LINKING PEOPLE, PLACE, AND POLICY:
A GISCIENCE APPROACH
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edited by
Stephen J. Walsh & Kelley A. Crews-Meyer
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CONTENTS
Chapters with Figures on the CD-ROM
CHAPTER 2
Continuous and Discrete: Where They Have Met in Nang
Rong, Thailand
Figure 10. Field maps used by the spatial team for year 2000 survey
-- image composite of aerial photography, cadastral lines,
village point location, and selected image annotation.
Ronald R. Rindfuss, Barbara Entwisle, Stephen J. Walsh, Pramote Prasartkul,
Yothin Sawangdee, Thomas W. Crawford, Julia Reade
Figure 11. Field maps arranged in a 3 x 3 map matrix covering 36
square kilometers and centered on the selected survey village.
Figure 1. Study area location, Nang Rong District, northeast Thailand.
CHAPTER 3
Figure 2. Landsat Thematic Mapper classification of the study area
for 1972/73 and 1997. Changes between the two dates are
evident throughout, but most conspicuous in the upland
southwest where forests have been replaced by field crops,
particularly cassava and to a lesser extent, sugar cane.
Land Use Strategies in the Mara Ecosystem: a Spatial
Analysis Linking Socio-Economic Data with Landscape
Variables
Figure 3. Nang Rong District, Thailand: 2-km radial buffers around
the 1994 survey villages.
Figure 1. Study area location.
Figure 4. Nang Rong District, Thailand: 3-km radial buffers around
the 1994 survey villages.
Figure 5. Unassigned areas in the study area for village territories
defined through a 2-km radial buffer; classified LULC mapped
through 1994/95 Landsat satellite data.
D. Michael Thompson, Suzanne Serneels, Eric F. Lambin
Figure 2. Expansion of mechanized agriculture in Loita plains in
1975 (left) and 1995 (right).
Figure 3. Conceptual model.
Figure 4. Spatial distribution of land use strategies.
Figure 6. Nang Rong District, Thailand: Thiessen polygons around
1994 survey villages; small (A) and large (B) polygons
identified.
CHAPTER 4
Figure 7. Nang Rong District, Thailand: Thiessen polygons around
1994 survey villages and hydrography superimposed.
Karen C. Seto, Robert K. Kaufmann, Curtis E. Woodcock
Figure 8. Fishbone land-ownership pattern characteristic of portions
of Amazonia (A) and the distributed pattern characteristics
of northeast Thailand (B).
Figure 9. Cadastral map coverage of Nang Rong District, Thailand
in 1999.
Monitoring Land Use Change in the Pearl River Delta,
China
Figure 1. Study area, Pearl River Delta, China.
Figure 2. Counties included in the image. The twelve counties
included in the study are: Nanhai, Dongguan, Panyu, Foshan,
Shunde, Shenzhen, Zengcheng, Huadu, Sanshui, Guangzhou,
Baoan, and Jiangmen.
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Figure 3a. Example of land use change in the Pearl River Delta.
The white areas in the western part of the Delta represent
new agricultural land. The white areas in the remainder of
the figure are urban areas, converted from either agriculture
or natural vegetation.
Figure 3b. Land-use change in the Pearl River Delta. Areas in red
represent new agricultural land reclaimed from the Delta.
Yellow regions are new urban areas, converted from agriculture.
New urban areas converted from natural vegetation or water
are shown in green.
CHAPTER 6
Understanding A Dynamic Landscape: Land Use, Land
Cover, and Resource Tenure in Northeastern Cambodia
Jefferson Fox
Figure 1. Study sites in Ratanakiri Province, northeast Cambodia.
Figure 2. Spatial history of Kres village between the early 1950’s
and today.
Figure 4. Agricultural land for ten counties in the Pearl River Delta,
1990.
CHAPTER 7
Figure 5. Agricultural land loss for ten counties in the Pearl River
Delta, 1990-1996.
Robert Walker, Charles H. Wood, David Skole, Walter Chomentowski
CHAPTER 5
Spatial Modeling Of Village Functional Territories to
Support Population-Environment Linkages
Thomas W. Crawford
Figure 1. Nang Rong study area, northeast Thailand.
Figure 2. Nuclear village settlement pattern with a residential core
and agricultural fields along the periphery.
Figure 3. Vertical and horizontal views of stacking grids and, for
each cell, selecting the kth closest village. Each village had
a circular grid that showed shortest-path distances to the
centrally located village point (not shown). Darker red shades
have higher cost distances. For simplicity, only four grids are
shown. Visually, “shadows” appear in the vertical view due
to the stacked nature of the grids.
The Impact of Land Titling on Tropical Forest Resources
Figure 1. Image mosaic of survey study area.
CHAPTER 8
Spatial and Temporal Dynamics of Ownership Parcels
and Forest Cover in Three Counties of Northern Lower
Michigan USA, ca. 1970 to 1990
Scott A. Drzyzga and Daniel G. Brown
Figure 1. The State of Michigan, its regions, the study area, and
large cities mentioned in the text.
Figure 2. Political entities in the study region.
Figure 3. Forest cover data for (a) 1973, (b) 1985, and (c) 1991.
Forested areas shown in green hue or gray tone. Inland
waters shown in black. Buffer omitted from display.
Figure 4. KSP diagram for Village A.
Figure 4. Concentric zones around Traverse City, Michigan.
Figure 5. Fuzzy functional village regions.
Figure 5. Frequency distributions for private parcels. Numbers of
parcels with area values less than 1ha omitted for display.
Omitted values are; 55874 (1970), 89697 (1980), and 94936
(1990).
