Modelling Land Change: The Scientific Challenges

1. 1st Brazilian Symposium on Global Enviromental Change, Rio de Janeiro, March 2007 Modelling Land Change: The Scientific Challenges Gilberto Câmara Director National Institute for Space Research Brazil

2. Model = a simplified description of a complex entity or process E0 E4 What is a Model? Deforestation deforest Farmer space owns • income • land use • soil type Model = entities+ relations + attributes + rules source: Carneiro (2006)

3. Application of multidisciplinary knowledge to produce a model. Modelling Complex Problems If (... ? ) then ... Desforestation?

4. What is Computational Modelling? • Design and implementation of computational enviroments for modelling • Requires a formal and stable description • Implementation allow experimentation • Rôle of computer representation • Bring together expertise in different field • Make the different conceptions explicit • Make sure these conceptions are represented in the information system

5. Scientific Challenges • “Third culture” • Modelling of physical phenomena • Understanding of human dimensions • How to model human actions? • What makes people do certain things? • Why do people compete or cooperate? • What are the causative factors of human actions?

6. Limits for Models Uncertainty on basic equations Social and Economic Systems Quantum Gravity Particle Physics Living Systems Global Change Hydrological Models Chemical Reactions Meteorology Solar System Dynamics Complexity of the phenomenon source: John Barrow (after David Ruelle)

7. Public Policy Issues • What are the acceptable limits to land cover change activities in the tropical regions in the Americas? • What are the future scenarios of land use? • How can food production be made more efficient and productive? • How can our biodiversity be known and the benefits arising from its use be shared fairly? • How can we manage our water resources to sustain our expected growth in urban population?

8. Modelling Land Change in Amazonia Territory (Geography) Money (Economy) Culture (Antropology) Modelling (GIScience)

9. Forecast tp + 10 Calibration Calibration Dynamic Spatial Models tp - 20 tp - 10 tp source: Cláudia Almeida

10. Soybeans Ranchers Small-scale Farming Challenge: How do people use space? Loggers Competition for Space Source: Dan Nepstad (Woods Hole)

11. Dynamic areas (current and future) Escada et al. (2005) New Frontiers INPE 2003/2004: Intense Pressure Deforestation Forest Future expansion Non-forest Clouds/no data

12. Altamira (Pará) – LANDSAT Image – 22 August 2003

13. Altamira (Pará) – MODIS Image – 07 May 2004

14. Altamira (Pará) – MODIS Image – 21 May 2004 Imagem Modis de 2004-05-21, com excesso de nuvens

15. Altamira (Pará) – MODIS Image – 07 June 2004

16. Altamira (Pará) – MODIS Image – 22 June 2004 6.000 hectares deforested in one month!

17. Altamira (Pará) – LANDSAT Image – 07 July 2004

18. Amazonian new frontier hypothesis (Becker) • “The actual frontiers are different from the 60’s and the 70’s • In the past it was induced by Brazilian government to expand regional economy and population, aiming to integrate Amazônia with the whole country. • Today, induced mostly by private economic interests and concentrated on focus areas in different regions.

19. Modelling Human Actions: Two Approaches • Models based on global factors • Explanation based on causal models • “For everything, there is a cause” • Human_actions = f (factors,....) • Emergent models • Local actions lead to global patterns • Simple interactions between individuals lead to complex behaviour • “More is different” • “The organism is intelligent, its parts are simple-minded”

20. Emergence: Clocks, Clouds or Ants? • Clocks • Paradigms: Newton’s laws (mechanistic, cause-effect phenomena describe the world) • Clouds • Stochastic models • Theory of chaotic systems • Ants • The colony behaves intelligently • Intelligence is an emergent property

21. Statistics: Humans as clouds • Establishes statistical relationship with variables that are related to the phenomena under study • Basic hypothesis: stationary processes • Exemples: CLUE Model (University of Wageningen) y=a0 + a1x1 + a2x2 + ... +aixi +E

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

23. Área de estudo – ALAP BR 319 e entorno (Aguiar, 2006b) ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas Rios principais Portos

