Project Title: Using lidar to improve the reliability of GNN vegetation and fuels maps for forest management and risk assessment
Principal Investigator: Janet Ohmann, US Forest Service, PNW Research Station
Collaborators: Harold Zald, Oregon State University; Robert McGaughey, PNW Research Station; Heather Roberts, Oregon State University; Mike Simpson, Central Oregon Ecology & Forest Health Protection Programs, Deschutes National Forest.
Key Issues/Problem Addressed:
Ideally, vegetation and fuels information to assist fire and fuel managers should: 1) integrate data on fuels, species composition, and forest structure in a common format, 2) be spatially explicit (i.e. mapped), 3) accurately portray fuels and vegetation with a small enough grain size to be useful for tactical (i.e. stand-level) decision making, 4) at the same time cover a large landscape or region to be useful for more strategic planning and decision making, and 5) provide information about the type, magnitude, frequency, and location of errors in each dataset. Unfortunately, information with all of these characteristics does not currently exist. The GNN vegetation maps that currently are available for most of the Pacific Northwest are based on Landsat satellite imagery and thus are appropriate for "mid-scale" applications in forest planning and policy, but are not considered sufficiently reliable for use in stand-level management.
Setting and Approach:
We are mapping vegetation and fuels over the lidar acquisition area (about 1.2 million acres) of interior dry forests of the eastern Cascades of Oregon using GNN. The GNN method uses multivariate direct gradient analysis to quantify relations among FIA plot data, satellite imagery, and mapped environmental variables. Individual pixels in resulting GNN maps are associated with plots that have the most similar spectral and environmental characteristics. A suite of detailed plot variables is imputed to each pixel, allowing simultaneous and consistent predicting of a wide range of vegetation attributes. Because the plot-level variables are attached to the GIS database, a wide array of summary variables and classifications can be portrayed to meet different objectives. This study will incorporate rasters describing the forest canopy (overall height, variability in height, and cover) derived from lidar point clouds, which will be developed by Bob McGaughey. We will quantify the improvement in map accuracy achieved through the addition of lidar data (compared to models which use Landsat, climate, and other spatial predictors).
Progress to Date:
We had anticipated that including lidar and precise FIA plot locations would greatly improve the accuracy and utility of GNN maps for local forest management for a variety of business needs. We still believe this is possible but with a couple caveats. First, we expect the addition of lidar and precise plot locations will improve accuracy and utility of GNN vegetation structure maps, but not necessarily GNN species composition maps. Second, we believe the GNN maps based on plot-level field data will be a large improvement over existing GNN products, but the finer spatial grain of sub-plot level data may not improve GNN maps. Improved GNN maps will provide resource managers within the study area unparalleled regional data, synthesizing fuels and vegetation information in one consistent data format to inform both tactical and regional fuels management. The strength of previous GNN products for regional assessments has been tempered by their limited applicability to stand-level decision making. If successful, the improved GNN map products will be a significant step towards providing natural resource managers with tactic-level information across large landscape and regions. This study would serve as a proof-of-concept for using lidar to downscale nearest-neighbor maps for local management, and our methods could be applied anywhere lidar data are available.
WWETAC ID: FY11NG97