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LC Mapping and Modeling Group Progress Summary NASA Meeting, 1 June 2006 Honolulu, HI John Vogler Jeff Fox Overview Large-scale Mapping Large-scale Modeling Fuzzy Cognitive Mapping MTCLIM Small-scale Modeling Future? Large-scale Mapping New Datasets - Thailand

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LC Mapping and Modeling Group

Progress Summary

NASA Meeting, 1 June 2006

Honolulu, HI

John Vogler Jeff Fox


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  • Overview

  • Large-scale Mapping

  • Large-scale Modeling

  • Fuzzy Cognitive Mapping

  • MTCLIM

  • Small-scale Modeling

  • Future?


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Large-scale Mapping

New Datasets - Thailand

  • Landsat ETM+ image acquired 29 Feb 2004

  • Aerial Photographs (via Alan Zieglar)

  • PKEW – jan1954, dec1995 @ 1:50,000

  • jan2002 @ 1:25,000

  • Mae Sa – jan1954, jan1968-70, dec1995 @ 1:50,000

  • jan2002 @ 1:25,000

  • 1:50,000 (and larger-scale) thematic layers from

  • USER (Louis Lebel)

  • FFORCCT (Chatchai & Royal Thai Forestry Dept.)

  • MCC (Methi Ekasingh & Chalermpol)

  • 20m DEM and Topographic Moisture Index (Mae Sa)


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Large-scale Mapping

New Datasets - Laos

  • Landsat ETM+ image acquired 25 March 2004

  • Detailed thematic layers for N. Laos districts

  • from Khamla

  • 1:50,000 (and larger scale) thematic datasets

  • from Yokoyama (EWC visiting researcher)

  • for all of Laos including:

  • - Admin, village, hydro, landuse,

  • road, builtup areas, elev points

  • - Contours and derived 30m DEMs

  • - b/w orthorectified SPOT(?) images


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Large-scale Mapping

New Datasets - Xishuangbanna

  • Landsat ETM+ image acquired 25 March 2004

  • Township-level 2000 Census data for Yunnan Province

  • Township boundaries (CBIK)

  • Daily observations for climate stations (CBIK)

  • Jinghong (1954 – 2001)

  • Menghai (1958 – 2001)

  • Mengla (1957 – 2001)

  • Damenglong (1958 – 1996)

  • 20m DEM and Topographic Moisture Index (Nam Ken)



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Large-scale Mapping

  • Above derived primarily from photo interp of 1:25k, 2002

  • Detailed participatory mapping this summer? (Pornwilai)


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Large-scale Modeling

Cellular Automata model

  • Develop annual dynamic simulations of land cover

  • to the years 2025 and 2050

  • for detailed simulation regions along road corridor

  • based on 3 interrelated LCLUC scenarios

  • 1) agricultural intensification

  • 2) road development

  • 3) growth of markets


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Large-scale Modeling

Cellular Automata

  • Mathematical object defined as:

  • n-dimensional cellular space, consisting of cells of equal size;

  • Cells in one of a discrete number of states;

  • Cells change state as the result of a transition rule;

  • Transition rule is defined in terms of the states of cells that are part of a neighbourhood;

  • Time progresses in discrete steps. All cells change state simultaneously.


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Large-scale Modeling

Cellular Automata example

Conway’s Life (Gardner, 1970)


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Large-scale Modeling

Cellular Automata model developments

  • Xishuangbanna model characteristics:

  • coded and run using IDL

  • grows rubber and rice annually (active classes)

  • from 1988 – 1999

  • using 3 x 3 neighborhood, 30m res. cells, 90x90km domain

  • random seeding to start

  • restricted areas include parks and protected areas

  • calculates suitability scores for active classes

  • reconciles rice vs. rubber

  • outputs annual maps

  • landscape and class-level pattern metrics

  • passive classes include forest, swidden, barren, urban, water

  • factor level and within-factor weights from AHP


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Large-scale Modeling

Cellular Automata model developments

  • Analytic Hierarchy Process Questionnaires:

  • Glean expert knowledge on conversion to rubber

  • Synthesized to determine relative weights of conversion factors

  • Factors (inputs): Weights (normalized 0-1):

  • d2procsuit 0.274

  • elevsuit 0.816

  • market price time-varying blanket weight

  • lcluwgt 0.296

  • - forest .55

  • - swidden 1

  • - rubber 1

  • - rice .24

  • - urban, water, barren 0

  • rubber score =

  • d2procsuit (wgt) * elevsuit (wgt) * mpwgt * lcluwgt (wgt)

rice score =

d2streamsuit *

riceslpsuit


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Fuzzy Cognitive Mapping

Cash Crops

Price

Cold

Land

Rubber

Seedlings

Inputs

Pests

Income

Infrastructure

Consensus Social Cognitive Map of Rubber Production

Damenglong and Meungpong Combined

Most Central Variables

Rubber

Inputs

Income

Pests

Price

Connections >= ABS(0.1, -0.1)

Feedbacks

Technology

0.13

0.15

- 0.41

0.15

0.34

0.31

- 0.46

0.1

0.63

0.19

0.19

- 0.3

0.1

- 0.2

0.15

0.23

0.13

Least Central

Variables:

(Centrality <= 0.5)

Labor

State Farms

Policy

Physical Environment

Government Ext.

