You are hereIntegrated, observation-based carbon monitoring for wooded ecosystems
Integrated, observation-based carbon monitoring for wooded ecosystems
Overall goal: Address carbon-relevant science questions by developing an empirical approach to monitor carbon stocks and fluxes in woodlands of Washington, Oregon, and California.
Approach: In a statistical framework known as GNN (Gradient Nearest Neighbor modeling), we create maps of forest condition by combining time-series Landsat Thematic Mapper (TM) data with plot data from the Forest Inventory and Analysis (FIA) program. We will then use high-quality estimates of biomass mapped at fine scales using laser imaging (lidar) to determine the reliability of our maps.
Presentation for June 19 project update meeting: Presentation file (Large: 60Mb)
Processing status. See how our underlying Landsat processing is going.
Attribution of change: Follow this link to learn more about how we make maps estimating what caused a given kind of forest change.
Science and management questions:
- How much have forest carbon pools or fluxes been affected by natural processes (insects, fire, wind, growth) versus anthropogenic processes (harvest, land-use change)? Are the relative impacts of those processes constant or changing as policy and climate also change?
- How have those processes of change been distributed across forest types, ownerships, management approaches, and policy periods?
- Has forest management intended to reduce susceptibility to insect and fire actually reduced vulnerability of carbon pools to unplanned loss a regional scale?
These science questions will be addressed by analysis of a set of key deliverables. These include annual, 30m resolution maps in Washington, Oregon, and California of:
- Forest aboveground live and dead carbon pools (Mg/ha)
- Changes in those pools from year to year
- Forest disturbance year and magnitude
- Agent of disturbance: clearcuts, partial cuts, insect mortality, fire, urbanization
- Forest regrowth rate and time period
- Forest type and condition
- Uncertainty estimation based on accumulated errors in monitoring as well as comparison with small-footprint lidar maps