Applications and Challenges Related to the Use of Unmanned Aircraft Systems in Environment Monitoring

This chapter gives an overview of the latest research and development activities conducted by VTT regarding environmental monitoring using unmanned aircraft systems (UAS) and discusses the associated challenges. An AI-based drone swarm technology in a unified framework can provide situational awareness and decision support tools for wildfire monitoring. The monitoring of floating waste from an unmanned aircraft (UA) with optical sensors suggests that multi-imaging with near-infrared (NIR) hyperspectral (HS), thermal infrared (TIR), and multicolor (RGB) sensors is a promising method for separating floating plastic waste from organic material. Monitoring of tailing ponds of mines with onboard hyperspectral and multispectral sensors indicated hints of seepage or water in spectral signatures of vegetation and ground along with general structural information, particularly of tailing pond dams. Hyperspectral data acquired by a UAS is well suited for monitoring vegetation’s biochemical composition, moisture content, and biodiversity since it offers unprecedented spatial resolution with pixel sizes comparable to the basic vegetation elements, leaves or flowers. VTT demonstrated the applicability of novel vegetation analysis algorithms based on the theory of spectral invariant theory to such ultra-high-resolution HS imagery for vegetation trait retrieval. The challenges related to the use of UAS are multifaceted. These include connectivity technologies and protocols, the operational limitations of UA, and the application of artificial intelligence (AI), data fusion, and machine learning methods. Also, the legislative demand for autonomous UAS operations, significantly beyond visual line of sight (BVLOS), requires a range of U-space services.