A Forest Fragmentation Index to Quantify the Rate of Forest Change James D. Hurd, Emily H. Wilson, Daniel L. Civco Center for Land Use Education and Research.

A Forest Fragmentation Index to Quantify the Rate of Forest Change James D. Hurd, Emily H. Wilson, Daniel L. Civco Center for Land Use Education and Research (CLEAR) Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT 06269-4087

OutlineOutline BackgroundBackground ObjectivesObjectives Study Area and DataStudy Area and Data MethodsMethods –Forest Fragmentation Model –State of Forest Fragmentation ResultsResults ConclusionsConclusions ERDAS Imagine GUIERDAS Imagine GUI BackgroundBackground ObjectivesObjectives Study Area and DataStudy Area and Data MethodsMethods –Forest Fragmentation Model –State of Forest Fragmentation ResultsResults ConclusionsConclusions ERDAS Imagine GUIERDAS Imagine GUI

Northeast Applications of Useable Technology In Land planning for Urban Sprawl A NASA Regional Earth Science Applications Center (RESAC) A NASA Regional Earth Science Applications Center (RESAC)

To educate the general public on the value and utility of geospatial technologies, particularly RS information. Our RESAC Mission To make the power of remote sensing technology available, accessible and useable to local land use decision makers as they plan their communities.

NAUTILUS Research Better land cover mapping and change detection Urban growth models and metrics Forest fragmentation models and metrics Improved impervious cover estimates

Research & Education Watersheds A range of land covers and issues Presumpscot SuAsCo Salmon Stonybrook

Salmon River Watershed, CT 140 Sq. Miles Focused watershed for NAUTILUS research Rapid Urbanization 5 out of 7 watershed towns are listed as the fastest growing towns in the State CES program and research already existing in watershed Key component of the lower Connecticut River Watershed State highway connects with major Hartford market

BackgroundBackground Need for effective methods for deriving information on –Land use change –Forest fragmentation –Urban growth –Loss of agricultural lands –Increase in impervious surface area Need for effective methods for deriving information on –Land use change –Forest fragmentation –Urban growth –Loss of agricultural lands –Increase in impervious surface area

ObjectiveObjective To derive a forest fragmentation index that provides a more informational assessment of the state of forest fragmentation within a given area beyond that provided through binary forest/non-forest approaches.

Forest Fragmentation Model Developed by Riitters et al. (2000) to assess global forest fragmentation from 1 km data. Adapted for use on TM derived land cover information. Categorizes forest pixels into 6 types: Interior forest Edge forest Perforated forest Transition forest Patch forest Undetermined forest Developed by Riitters et al. (2000) to assess global forest fragmentation from 1 km data. Adapted for use on TM derived land cover information. Categorizes forest pixels into 6 types: Interior forest Edge forest Perforated forest Transition forest Patch forest Undetermined forest

Forest Fragmentation Model Adapted from Riitters et al., 2000 5 x 5 roving window Non-forest pixels Forest pixels

Forest Fragmentation Model Considering pairs of pixels in cardinal directions, the total number of adjacent pixel pairs in a 5 x 5 window is 40. In this example, 32 pixel pairs contain at least 1 forest pixel, and of those 23 pairs contain 2 forest pixels. 5 x 5 roving window Non-forest pixels Forest pixels Adapted from Riitters et al., 2000

Forest Fragmentation Model  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0 Adapted from Riitters et al., 2000

Forest Fragmentation Model  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0.  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0. Adapted from Riitters et al., 2000

Forest Fragmentation Model  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0 Adapted from Riitters et al., 2000

Forest Fragmentation Model  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0  Undetermined forest - most of the pixels in the surrounding area are forested, but this center forest pixel could not be classified as a type of fragmentation in the surrounding area. Pf > 0.6 and Pf = Pff  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0  Undetermined forest - most of the pixels in the surrounding area are forested, but this center forest pixel could not be classified as a type of fragmentation in the surrounding area. Pf > 0.6 and Pf = Pff Adapted from Riitters et al., 2000

