Abstract
Mapping the microclimate effect of forest canopies on understory temperature requires spatially explicit predictors at very fine spatial resolutions. Light Detection And Ranging (LiDAR) offers promising prospects in that regard, as it allows capturing the vertical dimension of vegetation structure at a very high resolution over large areas.To explore the potential of airborne LiDAR-derived metrics to predict understory temperature, we focused on the forest of Blois (France), a 2740-ha lowland managed forest dominated by oak (Quercus petraea). We installed HOBO sensors measuring microclimate air temperature at one-metre height in 53 stands of contrasting vegetation structure, from open to very dense and from young regeneration to mature stages. Using a nearby weather station as the macroclimate temperature reference, we calculated the slope (log scale) coefficient of the linear regression between microclimate and macroclimate, as a simple parameter describing the microclimatic buffering (log(slope) < 0) or amplification (log(slope) > 0) capacity of the habitat.An airborne LiDAR flight was conducted during summer 2021, matching the timing of our temperature measurements. From the resulting 3D point cloud, three complementary metrics of forest structure were derived: the maximum height, the Plant Area Index and the Vertical Complexity Index. They were calculated for circular buffers of different radii (1 m to 100 m) centred on each HOBO sensor.We found that the 5-m radius combining the three metrics into a single multivariate model explained the greatest proportion of variance in the microclimate effect of each stand (R2 = 0.91). We mapped the buffering or amplification effect of vegetation structure on understory temperatures over the entire forest of Blois at a 10-m resolution. 91.4% of the surface of the forest was significantly buffered relative to macroclimate temperature, while 2.7% was amplified, especially in road verges, clear-cut and regeneration areas. Based on our simple linear model, we were able to derive understory air temperature maps for any temporal resolution (i.e. hourly, daily, or seasonal). The results highlight the great capacity of airborne LiDAR to retrieve forest structure parameters and generate high-resolution maps of the thermal environment.Applications for mapping the buffering or amplification of microclimate temperature are plentiful, especially in the context of climate change. They include improving the understanding of physiological processes such as thermoregulation or phenology, modelling the thermal connectivity of the landscape, improving species redistribution models, spotting microrefugia for conservation, informing forest management for tree regeneration or wildfire control, or even prioritising cooling recreational areas for humans to escape heatwaves in urban forests.