Multi-Perspective Wildlife Monitoring with Drones and Camera Traps Using Deep Learning and Spatial Analysis

Wildlife monitoring is essential for biodiversity conservation, habitat management, and ecological assessment in protected landscapes. This study develops a multi-sensor, multi-perspective remote sensing approach that integrates drone-based thermal and RGB imagery, camera trap imagery, and deep learning models for wildlife monitoring in Chitwan National Park, Nepal. Thermal infrared drone imagery and daytime visible/nighttime infrared-illuminated camera trap images were organized into two independently constructed but related datasets for automated detection and cross-platform comparison. YOLOv8 and YOLOv11 models were trained and evaluated on the drone thermal dataset, while YOLOv5 and YOLOv11 models were trained and evaluated on the camera trap dataset. For cross-platform comparison, the analyses focused on deer and rhinos, the two taxa shared by both datasets. Detection outputs for these species were used to calculate drone-based detection densities and camera trap-based relative abundance index (RAI) values for spatial and spatiotemporal comparison. Among the evaluated models, YOLOv11s achieved the best performance on both datasets. In the drone dataset, YOLOv11s achieved an overall precision of 91.2%, recall of 85.4%, and mean Average Precision-50 (mAP50) of 93.6%. In the camera trap dataset, it achieved an overall precision of 95.5%, recall of 92.9%, and mAP50 of 95.4%. Spatial and spatiotemporal analyses showed broad correspondence between drone-derived wildlife distribution patterns and camera trap observations. These findings demonstrate that integrating aerial and ground-based sensing with deep learning can leverage the complementary advantages of each platform and enhance scalable wildlife monitoring across heterogeneous protected landscapes.