Forest ecosystems are increasingly affected by a variety of stressors, including climate change, insect outbreaks, and disease. Early detection of tree stress is critical for understanding forest health dynamics and improving forest management. This project aims to develop and evaluate remote sensing approaches for detecting and monitoring tree stress at the individual tree level using high resolution mobile LiDAR and field based data.
We are interested in collecting backpack LiDAR data within the Harvard Forest ForestGEO megaplot to obtain high resolution three dimensional measurements of forest structure. These data will be integrated with existing tree census records to investigate structural indicators of tree stress and decline. The backpack LiDAR surveys will provide detailed information on canopy architecture and stem characteristics that are not readily captured by commonly used remote sensing platforms such as airborne or satellite systems.
The broader goal of this research is to improve methods for identifying stressed trees before mortality occurs and to better understand how forest structure and health respond to environmental disturbances. Findings from this work will contribute to the development of scalable remote sensing tools for long term forest health monitoring and support future ecological research and forest management efforts.