Figure 6. True village territories based on transect data (Evans
1998) are shown in gray lines – fuzzy functional village regions
are shown in grayscale.
Figure 7. Mean deviation between modeled boundaries and transect
endpoints.
Figure 6. Frequency distributions of forested parcel sizes. Numbers
of lots with area values less than 1ha not calculated.
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Figure 7. APS values for concentric zones by date.
Figure 8. Percent forest values for concentric zones by date.
Figure 9. CF index values for concentric zones by date.
CHAPTER 9
Characterizing and Modeling Patterns of Deforestation
and Agricultural Extensification in the Ecuadorian Amazon
Stephen J. Walsh, Joseph P. Messina, Kelley A. Crews-Meyer,
Richard E. Bilsborrow, William K.Y. Pan
Figure 1. Study area location, northeast Ecuador, the Oriente.
Figure 2. Band 4 Landsat MSS data with the boundary of the
Northern Intensive Study Area (ISA) identified.
CHAPTER 10
Deforestation Trajectories
in a Frontier Region of the Brazilian Amazon
Stephen D. McCracken, Bruce Boucek, Emilio F. Moran
Figure 1. Amazon-wide Arc of Deforestation (Source: Instituto de
Pesquisa Ambiental da Amazônia -IPAM).
Figure 2. Property grid overlay developed for study area, colonization
along the Transamazon Highway, Altamira, Pará.
Figure 3. The observed pace of deforestation among frontier farms
along the Transamazon Highway, km 20-140, 1970-96.
Figures 4a and 4b. Current composition of households arriving after
1985; current composition of households arriving before 1976.
Figure 3. The Ecuadorian Oriente, the Northern ISA classified image
for 1973.
Figure 5. A conceptual model of the domestic life cycle of households,
and expected trajectory of land use and evironmental change
at the farm-household level.
Figure 4. The Ecuadorian Oriente, the Northern ISA classified for
1999.
Figure 6. Map of properties by period forest clearing was initiated
(Farm Cohorts).
Figures 5a and 5b. Changes in pattern metric performance for the
five periods assessed – number of patches and patch density
respectively.
Figure 7. Properties by period forest clearing was initiated (Farm
Cohorts).
Figures 5c and 5d. Changes in pattern metric performance for the
five periods assessed – edge density and mean patch size
respectively.
Figure 8. Mean annual percent of farm area deforested by years of
farm occupation.
Figure 9a. Estimated percent farm area cleared annually from
regression results; various methods.
Figures 5e and 5f. Changes in pattern metric performance for the
five periods assessed – mean nearest neighbor and nearest
neighbor standard deviation.
Figure 9b. Predicted forest remaining based on regression results;
various methods.
Figures 5g and 5h. Changes in pattern metric performance for the
five periods assessed – landscape shape and interspersion
& juxtaposition.
Figure 10. Observed and expected area of remaining forest in the
study area, 1970-96, and predicted for 2010 and 2020; based
on farm level projections.
Figure 6. Structural design of the CA model.
Figure 11. Map of residuals of observed-predicted deforestation in
1996 based on quadratic equation regression line.
Figure 7. Simulated 1996 vs. reference 1996 output.
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CHAPTER 11
Multi-Resolution Classification Framework for Improving
Land Use/Cover Mapping
Figure 5. Newer housing has larger, flat roofs made of concrete or
metal. They are very light toned and spectrally similar to dry
soils or sand and gravel.
DongMei Chen, Douglas Stow, Arthur Getis
Figure 6. RADARSAT scene of Kathmandu, Nepal collected on
November 19,1998. Approximate scale is 1:100,000. The
light toned features, high backscatter, are primarily the urban
features.
Figure 1. Data flow for generating a GIS layer of land use/land
cover using remotely sensed data.
Figure 2. A proposed multi-resolution image analysis and classification
framework.
Figure 3. An example of multi-resolution images acquired from
different sensors for a subarea near Del Mar, San Diego,
USA.
Figure 4. An example of an image pyramid generated from a 1 m
DOQQ subset of single-residential area.
Figure 5. Scheme of a three-level hierarchical wavelet decomposed
analysis.
Figure 6. Three USGS CIR DOQQ subsets with single use/cover
components.
CHAPTER 13
FAO Methodologies for Land Cover Classification and
Mapping
John S. Latham, Changchui He, Luca Alinovi, Antonio DiGregorio, Zdenek
Kalensky
Figure 1. Overview of LCCS Modules and their links.
Figure 2. System flowchart for land cover mapping based on RS
and GIS.
CHAPTER 14
CHAPTER 12
Urban Growth in Kathmandu, Nepal: Mapping, Analysis,
and Prediction
Barry Haack, David Craven, Susan Jampoler, Elizabeth Solomon
Spatial Explicit Land Use Change Scenarios for Policy
Purposes: Some Applications of the CLUE Framework
A. Veldkamp, P.H. Verburg, K. Kok, G.H.J. De Koning, W. Soepboer
Figure 1. Six aggregation levels for China.
Figure 1. Typical Kathmandu Valley view of agricultural fields on
the flat valley floor and terraced slopes during the dry season.
Figure 2. General structure of the CLUE-modeling framework.
Figure 2. Aerial photograph of a portion of the Kathmandu Valley
from 1987. Approximate scale of 1:17,000.
Figure 3. Classified differences between land use in 1996 and 2010
under the Base3% scenario.
Figure 3. July 22 1989 SPOT Image in the red visible band. The
light tones are urban and a few areas of river gravel, the dark
tones are flooded or saturated rice fields in the early stages
of growth or vegetated areas of maize of forest. Approximate
scale of 1:100,000.
