Assessment of UAV-Based Remote Sensing for Environmental Surveillance

Abstract

Unmanned Aerial Vehicles (UAVs) have become indispensable tools for modern environmental surveillance due to their agility, cost-effectiveness, and ability to acquire high-resolution spatiotemporal data. This review evaluates the major technological, operational, and analytical components of UAV-based remote sensing and its applications across environmental monitoring domains. UAVs equipped with multispectral, hyperspectral, thermal, and LiDAR sensors enable detailed observations of vegetation health, water quality, soil conditions, wildlife distribution, and disaster-affected regions. The integration of geospatial analytics, machine learning algorithms, and cloud data platforms further enhances the interpretability and accuracy of UAV-derived information. Despite their advantages, UAV systems face challenges related to regulatory restrictions, limited battery endurance, weather sensitivity, data processing complexity, and the need for standardized methodologies. The review synthesizes current research findings to highlight the strengths and limitations of UAV-based environmental surveillance and identifies emerging trends such as swarm UAV systems, AI-driven image interpretation, and real-time mapping through edge computing. The study concludes that UAVs will continue to expand their role in environmental studies, making monitoring systems faster, more adaptive, and more data-rich.