Temperature buffering in temperate forests: Comparing microclimate models based on ground measurements with active and passive remote sensing

Abstract

Abstract The ability of a forest to buffer understory temperature extremes depends on the canopy structure, which is often measured from the ground. However, ground measurements provide only point estimates, which cannot be used for spatially explicit microclimate modeling. Canopy structures derived from airborne light detection and ranging (LiDAR) can overcome these limitations, but high point-density LiDAR is expensive and computationally challenging. Therefore, we explored whether unmanned aerial systems (UAS) processed with the structure-from-motion (SfM) algorithm could serve as an alternative source of canopy variables for forest microclimate modeling. Specifically, we compared the performance of the canopy cover and height derived from the ground measurements and passive (UAS-SfM) and active (UAS-LiDAR) remote sensing as predictors of air and soil temperature offsets (i.e. differences between the forest understory and treeless areas). We found that the maximum air temperatures were substantially lower inside than outside the forest, with differences ranging from 9.0 to 12.5\xa0°C. The soil temperatures under the canopy were also reduced, but the soil temperature offsets were lower and ranged from 1.1 to 2.8\xa0°C. The air and soil temperature offsets both increased with increasing tree height and canopy cover. However, the prediction ability of tree height and canopy cover differed if they were ground-based or remotely sensed. The remotely sensed canopy indices explained air temperature offsets better (UAS-SfM: R2\xa0=\xa00.59, RMSE\xa0=\xa00.75 °C; UAS-LiDAR: R2\xa0=\xa00.57, RMSE\xa0=\xa00.76 °C) than ground measurements (R2\xa0=\xa00.51, RMSE\xa0=\xa00.80 °C). Ground-based metrics explained soil temperature offsets better (R2\xa0=\xa00.37, RMSE\xa0=\xa00.36 °C) than passive remote sensing approach (UAS-SfM: R2\xa0=\xa00.27, RMSE\xa0=\xa00.39 °C), but comparably to active one (UAS-LiDAR: R2\xa0=\xa00.35, RMSE\xa0=\xa00.37 °C). Our results suggest that both UAS-SfM and UAS-LiDAR can substitute ground canopy measurements for air temperature modeling, but soil temperature modeling is more challenging. Overall, our results show that forest microclimate can be modelled at a very high spatial resolution using UAS equipped with inexpensive optical cameras. The increasingly available UAS-SfM approach can thus provide fine-resolution microclimatic data much needed for biologically relevant predictions of species responses to climate change.