Figure 4. Classified differences between land use in 1996 and 2010
under the Optimistic scenario.
Figure 4. Thatch is a common roofing material for older houses in
Kathmandu. These roofs are generally small, sloping, and
spectrally confused with wet soils.
Figure 5. Location of Northern Atlantic Zone (NAZ) in Costa Rica.
Figure 6. Input data and results of Base scenario for four land use
types.
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Figure 7. Distribution of forest with (left) and without (right) protection
of national parks.
Figure 8. Land use patterns in Sibuyan 1997 (A) and 2012 (B).
Figure 9. Differences in allocation pattern between a linear and
logistic demand development. Initial and end demand is
similar, only the pathway differs.
Figure 10. Modeled (y-axis) versus actual (x-axis) cover in hectares
for 1992 of four major kinds of land use, aggregated per
administrative unit.
Figure 11. The role of land use change modeling within studies
aiming at improving land use planning.
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Burma
Laos
Gulf of
Tonkin
Thailand
Nang
Rong
Cambodia
Andaman
Sea
Gulf of
Thailand
Figure 1. Study area location, Nang Rong District,
northeast Thailand.
CONTENTS
0
100 200 300
kilometers
LOCATION: Chapter 2, Figure 1
Malaysia
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Classification derived from
two Landsat MSS satellite
images.
Image acquisition dates:
18 December, 1972
28 February, 1973
High/Medium Density
Forest
Medium/Low Density
Forest
Upland Agriculture
Grass-Shurb Savanna
Other Agriculture
Water
Fallow/Bare Ground
Rice
Classification derived from
two Landsat TM satellite
images.
Image acquisition dates:
11 December, 1997
29 February, 1997
Figure 2. Landsat Thematic Mapper classification of the study area for 1976 and 1997. Changes
between the two dates are evident throughout, but most conspicuous in the upland southwest
where forests have been replaced by field crops, particularly cassava and to a lesser extent,
sugar cane.
CONTENTS
LOCATION: Chapter 2, Figure 2
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N
0
5
10
kilometers
Figure 3. Nang Rong District, Thailand: 2-km radial buffers around the 1994 survey villages.
CONTENTS
LOCATION: Chapter 2, Figure 3
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N
0
5
10
kilometers
Figure 4. Nang Rong District, Thailand: 3-km radial buffers around the 1994 survey villages.
CONTENTS
LOCATION: Chapter 2, Figure 4
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Background
High Density Forest
Low/Medium Density Forest
Grass-Shrub Savanna
Water
Upland Field Crops
Rice
Other Agriculture
Fallow/Bare Ground
Clouds
Fire Scars
N
0
5
10 kilometers
Figure 5. Unassigned areas in the study area for village territories defined through a 2-km radial buffer;
classified LULC mapped through 1994/95 Landsat satellite data.
CONTENTS
LOCATION: Chapter 2, Figure 5
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B
0
N
5
10
kilometers
Figure 6. Nang Rong District, Thailand: Thiessen polygons around 1994 survey villages; small (A) and large (B) polygons identified.
CONTENTS
LOCATION: Chapter 2, Figure 6
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N
0
5
10
kilometers
Figure 7. Nang Rong District, Thailand: Thiessen polygons around 1994 survey villages and hydrography superimposed.
CONTENTS
LOCATION: Chapter 2, Figure 7
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Figure 8. Fishbone land-ownership pattern characteristic of
portions of Amazonia (A) and the distributed pattern
characteristics of northeast Thailand (B).
CONTENTS
LOCATION: Chapter 2, Figure 8
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1-km by 1-km regions possessing cadastral maps
N
0
5
10
kilometers
Figure 9. Cadastral map coverage of Nang Rong District, Thailand in 1999.
CONTENTS
LOCATION: Chapter 2, Figure 9
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Figure 10. Field maps used by the spatial team for year 2000
survey -- image composite of aerial photography, cadastral
lines, village point location, and selected image annotation.
CONTENTS
0
LOCATION: Chapter 2, Figure 10
0.5 kilometers
N
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Figure 11. Field maps arranged in a 3 x 3 map matrix
covering 36 square kilometers and centered on the
selected survey village.
CONTENTS
0
1
LOCATION: Chapter 2, Figure 11
2
kilometers
N
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Kenya
Tanzania
Lemek GR
Lemek
Narok
Loita
Plains
Aitong
Koyaki GR
Masai Mara
Olkinyei GR
Maji Moto
GR
Talek
Siana
Hills
National Reserve
Siana GR
Settlements
Roads
Perennial Rivers
Figure 1. Study area location.
CONTENTS
Masai Mara National Reserve
Group Ranches
Narok District
Mechanized Agriculture 1995
N
0
LOCATION: Chapter 3, Figure 1
30
60 Km
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Figure 2. Expansion of mechanized agriculture in Loita plains in 1975 (left) and 1995 (right).
CONTENTS
LOCATION: Chapter 3, Figure 2
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Grasslands
NPP Area
Total biomass
Wildlife
avoidance
Tourism
revenues
Forage
Livestock
% redistributed
direct revenues
(craft, park jobs, etc.)
Figure 3. Conceptual model.
CONTENTS
Lease of
land
Total
wealth of
pastoralists
its
distribution
within the
community
LOCATION: Chapter 3, Figure 3
Area under
large-scale
mechanized
agriculture
Area under
small-scale
agriculture
Food
crops
Social structure,
Settlement structure
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(A) livestock herding
(B) livestock herding and income from tourism
(C) livestock herding and mechanised agriculture
(D) livestock herding, subsistence agriculture,
and income from tourism
N
0
20
40 km
Figure 4. Spatial distribution of land use strategies.
