New trends in Geoinformatics in a changing world

1. New trends in Geoinformatics in a changing world Gilberto Câmara National Institute for Space Research, Brazil

2. We need cooperation at a global level… By 2050... 8,5 billion people: 6 billion tons of GHG and 60 million tons of urban pollutants. Resource-hungry: We will withdraw 30% of available fresh water. Risky living: 80% urban areas, 25% near earthquake faults, 2% in coast lines less than 1 m above sea level. source: Guy Brasseur

3. Global Change: How is the Earth’s environment changing, and what are the consequences for human civilization? Global Change Where are changes taking place? How much change is happening? Who is being impacted by the change?

4. source: Global Land Project Science Plan (IGBP)

5. From land cover to land use Pasture Soybeans Abandoned Land

6. Land change is crucial for the world

7. Changes in dietary patterns: Meat consumption FAOSTAT 2007

8. Productivity and prices: the challenge source: The Economist

9. The food challenge source: Nature

10. The food challenge: technology gaps source: The Economist

11. Forests and food production: potential conflicts

12. 1973 1987 2000 Slides from LANDSAT 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? What techniques and tools are needed to model human-environment decision making?

13. Geoinformatics enables crucial links between nature and society Nature: Physical equations Describe processes Society: Decisions on how to Use Earth´s resources

14. Why GI Engineering? Chemistry Chemical Eng. Physics Electrical Eng. Computer Computer Eng. Science GI Science GI Engineering GI Engineering= “The discipline of systematic construction of GIS and associated technology, drawing on scientific principles”

15. Scientists and Engineers Photo 51(Franklin, 1952) Scientists build in ordertostudy Engineersstudy in order to build

16. What set of concepts drove GIS -20? Map-based (cartography) User-centered (user interfaces) Toblerianspaces (regionalized data analysis) Object-basedmodellingandspatialreasoning

17. Coverage Geo-field GIS-20: Object-oriented modelling Egenhofer, M. and A. Frank (1992). "Object-Oriented Modeling for GIS." URISA Journal 4(2): 3-19. SPRING´s object-oriented data model (1995) ARCGIS´s object-centred data model (2002) Spatial database contains contains Geo-object Cadastral Is-a Is-a Numerical Categorical

18. GIS-20: Topological Spatial Reasoning Egenhofer, M. and R. Franzosa (1991). "Point-Set Topological Spatial Relations." IJGIS 5(2): 161-174 OGC´s 9-intersection dimension-extended Open source implementations (GEOS)

19. GIS-20: User interfaces Jackson, J. (1990) Visualization of metaphors for interaction with GIS. M.S. thesis, University of Maine. Geographer´s desktop (1992) ArcView (1995)

20. GIS -20: Region-based spatial analysis GeoDA: Spatial data analysis SPRING´s Geostatistics Module

21. TerraAmazon – open source software for large-scale land change monitoring 116-112 116-113 Spatial database (PostgreSQL with vectors and images) 2004-2008: 5 million polygons, 500 GB images 166-112

22. mobiledevices sharedanalysis GIS-21 Data-centered, mobile-enabled, contribution-based, field-basedmodelling sensor networks ubiquitousimages

23. Data is coming... are we ready? 2014 2015 2012 2013 2011 CBERS-3 Amazônia-1 CBERS-4 Sentinel-2B Sentinel-2A Landsat-8 ResourceSat-2 ResourceSat-3

24. Current science practice based on data download Data Access Hitting a Wall How do you download a petabyte?

25. Current science practice based on data download Data Access Hitting a Wall How do you download a petabyte? You don’t! Move the software to the archive

26. Virtual Observatory If data is online, internet is the world’s best telescope Scientific Data Management in the Coming Decade (Jim Gray)

27. Sensor Webs source: ARGOS Tracking Positions collected over a fixed period of time Monitoring Data from remote stations, fixed or mobile

28. Earth observation satellites and geosensor webs provide key information about global change… …but that information needs to be modelled and extracted

29. What´s in an Image? “Remote sensing images provide data for describing landscape dynamics” (Câmara, Egenhofer et al., COSIT 2001).

30. GIS-21: Spatio-temporal semantics Different types of ST-objects (source: JP Cheylan)

31. GIS-21: Discovering the history of land change objects Reconstructing the history of a landscape

32. Land Use Change by Sugarcane expansion source: INPE

33. Sugarcane expansion source: Rudorff et al, Remote Sensing Journal (2010)

34. GIS-21: Spatio-temporal modelling “A dynamical spatial model is a computational representation of a real-world process where a location on the earth’s surface changes in response to variations on external and internal dynamics on the landscape” (Peter Burrough) f (It) f (It+1) f (It+2) f ( It+n ) F F . . DynamicSpatialModelsneedgood conceptual models

35. Spatially-explicit LUCC models • Explain past changes, through the identification of determining factors of land use change; • Envision which changes will happen, and their intensity, location and time; • Assess how choices in public policy can influence change, by building different scenarios considering different policy options.

36. Concepts for spatial dynamical models Events and processes Resilience

37. Concepts for spatial dynamical models vulnerability degradation

38. Concepts for spatialdynamicalmodels biodiversity sustainability and much more… Human-environmental models need to describe complex concepts (and store their attributes in a database)

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

40. Models: From Global to Local Athmosphere, ocean, chemistryclimatemodel (200 x 200 km) Atmosphereonly global climatemodel (50 x 50 km) Regional climatemodel (10 x 10 km) Hydrology, Vegetation SoilTopography (1 x 1 km) Regional land use change Socio-economicadaptation (e.g., 100 x 100 m)

41. Human-enviroment models should be multi-scale, multi-approach 25 x 25 km2 1 x 1 km2 [Moreira et al., 2008]

42. Multi-scale modelling using explicit relationships Express explicit spatial relationships between individual objects in different scales [Moreira et al., 2008] [Carneiro et al., 2008]

43. How can we express behavioural changes in human societies? photos: Isabel Escada When a small farmer becomes a medium-sized one, his behaviour changes Medium-SizedFarmers Small Farmers

44. Farms Settlements 10 to 20 anos Societal systems undergo phase transitions Isabel Escada, 2003 RecentSettlements (lessthan 4 years) OldSettlements (more than 20 years) [Escada, 2003]

45. Networks as enablersofhumanactions Bus traffic volume in São Paulo Innovation network in SiliconValley

46. GIS-21: Network-based analysis Emergentarea Consolidatedarea Modellingbeefchains in Amazonia

47. Cell Spaces Support for cellular automata and agents TerraME: Computational environment for developing human-environment models http://www.terrame.org [Carneiro, 2006]

48. TerraLib: spatio-temporal database as a basis for innovation Modelling (TerraME) Visualization (TerraView) Spatio-temporal Database (TerraLib) Data Mining(GeoDMA) Statistics (aRT)

49. R-Terralib interface R data from geoR package. Loaded into a TerraLib database, and visualized with TerraView.

50. Conclusions Managing change is a major challenge for the scientific community Images are a major source of new data Move the software, not the data We need new algebras, data representations and algorithms to handle spatio-temporal data