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USING GIS TO TARGET DEGRADED FORESTLAND FOR UST CARBON OFFSET PROJECTS Renee Huset University of Saint Thomas.

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Presentation on theme: "USING GIS TO TARGET DEGRADED FORESTLAND FOR UST CARBON OFFSET PROJECTS Renee Huset University of Saint Thomas."— Presentation transcript:

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2 USING GIS TO TARGET DEGRADED FORESTLAND FOR UST CARBON OFFSET PROJECTS Renee Huset University of Saint Thomas

3 Presentation Outline  Background  Research Question  Study Area  Methodology  Preliminary Findings  Further Analysis  Sources  Acknowledgements

4 BACKGROUND

5 Why worry about Carbon? Source:  Intergovernmental Panel on Climate Change (IPCC) 99% sure humans are responsible for global climate change

6 Global Concentrations of CO 2

7 Basics of Carbon Offsetting

8 Carbon Offsetting  Make a mess, Clean it up  Offset Carbon we add by subtracting it elsewhere

9 Going Carbon Neutral  Carbon Offset Projects:  Energy Efficiency  Fund Renewable Energies  Store Carbon: Afforestation, Reforestation, Peatland restoration

10 Green Intentions… 29/04/2006 How Coldplay's green hopes died in the arid soil of India

11 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”

12 UST’s Impact  UST’s Carbon footprint:  72,273 metric tonnes  If UST is going Carbon neutral, why not go neutral here in the state?  “Hands-on” Carbon neutral  Co-benefits: Larger Islands of habitat for wildlife, better water quality  Lab for students  Tangible symbol of commitment

13 Project Goal  Target degraded forestland next to Carbon-dense areas  Buy and restore enough of this land to offset UST’s Carbon emissions

14 RESEARCH QUESTION

15 Research Questions  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

16 STUDY AREA

17 Imperviousness  Unpaved land only

18 State Protection Levels  Level of Protection  GAP Data from MN DNR  Land ownership

19  Includes wetlands  GAP Stewardship Data Lakes and Rivers

20  Potentially suitable land  Unpaved  Unprotected  No Lakes or Rivers Study Area Detail

21 Study Area Model

22 METHODOLOGY

23 Processes  Polygon to Raster  Raster Calculator  Neighborhood Statistics

24 Polygon to Raster Conversion  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

25 Raster Calculator  Add and multiply data within and between data sets  Example: Raster calculator used to find exact Carbon figures from total biomass:  [(Digital Number/10)*900)/2] = Carbon in kg/m = 30 m. grid cell*30 m. grid cell Divided by two (2) because Carbon is roughly half of total aboveground biomass

26 Neighborhood Statistics  Creates new grid with SUM of Carbon in surrounding area  More gradual increases in concentrations with larger analysis windows  The larger the window, the more generalized the data  13x13 Window:  30 meter grid cell resolution  169 grid cells  152,100 meters 2

27 PRELIMINARY FINDINGS

28 Findings  Two parts:  Densest Carbon/ Deepest forests Southern Itasca County  Offsetting UST’s Carbon footprint Potential locations: Northeastern Washington County Southeastern Chisago County

29 Deepest Forest Southern Itasca County

30 Itasca County

31 13x13 Window  1,397 tons Carbon = Window SUM  Window Size: 152,100 m² = km²  Landscape-scale Carbon trends

32 Itasca County Neutral  UST generates 72,273 tons of Carbon per year  Restoring 7.84 km 2 of degraded, high quality forestland would eventually offset 1 year of UST Carbon emissions

33 Southeastern Chisago County Northeastern Washington County UST Carbon Neutral

34 Washington County

35 Northern Washington County  1,086 tons Carbon = Window SUM

36 Washington County: Google Earth

37 Chisago County

38  1,242 tons Carbon = Window SUM

39 Chisago County: Google Earth

40 Possible Locations

41 Near State Forests  Large concentrations of Carbon in proximity to existing state forests  Extend state forest habitats  Analysis: 5 kilometers around state forests

42 Southern Itasca County

43 FURTHER ANALYSIS

44 Spatial Filter  Displays high variability of Carbon levels  High and low edges of Carbon sinks

45 Why use it?  Shows high Carbon concentrations next to very low concentrations Clear-cuts Where Carbon will likely return in high numbers  More accurately pinpoint locations optimal for rehabilitation

46 Washington County  Carbon variability relative to edges  Red: Less Carbon  Blue: More Carbon

47 Standard Deviation  Much like Spatial Filter  Not high Carbon levels, but differences in Carbon levels  Relative variation  Example: Used oftentimes for steepness; not elevation, but relative elevation differences

48 Final Output  Locate areas on edges of Carbon sinks that can be rehabilitated to offset UST’s Carbon footprint  Possibly near State Forests or State Parks  Dependent upon further analysis of land values and tree types

49 DATA SOURCES AND REFERENCES

50 Data Sources  Cities: Municipal Boundaries, Minnesota Department of Transportation (Mn/DOT), 01/01/2001,  City Streets: City Streets, Minnesota Department of Transportation, Survey and Mapping, 01/01/2001,  County Boundary: Minnesota County Boundaries, Minnesota DNR - Minerals Division/Section of Wildlife,  GAP_Stewardship: GAP Stewardship All Ownership Types, Minnesota DNR - Division of Fish & Wildlife - Wildlife Unit, 1976 to 2007,  Imperviousness: National Land Cover Database Zone 41 Imperviousness Layer, U.S. Geological Survey, 2003,.  Land Cover: National Land Cover Database Zone 41 Land Cover Layer, U.S. Geological Survey, 2003,.

51 Data Sources (continued)  Major Roads: Major Roads, Minnesota Department of Transportation, Survey and Mapping, 01/01/2001,  Populated Places: Populated Places, DNR-MIS,  State Forests: State Forest Boundaries, DNR Forestry - Forest Resource Assessment, 2005,  State Outline: Minnesota State Boundary, Minnesota DNR - MIS Bureau,  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 The National Biomass and Carbon Dataset 2000 (NBCD 2000). The Woods Hole Research Center, Falmouth, MA.

52 References  Davies & Company, Forest Management Resources, “Climate Change: New Antarctic Ice Core Data,” May 30, 2000, New_Data/. 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

53 ACKNOWLEDGEMENTS

54 Many Thanks  Dr. Paul Lorah, Faculty Research mentor  Mr. Bob Douglas, UST Sustainability Committee


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