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Meeting Forest Carbon Planning Needs with Forest Service Data and Satellite Imagery. Sean Healey, Gretchen Moisen RMRS Inventory, Monitoring, and Analysis Program. Todd Morgan U. MT Bureau of Business and Economic Research. Greg Jones, Dan Loeffler RMRS Human Dimensions Program.

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Presentation Transcript
slide1

Meeting Forest Carbon Planning Needs with Forest Service Data and Satellite Imagery

Sean Healey, Gretchen Moisen

RMRS Inventory, Monitoring, and Analysis Program

Todd Morgan

U. MT Bureau of Business and Economic Research

Greg Jones, Dan Loeffler

RMRS Human Dimensions Program

Shawn Urbanski

RMRS Fire, Fuel, and Smoke Program

Jim Morrison, Barry Bollenbacher, Renate Bush, Keith Stockman

National Forest System, Region 1

slide2

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

managers and planners need comprehensive information about carbon stocks and flows
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”?
slide4

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.

slide5

Estimation of ecosystem flux Starting Point

Ravalli County (MT) forest volume, 1985

  • 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)

50 km

slide6

Estimation of ecosystem flux Starting Point

  • Spatial representations of reference data are prepared using satellite imagery

Forest Type

Disturbance

1985 Forest Volume

Fir, Spruce

Lodgepole

No disturbance

Burn

Greyscale: low to high

Non-forest

Non-forest/

Background

Cut

Doug Fir

Ponderosa

slide7

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

1985 Forest Volume

1987 Carbon

Forest Type

1989 Carbon

1985 Carbon

Disturbance

However, we know that there is uncertainty involved with each of the inputs …

1985 Carbon

slide8

Plot-Level Model Calibration

Plot-Level Basis for Simulation

Inventory Data

FVS Carbon Simulations

Forest Type

Population-Level Model Constraint

Forest Volume

Probabilistic Treatment of Spatial Inputs (PTSI)

Starting-Point Forest Condition Maps

Lookup tables linking the starting landscape variables and disturbance history of each SU with appropriate FVS carbon simulations

Spatial inputs of each SU are altered probabilistically to represent their random error and potential bias

+

10-ha Simulation Units (SU) are developed representing homogeneous groupings of pixels with identical combinations of starting conditions and disturbance parameters

Stocks and fluxes estimated within and summed across Simulation Units

Disturbance Type

% Cover Loss

ENDPOINT: Probability Density Function of Stock or Flux of interest

Volume harvested

Spatial disturbance data

slide9

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

slide11

Inputs, such as disturbance history, may be changed to derive estimates for alternative scenarios

Bars represent standard deviation of 2000 simulations

slide12

Unlike standard FIA carbon stock estimates, we can isolate individual processes contributing to overall carbon flux

1.9 million (±.4 million)

Average Annual Fossil Fuel Emissions

Bars indicate standard deviation of 2000 simulations

slide13

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

slide14

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

slide15

Framework

For each time period, PTSI-based ecosystem flux estimates may be combined with non-ecosystem flux estimates

Sequestration

Growth – undisturbed forests

Growth – recovering forests

Landscape Carbon Exchange (tonnes C)

Fossil fuel combustion – road building

Time

Fossil fuel combustion – timber haul

Combustion emissions

Net of all considered factors

Emission

slide16

The basic function of the system is to monitor (with uncertainty estimates) forest carbon flux over time. Alternative scenarios will be discussed later.

Sequestration

Landscape Carbon Exchange (tonnes C)

Time

Emission

slide17

Haul distances can be translated to fossil fuel emissions associated with timber transport

Transport emissions for Ravalli County timber

From: Healey and others, Carbon Balance and Management 4:9.

slide18

We can also estimate carbon emissions related to forest road-building over time

Source: Loeffler, Jones, Vonessen, Healey, Chung. 2008. 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.

slide19

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: http://www.treesearch.fs.fed.us/pubs/33355
slide20

Flux Diagnosis

Sequestration

Growth – undisturbed forests

Growth – recovering forests

Landscape Carbon Exchange (tonnes C)

Fossil fuel combustion – road building

Time

Fossil fuel combustion – timber haul

Combustion emissions

Net of all considered factors

Emission

slide21

Sequestration

Landscape Carbon Exchange (tonnes C)

Time

Emission

slide22

Alternative disturbance scenarios drive different flux trends

Landscape Carbon Exchange (tonnes C)

Time

summary
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
slide24

Questions?

Sean Healey

seanhealey@fs.fed.us, 801-625-5770