From Pixels to Processes: Detecting the Evolution of Agents in a Landscape

1. Department of Geography, SUNY Bufallo, February 2007 From Pixels to Processes: Detecting the Evolution of Agents in a Landscape Gilberto Câmara Director National Institute for Space Research Brazil

2. Knowledge gap for spatial data source: John McDonald (MDA)

3. The way remote sensing data is used • Exctracting information from remote sensing imagery • Most applications use the “snapshot” paradigm • Recipe analogy • Take 1 image (“raw”) • “Cook” the image (correction + interpretation) • All “salt” (i.e., ancillary data) • Serve while hot (on a “GIS plate”) • But we have lots of images! • Immense data archives (Terabytes of historical images)

4. The challenge of remote sensing data mining • How many cutting-edge applications exist for extracting information in large image databases? • How much R&D is being invested in spatial data mining in large repositories of EO data? • How do we put our image databases to more effective use?

5. Land remote sensing data mining: A GIScience view • A large remote sensing image database is a collection of snapshots of landscapes, which provide us with a unique opportunity for understanding how, when, and where changes take place in our world. • We should search for changes, not search for content • Research challenge: How do model land change for data extracted from a land remote sensing database?

6. MSS – Landsat 2 – Manaus(1977)

7. TM – Landsat 5 – Manaus (1987)

8. Can we avoid that this…. Source: Carlos Nobre (INPE)

9. Fire... ….becomes this? Source: Carlos Nobre (INPE)

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

11. Modelling Land Change in Amazonia • How much deforestation is caused by: • Soybeans? • Cattle ranching? • Small-scale setllers? • Wood loggers? • Land speculators? • A mixture of the above?

12. Agent-based models • Recent emphasis on agent-based modeling for simulation of social processes. • Simulations can generate patterns similar to real-life situations • How about real-life modelling? • We need to be able to describe the types of agents that operate in a given landscape.

13. Extracting Land Change Agents from Images • Land change agents can be inferred from land change segments extracted from remote sensing imagery. • Different agents can be distinguished by their different spatial patterns of land use. • This presentation • Description of methodology • Case studies in Amazonia

14. Research Questions • What are the different land use agents present in the database? • When did a certain land use agent emerge? • What are the dominant land use agents for each region? • How do agents emerge and change in time?

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

16. What Drives Tropical Deforestation? % of the cases  5% 10% 50% Underlying Factors driving proximate causes Causative interlinkages at proximate/underlying levels Internal drivers *If less than 5%of cases, not depicted here. source:Geist &Lambin

17. 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

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

19. photo source: Edson Sano (EMBRAPA)

20. Different agents, different motivations • Small-scale settlers • Associated to social movements • 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

21. Landscape Analysis: Land units associated to agents Space Partitions in Rondônia …linking human activities to the landscape

22. Is it enough to describe Amazonian land use patterns? Agent Typology: A simple example Tropical Deforestation Spatial Patterns: Corridor, Diffuse, Fishbone, Geometric (Lambin, 1997)

23. Landscape Ecology Metrics • Patterns and differences are immediately recognized by the eye + brain • Landscape Ecology Metrics allow these patterns in space to be described quantitatively Source: Phil Hurvitz

24. (image from Fragstats manual) Fragstats (patch metrics)

25. Some patch metrics • PARA = perimeter/area ratio • SHAPE = perimeter/ (perimeter for a compact region) • FRAC = fractal dimension index • CIRCLE = circle index (0 for circular, 1 for elongated) • CONTIG = average contiguity value • GYRATE = radius of gyration

26. 1975 1986 1992 Increased fragmentation on Rondonia, Brazil

27. Region-growing segmentation

28. Remote sensing image mining

29. Patterns of tropical deforestation (example 1)

30. Patch metrics for example 1

31. Decision tree classifier • C4.5 decision tree classifier (Quinlan 1993). • Each node matches a non-categorical attribute and each arc to a possible value of that attribute. • Each node is associated the numerical attribute which is most informative among the attributes not yet considered in the path from the root.

32. Decision tree for patterns metrics are: perimeter/area ratio (PARA) and fractal dimension (FRAC)

33. Validation set for decision tree (ex 1) Validation showed 81% correctness

34. Incra settlement projects • Small, medium and large farms • Started in the 70’s • Different spatial and temporal patterns • Lots size of 25 ha to 100 ha – Farms from 500 ha. • Cattle ranching Case Study 1:Rondônia Objective: To capture patterns and to characterize and model land use change processes TM/Landsat, 5, 4, 3 (2000) Prodes (INPE, 2000) Escada, 2003.

35. Spatial patterns in the Vale do Anari irregular, linear, regular

37. Decision tree for Vale do Anari

38. Changes in Incra parcels configuration by (Coy, 1987; Pedlowski e Dale, 1992; Escada 2003): • Fragmentation • Transference • Land concentration

39. Vale do Anari – 1982 -1985 REG Patterns/Typology IRR: Irregular – Colonist parcels LIN: Linear – roadside parcels REG: Regular agregation parcels Pereira et al, 2005 Escada, 2003

40. Vale do Anari – 1985 - 1988 REG Pereira et al, 2005 Escada, 2003

41. Vale do Anari – 1988 - 1991 REG Pereira et al, 2005 Escada, 2003

42. Vale do Anari – 1991 - 1994 Pereira et al, 2005 Escada, 2003

43. Vale do Anari – 1994 - 1997 REG Pereira et al, 2005 Escada, 2003

44. Vale do Anari – 1997 - 2000 REG Pereira et al, 2005 Escada, 2003

45. Confirmed by field work Vale do Anari – 1985 - 2000 REG REG Pereira et al, 2005 Escada, 2003

46. Marked land concentration Government plan for settling many colonists in the area has failed. Large farmers have bought the parcels in an illicit way

47. Case study 2: Xingi-Iriri watershed in the state of Pará

48. Spatial patterns in the Xingu-Iriri region linear, small irregular, irregular, medium regular, large regular

50. Decision tree for Terra do Meio spatial patterns