CONTENTS
LOCATION: Chapter 3, Figure 4
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Figure 1. Study area, Pearl River Delta, China.
CONTENTS
LOCATION: Chapter 4, Figure 1
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Fogang
Qingyuan
Longmen
Conghua
Huadu
Guangzhou
Huizhou
Nanhai
Gaoyan
Foshan
Shunde
Gaoming
Panyu
Huiyang
Dongguan
Baoan
Heshan
Figure 2. Counties included in the image. The twelve counties included
in the study are: Nanhai, Dongguan, Panyu, Foshan, Shunde,
Shenzhen, Zengcheng, Huadu, Sanshui, Guangzhou, Baoan, and
Jiangmen.
Bolo
Zengzheng
Sanshui
Zhongshan
Shenzhen
Xinhui
Jiangmen
Zhuhai
CONTENTS
LOCATION: Chapter 4, Figure 2
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Figure 3a. Example of land use change in the Pearl River Delta. Areas in yellow are new urban lands
converted from natural vegetation, water, or agricultural land. The salmon colored areas in the western
part of the Delta are new agricultural land.
CONTENTS
LOCATION: Chapter 4, Figure 3a
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Figure 3b. Land-use change in the Pearl River Delta. Areas in red
represent new agricultural land reclaimed from the Delta. Yellow
regions are new urban areas, converted from agriculture. New
urban areas converted from natural vegetation or water are shown
in green.
CONTENTS
LOCATION: Chapter 4, Figure 3b
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1400
Official estimates
Remote sensing estimates
1200
1000
800
km2
600
400
Guangzhou
Sanshui
Huadu
Zengcheng
Shenzhen
Shunde
Foshan
Panyu
Dongguan
0
Nanhai
200
Figure 4. Agricultural land for ten counties in the Pearl River Delta, 1990. (From Seto et al., 2000;
reprinted with permission from Nature.)
CONTENTS
LOCATION: Chapter 4, Figure 4
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300
Official estimates
Remote sensing estimates
250
200
km2
150
100
Guangzhou
Sanshui
Huadu
Zengcheng
Shenzhen
Shunde
Foshan
Panyu
Dongguan
0
Nanhai
50
Figure 5. Agricultural land loss for ten counties in the Pearl River Delta, 1990-1996. (From Seto
et al., 2000; reprinted with permission from Nature.)
CONTENTS
LOCATION: Chapter 4, Figure 5
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VIETNAM
MYANMAR
LAOS
Vientiane
Rangoon
THAILAND
Nang
Rong
Andaman
Sea
Bangkok
CAMBODIA
Phnom Penh
Gulf of
Siam
0
250
500
Kilometers
Figure 1. Nang Rong study area, northeast Thailand.
CONTENTS
LOCATION: Chapter 5, Figure 1
MALAYSIA
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Figure 2. Nuclear village settlement pattern with a residential core and agricultural fields along the periphery.
CONTENTS
LOCATION: Chapter 5, Figure 2
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Figure 3. Vertical and horizontal views of stacking grids and, for each cell, selecting the kth closest village. Each village had a
circular grid that showed shortest-path distances to the centrally located village point (not shown). Darker red shades have higher
cost distances. For simplicity, only four grids are shown. Visually, “shadows” appear in the vertical view due to the stacked
nature of the grids.
CONTENTS
LOCATION: Chapter 5, Figure 3
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1
0
1
2
3
4
5
6
Kilometers
Village A
Other Villages
Ordinary Voronoi Polygons
Shortest Path (SP) Voronoi Polygons
Kth Nearest Short Path (KSP)
Polygons for Village A (k=2)
Figure 4. KSP diagram for Village A.
CONTENTS
LOCATION: Chapter 5, Figure 4
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f = 0.10, less fuzzy
f = 0.20
Membership Function
f = 0.30, more fuzzy
.50 - .55
.56 - .61
.62 - .66
.67 - .72
.73 - .77
.78 - .83
.84 - .88
.89 - .94
.95 - .99
1.00
friction surface
1
0
1
2
3
4
5
km
Figure 5. Fuzzy functional village regions.
CONTENTS
LOCATION: Chapter 5, Figure 5
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1. Villages 127 and 152
2. Villages 42 and 117
3. Village 217
4. Village 35
Figure 6. True village territories based on transect data (Evans 1998) are shown in
yellow lines – fuzzy functional village regions are in shades of red. Yellow points
are village point locations.
CONTENTS
LOCATION: Chapter 5, Figure 6
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1500
2500
Radial Buffer Regions
0
500
MEAN DEVIATION
1500
500
0
MEAN DEVIATION
2500
Fuzzy KSP Regions
0
20
40
60
80
100
500
1000
1500
2000
2500
3000
Figure 7. Mean deviation between modeled boundaries and transect endpoints.
CONTENTS
LOCATION: Chapter 5, Figure 7
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Laos
Thailand
R.
n
a
Poey
es
Cambodia
iver
Mekong R
S
Banlung
Srepo
k R.
Vietnam
Phnom Penh
Ho Chi Minh City
Figure 1. Study sites in Ratanakiri
Province, northeast Cambodia.
Study sites
N
100
CONTENTS
0
100
200
LOCATION: Chapter 6, Figure 1
Kilometers
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(5)
Ta Veng
Ganchueng
(7)
(1)
(8)
Kameng
(4), (6)
(3)
Pin Pin
Koy
Satok
H.Q.
