Land Use Cover Change (LUCC) Modeling

Bryan C. PijanowskiPurdue University

Co-investigators

• David Campbell

• Jennifer Olson

• Nathan Torbick

• Zhen Lei

• Snehal Pithadia

• Konstantinos Alexandridis

Modeling Objectives

1. Quantify land use cover change across the region and within case study sites

2. Understand the nature of the drivers; how they operate over spatial and temporal scales

3. Develop reliable predictions of future change

4. Interface model output to biophysical forcing factors that influence local, regional and global climate change

Land Use Changes in East Africa• In rural areas, seeing a major shift from pastoralist society

to cropping society• Significant migrations toward urban areas (mostly males)• Cropping systems are intensifying and diversifying• Multiple use systems are common (charcoal, cropping,

grazing)• Use is dependent on unpredictable climate, scarce

resources (water, wood for fuel and building)• Large “shocks” to use due to policy change (adjudication,

global trade markets)• Major conflict in use of land for wildlife and cropping

(wildlife damage crops)• Wars and infectious disease effect population dynamics

and migration patterns

Role Playing Games

Expert Judgment

Case Study Data and

Stories

LUCC Data from

Remote Sensing

FO

RM

UL

AT

ION

LTM-Neural net

LTM-MCE

BayesianBelief

Networks

Multi-Agent

Simulations(MABEL)

MO

DE

LS

Expert Judgment

Uncertainty and Risk

Analysis

Model Ensemble Analysis

AS

SE

SS

ME

NT

Performance Analysis

Coupling Qualitative and Quantitative Approaches to Model LUCC

Global

Regional

Country

District

Town

Family/Farm

Individual/Pixel

Homogenous Zones

IPC

C/G

TA

PE

xper

ts/

Dem

ogra

phy

RP

S/C

ase

Stu

dies

SCALE SOURCE

MA

BE

L

LTM

“Bottom up”

“Top down”

Pot

entia

l

Cas

e S

tudi

es

Resolve

Modeling ScalesModeling Scales

LTM Results to Date

Africover-GLC Hybrid Land CoverAfricover-GLC Hybrid Land Cover

Land Cover and Coarse RCM GridLand Cover and Coarse RCM Grid

Land Cover and Both RCM GridsLand Cover and Both RCM Grids

Rainfed Herbaceous CropsRainfed Herbaceous Crops

This is what we are trying to predict locations of across the region

So…..• How well did we do?• Where does the model do poorly? • What are we missing? • What is need to improve the model

performance?• What specific scenarios can we

start to consider?

LTM Results - PreliminaryLTM Results - Preliminary

Areas in green are correctly predictedRed = LTM predicts rainfed but it doesn’t exist (over-predict)Yellow = LTM does not predict rainfed but it exists (under-predict)

Areas in green are correctly predictedRed = LTM predicts rainfed but it doesn’t exist (over-predict)Yellow = LTM does not predict rainfed but it exists (under-predict)

Kilimanjaro

Very good performance

Fair performance

Poor performance

Poor performance

LTM Results - PreliminaryLTM Results - Preliminary

Areas in green are correctly predictedRed = LTM predicts rainfed but it doesn’t exist (over-predict)Yellow = LTM does not predict rainfed but it exists (under-predict)

Areas in green are correctly predictedRed = LTM predicts rainfed but it doesn’t exist (over-predict)Yellow = LTM does not predict rainfed but it exists (under-predict)

LTM Results - PreliminaryLTM Results - Preliminary

Blue indicates 10% increase in this land use class regionally

Blue indicates 10% increase in this land use class regionally

Blue indicates 10% increase in this land use class regionally

Blue indicates 10% increase in this land use class regionally

LTM Results - PreliminaryLTM Results - Preliminary

Relative LTM Performance Metric

MABEL

• Economic and behavioral model of agents that interact in market model

• They calculate their expected utility from causal belief probability model (BBN)

• A statistical learning algorithm is introduced so that agents can adjust their beliefs according to rewards from actions (so they can be adaptive)

• It is multi-tool: Swarm, Netica, SPSS and ArcGIS based

MABEL Framework

Land Agent 1 …

“Market Model” or “Social Interaction Model”

Policy Maker Agent 1

Policy Maker Agent M

…

Land Agent 2 Land Agent N

Interface to MABEL Server for Decision Inference

Agents Grouped into Classes

MABEL Server

Land Partition Routine&

New Agent Creation

Policy Controls

Population of Agents in Each Client Simulation

Intented Decision's Expected Utility

60

65

70

75

80

85

90

95

100

01 2 3 4 5

67

89

1011

1213

1415

16

1718

1920

21

22

23

24

25

2627

2829

3031

32

3334

3536

3738

3940

4142

434445

464748495051525354555657

5859

6061

6263

64

6566

6768

69

7071

7273

7475

76

77

78

79

80

8182

8384

85

8687

8889

9091

9293

9495

96 97 98 99100

Cumulative Reward Value

-300

-200

-100

0

100

200

300

400

500

600

700

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Case Number

Cu

mu

lativ

e R

ew

ard

Step 0

Prior Probabilities of the FDS Belief Network

(Initial State)

Cumulative Reward Value

-300

-200

-100

0

100

200

300

400

500

600

700

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Case Number

Cu

mu

lativ

e R

ew

ard

Intented Decision's Expected Utility

60

65

70

75

80

85

90

95

100

01 2 3 4 5

67

89

1011

1213

1415

16

1718

1920

21

22

23

24

25

2627

2829

3031

32

3334

3536

3738

3940

4142

434445

464748495051525354555657

5859

6061

6263

64

6566

6768

69

7071

7273

7475

76

77

78

79

80

8182

8384

85

8687

8889

9091

9293

9495

96 97 98 99100

Step 100

Average Cumulative Rewards & 2nd Order Dynamics

y = 0.0235x2 - 0.0112x - 48.023R2 = 0.9646

-100

-50

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Case Number

Cum

ula

tive R

ew

ard

s

Average Cumulative Rewards

Poly. (Average Cumulative Rewards)

Losses Period (decreasing rate)

Break-even Period (steady rate)

Gains Period (increasing rate)

Higher Variability (high uncertainty,

slow learning)

Lower Variability (low uncertainty, faster learning)

What is needed with MABEL

• Need to define the social interaction(s) most important for land use change

• We have several candidate agents

• Need to determine a Belief system and general utility function for agents

• Can we collect this information from the group via the internet (we would develop a web-based input tool)