A Tree Level Model of Forests in the Western United States
Forest Service scientists sought to match plot data collected by the agency’s Forest Inventory Analysis (FIA) with characteristics of the landscape, as mapped on a 30 meter by 30 meter (98 feet by 98 feet) grid by the LANDFIRE project. The result is a map of plot identifiers, with the identifier of the best-matching plot assigned to each grid cell. The scientists used a modified random forests approach, which utilizes a “forest” of decision trees in order to identify the best-matching plot for each grid cell. One of the strengths of the random forests method is that it can model complex nonlinear relationships. map of plot identifiers can be linked to FIA’s databases to produce tree-level maps (aka “tree list”) or to map a number of other plot attributes. For example, they used the map of plot IDs to generate maps of forest cover, forest height, and existing vegetation group at 30 meter x 30 meter resolution for all forested pixels in the western United States.
They found high levels of agreement between our dataset and LANDFIRE data indicate our modified random forests model was able to identify forest plots that closely matched the landscape characteristics of 30 meter x 30 meter grid cells.
As the dataset in essence provides a tree-level model of forests in the western U.S., it greatly augments the information available to researchers and managers who previously relied on data from sparse forest plots or stand inventories.
|Utilizing random forests imputation of forest plot data for landscape-level wildfire analyses||(publication)|