Enhancing Tools and Geospatial Data to Support Operational Forest Management and Regional Forest Planning in the Face of Climate Change

Abstract:

A detailed understanding of how forest composition, structure, and function will be impacted by projected climate change and related adaptive forest management activities are particularly lacking at local scales, where on-the-ground management activities are implemented. Climate sensitive forest dynamics models may prove to be effective tools for developing a comprehensive understanding. However, to be applicable to both regional forest planning and operational forest management, modeling approaches must be capable of simulating forest dynamics across large spatial extents (required for regional planning) while maintaining a high-level of spatial detail (required for operational management). LiDAR remote sensing has shown great utility for operational forest inventory and management, including forest dynamics modeling, albeit across relatively small spatial extents. We present a remote sensing driven approach to spatially initialize a climate-sensitive forest dynamics model (LANDIS-II) in the Pacific Northwest of the US via an integration of airborne LiDAR data with satellite remote sensing data. The system provides detailed forest inventory information - at the landscape level - that is subsequently employed to demonstrate how such models can be used to 1) investigate the potential impacts of climate change on future forest composition and structure, and 2) assess how various forest management practices may either enhance or degrade forest resilience to changing climate and disturbance regimes.