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What is the Future of the Brazilian Amazon? The Challenges of Spatial Information Modelling Gilberto Câmara Director for Earth Observation National Institute.

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Presentation on theme: "What is the Future of the Brazilian Amazon? The Challenges of Spatial Information Modelling Gilberto Câmara Director for Earth Observation National Institute."— Presentation transcript:

1 What is the Future of the Brazilian Amazon? The Challenges of Spatial Information Modelling Gilberto Câmara Director for Earth Observation National Institute for Space Research Brazil

2 About... Gilberto Câmara is Director for Earth Observation at INPE.  Eletronics Engineer (ITA, 1979) with a PhD in Computer Science (INPE, 1995). Research interests  Geographical information science, spatial databases, spatial analysis and remote sensing image processing Achievements  Leader in the development of GIS and Image Processing technology in Brazil  Co-chair of the Brazilian Research Network on Environmental Modelling of the Amazon

3 INPE - brief description National Institute for Space Research  main civilian organization for space activities in Brazil  staff of 1,800 ( 800 Ms.C. and Ph.D.) Areas:  Space Science, Earth Observation, Meteorology and Space Engineering

4 Environmental activities at INPE Numerical Weather Prediction Centre  medium-range forecast and climate studies LANDSAT/SPOT Receiving and Processing Station  in operation since 1974 China-Brazil Earth Resources Satellite  5 bandas (3 visible, 1 IR) at 20 m resol. Research Activities in Remote Sensing  300 MsC and PhD graduates  ONU-funded Center for Africa and S. America

5 What is an Information Science Problem? Multidisciplinary issue  Different agents with conflicting interests  Computer representation is only part of the problem Rôle of the information science expert  Bring together expertise in different field  Make the different conceptions explicit  Make sure these conception are represented in the information system

6 The Future of Brazilian Amazon Why is this an information science problem? Amazonia is a key environmental resource Many different concerns  Environment and biodiversity conservation  Economic development  Native population

7 The forest... Source: Carlos Nobre (INPE)


9 The rains... Source: Carlos Nobre (INPE)

10 The rivers... Source: Carlos Nobre (INPE)

11 Amazonia at a glance... The Natural System Almost 6 million km2 of contiguous tropical forests Perhaps 1/3 of the planet's biodiversity Abundant rainfall (2.2 m annually) 18% of freshwater input into the global oceans (220,000 m 3 /s) Over 100 G ton C stored in vegetation and soil A multitude of ecosystems, biological and ethnic diversity Source: Carlos Nobre (INPE)

12 Population Growth and Land Use Change Modern occupation of Amazonia (since 1500): negligible land use change up to the 1960's, but large loss of ethnic diversity due to colonization Large land use change in the last 30 years Close to 600,000 km2 deforested in Brazilian Amazonia (15%) High annual rates of deforestation (15,000 to 30,000 km2/year) Source: Carlos Nobre (INPE)

13 Understanding Deforestation in Amazonia

14 Deforestation... Source: Carlos Nobre (INPE)

15 Fire... Source: Carlos Nobre (INPE)

16 Fire... Source: Carlos Nobre (INPE)

17 © Sebastião Salgado But there are millions of the beings All so well disguised That no-one asks From where such people come Chico Buarque Source: Carlos Nobre (INPE)

18 Amazon Deforestation 2003 Fonte: INPE PRODES Digital, 2004. Deforestation 2002/2003 Deforestation until 2002

19 Scientific Challenges “Third culture”  Modelling of physical phenomena  Understanding of human dimensions How to combine man-climate-earth?

20 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)

21 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

22 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

23 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?

24 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

25 Causes for Land Use Change Government plans to “integrate” Amazonia Build road network throughout the region Population growth in Amazonia: 3,5 million in 1970, up to 20 million in 2000, though 65% living in large and mid-size cities and towns Colonization projects: rush of landless people to small scale, low tech agriculture Subsidized cattle ranching Destructive logging as a vector to subsequent deforestation Large-scale soybean agriculture Source: Carlos Nobre (INPE)

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

27 1 9 7 3

28 1 9 9 1 Courtesy: INPE/OBT

29 1 9 9 9 Courtesy: INPE/OBT

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

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

32 CBERS Image

33 What do we do with so much spatial data? First, we collect it...  GPS, remote sensing, field surveys  Data conversion Then, we organize it...  Spatial modelling  Spatial databases  Spatial visualization But more important is to analyse and understand it!

34 ObjectsActions Space “Space is a system of entities and a system of actions” Milton Santos Material world Events

35 Spatial Data Natural Domain Human Domain IMAGES -planes -satellites ENVIRONMENTAL DATA -topography -soils -temperature -hidrography -geology CADASTRAL DATA -parcels -streets -land use CENSUS DATA -Demographics -Economics INFRASTRUCTURE -roads -utilities -dams


37 Remote Sensing LANDSAT 5 TM image of São Paulo, 1997

38 Aerial Photos Favela da maré, Rio de Janeiro - 2001

39 Choropletic Maps São Paulo - 96 districts per capita income São Paulo – 270 survey areas per capita income

40 Social Exclusion 1995 iex Trend Surfaces Social Exclusion 2002


42 The First Law of Geography Tobler’s Law  Everything is related to everything else, but near things are more related than distant things  We call this “spatial dependence” Can we see Tobler’s law in action? Yes, there are lots of exemples...Here are some....

43 The Future of Brazilian Amazonia? Scenarios for Amazônia in 2020 (Laurance et al., “Science”) Optimistic scenario  28% of deforestation Pessimistic scenario  42% of deforestation What’s the real science behind this work?

