Integrating UAV-Mounted Diffraction Spectroscopy and FBG Sensor Networks for Real-Time Forest Health Monitoring

Real-time forest health assessment demands both fine-scale physiological measurements and landscape-scale biochemical mapping, yet current monitoring strategies seldom integrate in-situ and airborne modalities into a unified operational framework. Here, we present an integrated forest monitoring platform that couples a distributed Fiber Bragg Grating (FBG) sensor network with a compact diffraction spectrometer mounted on an unmanned aerial vehicle (UAV). The FBG network, embedded within tree trunks and primary branches, provides continuous measurements of strain and temperature parameters directly linked to tree water status while the UAV-based spectrometer captures high-resolution reflectance spectra to estimate canopy chlorophyll content and other pigment indices. We develop and validate physics-based numerical models for both subsystems, simulating diurnal cycles of trunk deformation and atmospheric effects on aerial spectral acquisition. These models are evaluated in a virtual forest environment that replicates realistic ecological and microclimatic conditions. By fusing FBG-derived water potential proxies with UAV-derived pigment maps, the system generates georeferenced stress profiles from individual trees to the stand level. Simulation results indicate that the combined sensing approach enables early detection of water deficits and spatial mapping of canopy stress gradients, supporting precision forestry interventions such as targeted irrigation or selective thinning. This multimodal, scalable framework aligns with Sustainable Development Goal 15 (“Life on Land”) by enabling continuous, autonomous, and actionable forest health surveillance for climate-resilient ecosystem management.