Tannich
Klong
(9)
Svai
Kres
(10)
(2)
Kralaa
Ya Poey
(1) early 1950’s
(2) late 1950’s
(3) early 1960’s
(4) late 1960’s
(5) 1970-1979
(6) 1979-1983
(7) 1983-1987
(8) 1987-1990
(9) 1990-1992
(10) 1992-present
Road
Customary Boundary
Stream
Villages in Poey Commune
History of Kres Village Sites
Figure 2. Spatial history of Kres village between
the early 1950’s and today.
CONTENTS
N
1
0
1
2 Kilometers
LOCATION: Chapter 6, Figure 2
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Forested
Deforested
Regrowth Forest
Study Area
Cloud
ay
hw
n Hig
zo
ma
a
s
n
a
Tr
Water
Figure 1. Image mosaic of survey study area.
CONTENTS
LOCATION: Chapter 7, Figure 1
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uperior
Lake S
Western Upper Peninsula
Eastern Upper Peninsula
eH
ur
on
Lak
e
Mi
ch
i ga
n
L ak
Study area
Northern Lower Peninsula
Flint
Grand
Rapids
Lansing
Figure 1. The State of Michigan, its regions, the study
area, and large cities mentioned in the text.
CONTENTS
LOCATION: Chapter 8, Figure 1
Detroit
Southern Lower Peninsula
to Chicago, IL
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Acme
Long
Lake
Garfield
Grand Traverse
Green
Lake
Blair
East
Bay
Paradise
Grant
Mayfield
Whitewater
Peninsula
Clearwater
Rapid
River
Kalkaska
Cold
Springs
Boardman Orange
Fife
Lake
Springfield
Lovells
Maple
Forest
Frederic
Excelsior
Kalkaska
Union
Blue
Lake
Bear
Lake
Oliver
Garfield
Crawford
City of
Grayling
Beaver Creek
Grayling
South Branch
Figure 2. Political entities in the study region.
CONTENTS
LOCATION: Chapter 8, Figure 2
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Figure 3. Forest cover data for (a) 1973, (b) 1985, and
(c) 1991. Forested areas shown in green hue. Inland
waters shown in black. Buffer omitted from display.
(a) 1973
(b) 1985
(c) 1991
CONTENTS
LOCATION: Chapter 8, Figure 3
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Traverse
City
Figure 4. Concentric zones around Traverse City, Michigan.
CONTENTS
LOCATION: Chapter 8, Figure 4
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2000
1990
1990
1970
Frequency
1600
1200
800
400
0
0
4
8
12
16
20
24
28
32
36
40
44
48
52
56
60
64
68+
Average Parcel Size (ha)
Figure 5. Frequency distributions for private parcels. Numbers of parcels with area values less than 1ha
omitted for display. Omitted values are; 55874 (1970), 89697 (1980), and 94936 (1990).
CONTENTS
LOCATION: Chapter 8, Figure 5
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2000
1990
1990
1970
Frequency
1600
1200
800
400
0
0
4
8
12
16
20
24
28
32
36
40
44
48
52
56
60
64
68+
Average Parcel Size (ha)
Figure 6. Frequency distributions of forested parcel sizes. Numbers of lots with area values less than 1ha not calculated.
CONTENTS
LOCATION: Chapter 8, Figure 6
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18
Threshold
Average parcel size (ha)
1990
1990
1970
12
6
0
0
6
12
18
24
30
Midpoint distance from Traverse City (km)
Figure 7. APS values for concentric zones by date.
CONTENTS
LOCATION: Chapter 8, Figure 7
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Percent private land as forest
57
42
1991
27
1985
1973
12
0
6
12
18
24
30
Midpoint distance from Traverse City (km)
Figure 8. Percent forest values for concentric zones by date.
CONTENTS
LOCATION: Chapter 8, Figure 8
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1991
57
Forest fragmentation (CF index)
1985
1973
42
27
12
0
6
12
18
24
30
Midpoint distance from Traverse City (km)
Figure 9. CF index values for concentric zones by date.
CONTENTS
LOCATION: Chapter 8, Figure 9
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Pacific
Ocean
Path 8
Row 60
Path 9
Row 60
Esmeraldas
Tulcan
Path 642
Row 349
Lago Agrio
Shusufindi
Coca
Baeza
Napo
Tena
Puyo
Riobamba
Pastaza
Path 647
Row 354
Guayaquil
Gulf of
Guayaquil
Cuenca
Morona
Santiago
Path 9
Row 61
Path 8
Row 61
Machala
Peru
Loja
Zamora
Chinchipe
Figure 1. Study area location, northeast Ecuador, the Oriente.
CONTENTS
Peru
LOCATION: Chapter 9, Figure 1
0
100
200
kilometers
PREVIOUS
NEXT
Figure 2. Band 4 Landsat MSS data with the boundary of
the Northern Intensive Study Area (ISA) identified.
CONTENTS
LOCATION: Chapter 9, Figure 2
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LULC Classification
Background (incl. cloud)
Urban/Barren
Pasture/Ag/Low Density Forest
Secondary Forest
Background (incl. cloud)
Figure 3. The Ecuadorian Oriente, the Northern ISA classified image for 1973.
CONTENTS
LOCATION: Chapter 9, Figure 3
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LULC Classification
Background (incl. cloud)
Urban/Barren
Pasture/Ag/Low Density Forest
Secondary Forest
Background (incl. cloud)
Figure 4. The Ecuadorian Oriente, the Northern ISA classified for 1999.
CONTENTS
LOCATION: Chapter 9, Figure 4
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Number of Patches
12500
11500
10500
9500
Number of
Patches
8500
7500
6500
5500
1970
1980
1990
2000
Patch Density (#/100 ha)
Figures 5a and 5b. Changes in pattern metric
performance for the five periods assessed – number of
patches and patch density respectively.
