The Importance of Improving Collection and Access to Environmental Data in the Americas

1. The Importance of Improving Collection and Access to Environmental Data in the Americas Gilberto Câmara Director for Earth Observation National Institute for Space Research

2. With thanks to... • Carlos Nobre, CPTEC/INPE • Antonio Nobre, INPA • Eduardo Assad, EMBRAPA • João Vianei Soares, Miguel Monteiro, INPE • Daniel Hogan, UNICAMP • Ima Vieira, Peter Toledo, Mike Hopkins, MPEG • Leandro Ferreira, Ana Albernaz, MPEG • Luiz Bevilacqua, AEB/Brazilian Academy of Sciences • José Simeão Medeiros, INPE • and the whole INPE team....

3. What is Environmental Data? • Environment == “catch-all” word • “Enviromental Data”  Earth Sciences data • Athmosphere, oceans, biosphere • General feature • Collected on a geographical location • Either “in situ” or by remote sensing • In many cases, in “someone else’s backyard”

4. LBA Flux Towers on Amazonia Source: Carlos Nobre (INPE)

5. Source: Carlos Nobre (INPE) Biodiversity...

6. CBERS Image

7. Challenges of Sustainable Development Unlike other factors of production (such as capital and labor), natural resources are inflexible in their location. The Amazonian Forest is where it is; the water resources for our cities cannot be very far away from them. The challenge posed by sustainable development is that we can no longer consider natural resources as indefinitely replaceable, and move people and capital to new areas when existing resources become scarce or exhausted: there are no new frontiers in a globalized world. (Daniel Hogan)

8. Sustainability Science Core Questions • How can the dynamic interactions between nature and society be better incorporated in emerging models and conceptualizations that integrate the earth system, human development and sustainability? • How are long-term trends in environment and development, including consumption and population, reshaping nature-society interactions in ways relevant to sustainability? • What determines vulnerability/resilience of nature-society interactions for particular places and for particular types of ecosystems and human livelihoods? Source: Sustainability Science Workshop, Friibergh, SE, 2000

9. Sustainability Science Core Questions • Can scientifically meaningful ‘limits’ or ‘boundaries’ be defined that would provide effective warning of conditions beyond which the nature-society systems incur a significantly increased risk of serious degradation? • How can today’s relatively independent activities of research planning, monitoring, assessment and decision support be better integrated into systems for adaptative management and societal learning?” Source: Sustainability Science Workshop, Friibergh, SE, 2000

10. Public Policy Issues • What are the acceptable limits to land cover change activities in the tropical regions in the Americas? • What are the future scenarios of land use? • How can food production be made more efficient and productive? • How can our biodiversity be known and the benefits arising from its use be shared fairly? • How can we manage our water resources to sustain our expected growth in urban population?

11. The Importance of Environmental Data • Our knowledge of earth system science is very incomplete • Support for earth science modelling • Understanding of processes • Supporting “conjectures and refutations” • Helps address sustainability science questions • From scientific questions to public policy issues • Data collection brings new questions and helps formulate new ones • Breaking the five orders of ignorance

12. The Five Orders of Ignorance • 0th Order Ignorance (0OI): Lack of Ignorance • I (provably) know something • 1st Order Ignorance (1OI): Lack of Knowledge • I do not know something • 2nd Order Ignorance (2OI): Lack of Awareness • I do not know that I do not know something • 3rd Order Ignorance (3OI): Lack of Process • I do not know a suitably effective way to find out that I don’t know that I don’t know something • 4th Order Ignorance (4OI): Meta-Ignorance • I do not know about the Five Orders of Ignorance The five orders of ignorance, Phillip G. Armour, CACM,43(10), Oct 2000

13. Why is Environmental Data Different? • Cannot be re-created or synthesized in a laboratory • Unlike data in Physical, Chemical and Biological Sciences • Requirement of access to a data collection size • Granted by mutual consent • Implicitly conceded by international conventions • Remote Sensing is ruled by COPUOS • Biodiversity collection is guided by Biodiversity convention • Extremely sensitive topic • Many governments and politicians think of data collection as “stealing our valuable resources”

14. Amazonia (LBA - GEOMA): Scientific Questions that need Good Data • What is the age of the trees in Amazonia? • What is the extension of the Amazonian wetlands? • What is the environmental impact of the forest fires? • What is the CO2 balance of the rain forest? • What are the driving factors of deforestation? • What are the true extent of biodiversity in Amazonia?

15. The Challenges • Data Collection over large regions is tough work... • Consequences • Sparse data • In many cases, limited by reachability of field campaigns • Fast degradation of infra-structure • Can indirect data help? • How can improvements in Remote Sensing help us? • There is a need for much more in situ data collection • What do you do with bad or incomplete data?

16. LBA Sites Operational site Planned site Up to 5 years of data Up to 3 years of data 1 to 2 years of data

17. Dados com boa taxonomia e bons dados de distribuição......... Flora Neotropica etc: Mimosoideae: Inga; Lauraceae: Nectandra;Sapotaceae, Chrysobalanaceae, algumas Annonaceae, Marantaceae: Montagma, etc, 1425 spp geo-referenciadas até grau de longitude/latitude e mapeadas em Arcview.