Credit

0.25

0.23

0.1

0.15

0.2

Net Causal Relationships

after Additively Superimposing

16 FCMs and Normalizing results


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MT-CLIM

Mountain Climate Simulator for Excel

(Numerical Terradynamic Simulation Group, U. of Montana)

  • Extrapolates precipitation, max and min temperatures

  • at one location (“site”)

  • using daily climate data from known location (“base”)

  • and DEM (elevation, slope, aspect)

  • site latitude and lapse rate also required

  • Daily observations for climate stations (Jianchu)

  • Jinghong (1954 – 2001) Menghai (1958 – 2001)

  • Mengla (1957 – 2001) Damenglong (1958 – 1996)


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Areas

MT-CLIM


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Small-scale Modeling

Climate simulations …

  • Present climate (1998-2002; NCEP/NCAR) with 2025 LCLU

  • Present climate (1998-2002; NCEP/NCAR) with 2050 LCLU

  • Control climate (PCM 2045-55; Present CO2) with present LCLU

  • Control climate (PCM 2045-55; Present CO2) with 2050 LCLU

  • Projected 2050 climate (PCM 2045-55; SRES A2 CO2)

  • with present LCLU

  • Projected 2050 climate (PCM 2045-55; SRES A2 CO2)

  • with 2050 LCLU


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • Changing Land Use and its Effects (CLUE) modeling framework

  • LCLU type-specific

  • conversion settings

  • Transition sequences

  • (From-to matrix)

  • Conversion elasticity

  • (min and max t)

  • Spatial policies &

  • restrictions

  • Parks & protected areas

  • Restricted areas

  • Agricultural

  • development zones

CLUE

LCLU change

allocation

LCLU requirements

(demand)

Location characteristics

Location

factors:

soil, access.,

topography,

bioclimate,

demography,

socio-economic,

etc.

scenarios

Lclu

specific

location

suitability

aggregate

lclu

demand

Logistic

regression

trends

advanced

models

Source: The CLUE Group, Wageningen University, Netherlands, website: http://www.dow.wageningen-ur.nl/clue/


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • CLUE Allocation Procedure

  • Some allocations reversible

  • Some allocations dependent

  • on earlier time steps

LCLU type specific settings

Conversion

Elasticity

( ELASu )

Competitive

Strength

( ITERu )

Allowed

conversions

If No, then update

competitive strength

for those types not meeting demand

Is total lclu area

for each type equal to the

demand?

Calculation of

change

Land cover/use ( t)

LCLU ( t + 1)

Yes

For each grid cell i,

calc total probability

for each lclu type:

TPROPi,u = Pi,u +

ELASu + ITERu

Grid cell specific settings

Location

suitability

( Pi,u )

Neighborhood

weights

Spatial

policies

Regional

demand

Source: The CLUE Group, Wageningen University, Netherlands, website: http://www.dow.wageningen-ur.nl/clue/


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • Data Requirements (Raw data cleaned, vector – raster, 1km GRIDs cut to 6 different regions, GRIDs coverted to ASCII)

  • - Initial LC (year 2000; same LC used by Omer)

  • - Masks and Protected Areas (WDPA)

  • - Socio-economic (income, GDP, malnutrition rate, illiteracy, etc.)

  • - Demographic (population density (dynamic variable))

  • - Bioclimatic (subset of bioclimate variables from WorldClim)

  • - Geographic (distance to road, river, market (‘to road’ is dynamic variable))

  • - Topographic (elevation, slope, aspect)

  • - Soils/Geomorphology (soil type, soil degradation, landform)


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • LCLU Requirements (Demand Scenarios)

  • - by region (6 countries intersect MMSEA)

  • - by modeled LCLU type (vary by region)

  • - for years 2025 and 2050 specify % of total pixels (spreadsheet)

  • - linear step increases from 2000 - 2025 and 2025 – 2050

  • - Converted to ASCII text demand file for each region

  • demand.in1

  • 50

  • 19106 4184 1884 7 25318 120116

  • 19480 4301 1922 7 25216 119637

  • 19854 4418 1960 7 25114 119158

  • ... To 2025 and 2050


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • LCLU Requirements (Allowed Conversions Matrices)

