Degraded Minnesota Forestland and Carbon Offset Project

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Transcript Degraded Minnesota Forestland and Carbon Offset Project

USING GIS TO TARGET DEGRADED
FORESTLAND FOR UST CARBON
OFFSET PROJECTS
Renee Huset
University of Saint Thomas
Presentation Outline
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Background
Research Question
Study Area
Methodology
Preliminary Findings
Further Analysis
Sources
Acknowledgements
BACKGROUND
Why worry about Carbon?

Intergovernmental Panel on Climate Change (IPCC) 99% sure
humans are responsible for global climate change
Source: http://www.daviesand.com/Choices/Precautionary_Planning/New_Data/
Global Concentrations of CO2
Basics of Carbon Offsetting
Carbon Offsetting
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Make a mess, Clean it up
Offset Carbon we add by subtracting it
elsewhere
Going Carbon Neutral

Carbon Offset Projects:
 Energy
Efficiency
 Fund Renewable Energies
 Store Carbon: Afforestation, Reforestation, Peatland
restoration
Green Intentions…
How Coldplay's
green hopes
died in the arid
soil of India
29/04/2006
Why UST?

Presidents’ Climate Commitment
 Must
include “…actions to make climate neutrality and
sustainability a part of the curriculum and other
educational experience for all students”
UST’s Impact
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UST’s Carbon footprint:
 72,273
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metric tonnes
If UST is going Carbon neutral, why not go neutral here in
the state?
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“Hands-on” Carbon neutral
Co-benefits: Larger Islands of habitat for wildlife, better water
quality
Lab for students
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Tangible symbol of commitment
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Project Goal
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Target degraded forestland next to Carbon-dense
areas
Buy and restore enough of this land to offset UST’s
Carbon emissions
RESEARCH QUESTION
Research Questions
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Where is the greatest concentration of
aboveground biomass in Minnesota?
Target potentially suitable private lands
How much land required to take UST Carbon
neutral?
 Dependent
on tree types and corresponding Carbon
sequestration rates
STUDY AREA
Imperviousness
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Unpaved land only
State Protection Levels
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Level of Protection
GAP Data from MN DNR
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Land ownership
Lakes and Rivers
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Includes wetlands
GAP Stewardship
Data
Study Area Detail
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Potentially suitable
land
 Unpaved
 Unprotected
 No Lakes or
Rivers
Study Area Model
METHODOLOGY
Processes
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Polygon to Raster
Raster Calculator
Neighborhood Statistics
Polygon to Raster Conversion
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Changed polygon datasets to raster data sets for
further processing
Example: Converted vector data with private lands
to combine it with imperviousness for a final study
area
Raster Calculator
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Add and multiply data within and between data
sets
Example: Raster calculator used to find exact
Carbon figures from total biomass:
 [(Digital
 900
Number/10)*900)/2] = Carbon in kg/m2
= 30 m. grid cell*30 m. grid cell
 Divided by two (2) because Carbon is roughly half of total
aboveground biomass
Neighborhood Statistics
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Creates new grid with SUM of Carbon in
surrounding area
More gradual increases in concentrations with
larger analysis windows
 The
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larger the window, the more generalized the data
13x13 Window:
 30
meter grid cell resolution
 169 grid cells
 152,100 meters2
PRELIMINARY FINDINGS
Findings
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Two parts:
 Densest
Carbon/ Deepest forests
 Southern
 Offsetting
 Potential
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Itasca County
UST’s Carbon footprint
locations:
Northeastern Washington County
Southeastern Chisago County
Deepest Forest
Southern Itasca County
Itasca County
13x13 Window
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1,397 tons Carbon
= Window SUM
Window Size:
152,100 m² =