24. BASELINE SCENARIO – Hot spots of change (1997 a 2020) % mudança 1997 a 2020: 0.0 – 0.1 0.1 – 0.2 0.2 – 0.3 0.3 – 0.4 0.4 – 0.5 0.5 – 0.6 0.6 – 0.7 0.7 – 0.8 0.8 – 0.9 0.9 – 1.0 ALAP BR 319 Estradas pavimentadas em 2010 Estradas não pavimentadas source: Aguiar (2006b) Rios principais

25. GOVERNANCE SCENARIO – Differences from baseline scenario (Aguiar, 2006b) Differences: Protection areas ALAP BR 319 Estradas pavimentadas em 2010 Less: 0.0 -0.50 Sustainable areas Estradas não pavimentadas More: 0.0 0.10 Rios principais

27. The trouble with statistics • Extrapolation of current measured trends • How do we know if tommorow will be like today? • How do we incorporate feedbacks?

28. Complex adaptative systems • How come that a city with many inhabitants functions and exhibits patterns of regularity? • How come that an ecosystem with all its diverse species functions and exhibits patterns of regularity? • How can we explain how similar exploration patterns appear on the Amazon rain forest?

29. Results of human society such as economies Source: John Finnigan (CSIRO)

30. Farms Settlements 10 to 20 anos Recent Settlements (less than 4 years) Old Settlements (more than 20 years) Escada, 2003 Agents and CA: Humans as ants Identify different actors and try to model their actions

31. Different agents, different motivations • Intensive agriculture (soybeans) • export-based • responsive to commodity prices, productivity and transportation logistics • Extensive cattle-ranching • local + export • responsive to land prices, sanitary controls and commodity prices

32. photo source: Edson Sano (EMBRAPA) Large-Scale Agriculture

33. Different agents, different motivations • Small-scale settlers • Associated to social movements (MST, Church) • Responsive to capital availability, land ownership, and land productivity • Can small-scale economy be sustainable? • Wood loggers • Primarily local market • Responsive to prime wood availability, official permits, transportation logistics • Land speculators • Appropriation of public lands • Responsive to land registry controls, law enforcement

34. Agent model using Cellular Automata 1985 • Small farms environments: • 500 m resolution • Categorical variable: deforested or forest • One neighborhood relation: • connection through roads • Large farm environments: • 2500 m resolution • Continuous variable: • % deforested • Two alternative neighborhood • relations: • connection through roads • farm limits proximity 1997 1997

35. The trouble with agents • Many agent models focus on proximate causes • directly linked to land use changes • (in the case of deforestation, soil type, distance to roads, for instance) • What about the underlying driving forces? • Remote in space and time • Operate at higher hierarchical levels • Macro-economic changes and policy changes

36. Game theory and mobility • Two players get in a strive can choose shoot or not shoot their firearms. • If none of them shoots, nothing happens. • If only one shoots, the other player runs away, and then the winner receives $1. • If both decide to shoot, each group pays $10 due to medical cares.

37. Game theory and mobility Three strategies A - ((10%;; $200; 0) B - ((50%;; $200; 0) C - ((100%;; $200;; 0))

38. Game theory and mobility • What happens when players can move? If a player loses too much, he might move to an adjacent cell

39. Mobility breaks the Nash equilibrium!

40. TerraME Runtime Environment

41. Flexible neighbourhoods Consolidated area Emergent area

42. Scale is a generic concept that includes the spatial, temporal, or analytical dimensions used to measure any phenomenon. Extent refers to the magnitude of measurement. Resolution refers to the granularity used in the measures. Scale (Gibson et al. 2000)

43. Multi-scale approach

44. The trouble with current theories of scale • Conservation of “energy”: national demand is allocated at local level • No feedbacks are possible: people are guided from the above

45. The search for a new theory of scale • Non-conservative: feedbacks are possible • Linking climate change and land change • Future of cities and landscape integrate to the earth system

46. The big challenge: a theory of scale

47. U U U Nested Cellular Automata Environments can be nested Multiscale modelling Space can be modelled in different resolutions

48. Cell Spaces • Components • Cell Spaces • Generalizes Proximity Matriz – GPM • Hybrid Automata model • Nested enviroment Computational Modelling with Cell Spaces fonte: Carneiro (2006)

49. Cell Spaces

50. 2500 m 2.500 m e 500 m Cellular Data Base Resolution