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. Spatial segregation indexes Remote sensing image mining GI software: SPRING and TerraViewLand change modelling INPE´s strong point: a combination of problem-driven GI research and engineering

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

10. 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?” Human actions and global change photo: A. Reenberg

11. “By 2020, Brazil will reduce deforestation by 80% relative to 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. 166-112 116-113 116-112 How much it takes to survey Amazonia? 30 Tb of data 500.000 lines of code 150 man-years of software dev 200 man-years of interpreters

14. 166-112 116-113 116-112 TerraAmazon – open source software for large-scale land change monitoring 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+) Data source Data source Data source Modelling Data discoveryData accessAnalysis Remote Analysis

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

17. GEOSS

18. Improving GEOSS with brokers source: R.Shibasaki

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

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

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

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

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

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

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

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

27. Slides from LANDSAT Aral Sea 197319872000 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?

28. 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 FAPESP Climate Change Program Workshop 2011

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

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

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

32. Institutional analysis in Amazonia Identify different agents and try to model their actions Field work Land change patterns Land change models 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. (Getty Images, 2008) (PRODES, 2008) source: Espindola, 2012 Pathways: understanding the tradeoffs between land use, emissions and biodiversity

38. Ações de Mitigação (NAMAs)2020 (tendencial) Amplitude da redução 2020 Proporção de Redução (mi tCO2) Uso da terra1084669 24,70% Red Desmatamento Amazônia (80%) 564 20,90% Red Desmatamento no Cerrado (40%) 104 3,90% Agropecuária6271331664,90%6,10% Recuperação de Pastos 831043,10%3,80% ILP - Integração Lavoura Pecuária 18220,70%0,80% Plantio Direto 16200,60%0,70% Fixação Biológica de Nitrogenio 16200,60%0,70% Energia9011662076,10%7,70% Eficiência Energética 12150,40%0,60% Incremento do uso de biocombustíveis 48601,80%2,20% Expansão da oferta de energia por Hidroelétricas 79992,90%3,70% Fontes Alternativas (PCH, Bioeletricidade, eólica) 26331,00%1,20% Outros928100,30%0,40% Siderurgia – substituir carvão de desmate por plantado 8100,30%0,40% Total2703975105236,10%38,90%

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

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

45. Setting up the global research agenda

46. 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) Conclusions