Michael Falkowski, Michigan Technological University, firstname.lastname@example.org (Presenter)
Andrew Hudak, USFS Rocky Mountain Research Station, email@example.com
Linda Nagel, Michigan Technological University, firstname.lastname@example.org
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 growth models may prove to be effective tools for developing a more 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 management, including forest growth modeling, albeit across relatively small spatial extents. We present a geospatial modeling approach to spatially parameterize and supply critical initial conditions for two separate climate-sensitive forest growth models (Climate-FVS and LANDIS-II) across unique ecoregions (in terms of forest structure and composition) in the Pacific Northwest (PNW) of the US via an integration of sub-orbital LiDAR data with satellite remote sensing data (e.g., MODIS, Landsat, and GLAS). The system provides detailed forest inventory information - at both the landscape and ecoregion level - that is subsequently employed to demonstrate how climate-sensitive growth and yield 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.