Forest Fragmentation Model  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0  Undetermined forest - most of the pixels in the surrounding area are forested, but this center forest pixel could not be classified as a type of fragmentation in the surrounding area. Pf > 0.6 and Pf = Pff  Transitional forest - about half of the cells in the surrounding area are forested and the center forest pixel may appear to be part of a patch, edge, or perforation depending on the local forest pattern. 0.4 < Pf < 0.6  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0  Undetermined forest - most of the pixels in the surrounding area are forested, but this center forest pixel could not be classified as a type of fragmentation in the surrounding area. Pf > 0.6 and Pf = Pff  Transitional forest - about half of the cells in the surrounding area are forested and the center forest pixel may appear to be part of a patch, edge, or perforation depending on the local forest pattern. 0.4 < Pf < 0.6 Adapted from Riitters et al., 2000

Forest Fragmentation Model  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0  Undetermined forest - most of the pixels in the surrounding area are forested, but this center forest pixel could not be classified as a type of fragmentation in the surrounding area. Pf > 0.6 and Pf = Pff  Transitional forest - about half of the cells in the surrounding area are forested and the center forest pixel may appear to be part of a patch, edge, or perforation depending on the local forest pattern. 0.4 < Pf < 0.6  Patch forest - pixel is part of a forest patch on a non-forest background, such as a small wooded lot within an urban region. Pf < 0.4  Interior forest - all of the pixels surrounding the center pixel are forest. Pf = 1.0  Perforated forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the inside edge of a forest patch, such as would occur if a small clearing was made within a patch of forest. Pf > 0.6 and Pf - Pff > 0  Edge forest - most of the pixels in the surrounding area are forested, but the center pixel appears to be part of the outside edge of forest, such as would occur along the boundary of a large urban area, or agricultural field. Pf > 0.6 and Pf - Pff < 0  Undetermined forest - most of the pixels in the surrounding area are forested, but this center forest pixel could not be classified as a type of fragmentation in the surrounding area. Pf > 0.6 and Pf = Pff  Transitional forest - about half of the cells in the surrounding area are forested and the center forest pixel may appear to be part of a patch, edge, or perforation depending on the local forest pattern. 0.4 < Pf < 0.6  Patch forest - pixel is part of a forest patch on a non-forest background, such as a small wooded lot within an urban region. Pf < 0.4 Adapted from Riitters et al., 2000

Forest Fragmentation Model Example Calculation 1 Landsat Derived Land Cover White box represents 5X5 pixel area. Identifies center forest pixel. Forest Fragmentation (center forest pixel identified as perforated) INTERIOR FOR. PERFORATED FOR. EDGE FOREST TRANSITIONAL FOR. PATCH FOREST UNDETERMINED FOREST IKONOS Image of Area

Non-water pixels = 25 Forest pixels = 24 Pixel pairs, both forest = 36 Pixel pairs, one forest = 4 Forest Fragmentation Model Example Calculation 1 Forest Fragmentation INTERIOR FOR. PERFORATED FOR. EDGE FOREST TRANSITIONAL FOR. PATCH FOREST UNDETERMINED FOREST Landsat Derived Land Cover Perforated forest = Pf > 0.6 and Pf - Pff > 0 Pf is greater than 0.6 and Pf - Pff (0.06) is greater than 0. Within 5x5 window

Forest Fragmentation Model Example Calculation 2 Landsat Derived Land Cover Forest Fragmentation (center forest pixel identified as edge) INTERIOR FOR. PERFORATED FOR. EDGE FOREST TRANSITIONAL FOR. PATCH FOREST UNDETERMINED FOREST IKONOS Image of Area White box represents 5X5 pixel area. Identifies center forest pixel.

Non-water pixels = 25 Forest pixels = 18 Pixel pairs, both forest = 26 Pixel pairs, one forest = 6 Forest Fragmentation INTERIOR FOR. PERFORATED FOR. EDGE FOREST TRANSITIONAL FOR. PATCH FOREST UNDETERMINED FOREST Landsat Derived Land Cover Edge forest = Pf > 0.6 and Pf - Pff < 0 Pf is greater than 0.6 and Pf - Pff (-0.09) is less than 0. Forest Fragmentation Model Example Calculation 2 Within 5x5 window

Forest Fragmentation Model Example Calculation 3 Landsat Derived Land Cover IKONOS Image of Area White box represents 5X5 pixel area. Identifies center forest pixel. Forest Fragmentation (center forest pixel identified as patch) INTERIOR FOR. PERFORATED FOR. EDGE FOREST TRANSITIONAL FOR. PATCH FOREST UNDETERMINED FOREST