18. Data from Floras.................. Reserva Ducke: “Best kinown area in Amazonia” in 1993 (ca. 1100 spp.) By 1999, it had 2175 species, including between 50 – 100 undescribed ones........ Também: Saül (Guiana Francesa – Mori et al.) – 1997 & 2002. Iquitos (Vásquez et al.) - 1997 Flora of Ecuador (Renner et al.) – em progresso

19. Sapotaceae “densidade das espécies” Alto Rio Negro Saül Belém Santarém Tabatinga Rio de Janeiro

20. “1425 espécies” Isso é realmente a distribuição da diversidade de espécies neotropicais??? De jeito nenhum!!!!!

21. What are we doing? • INPE’s role • Production of basic data • CBERS, LANDSAT, NOAA imagery • LBA data • Integration of Remote Sensing, GIS, Meteorology, Climatology, Earth Sciences in Environmental Models • Some Programmes we are participating • Monitoring Forest Fires • Monitoring and Modelling Deforestation • LBA Experiment in Amazonia • Land management and zoning for Brazil

22. Land Management: Dealing with Old Data

23. Land Management: Dealing with Old Data

24. Land Management: RADAM x SRTM

25. Land Management: RADAM x LANDSAT/NASA

26. Landsat/CBERS Reception Fire Monitoring in Brazil Cartographic Base Imagem TM FOREST FIRE MONITORING Products NOAA Image NOAA Reception Internet CPTEC Weather Forecast Decision Making

28. “Risque” soil moisture model (Woods Hole) integrated with INPE/CPTEC data/models • D. Nepstad • C. Nobre • A Setzer • J. Tomasella • U. Lopes • P. Lefebvre

29. CO2 FLUXES OVER PANTANAL REGION UNDER DRY AND FLOOD CONDITIONS POSTER 20 cm 10 cm Start of flooding water layer height 20 cm 55 cm 14 cm Plinio Alvalá1, C. von Randow2, A. O. Manzi2, A. de Souza3, L. Sá1, R. Alvalá1

30. Deforestation...

31. 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

32. 1 9 7 3

33. 1 9 9 1 Courtesy: INPE/OBT

34. 1 9 9 9 Courtesy: INPE/OBT

35. Deforestation in Amazonia PRODES (Total 1997) = 532.086 km2 PRODES (Total 2001) = 607.957 km2

36. Desmatamentos Ocorridos em Áreas Prioritárias à Conservação-2002 PA AM TO MT Desmat. em Área Prioritária Desmat. em Outras Áreas Fonte: MMA/SBF

37. Análise de tendências • Modelos econômicos Modelling Tropical Deforestation Coarse: 100 km x 100 km grid Fine: 25 km x 25 km grid

38. Factors Affecting Deforestation

39. Coarse resolution: candidate models

40. Terra do Meio, Pará State South of Amazonas State Hot-spots map for Model 7: (lighter cells have regression residual < -0.4) Coarse resolution: Hot-spots map

41. Modelling Deforestation in Amazonia • High coefficients of multiple determination were obtained on all models built (R2 from 0.80 to 0.86). • The main factors identified were: • Population density; • Connection to national markets; • Climatic conditions; • Indicators related to land distribution between large and small farmers. • The main current agricultural frontier areas, in Pará and Amazonas States, where intense deforestation processes are taking place now were correctly identified as hot-spots of change.

42. Deforestation Alert – Sensors TERRA e AQUA MODIS - Moderate-resolution Imaging Spectroradiometer 36 bandas Resolução temporal: Diária Resolução espacial: 250 m CBERS - China-Brazil Earth Resources Satellite Sensor WFI 2 bandas 260 m de resolução Repetitividade: 5 dias

43. MODIS R (MIR) G (NIR) B (RED) - 08/AGOSTO/2003

44. MODIS R (MIR) G (NIR) B (RED) - 09/AGOSTO/2003

45. MODIS R (MIR) G (NIR) B (RED) - 10/AGOSTO/2003

46. MODIS R (MIR) G (NIR) B (RED) - Mosaico/AGOSTO/2003

47. PRODES Digital 2002 - MODIS MAIO 2003 (RGB)

48. PRODES Digital 2002 - MODIS JUNHO 2003 (RGB)

49. PRODES Digital 2002 - MODIS JULHO 2003 (RGB)

50. Environmental Modelling in Brasil • GEOMA: “Rede Cooperativa de Modelagem Ambiental” • Cooperative Network for Environmental Modelling • Established by Ministry of Science and Technology • INPE/OBT, INPE/CPTEC, LNCC, INPA, IMPA, MPEG • Long-term objectives • Develop computational­-mathematical models to predict the spatial dynamics of ecological and socio-economic systems at different geographic scales, within the framework of sustainability • Support policy decision making at local, regional and national levels, by providing decision makers with qualified analytical tools.