0.1521 km²
Landscape-scale
Carbon trends
Itasca County Neutral
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UST generates 72,273 tons of Carbon per year
Restoring 7.84 km2 of degraded, high
quality forestland would eventually offset 1
year of UST Carbon emissions
UST Carbon Neutral
Southeastern Chisago County
Northeastern Washington County
Washington County
Northern Washington County
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1,086 tons Carbon =
Window SUM
Washington County: Google Earth
Chisago County
Chisago County
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1,242 tons Carbon =
Window SUM
Chisago County: Google Earth
Possible Locations
Near State Forests
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Large concentrations of Carbon in proximity to
existing state forests
Extend state forest habitats
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Analysis:
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5
kilometers around state forests
Southern Itasca County
FURTHER ANALYSIS
Spatial Filter
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Displays high variability of Carbon levels
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High and low edges of Carbon sinks
Why use it?
 Shows
high Carbon concentrations next to very low
concentrations
 Clear-cuts
 Where
 More
Carbon will likely return in high numbers
accurately pinpoint locations optimal for
rehabilitation
Washington County
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Carbon variability
relative to edges
 Red:
Less Carbon
 Blue: More Carbon
Standard Deviation
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Much like Spatial Filter
Not high Carbon levels, but differences in Carbon
levels
 Relative
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variation
Example: Used oftentimes for steepness; not
elevation, but relative elevation differences
Final Output
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Locate areas on edges of Carbon sinks that can be
rehabilitated to offset UST’s Carbon footprint
 Possibly
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near State Forests or State Parks
Dependent upon further analysis of land values and
tree types
DATA SOURCES AND
REFERENCES
Data Sources
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Cities: Municipal Boundaries, Minnesota Department of Transportation
(Mn/DOT), 01/01/2001, http://rocky.dot.state.mn.us/BaseMap .
City Streets: City Streets, Minnesota Department of Transportation, Survey
and Mapping, 01/01/2001 , http://deli.dnr.state.mn.us/ .
County Boundary: Minnesota County Boundaries, Minnesota DNR Minerals Division/Section of Wildlife, http://deli.dnr.state.mn.us/.
GAP_Stewardship: GAP Stewardship 2008 - All Ownership Types,
Minnesota DNR - Division of Fish & Wildlife - Wildlife Unit, 1976 to 2007,
http://deli.dnr.state.mn.us/.
Imperviousness: National Land Cover Database Zone 41 Imperviousness
Layer, U.S. Geological Survey, 2003, <http://www.mrlc.gov>.
Land Cover: National Land Cover Database Zone 41 Land Cover Layer,
U.S. Geological Survey, 2003, <http://www.mrlc.gov>.
Data Sources (continued)
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Major Roads: Major Roads, Minnesota Department of Transportation,
Survey and Mapping, 01/01/2001 , http://deli.dnr.state.mn.us/ .
Populated Places: Populated Places, DNR-MIS,
http://deli.dnr.state.mn.us/.
State Forests: State Forest Boundaries, DNR Forestry - Forest Resource
Assessment, 2005, http://deli.dnr.state.mn.us/ .
State Outline: Minnesota State Boundary, Minnesota DNR - MIS Bureau,
2007. http://deli.dnr.state.mn.us/ .
The National Biomass and Carbon Dataset for the year 2000 (NBCD
2000): Kellndorfer, J., Walker, W., Kirsch, K., Fiske, G., Bishop, J., LaPoint,
L., Hoppus, M., and Westfall, J. 2007-2009. The National Biomass and
Carbon Dataset 2000 (NBCD 2000). The Woods Hole Research Center,
Falmouth, MA.
References
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Davies & Company, Forest Management Resources, “Climate
Change: New Antarctic Ice Core Data,” May 30, 2000,
http://www.daviesand.com/Choices/Precautionary_Planning/
New_Data/.
Google Earth Images
Lennon, Megan J. and Edward A. Nater. “Biophysical Aspects
of Terrestrial Carbon Sequestration in Minnesota.” University
of Minnesota, Minnesota Terrestrial Carbon Sequestration
Project. 2006. http://wrc.umn.edu/outreach/carbon/.
ACKNOWLEDGEMENTS
Many Thanks
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Dr. Paul Lorah, Faculty Research mentor
Mr. Bob Douglas, UST Sustainability Committee