CONTENTS
23.00
21.00
19.00
17.00
15.00
13.00
11.00
9.00
7.00
1970
Patch Density
(#/100 ha)
1980
1990
LOCATION: Chapter 9, Figures 5a & 5b
2000
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Edge Density (m/100 ha)
120.00
110.00
100.00
90.00
Edge Density
(m/ha)
80.00
70.00
60.00
50.00
40.00
1970
1980
1990
2000
Mean Patch Size (ha)
11.00
10.00
9.00
Mean Patch Size
(ha)
8.00
7.00
Figures 5c and 5d. Changes in pattern metric performance for
the five periods assessed – edge density and mean patch size
respectively.
CONTENTS
6.00
5.00
1970
1980
1990
LOCATION: Chapter 9, Figures 5c & 5d
2000
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Mean Nearest Neighbor (m)
98.00
96.00
94.00
92.00
Mean Nearest
Neighbor (m)
90.00
88.00
86.00
84.00
1970
1980
1990
2000
Nearest Neighbor Standard Deviation (m)
Figures 5e and 5f. Changes in pattern metric performance for the
five periods assessed – mean nearest neighbor and nearest
neighbor standard deviation.
CONTENTS
150.00
140.00
130.00
120.00
110.00
100.00
90.00
80.00
70.00
1970
Nearest
Neighbor
Standard
Deviation (m)
1980
LOCATION: Chapter 9, Figures 5e & 5f
1990
2000
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Landscape Shape Index
105.00
95.00
85.00
Landscape Shape
Index
75.00
65.00
55.00
45.00
1970
1980
1990
2000
Interspersion/Juxtaposition Index (%)
Figures 5g and 5h. Changes in pattern metric performance
for the five periods assessed – landscape shape and
interspersion & juxtaposition.
CONTENTS
95.00
90.00
85.00
80.00
75.00
70.00
65.00
60.00
55.00
50.00
45.00
1970
Interspersion/
Juxtaposition
Index (%)
1980
1990
LOCATION: Chapter 9, Figures 5g & 5h
2000
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LULC
Terrain
GIS Layers
Roads
Rivers
Spatial Partitioning
Defaults
Random #
Generator
Organic
Annual
Output
Limits
Scalars
Diffusive
Access
Figure 6. Structural design of the CA model.
CONTENTS
LOCATION: Chapter 9, Figure 6
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Figure 7. Simulated 1996 vs. reference 1996 output.
CONTENTS
LOCATION: Chapter 9, Figure 7
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Figure 1. Amazon-wide Arc of Deforestation (Source: Instituto
de Pesquisa Ambiental da Amazônia -IPAM).
CONTENTS
LOCATION: Chapter 10, Figure 1
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Figure 2. Property grid overlay developed for study area, colonization along the Transamazon Highway, Altamira, Pará.
CONTENTS
LOCATION: Chapter 10, Figure 2
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Grid
North
Outside Study
Forest Remaining
Def. < 1970
Def. 70-73
Def. 73-76
Meters
23,360.00
Def. 76-79
Def. 79-85
Def. 85-91
Def. 91-96
For-91/Clouds-96
Figure 3. The observed pace of deforestation among frontier farms along the Transamazon Highway, km 20-140, 1970-96.
CONTENTS
LOCATION: Chapter 10, Figure 3
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Age & Sex of Households Arriving After 1985
70+
65-69
60-64
Age & Sex of Households Arriving Before 1976
70+
Female
Male
65-69
Female
Male
60-64
55-59
55-59
50-54
50-54
45-59
45-59
40-44
40-44
35-39
35-39
30-34
30-34
25-29
25-29
20-24
20-24
15-19
15-19
10-14
10-14
5-9
5-9
<5
<5
10.00 8.00 6.00 4.00 2.00 0.00 2.00 4.00 6.00 8.00 10.00
10.00 8.00 6.00 4.00 2.00 0.00 2.00 4.00 6.00 8.00 10.00
Percent
Percent
Figures 4a &4b. (a) Current composition of households arriving after 1985; (b) current composition of
households arriving before 1976
CONTENTS
LOCATION: Chapter 10, Figures 4a & 4b
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Land Use & Environmental Change
Stage I
Stage II
Stage III
Stage IV
Stage V
Deforestation:
SS/Fallows:
Annual Crops:
Fruit Tree Prod.:
Agroforestry:
Cattle Grazing:
H
o
u
s
e
h
o
l
d
S
t
a
g
e
s
Figure 5. A conceptual model of the domestic life
cycle of households, and expected trajectory of land
use and evironmental change at the farm-household
level.
CONTENTS
Household Composition
Nuclear - Young Adults
with Small Children
I
5-yrs.
II
Time Since Initial Settlement
10-yrs.
III
IV
Sampling Strata
V
LOCATION: Chapter 10, Figure 5
Nuclear - Adults with
Older Children
Nuclear - Adults with
Teenage Children
15-yrs.
Nuclear - Older Adults
with Teenage & Young
Adult Children
Multi-Generational HHs;
Second Generation HHs
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Cohort of Farm Property Occupancy
<1970
70-73
73-76
76-79
79-85
85-91
91-96
Not Occ
by 96
Figure 6. Map of properties by period forest clearing was initiated (Farm Cohorts).
