1. Land Use Cover Change (LUCC) Modeling Bryan C. Pijanowski Purdue University

2. Co-investigators David Campbell Jennifer Olson Nathan Torbick Zhen Lei Snehal Pithadia Konstantinos Alexandridis

3. 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

4. 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

5. Role Playing Games Expert Judgment Case Study Data and Stories LUCC Data from Remote Sensing FORMULATION LTM- Neural net LTM- MCE Bayesian Belief Networks Multi- Agent Simulations (MABEL) MODELS Expert Judgment Uncertainty and Risk Analysis Model Ensemble Analysis ASSESSMENT Performance Analysis Coupling Qualitative and Quantitative Approaches to Model LUCC

6. Global Regional Country District Town Family/Farm Individual/Pixel Homogenous Zones IPCC/GTAP Experts/ Demography RPS/Case Studies SCALE SOURCE MABEL LTM “Bottom up” “Top down” Potential Case Studies Resolve Modeling Scales

7. LTM Results to Date

8. Africover-GLC Hybrid Land Cover

9. Land Cover and Coarse RCM Grid

10. Land Cover and Both RCM Grids

13. Rainfed Herbaceous Crops This is what we are trying to predict locations of across the region

14. 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?

15. LTM Results - Preliminary Areas in green are correctly predicted Red = 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 predicted Red = LTM predicts rainfed but it doesn’t exist (over- predict) Yellow = LTM does not predict rainfed but it exists (under-predict)

16. Kilimanjaro Very good performance Fair performance Poor performance LTM Results - Preliminary Areas in green are correctly predicted Red = 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 predicted Red = LTM predicts rainfed but it doesn’t exist (over- predict) Yellow = LTM does not predict rainfed but it exists (under-predict)

17. LTM Results - Preliminary Blue indicates 10% increase in this land use class regionally

18. LTM Results - Preliminary

19. Relative LTM Performance Metric

20. 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

21. MABEL Framework Land Agent 1 … “Market Model” or “Social Interaction Model” Policy Maker Agent 1 Policy Maker Agent M … Land Agent 2Land 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

22. Step 0 Prior Probabilities of the FDS Belief Network (Initial State)

23. Step 100

24. 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)

25. 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)