44 The Future of Brazilian Amazonia(Laurance) Optimistic scenario  Complete degradation up to 20 km from roads (existing and projected)  Moderate degradation up to 50 km from roads  Reduced degradation up to 100 km from roads Pessimistic scenario  Complete degradation up to 50 km from roads (existing and projected)  Moderate degradation up to 100 km from roads What’s wrong with this approach?

45 Scenarios and Models Scenarios require models! Models  Describe quantitatively a phenomenon and predict its evolution in space and time A model must answer:  What changes?  When changes take place?  Where changes take place?  Why are there changes?

46 Modelling and Laurance’s work “The Future of the Brazilian Amazon”? What changes?  Is constrast forest-deforestation enough? Where changes take place?  Model is spatially explicit - OK When changes take place?  No change equations Why are there changes?  Model does not indicate causes…

47 Alternatives to Simplistic Models Multidisciplinary work  Geography, Demography, Antropology, Computer Science, Statistics, Ecology Use of empirical evidence  Census surveys  On-situ visit  Remote Sensing Models grounded on hard data

48 Competition for Space Loggers Competition for Space Soybeans Small-scale Farming Ranchers Source: Dan Nepstad (Woods Hole)

49 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 (Université Louvain)  5% 10% 50% % of the cases

50 Source: LUCC

51 Modelling and Public Policy System Ecology Economy Politics Scenarios Decision Maker Desired System State External Influences Policy Options

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

53 Factors Affecting Deforestation

54 Coarse resolution: candidate models

55 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)

56 Modelling Deforestation in Amazonia High coefficients of multiple determination were obtained on all models built (R 2 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.

57 Fatores Correlacionados ao Desmatamento Sete fatores estão relacionados à variação de 83% das taxas de desmatamento na Amazônia nos últimos anos: (a) Estrutura Agrária (2 fatores): percental de área ocupada por grandes fazendas e número de pequenas propriedades. (b) Ocupação Populacional (1 fatores): densidade de população. (c) Condições do Meio Físico (2 fatores): Precipitação média e percentual de solos férteis. (d) Infraestrutura (1 fator): distância a estradas. (e) Presença do Estado (1 fator): percentagem de áreas indígenas

58 Clocks, Clouds or Ants? Clocks  Paradigms: Netwon’s laws (mechanistic, cause-effect phenomena describe the world) Clouds  Stochastic models  Suporte: Teoria de sistemas caóticos Formigas  Modelos emergentes  Suporte: teoria de sistemas complexos  Exemplos: automata celulares

59 Modelos Espaciais: Princípios Básicos Célula: localização Input: processo ocorre no lugar (ex. chuva) Função: entrada -> estado f ( I (t n ))... FF f ( I (t) )f ( I (t+1) )f ( I (t+2) )

60 Ambientes Computacionais para Modelagem Espaços celulares Componentes  conjunto de células georeferenciadas  identificador único  vários atributos por células  matriz genérica de proximidade - GPM superfície discreta de células retangulares multivaloradas possivelmente não contíguas

61 O modelo ambiental Um ambiente possui 3 submodelos:  Modelo Espacial: espaços celulares + regiões + GPM  Modelo Comportamental: teoria de sistemas + autômatos celulares híbridos + agentes situados  Modelo Temporal: simulador de eventos discretos definidos de forma recorrente A estrutura espacial e temporal é compartilhada por vários agentes. GIS E1E1 E2E2 E3E3 possui é um E4E4  proprietário espaço trator desmata cobertura uso tipo de solo custo capacidade depreciação posição f(‘floresta’, trator)  ‘solo exposto’ como? g(‘floresta’, trator )  ‘pasto’ Desmatamento renda X

62 A estrutura do espaço é heterogênea U U U Ambientes definidos de forma recorrente Porções distintas do espaço podem ter escalas diferentes É possível construir modelos multiescalas

63 Ambiente Computacional de Modelagem: TerraLib GPM+Lote GPM 1991 1988 MooreRealidade Geoinfo (Aguiar, 2003), Submetido GIScience (Câmara et al, 2004)

64 Laurance et al., 2001 – Pessimist scenario (2020): Savannas, non-forested areas, deforested or heavely degrated Moderately degrated Lightly degrated Pristine Fonte: INPE PRODES Digital, 2004. Deforestation 2002/2003 Deforestation until 2002

65 Conjectures and Refutations on Third Culture... Amazon Deforestation Models: Challenging the Only- Roads Approach Deforestation predictions presented by Laurance et al. are based on the assumption that the road infrastructure is the prime factor driving deforestation. Deforestation rates have increased significantly in the last two years, but very few Federal investments on roads have effectively been made since the 80s. Simplistic models such as Laurance et al. may deviate attention from real deforestation causes, being potentially misleading in terms of deforestation control There is an urgent need to understand the genesis of the new Amazon frontiers.

66 How Ethical is Science Judgment? From: Brian White > Date: 09/02/04 09:55:22 > TO: > Dear Dr. Laurance, We have recently sent letters about your Policy Forum published in Science to which you have responded. Following is another letter we have received about the same paper. If possible, we would like your response to this comment as well. Sincerely, Etta Kavanagh Associate Letters Editor

67 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.

68 The Road Ahead: Can Technology Help? Advances in remote sensing are giving computer networks new eyes and ears. Sensors detect physical changes and then send a signal to a computer. Scientists expect that billions of these devices will someday put the environment itself online. (Rand Corporation, “The Future of Remote Sensing”)

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

70 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

71 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)

72 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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