WWETAC Projects

Principal Investigator: Janet L. Ohmann, PNW Research Station, USDA Forest Service and the Landscape Ecology, Modeling, Mapping, and Analysis (LEMMA) Team

johmann[at]fs.fed.us

Collaborators: Western Wildland Environmental Threat Assessment Center, USFS; Department of Forest Ecosystems and Society, Oregon State University (OSU); Interagency Mapping and Assessment Project (IMAP), USFS, Oregon Department of Forestry, BLM; Northwest Forest Plan Effectiveness Monitoring, USFS and BLM; Remote Sensing Applications Center, USFS; Forest Inventory and Analysis (FIA), USFS; Institute of Natural Resources, OSU

Key Issues/Problems Addressed:

Information about the distribution and characteristics of forest vegetation and land cover is fundamental to a wide array of issues related to sustainable ecosystem management.  Comprehensive and detailed vegetation maps are necessary to assess the landscape conditions of various habitats, biodiversity indicators, and forest resources or concerns (e.g., timber supply, carbon budgets, and fuel conditions/fire risk).

Setting and Approach:

This project developed detailed maps of existing forest vegetation/land cover across the Pacific Coast States (Oregon, Washington, and parts of California) using the gradient nearest neighbor (GNN) method.  GNN, a form of imputation mapping, integrates plot and spatial (GIS) data, including satellite imagery, to map detailed forest composition attributes and structure across large, multi-ownership regions. The mapping process used ongoing sample-based forest inventories for the region, including the Forest Inventory and Analysis (FIA) and Current Vegetation Survey (CVS). Multivariate gradient modeling was used to integrate data from FIA field plots with satellite imagery and mapped environmental data. This involved imputing a suite of fine-scale plot variables was imputed to each pixel in a digital map. All GNN map products are grid-based at 30-m spatial resolution.

Key Findings:

  • GNN map accuracy is excellent at the regional level and good-to poor at the local scale, depending on vegetation attribute and location.
  • At the local scale, the covariance structure of all vegetation components within a map unit is maintained, avoiding unrealistic co-occurrences of species and forest structures.
  • The inherent capabilities of the Landsat sensor limit local accuracy. Canopy measures are mapped more accurately than subcanopy features and accuracy tends to be better in west side than in east side forests in Pacific Coast States.
  • GNN data are expected to be reliable at the level of fifth- and sixth-field hydrologic units, although independent data for accuracy assessment is lacking at the small landscape scale.

Impacts/Applications:

GNN maps are useful when information on a wide range of forest attributes is needed for analysis at intermediate (i.e., landscape to ecoregional) scales. GNN vegetation/land cover maps and models for identified regions within the Pacific Coast States are available online ("http://www.fsl.orst.edu/lemma/gnnpac/").

WWETAC Project ID:  FY05JB5