GEOG591 Final Project
By: Sophie Wilderotter, Tyler
Tran, Casey Stern, Sarah
Rhodes and Kaitlin Finan

INTRODUCTION
• Negative Impacts of Urbanization and Impervious Cover:
 Physical impacts: Increased velocity /volume of runoff, Flooding, Erosion of Stream Banks, Decline in Infiltration and Groundwater Recharge,
Degradation of Riparian Zones
 Ecological Impacts: Contamination of water bodies by non-point source pollution can cause stress for aquatic organisms such as: hypoxia by nutrients (N &P), pathogen contamination of shellfish beds, physiological effects by toxic chemicals
(Arnold and Gibbons, 1996)

A STORM-WATER MANAGEMENT TOOL: REFORESTATION
• Increased evapotranspiration
• Reduced runoff
• Potential tool for Carbon sequestration

STUDY OBJECTIVES
(1) Quantify and characterize the tree cover, including the life form, extent of trees and LAI, in Chapel Hill and Carrboro, NC
(2) Determine suitable regions for reforestation in Chapel Hill and
Carrboro alongside roadways and sidewalks and areas of low LAI
(3) Estimate the water balance change that would result from increasing tree cover in this region.

FLOW CHART
Current Tree Extent
(Sophie, Casey, Kaitlin)
•LAI
•Tree Type
Potential for
Reforestation
(Sarah, Tyler)
•Land Cover
•Buffer
•Interception
Water Balance Change
(Everyone)
•Evapotranspiration
•50 vs 100% of potential

STUDY AREA
• Municipalities of Chapel Hill and Carrboro combined
• 77.2 square kilometers
• Created a shape file of the outline of our study area

METHODS: LAI AND TREE EXTENT
 LAI from EVI and Dr. Song, Forests from NLCD
 Raster Calculator
• BL_LAI = (NLCD==41)*LAI
• CN_LAI = (NLCD==42)*LAI
• MX_LAI = (NLCD==43)*LAI
 Weighted Average LAI by tree type in Excel
 Reclassification to find extent of Trees

TREE COVER EXTENT BY LAI
Average LAI Results:
 Conifer: 6.06
 Deciduous: 8.32
 Mixed: 6.64

RESULTS: TREE COVER EXTENT BY TREE TYPE
 Conifer=
6.3 km
2
, 8.2%
 Deciduous=
11.9 km
2
, 15.4%
 Mixed=
1.3 km
2
, 1.7%

METHODS: REFORESTATION POTENTIAL
 Masked South Orange Classification with Chapel
Hill/Carrboro Boundaries
 Created 2m buffer around impervious and found area of grass, barren within buffer
 Calculated areas using pixel count (1m resolution)
 Calculated impervious that would be covered by canopy:
 Assumed stems would be planted 1m from edge of impervious
 Used average crown areas of broadleaf and loblolly to calculate area of impervious covered
 Total area to reforest = area of grass, barren + area of impervious covered by canopy

RESULTS: POTENTIAL REFORESTATION SITES
•Suitable Sites?
o Grass/barren land o Parking lots o Alongside streams: Buffers o Alongside impervious: sidewalks and roads
Cover Type
Trees
Water
Impervious
Buildings
Grass
Barren
Area (m 2 )
43,454,039
533,369
10,778,014
4,874,686
7,743,444
284,467

RESULTS: POTENTIAL FOR REFORESTATION
ALONGSIDE IMPERVIOUS SURFACES
 Area converted from impervious surface to canopy cover :
 Broadleaf species: 3,858,701 m 2
 Loblolly pine: 2,875,216 m 2
 Total reforestation area across Chapel Hill and Carrboro, assuming 100% successful reforestation:
 Broadleaf: 6.008 km 2
 Loblolly: 5.020 km 2 for loblolly pines, assuming 100% successful reforestation

METHODS: WATER BALANCE CHANGE
 Calculated perimeter [L] of impervious area where buffer was grass or barren
 Divided area of grass, barren [L 2 ] by 2 (since each pixel is 1 m 2 )
 Found average evapotranspiration rates for loblolly and broadleaf
 Loblolly = 0.7360 meters per year, broadleaf = 0.6045 meters per year
 Calculated water balance change [L 3 T -1 ] through evapotranspiration
 Multiplied area of reforestation [L 2 ] by ET rate [LT -1 ]
 Calculated for both loblolly and broadleaf and both 50% and 100% reforestation
 Calculated percent increase of ET from current ET rates
 Solved system of equations using average LAI to determine makeup (in terms of
% conifer and % deciduous) and ET rate of mixed forests
 Mixed forest ET rate = 0.7023 meters per year

