Although endemic to North America and having an important role in forest ecosystem processes, the most recent outbreak of mountain pine beetle (Dendroctonus ponderosae) shows impacts on forest structure and ecosystem processes that are acute and widespread. The current outbreak may heavily impact water runoff, water quality, nutrient cycling, forest regeneration and regrowth, native and non-native plant communities, soil chemistry and wildlife habitat.
The use of legislative acts to address bark beetle outbreaks and collaboration with communities of interest has become a key approach to the Forest Service’s management strategy. Effective detection and mapping of beetle mortality across multiple scales is urgently needed to assist in determining high risk areas in need of mitigation and restoration. Accurate baseline maps displaying the abundance of tree species across the landscape have become a necessary tool for addressing the array of important and often overlapping concerns of land managers, policy-makers and the public.
This project demonstrated the utility of Boosted Regression Tree modeling in concert with Landsat imagery to create a fine resolution forest cover classification model with limited predictor data, and acts as an important baseline for subsequent model calibration by future NASA DEVELOP teams. Ultimately this methodology can be a useful decision support tool for resource managers and community members affected by mountain pine beetle. The decision support tools, including forest cover classification maps, modeling methodologies and field methods tutorials, were delivered directly to the resource managers. Furthermore, all methods and remote sensing resources will be available to interested community members through the ColoradoView website, and the team will share the project results at the annual American Geophysical Union (AGU) Fall Meeting in San Francisco, CA in early December 2012.
Excerpted From: Matthew Luizz and Steve Chignell, Directions Magazine
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+ NASA DEVELOP: Colorado Ecological Forecasting