Land change modelling Gilberto Câmara, Pedro Andrade Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike

Slides from LANDSAT Aral Sea images: USGS Modelling Human-Environment Interactions How do we decide on the use of natural resources? What are the conditions favoring success in resource mgnt? Can we anticipate changes resulting from human decisions?

TerraME: Computational environment for developing human-environment models Cell Spaces T. Carneiro, P. Andrade, G. Câmara, A. Monteiro, R. Pereira, “TerraME: an extensible toolbox for modeling nature-society interactions” (under review).

What models are needed to describe human actions? Modelling human-environment interactions

Modelling and Public Policy System Ecology Economy Politics Scenarios Decision Maker Desired System State External Influences Policy Options

Models need to be Calibrated and “Validated” t p - 20 t p - 10 tptp Calibration Validation t p + 10 Scenario Source: Cláudia Almeida

Clocks, clouds or ants? Clocks: deterministic equations Clouds: statistical distributions Ants: emerging behaviour

Statistics: Humans as clouds Establishes statistical relationship with variables that are related to the phenomena under study Basic hypothesis: stationary processes y=a 0 + a 1 x 1 + a 2 x a i x i +E Fonte: Verburg et al, Env. Man., Vol. 30, No. 3, pp. 391–405

Statistical-based land use models

Statistics: Assessment of land use drivers A. Aguiar, G. Câmara, I. Escada, “Spatial statistical analysis of land-use determinants in the Brazilian Amazon”. Ecological Modelling, 209(1-2):169–188, G. Espíndola, A. Aguiar, E. Pebesma, G. Câmara, L. Fonseca, “Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data”, Applied Geography, 32(2): , Land use models are good at allocating change in space. Their problem is: how much change will happen?

Driving factors of change (deforestation) source: Aguiar (2006)

Amazônia in 2007 x All Variables Variables Transportation (11) Distance Markets(7) Demography (3) Tecnology (2) Environmental (20) Public Policy(8) Market (8) Agrarian Structure(6)

Statistics: Humans as clouds Statistical analysis of deforestation source: Aguiar (2006)

Amazônia in 2007 x All Variables 1)transformações e análises de correlação de 65 para 31 variáveis 2)seleção do melhor modelo de 31 para 10 variáveis 3)regressão linear (AIC = ) 4)regressão espacial (AIC = ) R-squared Betap-level log(N_TRATOR_ ) <2e-16 sqrt(PSI_ASSENTAMENTOS_CLASSICOS) <2e-16 FERTIL_ALTA <2e-16 log(SOJA_ ) <2e-16 sqrt(PSI_GPM_SEDE_AMZ) <2e-16 sqrt(PSI_GPM_CAPITAIS) <2e-16 sqrt(PSI_PVM_IUMID) <2e-16 log(DIST_MIN_MAD + 1) <2e-16 TI_ <2e-16 UC_ <2e Cells

R-squared Betap-level FERTIL_ALTA <2e-16 log(DIST_MIN_MAD + 1) <2e-16 log(PREC_INV + 1) <2e-16 TI_ <2e-16 ASSENT_06_NUNFAM <2e-16 DIST_MIN_PORTOS <2e-16 FERTIL_MUITOBAIXA <2e-16 log(DIST_MIN_ROD_PAV + 1) <2e-16 UC_ <2e-16 R-squared Betap-level FERTIL_ALTA <2e-16 log(DIST_MIN_MAD_96 + 1) <2e-16 log(DIST_MIN_ROD_PAV_96 + 1) <2e-16 DIST_MIN_PORTOS <2e-16 A_UC_ <2e-16 log(POP_RUR_ ) <2e-16 A_ASSENT_96_NUNFAM <2e-16 log(PREC_INV + 1) <2e-16 A_NU_AGR_MEDIUM_ <2e Years 25Km Amazônia x Variables of 1996/ Variáveis

Statistical-based land use models

Allocation of change combining demand and cell potential at time t (ALLOCATION) Cell suitability for each land use at time t (POTENTIAL) Rate and magnitude of change for each land use at time t (DEMAND) Land use at time t-1 Land use map at time t Time Loop Top-down constraint Bottom-up calculation Feedbacks Driving factors of land use/cover change QUANTITY (at time t) Driving factors of land use/cover change LOCATION (at time t) Feedback on spatial drivers sources: P. Verburg, A.P. Aguiar Statistical-based land use models

Simple Demand Demand submodel Difference between years

Potential map Driving factors Neural Network Multivariate Statistics Mathematics Potential Transition potential submodel

Potential map Potential – CLUE like Transition potential submodel Protected Areas Roads Ports Deforestation Subtract from Deforestation

