Data-intensive Geoinformatics

1. Data-intensive Geoinformatics Gilberto Câmara October 2012 Licence: Creative Commons ̶̶̶̶ By Attribution ̶̶̶̶ Non Commercial ̶̶̶̶ Share Alike http://creativecommons.org/licenses/by-nc-sa/2.5/

2. Spatialsegregation indexes Remotesensingimagemining INPE´s strong point: a combination of problem-driven GI research and engineering GI software: SPRING andTerraView Landchangemodelling

3. Data-intensive Geoinformatics = principles and applications of spatial information science to extract information from very large data sets image: NASA

4. Enhanced environmental information service provision to users through knowledge platforms: Delivering applied knowledge to support innovative adaptation and mitigation solutions, based on the observations and predictions

5. Nature, 29 July 2010

6. Nature, 29 July 2010 Brazil is the world’s current largest experiment on land change and its effects: will it also happen elsewhere? Today’s questions about Brazil could be tomorrow’s questions for other countries

7. Where is the food coming from and going to? graphics: The Economist

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

9. Human actions and global change Global Change Where are changes taking place? How much change is happening? Who is being impacted by the change? What is causing change? photo: A. Reenberg

10. Human actions and global change Global Change • “Can we improve the architecture of land use information systems to increase their capacity to deal with big geospatial data sets and to provide better information for researchers and decision-makers?” • “Can we develop models and statistical analysis methods that increase our knowledge of what causes land change and our capacity to project scenarios of future change?” photo: A. Reenberg

11. “By 2020, Brazilwillreducedeforestationby 80% relativeto 2005.” (pres. Lula in Copenhagen COP-15)

12. “Deforestation in Brazilian Amazonia is down by a whopping 78% from its recent high in 2004. If Brazil can maintain that progress — and Norway has put a US$1-billion reward on the table as encouragement — it would be the biggest environmental success in decades” (Nature, Rio + 20 editorial)

13. How much it takes to survey Amazonia? 116-112 30 Tb of data 500.000 lines of code 150 man-years of software dev 200 man-years of interpreters 116-113 166-112

14. TerraAmazon – open source software for large-scale land change monitoring 116-112 116-113 166-112 Ribeiro V., Freitas U., Queiroz G., Petinatti M., Abreu E. , “The Amazon Deforestation Monitoring System”. OSGeo Journal 3(1), 2008.

15. Stage 3 – Multidatabase access (Terralib 5+) Modelling Data discovery Data access Analysis Data source Data source Data source Remote Analysis Remote Analysis Remote Analysis

16. Data discovery: the whole earth catalogue ? answers questions What data exists about Quixeramobim? When did this flood happen? Where do I find data on forest degradation?

17. GEOSS

18. catalogue GEOSS catalogue Broker catalogue catalogue Improving GEOSS with brokers source: R.Shibasaki

19. Linking INPE’s data to a semantic search engine EuroGEOSS broker Some experiments linking EuroGEOSS broker with INPE’s data base show potential (credits to LubiaVinhas)… but there’s much to be done…

20. Semantic data discovery in Terralib 5+? Modelling Data discovery Data access Analysis internet Data source Data source Data source Remote Analysis Remote Analysis Remote Analysis

21. What do we know we don’t know? Representing concepts is hard vulnerability? climate change? poverty? image: WMO

22. What do we know we don’t know? We’re bad at representing meaning Representing concepts is hard degradation deforestation? degradation? disturbance?

23. Geosemantics: representing concepts is hard vulnerability degradation image: Y.A. Bertrand

24. Geosemantics: representing concepts is hard sustainability resilience image: Y.A. Bertrand Human-environmental models need to describe complex concepts (and store their attributes in a database)

25. What do we know we don’t know? Representing change is very hard images: USGS

26. What do we know we don’t know? Describing events and processes is very hard When did the flood occur?

27. images: USGS Slides from LANDSAT 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? Aral Sea 1973 1987 2000

28. FAPESP Climate Change Program Workshop 2011 Land Use Change in Amazonia: Institutional analysis and modeling at multiple temporal and spatial scales Gilberto Câmara, Ana Aguiar, Roberto Araújo, Patrícia Pinho, Luciano Dutra, Corina Freitas, Leila Fonseca, Isabel Escada, Silvana Amaral, Pedro Andrade-Neto

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

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

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

32. Institutional analysis in Amazonia Identifydifferentagentsandtry to modeltheiractions Land changepatterns Field work Land changemodels Urban networks

33. Modelling collective spatial actions S. Costa, A. Aguiar, G. Câmara, T. Carneiro, P. Andrade, R. Araújo, “Using institutional arrangements and hybrid automata for regional scale agent-based modelling of land change” (under review), 2012.

34. Linking remote sensing and census: population models S. Amaral, A. Gavlak , I. Escada, A. Monteiro, “Using remote sensing and census tract data to improve representation of population spatial distribution: Case studies in the Brazilian Amazon”. Population and Environment, 34(1): 142-170, 2012.

35. Radar images for land cover classification Li, G. ; Lu, D.; Moran, E. ; Dutra, L.; Batistella, M. . A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. ISPRS Journal of Photogrammetry and Remote Sensing, 70:26-38, 2012.

36. REDD+ and Land Use Policy Assessment Centre

37. Pathways: understanding the tradeoffs between land use, emissions and biodiversity (PRODES, 2008) (Getty Images, 2008) source: Espindola, 2012

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

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

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

42. 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, 2007. 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):240-252, 2012. Land use models are good at allocating change in space. Their problem is: how much change will happen?

43. Information extraction from image time series “Remote sensing images provide data for describing landscape dynamics” (Câmara et al., "What´s in An Image?“ COSIT 2001) data source: B. Rudorff (LAF/INPE)

44. MODIS time series describe changes in land use Land use change by sugarcane expansion data source: B. Rudorff (LAF/INPE)

45. Setting up the global research agenda

46. Conclusions We need to be part of the community that sets up the scientific agenda for global change We can develop new technology and models to build enhanced environmental information services (knowledge platforms)