Forest Fragmentation Model Example Calculation 3 Non-water pixels = 25 Forest pixels = 6 Pixel pairs, both forest = 2 Pixel pairs, one forest = 15 Forest Fragmentation INTERIOR FOR. PERFORATED FOR. EDGE FOREST TRANSITIONAL FOR. PATCH FOREST UNDETERMINED FOREST Landsat Derived Land Cover Patch forest = Pf < 0.4 Pf is less than 0.4. Within 5x5 window

Forest Fragmentation Model 30 meter interior forest buffer. Size of window determines width of fragmentation classes (5x5 window = 60 meter width). User option to buffer interior forest to generate 30 meter wide fragmentation classes. Size of window determines width of fragmentation classes (5x5 window = 60 meter width). User option to buffer interior forest to generate 30 meter wide fragmentation classes. Forest Frag. Before Buffer Forest Frag. post-Buffer

Forest Fragmentation Model 1999 Forest Fragmentation post-Buffer 1999 Forest Fragmentation 1999 Land Cover Salmon River Watershed

Forest Fragmentation Model August 9, 1985August 30, 1990August 28, 1995August 31, 1999 URBAN NON-WOODY VEG DECIDUOUS FOR. CONIFEROUS FOR. WATER WETLAND BARREN Land Cover INTERIOR FOR. PERFORATED FOR. EDGE FOREST TRANSITIONAL FOR. PATCH FOREST UNDETERMINED FOREST Fragmentation Landsat TM RED = Band 4 (NIR) GREEN = Band 5 (MIR) BLUE = Band 6 (red)

State of Forest Fragmentation Input information is the result from the forest fragmentation model. Two components: –Proportion of forest area to total area (excluding water). (TFP) –Forest continuity (FC) The product of: –the proportion of weighted area of fragmented forest to total forest area, and… –…the proportion of the largest interior forest patch to total forest area. Input information is the result from the forest fragmentation model. Two components: –Proportion of forest area to total area (excluding water). (TFP) –Forest continuity (FC) The product of: –the proportion of weighted area of fragmented forest to total forest area, and… –…the proportion of the largest interior forest patch to total forest area.

State of Forest Fragmentation determining TFP Interior forest3624 Patch forest 88 Transitional forest 350 Perforated Forest 346 Edge Forest 318 TOTAL FOREST 4,726 CLASS PIXELS Non-water 5,980 Water 94 TOTAL WATERSHED 6,074 CLASS PIXELS 4,726/5,980 = 0.79 Proportion of Forest (non-water) Proportion of Forest (non-water)

State of Forest Fragmentation determining FC, part 1: weighted forest area Interior forest3,624 X 1.0 3,624.0 Patch forest 88 X 0.2 17.6 Transitional forest 350 X 0.5 175.0 Perforated Forest 346 X 0.8 276.8 Edge Forest 318 X 0.8 254.4 TOTAL FOREST 4,7264,347.8 CLASS PIXELS WEIGHT The weighting values are based on the Pf values of Riitters model. 1.0 - 0.8 - 0.5 - 0.2 - (4,347.8 / 4726) = 0.92 Weighted forest area

State of Forest Fragmentation determining FC, part 2: largest interior forest Interior forest3,624 Patch forest 88 Transitional forest 350 Perforated Forest 346 Edge Forest 318 TOTAL FOREST 4,726 CLASS PIXELS Largest Interior Forest Patch 2,443 pixels 2443 / 4726 = 0.52 Largest interior forest patch proportion Largest interior forest patch proportion

Proportion of weighted forest x Proportion of largest interior forest patch = Forest Continuity Forest Continuity = 0.92 * 0.52 = 0.48 State of Forest Fragmentation determining FC

State of Forest Fragmentation Hypothetical Example 1 Interior forest2483 Patch forest 0 Transitional forest 15 Perforated Forest 4 Edge Forest 13 CLASS PIXELS Urban 307 Forest 2515 Barren 1 CLASS PIXELS 0 Proportion of Forest Forest Continuity 1 1 Proportion of Forest = 0.89 Forest Continuity = 0.98

