Fixed-ground multisensor monitoring resolves longitudinal vegetation condition and fuel-state structure in Southern California chaparral and oak scenes

Andrew Lee

Wildfire-relevant vegetation and fuel conditions evolve through interacting changes in vegetation condition, three-dimensional fuel structure, thermal response, and atmospheric drying, yet these dimensions are commonly monitored separately. This study evaluated repeated fixed-ground multisensor monitoring at two fixed vegetation scenes in Placerita Canyon, California. Multispectral imaging, radiometric thermal imaging, light detection and ranging (LiDAR), and local atmospheric measurements were integrated across persistent dense, mixed, and dry vegetation regions during 10 quality-controlled visits at a native-chaparral hillside and 24 visits at an oak/canopy-centered scene. Persistent vegetation regions remained strongly differentiated across sensing domains while also exhibiting longitudinal change. Dense vegetation exceeded dry vegetation in median normalized difference vegetation index (NDVI) by 0.259 and 0.113 at the two sites, showed 4.11 m and 0.56 m greater LiDAR vertical spread, and remained 3.37 °C and 3.88 °C cooler relative to ambient air. Dense chaparral NDVI declined markedly, while dense, mixed, and dry regions at the oak-centered site showed sustained NDVI declines. Corresponding decreases across three additional spectral indices and spatially differentiated oak-canopy changes showed that the temporal signal extended beyond a single spectral index or whole-canopy average. Multisensor analysis of 96 complete visit × site × region observations showed vegetation region as the dominant source of multivariate separation (partial R² = 0.491, p = 0.0005). By preserving spatial identity while integrating spectral, structural, thermal, and atmospheric measurements, this fixed-ground framework provides a local longitudinal approach for characterizing evolving vegetation and fuel conditions before ignition.