Presentation is loading. Please wait.

Presentation is loading. Please wait.

Meeting Forest Carbon Planning Needs with Forest Service Data and Satellite Imagery Sean Healey, Gretchen Moisen RMRS Inventory, Monitoring, and Analysis.

Similar presentations


Presentation on theme: "Meeting Forest Carbon Planning Needs with Forest Service Data and Satellite Imagery Sean Healey, Gretchen Moisen RMRS Inventory, Monitoring, and Analysis."— Presentation transcript:

1 Meeting Forest Carbon Planning Needs with Forest Service Data and Satellite Imagery Sean Healey, Gretchen Moisen RMRS Inventory, Monitoring, and Analysis Program Greg Jones, Dan Loeffler RMRS Human Dimensions Program Shawn Urbanski RMRS Fire, Fuel, and Smoke Program Todd Morgan U. MT Bureau of Business and Economic Research Jim Morrison, Barry Bollenbacher, Renate Bush, Keith Stockman National Forest System, Region 1

2 Montana Idaho The Forest Carbon Management Framework (ForCaMF) has been piloted in Ravalli County, MT, and is currently being applied across the NFS Northern region

3 Managers and planners need comprehensive information about carbon stocks and flows Big Picture: How much carbon is the landscape storing or emitting? What are the immediate and long-term effects of natural disturbance on carbon storage? How does carbon accumulation in undisturbed parts of the landscape compare with disturbance losses? What is the magnitude of harvest effects vs. “natural processes”?

4 The Forest Service maintains a stand dynamics tool (Forest Vegetation Simulator - FVS) that is used in ForCaMF to govern carbon accumulation and emission across the landscape.

5 Mid-1980s imagery is used spatially represent FIA estimates from the same era. The landscape matches FIA in the following ways: Area of forest Area of forest by forest type Mean volume Distribution of volume (right number of low-, medium-, and high-volume pixels) Ravalli County (MT) forest volume, km Estimation of ecosystem flux Starting Point

6 Non-forest/ Background Fir, Spruce Cut No disturbance Burn Doug Fir Lodgepole Ponderosa Non- forest Greyscale: low to high 1985 Forest Volume Forest TypeDisturbance Estimation of ecosystem flux Starting Point Spatial representations of reference data are prepared using satellite imagery

7 1985 Forest Volume Forest Type Disturbance 1985 Carbon 1987 Carbon 1989 Carbon 1985 Carbon FVS-derived carbon dynamics are applied according to the spatial inputs to create the best available spatial representation of carbon sequestration over time However, we know that there is uncertainty involved with each of the inputs …

8 Forest Type Forest Volume Starting-Point Forest Condition Maps FVS Carbon Simulations Lookup tables linking the starting landscape variables and disturbance history of each SU with appropriate FVS carbon simulations % Cover Loss Volume harvested Spatial disturbance data 10-ha Simulation Units (SU) are developed representing homogeneous groupings of pixels with identical combinations of starting conditions and disturbance parameters Spatial inputs of each SU are altered probabilistically to represent their random error and potential bias Plot-Level Model Calibration Population- Level Model Constraint Plot-Level Basis for Simulation ENDPOINT: Probability Density Function of Stock or Flux of interest Probabilistic Treatment of Spatial Inputs (PTSI) Disturbance Type + Stocks and fluxes estimated within and summed across Simulation Units

9 Probability Density Function (PDF) Used in ForCaMF to describe and simulate uncertainty of inputs due to random error and bias as well as uncertainty Also used to describe ForCaMF outputs Figure from wikipedia.org

10 SourceTypePDF σRationale Starting Volume Bias0.08 of mapped volume Landscape is matched to FIA's estimate of forest volume; σ is taken from the standard error of this estimate Starting Volume Random Error 1611 ft/acre Root mean square error of independent test set Forest Type Bias from 0.12 to 0.25, depending upon type Landscape is matched to FIA's estimates of area by forest type; σ is taken from the standard errors of these estimates Forest Type Random Error PDF not used - 30% chance of error, assumed to be distributed evenly among types Error structure drawn from error matrix of independent test set Area Disturbed by Year Bias0.15 of mapped area Conservative estimate drawn from literature involving similar products % Cover Loss due to Fire Random Error 26% loss Taken from predicted vs. observed pair-wise cover differences from the independent test set % Volume Removal due to Harvest Random Error 26% removal Arbitrarily set to uncertainty associated with cover loss FVS Link Function Random Error 0.27 of carbon stocks projected via FVS lookup table Represents the average variation in carbon stocks among simulations binned within each cell of the lookup table Uncertainty built into the simulations is estimated from the best available sources, including FIA

11 Inputs, such as disturbance history, may be changed to derive estimates for alternative scenarios Bars represent standard deviation of 2000 simulations

12 Bars indicate standard deviation of 2000 simulations 1.9 million (±.4 million) Average Annual Fossil Fuel Emissions Unlike standard FIA carbon stock estimates, we can isolate individual processes contributing to overall carbon flux

13 We see that the net effect of fire on carbon stores actually increases for decades after the fire Estimated stand carbon in forest population affected by fire in the year 2000 in Ravalli County, MT Tonnes Carbon

14 Preliminary programming has occurred to embed PTSI in a decision support tool for the NFS Northern Region

15 Time Landscape Carbon Exchange (tonnes C) Sequestration Emission Framework Growth – undisturbed forests Growth – recovering forests Combustion emissions Fossil fuel combustion – road building Fossil fuel combustion – timber haul For each time period, PTSI- based ecosystem flux estimates may be combined with non-ecosystem flux estimates Net of all considered factors

16 Time Landscape Carbon Exchange (tonnes C) Sequestration Emission The basic function of the system is to monitor (with uncertainty estimates) forest carbon flux over time. Alternative scenarios will be discussed later.

17 From: Healey and others, Carbon Balance and Management 4:9. Haul distances can be translated to fossil fuel emissions associated with timber transport Transport emissions for Ravalli County timber

18 Source: Loeffler, Jones, Vonessen, Healey, Chung Estimating Diesel Fuel Consumption and Carbon Dioxide Emissions from Forest Road Construction. In: Forest Inventory and Analysis (FIA) Symposium; October 21-23, 2008; Park City, UT. Proc. RMRS-P- 56CD. We can also estimate carbon emissions related to forest road-building over time

19 Using dynamics in the forest product life cycle literature with harvest records, we can track emissions from historically harvested timber More on this method: Healey et al., 2008:

20 Time Landscape Carbon Exchange (tonnes C) Sequestration Emission Flux Diagnosis Growth – undisturbed forests Growth – recovering forests Combustion emissions Fossil fuel combustion – road building Fossil fuel combustion – timber haul Net of all considered factors

21 Time Landscape Carbon Exchange (tonnes C) Sequestration Emission

22 Time Landscape Carbon Exchange (tonnes C) Alternative disturbance scenarios drive different flux trends

23 Summary Probabilistic treatment of spatial inputs (PTSI) allows us to link satellite and inventory data with FVS to understand landscape carbon dynamics and associated uncertainties We can combine ecosystem and non- ecosystem fluxes to comprehensively track effects of disturbance and management on forest carbon storage, using both observed and hypothetical scenarios

24 Questions? Sean Healey


Download ppt "Meeting Forest Carbon Planning Needs with Forest Service Data and Satellite Imagery Sean Healey, Gretchen Moisen RMRS Inventory, Monitoring, and Analysis."

Similar presentations


Ads by Google