Proportion of Forest = 0.89 Forest Continuity = 0.37 Interior forest2179 Patch forest 14 Transitional forest 202 Perforated Forest 53 Edge Forest 67 CLASS PIXELS Urban 307 Forest 2515 Barren 1 CLASS PIXELS State of Forest Fragmentation Hypothetical Example 2 0 Proportion of Forest Forest Continuity 1 1

State of Forest Fragmentation Based on current literature, forest fragmentation becomes more severe at about 80% forest cover. The potential for improving forest connectivity by connecting adjacent forest patches is greatest between 60% and 80% forest cover. Below 60% forest cover there tends to be more numerous and smaller forest patches. Based on current literature, forest fragmentation becomes more severe at about 80% forest cover. The potential for improving forest connectivity by connecting adjacent forest patches is greatest between 60% and 80% forest cover. Below 60% forest cover there tends to be more numerous and smaller forest patches. With regards to forest proportion:

State of Forest Fragmentation Analyzing only the forest area, forest will range from fully intact with little or no fragmentation to completely fragmented or non-existent forest. With regards to forest continuity:

State of Forest Fragmentation 0 Proportion of Forest Forest Continuity 1 1.8.6.5 High amount of forest, High forest continuity High amount of forest, Low forest continuity Moderate amount of forest, High forest continuity Moderate amount of forest, Low forest continuity Low amount of forest, High forest continuity Low amount of forest, Low forest continuity

State of Forest Fragmentation Real World Example 1 Proportion of Forest = 0.76 Proportion of Forest = 0.76 Forest Continuity = 0.66 Forest Continuity = 0.66 Interior forest2981 Patch forest 56 Transitional forest 174 Perforated Forest 408 Edge Forest 199 CLASS PIXELS Urban 307 Non-woody Veg 595 Forest/Wetland3818 Water 14 Barren 14 CLASS PIXELS 0 Proportion of Forest Forest Continuity 1 1.8.6.5

State of Forest Fragmentation Real World Example 2 Proportion of Forest = 0.79 Proportion of Forest = 0.79 Forest Continuity = 0.46 Forest Continuity = 0.46 0 Proportion of Forest Forest Continuity 1 1.8.6.5 Interior forest3624 Patch forest 88 Transitional forest 350 Perforated Forest 346 Edge Forest 318 CLASS PIXELS Urban 689 Non-woody Veg 546 Forest/Wetland4726 Water 94 Barren 19 CLASS PIXELS

State of Forest Fragmentation Real World Example 3 Proportion of Forest = 0.43 Proportion of Forest = 0.43 Forest Continuity = 0.66 Forest Continuity = 0.66 0 Proportion of Forest Forest Continuity 1 1.8.6.5 Interior forest418 Patch forest 21 Transitional forest 36 Perforated Forest 48 Edge Forest 36 CLASS PIXELS Urban 77 Non-woody Veg 650 Forest/Wetland 568 Water 32 Barren 6 CLASS PIXELS

ResultsResults Town of Marlborough, CT and intersecting watersheds Town of Marlborough, CT and intersecting watersheds 1985 State of Fragmentation High amount of forest, High forest continuity High amount of forest, Low forest continuity Moderate amount of forest, High forest continuity Moderate amount of forest, Low forest continuity Low amount of forest, High forest continuity Low amount of forest, Low forest continuity

Town of Marlborough, CT and 1 kilometer grid regions Town of Marlborough, CT and 1 kilometer grid regions 1985 State of Fragmentation High amount of forest, High forest continuity High amount of forest, Low forest continuity Moderate amount of forest, High forest continuity Moderate amount of forest, Low forest continuity Low amount of forest, High forest continuity Low amount of forest, Low forest continuityResultsResults

ResultsResults Town of Marlborough, CT and 1 kilometer grid regions Town of Marlborough, CT and 1 kilometer grid regions 1990 State of Fragmentation High amount of forest, High forest continuity High amount of forest, Low forest continuity Moderate amount of forest, High forest continuity Moderate amount of forest, Low forest continuity Low amount of forest, High forest continuity Low amount of forest, Low forest continuity

