Plant canopy temperature (T_c) is partly regulated by evaporation and transpiration from the canopy surface and can be used to infer changes in stomatal regulation and vegetation water stress. In this study, we used a thermal Unmanned Aircraft Systems in conjunction with eddy covariance, sap flow, and spectral reflectance data to assess the diurnal characteristics of T_c and water stress status over a semiarid mixed conifer forest in Arizona, USA. Diurnal T_c dynamics were closely related to tree sap flow and changes in spectral reflectance associated with stomatal regulation. Consistent with previously reported deviations, we found that on average T_c was 1.8°C lower than the above canopy air temperature (T_a). However, the relationship between T_c and T_a varied significantly according to tree density and tree height classes, with taller and denser trees exhibiting relatively low |T_c‐T_a| (2.4 and 2.1°C cooler canopies, respectively) compared to shorter and less‐dense tree stands (1.7 and 1.5°C cooler canopies, respectively). We used these data to evaluate space‐borne diurnal measurements of T_c and water stress from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. We found that ECOSTRESS observations of T_c accurately tracked seasonal shifts in diurnal surface temperatures and vegetation water stress, and that site‐level observations of heterogeneity in forest composition and structure could be applied to separate the processes of canopy transpiration and soil evaporation within the ECOSTRESS footprint. This study demonstrates how proximal and satellite remote sensing approaches can be combined to reveal the diurnal and seasonally dynamic nature of T_c and water stress.

Pre-fire environmental conditions play a critical role in wildfire severity. This study investigated the impact of pre-fire forest conditions on burn severity as a result of the 2020 Bighorn Fire in the Santa Catalina Mountains in Arizona. Using a stepwise regression model and remotely sensed data from Landsat 8 and LiDAR, we analyzed the effects of structural and functional vegetation traits and environmental factors on burn severity. This analysis revealed that the difference normalized burn ratio (dNBR) was a more reliable indicator of burn severity compared to the relative dNBR (RdNBR). Stepwise regression identified pre-fire normalized difference vegetation index (NDVI), canopy cover, and tree density as significant variables across all land cover types that explained burn severity, suggesting that denser areas with higher vegetation greenness experienced more severe burns. Interestingly, residuals between the actual and estimated dNBR were lower in herbaceous zones compared to denser forested areas at similar elevations, suggesting potentially more predictable burn severity in open areas. Spatial analysis using Geary’s C statistics further revealed a strong negative autocorrelation: areas with high burn severity tended to be clustered, with lower severity areas interspersed. Overall, this study demonstrates the potential of readily available remote sensing data to predict potential burn severity values before a fire event, providing valuable information for forest managers to develop strategies for mitigating future wildfire damage.

How forest structure regulates the amount and timing of snowmelt across a climatic gradient of snow stability