A Forest Fragmentation Index to Quantify the Rate of Forest Change James D. Hurd, Emily H. Wilson,...

A Forest Fragmentation Index to Quantify the Rate of Forest

Change


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 ConnecticutU-4087, Room 308, 1376 Storrs Road

Storrs, CT 06269-4087

OutlineOutlineOutlineOutline

• BackgroundBackground• ObjectivesObjectives• Study Area and DataStudy Area and Data• MethodsMethods

– Forest Fragmentation ModelForest Fragmentation Model– State of Forest FragmentationState of Forest Fragmentation

• ResultsResults• ConclusionsConclusions• ERDAS Imagine GUIERDAS Imagine GUI

• BackgroundBackground• ObjectivesObjectives• Study Area and DataStudy Area and Data• MethodsMethods

– Forest Fragmentation ModelForest Fragmentation Model– State of Forest FragmentationState of Forest Fragmentation

• ResultsResults• ConclusionsConclusions• ERDAS Imagine GUIERDAS Imagine GUI

Northeast Applications of Useable Technology In Land planning for

Urban Sprawl

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.

To educate the general public on the value and utility of geospatial technologies, particularly RS information.

Our RESAC MissionOur 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 ResearchNAUTILUS Research

Better land cover mapping and Better land cover mapping and change detectionchange detection

Better land cover mapping and Better land cover mapping and change detectionchange detection

Urban growth models and Urban growth models and metricsmetrics

Urban growth models and Urban growth models and metricsmetrics

Forest fragmentation Forest fragmentation models and metricsmodels and metrics

Forest fragmentation Forest fragmentation models and metricsmodels and metrics

Improved Improved impervious cover impervious cover

estimatesestimates

Improved Improved impervious cover impervious cover

estimatesestimates

Research & Education Research & Education WatershedsWatersheds

Research & Education Research & Education WatershedsWatersheds

A range of land covers and issues

A range of land covers and issues

PresumpscotPresumpscot

SuAsCoSuAsCo

SalmonSalmon

StonybrookStonybrook

Salmon River Watershed, CTSalmon 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

BackgroundBackgroundBackgroundBackground

• 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

ObjectiveObjectiveObjectiveObjective

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.

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 ModelForest Fragmentation ModelForest Fragmentation ModelForest 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


Adapted from Riitters et al., 2000

64.025

16

pixelsnonwater all

pixelsforest of#Pf

5 x 5 roving window

Non-forest pixels

Forest pixels


64.025

16

pixelsnonwater all

pixelsforest of#Pf

72.032

23

forest is oneleast at direction cardinalin pairs pixel

forest pixelsboth direction cardinalin pairs pixelPff

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.

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


Forest Fragmentation ModelForest Fragmentation ModelForest Fragmentation ModelForest 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


Forest Fragmentation ModelForest Fragmentation ModelForest Fragmentation ModelForest 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.



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


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





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






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 < 0.6Adapted from Riitters et al., 2000






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





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 ModelForest Fragmentation ModelExample Calculation 1Example Calculation 1Forest Fragmentation ModelForest Fragmentation ModelExample Calculation 1Example Calculation 1

Landsat DerivedLand Cover

White box represents 5X5 pixel area.

Identifies center forest pixel.

Forest Fragmentation(center forest pixel identified as perforated)

INTERIOR FOR.PERFORATED FOR.EDGE FORESTTRANSITIONAL FOR.PATCH FORESTUNDETERMINEDFOREST

IKONOS Imageof Area

Non-water pixels = 25Forest pixels = 24Pixel pairs, both forest = 36Pixel pairs, one forest = 4


Forest Fragmentation


Landsat Derived Land Cover

96.025

24Pf 9.0

40

36Pff

Perforated forest = Pf > 0.6 and Pf - Pff > 0Perforated forest = Pf > 0.6 and Pf - Pff > 0

Pf is greater than 0.6 andPf - Pff (0.06) is greater than 0.

Within 5x5 window



Forest Fragmentation(center forest pixel identified as edge)


IKONOS Imageof Area







72.025

18Pf 81.0

32

26Pff

Edge forest = Pf > 0.6 and Pf - Pff < 0Edge forest = Pf > 0.6 and Pf - Pff < 0

Pf is greater than 0.6 andPf - Pff (-0.09) is less than 0.


