Upload
aiko
View
39
Download
1
Tags:
Embed Size (px)
DESCRIPTION
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. - PowerPoint PPT Presentation
Citation preview
Meeting Forest Carbon Planning Needs with Forest Service Data and
Satellite Imagery
Sean Healey, Gretchen MoisenRMRS Inventory, Monitoring, and Analysis Program
Greg Jones, Dan LoefflerRMRS Human Dimensions Program
Shawn UrbanskiRMRS Fire, Fuel, and Smoke Program
Todd MorganU. MT Bureau of Business and Economic Research
Jim Morrison, Barry Bollenbacher, Renate Bush, Keith Stockman
National Forest System, Region 1
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
• 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”?
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.
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, 1985
50 km
Estimation of ecosystem flux Starting Point
Non-forest/Background
Fir, Spruce
Cut
No disturbanceBurn
Doug Fir
Lodgepole
PonderosaNon-forestGreyscale: low to high
1985 Forest Volume Forest Type Disturbance
Estimation of ecosystem flux Starting Point
•Spatial representations of reference data are prepared using satellite imagery
1985 Forest Volume
Forest Type
Disturbance
1985 Carbon
1987 Carbon
1989 Carbon
1985 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 …
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
Inventory Data
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
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
Source Type PDF σ RationaleStarting Volume Bias 0.08 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 typesError structure drawn from error matrix of
independent test set
Area Disturbed by Year Bias 0.15 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
Inputs, such as disturbance history, may be changed to derive estimates for alternative scenarios
Bars represent standard deviation of 2000 simulations
1985-86
1986-88
1988-91
1991-93
1993-95
1995-97
1997-98
1998-99
1999-01
2001-03
2003-05
0
50,000
100,000
150,000
200,000Ravalli County, Montana, Fire Emissions (tonnes C)
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
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
Tonn
es
Carb
on
Preliminary programming has occurred to embed PTSI in a decision support tool for
the NFS Northern Region
Time
Land
scap
e Ca
rbon
Exc
hang
e (t
onne
s 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
Time
Land
scap
e Ca
rbon
Exc
hang
e (t
onne
s 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.
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
Road construction activityCarbon dioxide
emissions (pounds/foot)
Carbon dioxide emissions
(pounds/mile)
Carbon equivalent (pounds/foot)
Carbon equivalent (pounds/mile)
Pioneering 0.31345 1,655 0.08548 451
Clearing and grubbing 1.40834 7,436 0.38409 2,028
Sub-grade excavation with sidecasting
0.81769 4,317 0.22301 1,177
Total of all activities 2.53947 13,408 0.69258 3,657
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.
We can also estimate carbon emissions related to forest road-building over time
0
2000
4000
6000
8000
10000
12000
14000
16000
1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007
Emis
sion
(ton
nes
C)
Cut 1945-1979, Dump
Cut 1945-1979, Landfill
Cut 1980-2007, Landfill
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
Time
Land
scap
e Ca
rbon
Exc
hang
e (t
onne
s 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
Time
Land
scap
e Ca
rbon
Exc
hang
e (t
onne
s C)
Sequestration
Emission
Time
Land
scap
e Ca
rbon
Exc
hang
e (t
onne
s C)
Alternative disturbance scenarios drive different flux trends
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