A simplified multi-model statistical approach for predicting the effects of forest management on land surface temperature in Fennoscandia ...

• Main Authors: Huang, Bo, Li, Yan, Liu, Yi, Hu, Xiangping, Zhao, Wenwu, Cherubini, Francesco
• Format: Text
• Language: unknown
• Published: Freie Universität Berlin 2023
• Subjects: Forest management; Climate change; Surface temperature; Machine learning; 500 Naturwissenschaften und Mathematik550 Geowissenschaften, Geologie550 Geowissenschaften; Fennoscandia
• Online Access: https://dx.doi.org/10.17169/refubium-38614; https://refubium.fu-berlin.de/handle/fub188/38898

Forests interact with the local climate through a variety of biophysical mechanisms. Observational and modelling studies have investigated the effects of forested vs. non-forested areas, but the influence of forest management on surface temperature has received far less attention owing to the inherent challenges to adapt climate models to cope with forest dynamics. Further, climate models are complex and highly parameterized, and the time and resource intensity of their use limit applications. The availability of simple yet reliable statistical models based on high resolution maps of forest attributes representative of different development stages can link individual forest management practices to local temperature changes, and ultimately support the design of improved strategies. In this study, we investigate how forest management influences local surface temperature (LSTs) in Fennoscandia through a set of machine learning algorithms. We find that more developed forests are typically associated with higher ...