Within 5x5 window



IKONOS Imageof Area



Forest Fragmentation(center forest pixel identified as patch)







24.025

6Pf

Patch forest = Pf < 0.4 Patch forest = Pf < 0.4

Pf is less than 0.4.

Within 5x5 window


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


1999 ForestFragmentation post-Buffer

1999 ForestFragmentation

1999 Land Cover

Salmon RiverWatershed

Forest Fragmentation ModelForest Fragmentation ModelForest Fragmentation ModelForest Fragmentation ModelAugust 9, 1985 August 30, 1990 August 28, 1995 August 31, 1999

URBANNON-WOODY VEGDECIDUOUS FOR.CONIFEROUS FOR.WATERWETLANDBARREN

Land Cover


Fragmentation

Landsat TM

RED = Band 4 (NIR)GREEN = Band 5 (MIR)BLUE = Band 6 (red)

State of Forest FragmentationState of Forest FragmentationState of Forest FragmentationState 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 FragmentationState of Forest Fragmentationdetermining TFPdetermining TFPState of Forest FragmentationState of Forest Fragmentationdetermining TFPdetermining TFP

Interior forest 3624Patch forest 88Transitional forest 350Perforated Forest 346Edge Forest 318TOTAL FOREST 4,726

CLASS PIXELS

Non-water 5,980Water 94TOTAL WATERSHED 6,074

CLASS PIXELS

4,726/5,980 = 0.794,726/5,980 = 0.79

Proportion of Forest(non-water)

Proportion of Forest(non-water)

State of Forest FragmentationState of Forest Fragmentationdetermining FC, part 1: weighted forest areadetermining FC, part 1: weighted forest areaState of Forest FragmentationState of Forest Fragmentationdetermining FC, part 1: weighted forest areadetermining FC, part 1: weighted forest area

Interior forest 3,624 X 1.0 3,624.0Patch forest 88 X 0.2 17.6Transitional forest 350 X 0.5 175.0Perforated Forest 346 X 0.8 276.8Edge Forest 318 X 0.8 254.4TOTAL FOREST 4,726 4,347.8

CLASS PIXELS WEIGHT

The weighting values are based onthe Pf values of Riitters model.

1.0 -0.8 -

0.5 -

0.2 -

(4,347.8 / 4726)(4,347.8 / 4726)= 0.92= 0.92Weighted forest areaWeighted forest area

State of Forest FragmentationState of Forest Fragmentationdetermining FC, part 2: largest interior forestdetermining FC, part 2: largest interior forestState of Forest FragmentationState of Forest Fragmentationdetermining FC, part 2: largest interior forestdetermining FC, part 2: largest interior forest

Interior forest 3,624Patch forest 88Transitional forest 350Perforated Forest 346Edge Forest 318TOTAL FOREST 4,726

CLASS PIXELS

Largest Interior Forest Patch2,443 pixels

2443 / 4726 = 0.522443 / 4726 = 0.52

Largest interior forestpatch proportion

Largest interior forestpatch proportion

Proportion of weighted forest x Proportion of largest interior forest patch

= Forest Continuity

Forest Continuity = 0.92 * 0.52 = 0.48Forest Continuity = 0.92 * 0.52 = 0.48

State of Forest FragmentationState of Forest Fragmentationdetermining FCdetermining FCState of Forest FragmentationState of Forest Fragmentationdetermining FCdetermining FC

State of Forest FragmentationState of Forest FragmentationHypothetical Example 1Hypothetical Example 1State of Forest FragmentationState of Forest FragmentationHypothetical Example 1Hypothetical Example 1

Interior forest 2483Patch forest 0Transitional forest 15Perforated Forest 4Edge Forest 13

CLASS PIXELS

Urban 307Forest 2515Barren 1

CLASS PIXELS

0Proportion of Forest

For

est

Con

tinui

ty

1

1

Proportion of Forest = 0.89Proportion of Forest = 0.89

Forest Continuity = 0.98Forest Continuity = 0.98

Proportion of Forest = 0.89Proportion of Forest = 0.89

Forest Continuity = 0.37Forest Continuity = 0.37


CLASS PIXELS

Urban 307Forest 2515Barren 1

CLASS PIXELS

State of Forest FragmentationState of Forest FragmentationHypothetical Example 2Hypothetical Example 2State of Forest FragmentationState of Forest FragmentationHypothetical Example 2Hypothetical Example 2


For

est

Con

tinui

ty

1

1


• 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:With regards to forest proportion:


• Analyzing only the forest area, forest will range from fully intact with little or no fragmentation to completely fragmented or non-existent forest.