Transition Matrix (Markov chain) Global economic model Trend analysis Building Scenarios Demand

Potential map at t Landscape map at t Landscape map at t+1 Demand t+1 Rank-order Stochastic Iterative Allocation submodel Allocation

Scenario exploration: linking to process knowledge Cellular database construction Exploratory analysis and selection of subset of variables Porto Velho- Manaus BR 163 Cuiabá-Santarém São Felix/ Iriri ApuíHumaitá Boca do Acre Santarém Manaus- Boa Vista Aripuanã Scenario exploration

Scenarios for deforestation in Amazonia (2020)

SimAmazonia Modeling conservation in the Amazon basin Soares Filho et al., Nature, 2006 Simamazonia

SimAmazonia Subregiões do modelo SimAmazonia. Modeling conservation in the Amazon basin Soares Filho et al., Nature, 2006

SimAmazonia: deforestation scenarios Business-as-usualGovernance Modeling conservation in the Amazon basin Soares Filho et al., Nature, 2006

How good are statistical models?

Agents as basis for complex systems Agent: flexible, interacting and autonomous An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.

Agents: autonomy, flexibility, interaction Synchronization of fireflies

Bird Flocking No central authority: Each bird reacts to its neighbour Not possible to model the flock in a global manner. Need to necessary to simulate the INTERACTION between the individuals

Agent Space Space Agent Benenson and Torrens, “Geographic Automata Systems”, IJGIS, 2005 (but many questions remain...) Modelling collective spatial actions

Agent-Based Modelling: Computing approaches to complex systems Goal Environment Representations Communication Action Perception Communication source: Nigel Gilbert

Agent Space Space Agent source: Benenson and Torrens, “Geographic Automata Systems”, IJGIS, 2005 Modelling collective spatial actions

Agent-Based Modeling of Land change Theoretical Models Empirical models Agent-based models (ABM) range from theoretical to empirical. Theoretical models use simple generalizable ideas, whereas empirical models require more complexity and case-specific data..

61,000 ha 30,000 ha ha 60,000 ha 30,000 ha 50 ha 20,000 ha 200 ha Different farm sizes, different actors

Frontier areas Frontier: an area of changing resource use whose boundaries are continually evolving

Census data for São Félix do Xingu

Evolution of cattle/deforestation in São Félix do Xingu

Different actors and strategies in SFX

Model initialization (estimated farm distribution) (a) Deforested areas in 1985 (b) Estimated distribution of farms in 1985 Deforestation

Deforestation maps (PRODES) The frontier “exploded” between 1985 and 1997

Land concentration in Tucumã

Evolution of land tenure in São Felix

How to place agents in a frontier? São Felix do Xingu in 1985: farms are there, deforestation not yet started

Division of frontier in large and small farms Use the history of São Felix do Xingu to make assumption about farms

Extracting patterns from sequences of images M. Silva, G.Câmara, M.I. Escada, R.C.M. Souza, “Remote Sensing Image Mining: Detecting Agents of Land Use Change in Tropical Forest Areas”. International Journal of Remote Sensing, vol 29 (16): 4803 – 4822, 2008.

Hypothetical land tenure in 1985 (a) Deforested areas in 1985 (b) Estimated distribution of farms in 1985 Deforestation

Patterns of tropical deforestation

Agent states in São Felix do Xingu

Pasture management model

Deforestation: simulated x observed

Frontier evolution in Sao Felix Consolidated (dark red), pre-frontier (light red), frontier (light green) and post-frontier (dark green).

Both the spatial values and the deforestation totals emerge as a result of the agent’s decisions Model updates the support capacity of the region changes in response to agents’ decision. Agents then sense how geographical space has changed and use this information in their decision-making. Model results

“Agent-based modeling meets an intuitive desire to explicitly represent human decision making. (…) However, by doing so, the well-known problems of modeling a highly complex, dynamic spatial environment are compounded by the problems of modeling highly complex, dynamic decision-making. (…) The question is whether the benefits of that approach to spatial modeling exceed the considerable costs of the added dimensions of complexity introduced into the modeling effort. The answer is far from clear and in, my mind, it is in the negative. But then I am open to being persuaded otherwise ”. (from “Why I no longer work with agents”, 2001 LUCC ABM Workshop) Some caution necessary... Helen Couclelis

GLOBIOM: a global model for projecting how much land change could occur source: A. Mosnier (IIASA)

GLOBIOM: land use types and products source: A. Mosnier (IIASA)

REDD-PAC: land use policy assessment Land use data and drivers for Brazil Model cluster - realistic assumptions Globally consistent policy impact assessment Information infrastructure GLOBIOM, G4M, EPIC, TerraME TerraLib