Remote Sensing & Agro-informatics Lab

​Dryland forest ecosystems throughout the globe are increasingly threatened by climate change. Higher drought frequency in these systems often leads to vegetation mortality and deterioration of important ecosystem services. This trend is projected to increase as a consequence of climate change. The study will investigate the responses of dryland forest ecosystems to increasing drought stress based on remote sensing of vegetation structural dynamics. We will develop a new approach based on 'leaf area organization' (LAO) to investigate forest ecosystem adjustments to increasing drought stress in its main functional parameters: primary production, rates of carbon assimilation, and evapotranspiration. This study is now possible by utilizing the exceptional remote-sensing capabilities of the recently launched Venµs satellite, combined with airborne Light Detection and Ranging (LiDAR) measurements and two long-term ecological research (LTER) sites - Kedoshim and Yatir, established in 2009 in Mediterranean-semiarid conifer forests in Israel, across a steep climatic gradient (reaching up to the dry tree line). The two sites are similar in forest type (Pinus halepensis), edaphic conditions, and silvicultural history and share a parallel experimental structure in which forest plots (70×70 m) were thinned to several different levels. The unique capabilities of Venµs and LiDAR will allow for the first time to carry out research using a remote sensing approach on a plot-size manipulation experiment. This unique setup will enable us to investigate forest structural and functional self-organization as a basis to understand its response to spatial and temporal variation in drought stress and provide estimates of forest response to predicted climate change and, in turn, contribute to the development of silvicultural strategies for the management, conservation, and restoration of dryland forests.