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

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Presentation on theme: "The Importance of Improving Collection and Access to Environmental Data in the Americas Gilberto Câmara Director for Earth Observation National Institute."— Presentation transcript:

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 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 elses backyard

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

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

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 todays 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 dont know that I dont 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 CO 2 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 Operational site Planned site Up to 5 years of data Up to 3 years of data 1 to 2 years of data LBA Sites

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 spp.) By 1999, it had 2175 species, including between 50 – 100 undescribed ones Também: Saül (Guiana Francesa – Mori et al.) – 1997 & Iquitos (Vásquez et al.) Flora of Ecuador (Renner et al.) – em progresso

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

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

21 What are we doing? INPEs 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


24 Land Management: RADAM x SRTM

25 Land Management: RADAM x LANDSAT/NASA

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


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 Plinio Alvalá 1, C. von Randow 2, A. O. Manzi 2, A. de Souza 3, L. Sá 1, R. Alvalá 1 CO 2 FLUXES OVER PANTANAL REGION UNDER DRY AND FLOOD CONDITIONS POSTER Start of flooding water layer height 20 cm 10 cm 55 cm 14 cm

30 Deforestation...

31 What Drives Tropical Deforestation? 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 5% 10% 50% % of the cases


33 Courtesy: INPE/OBT

34 Courtesy: INPE/OBT

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

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

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

38 Factors Affecting Deforestation

39 Coarse resolution: candidate models

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

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 WFI/CBERS - 25/03/2000 – Mato Grosso

48 WFI/CBERS – Mosaico Março 2000 – Mato Grosso

49 MODIS (agosto de 2000)

50 PRODES Digital MODIS MAIO 2003 (RGB)

51 PRODES Digital MODIS JUNHO 2003 (RGB)

52 PRODES Digital MODIS JULHO 2003 (RGB)

53 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.

54 Environmental Modelling in Brazil GEOMA Network Three Year Focus ( ) Amazon region Modelling Land Use and Land Cover Change Population dynamics Wetlands Biodiversity Hydrological systems Regional economics

55 The Road Ahead: Can Technology Help? Advances in remote sensing are giving computer networks the eyes and ears they need to observe their physical surroundings. Sensors detect physical changes in pressure, temperature, light, sound, or chemical concentrations and then send a signal to a computer that does something in response. Scientists expect that billions of these devices will someday form rich sensory networks linked to digital backbones that put the environment itself online. (Rand Corporation, The Future of Remote Sensing)

56 The Road Ahead: Smart Sensors Sources: Silvio Meira and Univ Berkeley, SmartDust project SMART DUST Autonomous sensing and communication in a cubic millimeter

57 The Road Ahead: Improving Models

58 The Carbonsink of Amazonian Forest 2 Sink Strength 1 to 7 t C ha -1 yr -1 and climate Preliminary synthesis of the carbon cycle for Amazonian forests. Units: t C ha -1 yr -1. GPP= gross primary productivity; R a = autotrophic respiration; R h =heterotrophic respiration; VOC= volatile organic carbon compounds. Source: Carlos Nobre, Alterra, INPA, IH, Edinburgh Un., Washington Un ?

59 Source: LUCC

60 Limits for Models source: John Barrow Complexity of the phenomenon Uncertainty on basic equations Solar System Dynamics Meteorology Chemical Reactions Applied Sciences Particle Physics Quantum Gravity Living Systems Global Change Social and Economic Systems

61 The Road Ahead... Producing environmental data in the Americas Tremendous impact of in the management of our natural resources Task outside of the resources and capabilities of a single country Breaking the bottleneck Establishment of continental research networks Adherence to agreed international protocols (Biodiversity Convention, Kyoto Protocol)

62 The Rôle of Science and Scientists Science is more than a body of knowledge; it is a way of thinking. [...] The method of science... is far more important than the findings of science. (Carl Sagan) Scientists have to understand the sensitivities involved in collecting, using and disseminating environmental data

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