Forest microclimate modelling using remotely sensed data

Forest gaps creation can affect the microclimate and soil water balance which strongly influences the process of regeneration and biodiversity within forest ecosystems. Understanding the microclimatic conditions in canopy gaps is needed in developing and improving techniques for forest conservation and management. However, information is scarce on how forest gaps and their spatial distribution affect the microclimate and soil water balance across forest stands. In this study, the potential for retrieving forest gap and canopy attributes from LiDAR and multispectral sensors in order to provide new opportunities for modelling forest microclimates were investigated. A spatially explicit microclimate model (FORGAP-BD) was developed which could be driven using spatial inputs from remote sensing. The model was implemented for a study site in the broadleaved deciduous forest, Eaves Wood, UK in order to quantify the spatio-temporal dynamics of microclimates over an entire forest stands