• 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:With regards to forest continuity:



Fo

rest

Co

nti

nu

ity

1

1.8.6

.5

High amount of forest,High forest continuityHigh amount of forest,Low forest continuityModerate amount of forest,High forest continuityModerate amount of forest,Low forest continuityLow amount of forest,High forest continuityLow amount of forest,Low forest continuity

State of Forest FragmentationState of Forest FragmentationReal World Example 1Real World Example 1State of Forest FragmentationState of Forest FragmentationReal World Example 1Real World Example 1

Proportion of Forest= 0.76


Forest Continuity= 0.66



CLASS PIXELS

Urban 307Non-woody Veg 595Forest/Wetland 3818Water 14Barren 14

CLASS PIXELS


For

est

Con

tinui

ty

1

1.8.6

.5







For

est

Con

tinui

ty

1

1.8.6

.5


CLASS PIXELS


CLASS PIXELS

ResultsResultsResultsResults

Town of Marlborough, CTand intersecting watershedsTown of Marlborough, CT

and intersecting watersheds

1985 State of Fragmentation1985 State of Fragmentation


Town of Marlborough, CTand 1 kilometer grid regionsTown of Marlborough, CT

and 1 kilometer grid regions







1985 Forest Fragmentation1985 Forest Fragmentation

CLASS

Interior Forest 6,481

Water 130Developed 807Non-woody Vegetation 930

Patch Forest 62Transitional Forest 373Perforated Forest 740Edge Forest 476

Town BoundaryWatershed Boundary

HECTARES1985

Total Forest 8,132





CLASS

Interior Forest 6,481 6,027

Water 130 139Developed 807 997Non-woody Vegetation 930 979

Patch Forest 62 83Transitional Forest 373 442Perforated Forest 740 836Edge Forest 476 496


HECTARES1985 1990

Total Forest 8,132 7,884

CLASS


Water 139 139Developed 997 1,111Non-woody Vegetation 979 1,119



HECTARES1990 1995










CLASS


Water 139 139Developed 1,111 1,191Non-woody Vegetation 1,119 1,151



HECTARES1995 1999



State of Forest Fragmentation

0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90

Total Forest Proprtion (excluding water)

Fo

rest

Co

ntin

uity

1985

1990

1995

1999

19851985

TFP = 0.81 FC = 0.60

Land CoverLand Cover

ForestFragmentationForestFragmentation

Landsat TMAug. 9, 1985 Landsat TMAug. 9, 1985



0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90


Fo

rest

Co

ntin

uity

1985

1990

1995

1999

1985 19901990

TFP = 0.76 FC = 0.47






0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90


Fo

rest

Co

ntin

uity

1985

1990

1995

1999

1985 1990 19951995

TFP = 0.72 FC = 0.42






0.30

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

0.55 0.60 0.65 0.70 0.75 0.80 0.85 0.90


Fo

rest

Co

ntin

uity

1985

1990

1995

1999

1985 1990 1995 19991999

TFP = 0.69 FC = 0.38




ConclusionsConclusionsConclusionsConclusions

• 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.1. An easy to understand map that identifies the state of forest fragmentation for a region.

Regions of highforest fragmentation

Regions of lowforest fragmentation



2. A way in which to easily identify regions undergoing land use change.2. A way in which to easily identify regions undergoing land use change.


1985 1999




3. A manner in which to quantify the severity of forest fragmentation.3. A manner in which to quantify the severity of forest fragmentation.

1985 1999

YEAR TFP FC1985 0.70 0.571990 0.67 0.521995 0.63 0.411999 0.60 0.32




4. And a way in which to identify the cause and amount of forest fragmentation.4. And a way in which to identify the cause and amount of forest fragmentation.

1999

ERDAS Imagine GUIERDAS Imagine GUIERDAS Imagine GUIERDAS Imagine GUI

AcknowledgementAcknowledgementAcknowledgementAcknowledgement

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 presentationis available at

resac.uconn.edu


Change


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 ConnecticutU-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,...

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