Abstract: Forests play a vital role in mitigating climate change through their physiological functions and metabolic processes, including their ability to convert solar energy into biomolecules. However, further research is necessary to elucidate how structural characteristics of a forest and topographic settings influence energy conversion and surface temperature of a forest. In this study, we investigated a beech forest in central Germany using airborne laser scanning (ALS) point cloud data and land surface temperature (LST) data derived from Landsat 9 satellite imagery. We constructed 30 m × 30 m plots across the study area (approximately 17 km²) to align the spatial resolution of the satellite imagery with the ALS data. We analyzed topographic variables (surface elevation, aspect and slope), forest attributes (canopy cover, canopy height, and woody area index), as well as forest structural complexity, quantified by the box-dimension (D_b). Our analysis revealed that LST is significantly influenced by both forest attributes and topographic variables. A multiple linear regression model demonstrated an inverse relationship (R² = 0.38, AIC = 8105) between LST and a combination of D_b, elevation, slope, and aspect. However, the model residuals exhibited significant spatial dependency, as indicated by Moran’s I test. To address this, we applied a spatial autoregressive model, which effectively accounted for spatial autocorrelation and improved the model fit (AIC = 746). Our findings indicate that elevation exerts the most substantial influence on LST, followed by forest structural complexity, slope, and aspect. We conclude that forest management practices that enhance structural complexity can effectively reduce land surface temperatures in forested landscapes.

Key words: Airborne laser scanning, Topography, Box-dimension, Landsat satellite imagery, Land surface temperature