1. Databases and Global Environmental Change Gilberto Câmara Diretor, INPE

2. source: IGBP How is the Earth’s environment changing, and what are the consequences for human civilization? The fundamental question of our time

3. Earth is a system of systems Human actions are changing the balance!

5. Earth as a system

6. sources: IPCC and WMO Impacts of global environmental change By 2020 in Africa, agriculture yields could be cut by up to 50%

7. Precipitation anomalies [(2071-2100)- (1961-90)] in mm/day A2 Temperature anomalies [(2071-2100)- (1961-90)] in o C B2 Seco Quente Climate change scenarios in Brazil

8. T min up 1 C! Source: (Obregón e Marengo, 2007) Average temp raised 0,7 C in 50 years in Brazil

9. Fonte: Eduardo Assad, Embrapa Impacts on Agriculture

10. Collapse of Amazon Rain Forest? source: Oyama and Nobre, 2003 Is there a tipping point for Amazonia? forest savanna caatinga pastures desert 2000 2100

11. Hidrological Balance – NE Brazil Less Water for Agriculture! 1961-1990 2071-2100 Source: Marengo and Salati, 2007 Impacts on Water Availability in NE Brazil

12. source: Greenpeace Amazônia in 2005

13. Amazônia in 2015? fonte: Aguiar et al., 2004

14. Great challenge: Database support for earth system science source: NASA

15. Global Change Where are changes taking place? How much change is happening? Who is being impacted by the change?

16. Global Land Project What are the drivers and dynamics of variability and change in terrestrial human- environment systems? How is the provision of environmental goods and services affected by changes in terrestrial human- environment systems? What are the characteristics and dynamics of vulnerability in terrestrial human- environment systems?

17. Data chain in Earth System Science fonte: NASA

18. 150 TF 5 TF 2 TF 40 GF 8 GF #1 trend INPE´s supercomputers and world´s TOP 500 #500 trend INPE (MPP equivalent peak performance) Sum top 500

19. Índice de Vegetação Large Scale Data Earth System Science Data Handling PetaFlop Centres CO 2 Emissions Megascenarios Regional Centers B1-low Regional Scenarios Policy Options

20. Terrestrial Airborne Near- Space LEO/MEO Commercial Satellites and Manned Spacecraft Far- Space L1/HEO/GEO TDRSS & Commercial Satellites Deployable Permanent Forecasts & Predictions Aircraft/Balloon Event Tracking and Campaigns User Community Vantage Points Capabilities Global Earth Observation System of Systems

21. Weather and climate source: WMO 11,000 land stations (3000 automated) 900 radiosondes, 3000 aircraft 6000 ships, 1300 buoys 5 polar, 6 geostationary satellites

22. ARGOS Data Collection System (16000 plats) 650,000 messages processed daily

23. Tracking Positions collected over a fixed period of time Monitoring Data from remote stations, fixed or mobile Data collection services

24. Argo bouy network

25. I am the Walrus

26. Models: From Global to Local Athmosphere, ocean, chemistry climate model (resolution 200 x 200 km) Atmosphere only climate model (resolution 50 x 50 km) Regional climate model (resolution e.g 10 x 10 km) Hydrology, Vegetation Soil Topography (e.g, 1 x 1 km) Regional land use change Socio-economic changes Adaptative responses (e.g., 10 x 10 m)

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

28. augmented reality sensor networks mobile devices ST DBMS-21 ubiquitous images and maps Data-centered, mobile-enabled, contribution-based, field-based modelling

29. Slides from LANDSAT Aral Sea Bolivia 1975 19922000 197319872000 source: USGS Databases and Change: A Research Programme Understanding how humans use space Predicting changes resulting from human actions Modeling the interaction between society and nature

30. How can DBMS technology handle Earth System Science data? What algebra is needed for spatio-temporal data? How can this algebra be handled in an object- relational DBMS?

31. Identity conditions on ST data Average temp for IPCC scenarios Continuous fields (x,y,z,t)

32. land_cover cells in 1985 Identity conditions on ST data land_cover cells in 2000 Individual objects (id, {t,{(x,y,z)}})

33. Identity conditions on ST data: Images “Remotely sensed images are ontologically instruments for capturing landscape dynamics” 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.

