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.

Biomass examples:   See a figure showing how maps of biomass over time can provide insight into losses caused by fire and insect. 

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:

  1. 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?
  2. How have those processes of change been distributed across forest types, ownerships, management approaches, and policy periods? 
  3. 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





More details:

Meeting presentation Jan 20, 2011

Study area

Project schematic

Full project text

Panel review