CONTENTS
LOCATION: Chapter 10, Figure 6
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1000
983
900
781
800
Number of Farm Lots
700
600
549
500
460
406
400
400
346
300
200
100
0
66
Pre-1970
1970-73
1973-76
1976-79
1979-85
1985-91
1991-96
Unoccupied
Period
Figure 7. Properties by period forest clearing was initiated (Farm Cohorts).
CONTENTS
LOCATION: Chapter 10, Figure 7
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30
Number of Farm Lots
25
20
15
10
5
0
1
5
10
15
20
25
Years of Occupation on Farm
Figure 8. Mean annual percent of farm area deforested by years of farm occupation.
CONTENTS
LOCATION: Chapter 10, Figure 8
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8.00
Linear
7.00
Squared
Quadratic
% Farm Area Cleared
6.00
5.00
4.00
3.00
2.00
1.00
0.00
0 1
2
3 4
5
6 7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
Years of Farm Activity on the Lot
Figure 9a. Estimated percent farm area cleared annually from regression results; various methods.
CONTENTS
LOCATION: Chapter 10, Figure 9a
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100
Linear
90
Squared
Quadratic
Percent Farm Area in Forest
80
70
60
50
40
30
20
10
0
0 1
2
3 4
5
6 7 8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51
Years
Figure 9b. Predicted forest remaining based on regression results; various methods.
CONTENTS
LOCATION: Chapter 10, Figure 9b
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450
120
Forest Area
400
% Forest
100
350
80
250
60
Percent
Hectares (Thousands)
300
200
40
150
100
20
50
Figure 10. Observed and expected area of remaining forest
in the study area, 1970-96, and predicted for 2010 and 2020;
based on farm level projections.
0
1970 1973 1976 1979 1985 1991 1996
2010
2020
0
Year
CONTENTS
LOCATION: Chapter 10, Figure 10
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Flat Terrain
Medicilandia
Crossroad
“Crazy 12”
Altamira
(20 Kms)
10 Kilometers
from Sugar Refinery
Brasil Novo
n
Indige
ese
ous R
rve
Residuals of Observed-Expected
Deforestation in 1996
-21.6 < (<1 Std. Dev.)
5
0
5
10
15
20 Kilometers
-21.622-11.558
Sugar Refinery
>11.5 (>1 Std. Dev.)
Roads
Figure 11. Map of residuals of observed-predicted deforestation in 1996 based on quadratic equation regression line.
CONTENTS
LOCATION: Chapter 10, Figure 11
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Feature image
Multispectral
image
Feature
transformation
Design classification
scheme
Extracting Training
data
Applying classification
algorithms
Visual interpretation
and manual digitization
Classification decision
algorithms
Post-classification
filtering and clumping
Vector thematic map
Raster thematic map
Vectorizing
GIS
Figure 1. Data flow for generating a GIS layer of land use/land cover using remotely sensed data.
CONTENTS
LOCATION: Chapter 11, Figure 1
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Acquiring/generating multiple resolution images
R1
R2
Rn
Extract
representative sites
for each class
Feature
transformation
Establish
classification
scheme
Extract training data
Exploratory
spatial data
analysis
Classification
decision algorithms
Apply multi-resolution strategies
Reference
Accuracy
evaluation
LU/LC map
Figure 2. A proposed multi-resolution image analysis and classification framework.
CONTENTS
LOCATION: Chapter 11, Figure 2
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TM 30m
SPOT 10m
DOQQ 1.0m
PHOTO 0.5m
Figure 3. An example of multi-resolution images acquired from different sensors for a subarea near
Del Mar, San Diego, USA.
CONTENTS
LOCATION: Chapter 11, Figure 3
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4m
8m
12m
16m
Figure 4. An example of an image pyramid generated from a 1 m DOQQ subset of single-residential area.
CONTENTS
LOCATION: Chapter 11, Figure 4
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0a
(Original
image)
1a
1b
1c
1d
2a
2b
2c
2d
1c
1b
1d
0a: Resolution level 0 image (with the finest resolution R)
1a, 2a: Context images at resolution level 1 (2R) and 2 (4R)
1b, 2b: Horizontal sub-images at resolution level 1 and 2
1c, 2c: Vertical sub-images at resolution level 1 and 2
1d, 2d: Diagonal sub-images at resolution level 1 and 2
Figure 5. Scheme of a three-level hierarchical wavelet decomposed analysis.
CONTENTS
LOCATION: Chapter 11, Figure 5
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(a) Single-family residential
(b) Industry
(c) Irrigated grassland
Figure 6. Three USGS CIR DOQQ subsets with single use/cover components.
CONTENTS
LOCATION: Chapter 11, Figure 6
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Figure 1. Typical Kathmandu Valley view of agricultural fields on the flat valley floor and terraced slopes during the dry season.
CONTENTS
LOCATION: Chapter 12, Figure 1
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Figure 2. Aerial photograph of a portion of the Kathmandu Valley
from 1987. Approximate scale of 1:17,000.
CONTENTS
LOCATION: Chapter 12, Figure 2
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Figure 3. July 22 1989 SPOT Image in the
red visible band. The light tones are urban
and a few areas of river gravel, the dark tones
are flooded or saturated rice fields in the early
stages of growth or vegetated areas of maize
of forest. Approximate scale of 1:100,000.
CONTENTS
LOCATION: Chapter 12, Figure 3
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Figure 4. Thatch is a common roofing material for older houses in Kathmandu. These roofs are generally small, sloping,
and spectrally confused with wet soils.
CONTENTS
LOCATION: Chapter 12, Figure 4
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Figure 5. Newer housing has larger, flat roofs made of concrete or metal. They are very light toned and spectrally
similar to dry soils or sand and gravel.
