This study presents a real-time, adaptive UAV system designed to enhance ecological surveys by overcoming the trade-off between wide-area coverage and high-resolution data collection. The Modular Detection and Targeting System (MDTS) integrates thermal imaging for broad detection and high-resolution RGB zoom imaging for precise species identification. Field trials demonstrated the system’s ability to detect and record both avian and mammalian species with significantly reduced redundant data and improved survey efficiency. Compared to traditional UAV methods, the MDTS achieved over 300-fold improvements in image resolution and up to a 1000-fold reduction in data volume. The system’s modular design enables rapid adaptation to diverse ecological applications, providing classification-ready data while minimizing post-processing demands. These results highlight the MDTS as a scalable, efficient tool for wildlife monitoring and environmental research, bridging the gap between detection and actionable ecological insights.

Accurate classification of salt marsh vegetation is vital for conservation efforts and environmental monitoring, particularly given the critical role these ecosystems play as carbon sinks. Understanding and quantifying the extent and types of habitats present in Ireland is essential to support national biodiversity goals and climate action plans. Unmanned Aerial Vehicles (UAVs) equipped with optical sensors offer a powerful means of mapping vegetation in these areas. However, many current studies rely on single-sensor approaches, which can constrain the accuracy of classification and limit our understanding of complex habitat dynamics. This study evaluates the integration of Red-Green-Blue (RGB), Multispectral Imaging (MSI), and Hyperspectral Imaging (HSI) to improve species classification compared to using individual sensors. UAV surveys were conducted with RGB, MSI, and HSI sensors, and the collected data were classified using Random Forest (RF), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM) algorithms. The classification performance was assessed using Overall Accuracy (OA), Kappa Coefficient (k), Producer’s Accuracy (PA), and User’s Accuracy (UA), for both individual sensor datasets and the fused dataset generated via band stacking. The multi-camera approach achieved a 97% classification accuracy, surpassing the highest accuracy obtained by a single sensor (HSI, 92%). This demonstrates that data fusion and band reduction techniques improve species differentiation, particularly for vegetation with overlapping spectral signatures. The results suggest that multi-sensor UAV systems offer a cost-effective and efficient approach to ecosystem monitoring, biodiversity assessment, and conservation planning.