessment for wildlife management, movement corridor identification, and marine mammal and seabird colonymonitoring. 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 toconventional ground counts. Satellite-based species distribution modelling has been validated for 284 Europeanvertebrate species, with mean AUC 0.82 for habitat-specialist species. LiDAR-derived structural habitat variables explain42-68% of variance in bird species richness across forest systems. Machine learning integration -- particularlyconvolutional neural networks for automated species detection from aerial imagery -- has reduced manual imageanalysis time by 78-94% while maintaining detection accuracy. A practical framework for remote sensing platform andmethod selection for European faunal monitoring applications is presented.