  • - by region (6 countries intersect MMSEA)

  • - by modeled LCLU type (vary by region)


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • LCLU Requirements (Allowed Conversions Matrix)

  • - by region (6 countries intersect MMSEA)

  • - by modeled LCLU type (vary by region)

  • - converted to ASCII matrix

allow.txt

1 1 0 0 1 0 0 1 1 0 0 0 0 1 0 0 1

1 1 1 0 1 1 1 1 0 0 0 0 0 1 1 1 1

1 0 1 0 1 0 0 1 0 0 0 0 0 1 0 1 0

0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1

1 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 1

1 0 1 0 1 1 1 1 0 0 0 0 0 1 1 1 1

0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0

1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0

1 1 1 0 1 1 0 1 0 0 0 0 0 1 0 1 1

1 0 0 0 1 0 0 1 0 0 0 0 0 0 1 1 0

1 1 0 0 1 1 0 1 0 0 0 0 0 1 1 1 1

1 1 1 0 1 1 0 1 0 0 0 0 0 1 1 1 1


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • LCLU Requirements (Conversion Elasticities)

  • - Codes for allowed changes and behaviors of LC types

  • 3 Parameters:

  • 1) No consideration of present land cover to high preference for current land cover (0 – 1)

  • 2) Minimum number of years a cell must remain in specific LC type

  • (e.g. regrowth of forest from grassland to forest)

  • 3) Maximum number of years a cell can remain in specific LC type

  • (e.g. crop rotations)


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • Spatial Policy Requirements (Restricted Areas)

  • - by region (6 countries intersect MMSEA)

  • - conversion restricted in parks and protected areas

  • - WDPA restricted grid cells recoded to –9998 (no change)

  • - Active cells recoded to 0; NoData cells to –9999

  • - With exception of Laos, all region models use restricted areas

  • - But scenarios can be run with/without restricted areas


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • LC Location Characteristics (Allocation Regressions)

  • - by region (6 intersect MMSEA) and by model LC types (14 – 17)

  • - Binary logistic regression using SPSS

  • - All GRIDs stripped of NoData values and fed into SPSS

  • - Modeled LC types become dependent variables with value 0 or 1

  • - Hypothesized drivers are independent variables

  • - Separate equations for every LC type modeled in each region

  • - Constant and variable coefficients retained for use in CLUE

  • - Goodness of fit measured using ROC characteristic, not R2


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • LC Location Characteristics (Drivers)

  • - driving variables numbered sequentially

  • - most driving variables are stable

  • - dynamic variables change annually

  • - new ASCII grid accessed each year

  • - Dynamic population density (US Census IDB)

  • using projected annual growth rates

  • - Dynamic distance to road

6

1

1

0 1

7

-4.266

5

-0.027 22

-0.013 20

-0.175 26

0.003 1

0.041 27

8

1.178

6

-0.012 22

-0.023 20

0.028 26

-0.001 1

0.066 27

-0.007 21


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • Main Parameter File

  • - by regional model (6 intersect MMSEA)

16

1

8

33

560

1039

1 (not 0.0083333333333)

97.53749999999

21.137499999959

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

0 0.5 5

2000 2050

1 1

1

0

1 25

0

0

  • 1 Number of land use types

  • Number of regions

  • Max. number of independent variables in a regression equation

  • Total number of driving factors

  • Number of rows

  • Number of columns

  • Cell area

  • 8 xll coordinate

  • 9 yll coordinate

  • 10 Number coding of the land use types

  • 11 Codes for conversion elasticities

  • 12 Iteration variables

  • 13 Start and end year of simulation

  • 14 Number and coding of explanatory factors that change every year

  • 15 Output file choice 1, 0, -2 or 2

  • 16 Region specific regression choice 0, 1 or 2

  • 17 Initialization of land use history 0, 1 or 2

  • 18 Neighborhood calculation choice 0, 1 or 2

  • 19 Location specific preference addition


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Small-scale Modeling

MMSEA Land Cover / Land Use Simulations

  • Overall MMSEA Results

Increase

Decrease

Little/No change













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Future Steps

  • What Next?

  • Map rubber over time at larger scale

  • Refine and expand large-scale CA modeling

  • Incorporate narratives/livelihoods into model

  • Explore Agent-based modeling

  • Refine small-scale CLUE modeling MMSEA

  • - Neighborhood characteristics

  • - Dynamic distance to roads

  • - Model without protected areas

  • - Addition of more exploratory drivers

  • - More LC elasticity testing

  • Publications


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