ResultsResults Town of Marlborough, CT and intersecting watersheds Town of Marlborough, CT and intersecting watersheds 1985 Forest Fragmentation CLASS Interior Forest6,481 Water 130 Developed 807 Non-woody Vegetation 930 Patch Forest 62 Transitional Forest 373 Perforated Forest 740 Edge Forest 476 Town Boundary Watershed Boundary HECTARES 1985 Total Forest8,132

ResultsResults Town of Marlborough, CT and intersecting watersheds Town of Marlborough, CT and intersecting watersheds 1990 Forest Fragmentation CLASS Interior Forest6,481 6,027 Water 130 139 Developed 807 997 Non-woody Vegetation 930 979 Patch Forest 62 83 Transitional Forest 373 442 Perforated Forest 740 836 Edge Forest 476 496 Town Boundary Watershed Boundary HECTARES 1985 1990 Total Forest8,132 7,884

CLASS Interior Forest6,027 5,625 Water 139 139 Developed 997 1,111 Non-woody Vegetation 979 1,119 Patch Forest 83 103 Transitional Forest 442 490 Perforated Forest 836 879 Edge Forest 496 533 Town Boundary Watershed Boundary HECTARES 1990 1995 Total Forest7,884 7,630 ResultsResults Town of Marlborough, CT and intersecting watersheds Town of Marlborough, CT and intersecting watersheds 1995 Forest Fragmentation

Town of Marlborough, CT and intersecting watersheds Town of Marlborough, CT and intersecting watersheds 1999 Forest Fragmentation ResultsResults CLASS Interior Forest5,625 5,444 Water 139 139 Developed 1,111 1,191 Non-woody Vegetation 1,119 1,151 Patch Forest 103 113 Transitional Forest 490 519 Perforated Forest 879 892 Edge Forest 533 551 Town Boundary Watershed Boundary HECTARES 1995 1999 Total Forest7,630 7,519

ResultsResults 1985 TFP = 0.81 FC = 0.60 Land Cover Forest Fragmentation Forest Fragmentation Landsat TM Aug. 9, 1985 Landsat TM Aug. 9, 1985

ResultsResults 1985 1990 TFP = 0.76 FC = 0.47 Land Cover Forest Fragmentation Forest Fragmentation Landsat TM Aug. 30, 1990 Landsat TM Aug. 30, 1990

ConclusionsConclusions The results of this research provide several forms of information: 1. An easy to understand map that identifies the state of forest fragmentation for a region. Regions of high forest fragmentation Regions of low forest fragmentation

The results of this research provide several forms of information: 2. A way in which to easily identify regions undergoing land use change. ConclusionsConclusions 19851999

ConclusionsConclusions The results of this research provide several forms of information: 3. A manner in which to quantify the severity of forest fragmentation. 19851999 YEAR TFP FC 1985 0.70 0.57 1990 0.67 0.52 1995 0.63 0.41 1999 0.60 0.32

ConclusionsConclusions The results of this research provide several forms of information: 4. And a way in which to identify the cause and amount of forest fragmentation. 1999

ERDAS Imagine GUI

AcknowledgementAcknowledgement National Aeronautics and Space Administration Grant NAG13-99001/NRA-98-OES-08 RESAC- NAUTILUS, Better Land Use Planning for the Urbanizing Northeast: Creating a Network of Value- Added Geospatial Information, Tools, and Education for Land Use Decision Makers. Northeast Applications of Useable Technology In Land planning for Urban Sprawl

This presentation is available at resac.uconn.edu

A Forest Fragmentation Index to Quantify the Rate of Forest Change James D. Hurd, Emily H. Wilson, Daniel L. Civco Center for Land Use Education and Research (CLEAR) Department of Natural Resources Management & Engineering The University of Connecticut U-4087, Room 308, 1376 Storrs Road Storrs, CT 06269-4087

A Forest Fragmentation Index to Quantify the Rate of Forest Change James D. Hurd, Emily H. Wilson, Daniel L. Civco Center for Land Use Education and Research.

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A Forest Fragmentation Index to Quantify the Rate of Forest Change James D. Hurd, Emily H. Wilson, Daniel L. Civco Center for Land Use Education and Research.

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Presentation on theme: "A Forest Fragmentation Index to Quantify the Rate of Forest Change James D. Hurd, Emily H. Wilson, Daniel L. Civco Center for Land Use Education and Research."— Presentation transcript:

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