CONTENTS
LOCATION: Chapter 12, Figure 5
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Figure 6. RADARSAT scene of Kathmandu,
Nepal collected on November 19,1998.
Approximate scale is 1:100,000. The light
toned features, high backscatter, are primarily
the urban features.
CONTENTS
LOCATION: Chapter 12, Figure 6
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Classification Module
Overview of the software application
FIELD DATA MODULE
standardized general field
data collection
specific field data
collection
automatic extraction of
land cover class from field
data
saving of field data in
synthetic form
print and export
LEGEND MODULE
CLASSIFICATION MODULE
build up legend
all classifiers and attributes
edit classes
glossary
create user-defined land
cover classes
conditions to create land cover
classes
database of all possible
classes, including name, code
and description
images and interpretation
database
TRANSLATOR MODULE
translation of external
similarity of external
comparison of two
classifications
single classes through
external classifications
into LCCS
LCCS of LCCS Modules
through
LCCSlinks.
Figure 1. Overview
and their
display legend
save and retrieve
print
export
comparison of two
LCCS classes
Figure 1. Overview of LCCS Modules and their links.
CONTENTS
LOCATION: Chapter 13, Figure 1
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NEW RS
DATA
MAPPING
CONTROL
TOPOMAPS
GPS
EXISTING
THEMATIC
GEOSPATIAL
DATA
DATA
CORRECTION
DATA
CORRECTION
DATA
ENHANCEMENT
DATA
DIGITIZATION
DATA
ANALYSIS
DATA
EDITING
COLLATERAL
DATA
INFORMATION
FROM EXISTING
GEOSPATIAL
DATA
INFORMATION
FROM NEW
RS DATA
GIS
TRANSFORMATION
INTEGRATION
PROCESSING
INTRANET
COMPREHENSIVE
LAND COVER
DATABASE
INTERNET
MATHEMATICAL
MODELING
LAND COVER
MAPPING
Figure 2. System flowchart for land cover mapping based
on RS and GIS.
CONTENTS
LAND COVER MAP
& L.C. STATISTICS
ENVIRONMENTAL
MONITORING
CHANGE MAP
& CHANGE
STATISTICS
LOCATION: Chapter 13, Figure 2
ASSESSMENTS
LAND DEGRADATION
DEFORESTATION RATE
LAND SUITABILITY
etc
FORECASTING
AGR. CROP
PRODUCTION
AGR. DROUGHT RISK
etc.
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base grid 32x32 km
128x128 km grid
64x64 km grid
96x96 km grid
160x160 km grid
192x192 km grid
Figure 1. Six aggregation levels for China.
CONTENTS
LOCATION: Chapter 14, Figure 1
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demand module
national level
consumption
demand for
agricultural
commodities
export / import
productivity
population
growth
allocation module
grid level
land use
change
biophysical and
socio-economic
conditions
Figure 2. General structure of the CLUE-modeling framework.
CONTENTS
LOCATION: Chapter 14, Figure 2
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Annuals
Bananas
Coffee
Sugar cane
Pasture
Natural vegetation
Figure 3. Classified differences between land use in 1996
and 2010 under the Base3% scenario.
200
CONTENTS
LOCATION: Chapter 14, Figure 3
0
200
400
600
kilometers
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Annuals
Bananas
Coffee
Sugar cane
Pasture
Natural vegetation
Figure 4. Classified differences between land use in 1996
and 2010 under the Optimistic scenario.
200
CONTENTS
LOCATION: Chapter 14, Figure 4
0
200
400
600
kilometers
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Nicaragua
N
NAZ
Limon
Panama
50
0
50 Kilometers
Figure 5. Location of Northern Atlantic Zone (NAZ) in Costa Rica.
CONTENTS
LOCATION: Chapter 14, Figure 5
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1984
2005
forest
pasture
bananas
annuals
Figure 6. Input data and results of Base scenario for four land use types.
CONTENTS
LOCATION: Chapter 14, Figure 6
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park protection
liberalisation
Figure 7. Distribution of forest with (left) and without (right) protection of national parks.
CONTENTS
LOCATION: Chapter 14, Figure 7
PREVIOUS
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A
10
B
0
10
kilometers
Forest
Coconut
Grassland
Rice
Others
Figure 8. Land use patterns in Sibuyan 1997 (A) and 2012 (B).
CONTENTS
LOCATION: Chapter 14, Figure 8
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Figure 9. Differences in allocation between a linear and logistic demand development. Initial and end demand is
similar, only the pathway differs.
CONTENTS
LOCATION: Chapter 14, Figure 9
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Pasture
Forest
25000
12000
20000
9000
15000
6000
10000
3000
5000
0
0
0
3000
6000
9000
12000
Banana
6000
0
2000
2000
1000
0
2000
4000
10000
6000
0
15000
20000
25000
2
Annuals
3000
4000
0
5000
0
1000
2000
3000
Figure 10. Modeled (y-axis) versus actual (x-axis) cover in hectares for 1992 of four major
kinds of land use, aggregated per administrative unit.
CONTENTS
LOCATION: Chapter 14, Figure 10
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ry
to
ec
tr
aj
lan
present land use
se
du
ch
an
ge
scenario: urbanization
land use plan
scenario: protectionism
policy objectives
international agreements
goals
scenario: increasing
domestic demand
Land use change modelling:
simulation of near-future land use change trajectories
Assessment of land-use change effects:
erosion, greenhouse gas emission, etc.
Land use planning:
design and negotiation
about alternative land use
trajectory
Figure 11. The role of land use change modeling within studies aiming at improving land use planning.
CONTENTS
LOCATION: Chapter 14, Figure 11
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