RESULTS:
THE EFFECT OF REFORESTATION ON WATER BALANCE
Table 1. Current evapotranspiration rates and areas for conifer, broadleaf, and mixed forests .
Tree Type Area (km 2 ) ET Rate (mm year -1 ) Current ET for total area (m 3 year -1 )
Conifer
Broadleaf
Mixed
Total
6.3
1.3
11.9
736
604.5
702.3
4,636,800
785,900
8,357,900
13,780,600
Table 2. Water balance changes for half and complete reforestation in potential areas.
% Reforestation
50
100
Conifer ET
(m 3 year -1 )
1849166.8
3698333.5
Broadleaf ET (m 3 year -1 )
1816037.1
3629070.4

RESULTS:
BROADLEAF VS. LOBLOLLY EVAPOTRANSPIRATION
Table 3. Percent ET increase over current ET for each reforestation scenario
.
Scenario
100% of potential reforested with conifer
100% of potential reforested with broadleaf
% Increase over current ET
126.8
126.3
113.4
50% of potential reforested with conifer
50% of potential reforested with broadleaf
113.2

LIMITATION: IKONOS VS. LANDSAT 7
Trade-off:
Spatial resolution or
Spectral resolution
LAI calculated using
Landsat image

CONIFER VS. BROADLEAF

CONIFER VS. BROADLEAF
Higher LAI values
Offer greater interception, less runoff
Spring and summer months
Reforest in areas where LAI is low

CONIFER VS. BROADLEAF
Higher LAI values
Offer greater interception, less runoff
Spring and summer months
Reforest in areas where LAI is low

FUTURE DIRECTIONS
Incorporate successful aspects of:
• NYC Million tree project
• Greening of Detroit

REFERENCES: LITERATURE
Arnold, C. L., and C. J. Gibbons. 1996. Impervious Surface Coverage: The Emergence of a Key Environmental Indicator. Jounral of the
American Planning Association 62(2):243-5.
Buttle, J. M. 1994. Hydrological Response to Reforestation in the Ganaraska River Basin, Southern Ontario. The Canadian
Geographer 38(3):240-53.
Harms, W. R., D. S. DeBell, and C. D. Whitesell. 1994. Stand and tree characteristics in Pinus taeda plantations in Hawaii and South
Carolina. Canadian Journal of Forest Research 24:511-521.
Kim, S. 2007. Individual tree species identification using LIDAR-derived crown structures and intensity data. Ph.D. dissertation,
University of Washington, College of Forest Resources, 137 pages.
McNulty, S. G., J. M. Vose, and W. T. Swank. 1996. Loblolly pine hydrology and productivity across the southern United States. Forest
Ecology and Management, 86:241-251.
Million Trees NYC. [Internet]. 2012. New York City: Million Trees NYC. [cited 2012 04. 29] Available from: http://www.milliontreesnyc.org/
Oishi, A. C., R. Oren, and P. C. Stoy. 2008. Estimating components of forest evapotranspiration: A footprint approach for scaling sap flux measurements. Agricultural and Forest Meteorology, 148 (11):1719-1732.
Ray, D. K., J. M. Duckles, and B. C. Pijanowski. 2010. The Impact of Future Land Use Scenarios on Runoff Volumes in the Muskegon
River Watershed. Environmental Management 46:351-66.
The Greening of Detroit. [Internet]. 2012. Detroit, MI: The Greening of Detroit. [cited 2012 Apr 29] Available from: http://greeningofdetroit.com/contact-us/
US Geological Survey. 2011 Oct 27. Earth Resources Observation and Science (EROS) Center, Dept. of the Interior. Retrieved from: http://www.mrlc.gov/nlcd06_leg.php
Woodland Owner Notes: Thinning Pine Stands. [Internet]. 2012. North Carolina: NC State University and NC A&T State University:
North Carolina Cooperative Extension. [cited 2012 04. 29] Available from: http://www.ces.ncsu.edu/forestry/pdf/WON/won13.pdf.
Peduzzi, A., R.Wynne, T. Fox, R. Nelson, and V. Thomas. 2012. Estimating Leaf Area Index in Intensively Managed Pine Plantations
Using Airborne Laser Scanner Data. Forest Ecology and Management 270:54-65.

REFERENCES: IMAGES http://www.chapel-hill-north-carolina.com/demographics/ http://www.unc.edu/community/fire_station.html
http://www.regent.qc.ca/products/scanopy/winscanopymorefeatures.html
http://26.media.tumblr.com/tumblr_l7lcjyuJiX1qdstgoo1_500.jpg: Algal blooms http://media.treehugger.com/assets/images/2011/10/peru-national-reforestationcampaign.jpg: planting woman