34. Landsat Image 13/Ago/2003 Identity conditions on ST data: Images

35. Deforestation 13/Ago/2003 until 07/Mai/2004 Deforestation in 13/Aug/2003 (yellow) + deforestation from 13/Aug/2003 until 07/mai/2004 (red) Identity conditions on ST data: Images

36. Deforestation on 21/May/2004 Deforestation in 13/Aug/2003 (yellow) + deforestation from 13/Aug/2003 until 07/May/2004 (red) + deforestation on 21/May/2004 (orange) Identity conditions on ST data: Images

37. Identity conditions have uncertain cases! Furacão Catarina (março/2004) Imagem NASA

38. Modelling change…from practice to theory Outiline of a theory for change modelling in spatio-temporal data

39. What is a geo-sensor? measure (s,t) = v s ⋲ S - set of locations in space t ⋲ T - is the set of times. v ⋲ V - set of values Basic spatio-temporal types S: set of locations (space) T: set of intervals (time) I: set of identifiers (objects) V: set of values (attributes)

40. What is a geo-sensor? measure (s,t) = v s ⋲ S - set of locations in space t ⋲ T - is the set of times. v ⋲ V - set of values Field (static) field : S  V The function field gives the value of every location of a space

41. Slides from LANDSAT Aral Sea Bolivia snap (1973) Time-varying fields are modelled by snapshots snap : T  Field snap : T  (S  V) The function snap produces a field with the state of the space at each time. snap (1987)snap (2000) snap (1975)snap (1992)snap (2000)

42. Sensors: sources of continuous information

43. Sensors: water monitoring in Brazilian Cerrado Wells observation 50 points 50 semimonthly time series (11/10/03 – 06/03/2007) Rodrigo Manzione, Gilberto Câmara, Martin Knotters

44. Fixed sensors: time series (histories) Well 30 Well 40 Well 56 Well 57 hist: S  (T  V) each sensor (fixed location) produces a time series

45. Evolving (modifiable) object life: I  (T  (S,V)) The function life produces the evolution of a modifiable object

46. A life´s trajectory life : I ⟶ (T ⟶ (S,V)) The life of the object is also a trajectory

47. Which objects are alive at time T and where are they? exist : T ⟶ (I ⟶ (S,V))

48. Models: From Global to Local snap: T  (S  V) evolution of a landscape hist: S  (T  V) History of a location life : I  (T  (S,V)) the life of an object in space-time exist: T  (I  (S,V)) objects alive in a time T

49. A model for time-varying geospatial data.... Temporal entity T-field (coverage set) T-object hist(o i ) (feature) snap(t) (coverage [t]) Feature instance[t] set has-a is-a has-a location has-a T-fields have snapshotsT-objects have histories

50. ST DBMS as a basis for data integration Visualization (TerraView) Spatio-temporal Database (TerraLib) Modelling (TerraME) Data Mining(GeoDMA)Statistics (aRT)

51. GIS-21: Dynamical modelling integrated in a spatio-temporal database Spatio-temporal database G. Câmara, L. Vinhas, G. Queiroz, K. Ferreira, A.M.V. Monteiro, M. Carvalho, MA Casanova. “TerraLib: An open-source GIS library for large-scale environmental and socio-economic applications”. In: B. Hall, M. Leahy (eds.), “Open Source Approaches to Spatial Data Handling”. Berlin, Springer, 2008.

52. GIS-21: Dynamical modelling integrated in a spatio-temporal database

53. Consolidated area GIE-21: Network-based analysis Emergent area Modelling beef chains in Amazonia

54. GIS-21: Dynamical spatial modelling with Agents in Cell Spaces Cell Spaces Generalized Proximity Matrix – GPM Hybrid Automata model Nested scales TerraME: Based on functional programming concepts (second-order functions) to develop dynamical models Tiago Garcia de Senna Carneiro, “"Nested-CA: A Foundation for Multiscale Modelling of Land Use and Land Cover Change”. PhD Thesis, INPE, june 2006

55. 166-112 116-113 116-112 TerraAmazon – open source software for large-scale land change monitoring Spatial database (PostgreSQL with vectors and images) 2004-2008: 5 million polygons, 500 GB images

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

57. Earth System Science data management poses a major challenge for the database community We need new algebras and data representation and handling techniques to deal with ESS data Conclusions