The influence of topography on the forest surface temperature retrieved from Landsat TM, ETM + and ASTER thermal channels

The objective of this study was to assess the influence of topography on the surface temperature (ST) of spruce forest in the central part of the Šumava Mountains, Czech Republic. The aim was to design a method for surface temperature normalisation of forest stands in rugged relief. In this study, two Landsat scenes, ETM + and TM, and one TERRA ASTER scene were used. The different spatial (60 m–90 m–120 m) and/or radiometric (8–12 bits) resolutions of these scenes enabled the assessment of the influence of these parameters on the accuracy of surface temperature models at the mesoscale landscape context. These models are based on the effects of complex topography (digital elevation model — DEM, and illumination — Hillshade) on surface temperature. Only homogeneous spruce forest stands were used for surface temperature modeling. The influence of topography on surface temperature in spruce forest was confirmed in all types of satellite data used. Three different sampling approaches were used to increase the accuracy of the models. Predictability increased with forest content in the thermal pixel (sampling approach 1). The resulting R 2 values (0.47–0.49) were similar between all three scenes. Sampling approach 2 is based on the weighting of thermal pixels by the forest content ( R 2 : 0.32–0.39). We also assessed the influence of spruce forest edge effect on the accuracy of thermal models (sampling approach 3). Removing forest buffer zones resulted in greater statistical significance (approximately by 25%–40%). The optimal width of forest edge removed was determined to be 90 m. The resulting explained variability ( R 2 ) improved by forest edge removal was 0.57, 0.52, 0.47 in the case of ETM + , ASTER, and TM, respectively. These values correspond with the spatial resolution. However, the differences are not significant indicating that all these data are useful for ST modeling of spruce forest. The potential use of ST modeling is to identify temperature anomalies caused by different types of forest disturbance (e.g. harvesting, disease, insect attack) or changes in the condition of stands (e.g. water stress).