Transcript Slide 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
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
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
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
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
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
Source: Carlos Nobre (INPE)
The forest...
Source: Carlos Nobre (INPE)
The rains...
Source: Carlos Nobre (INPE)
The rivers...
Source: Carlos Nobre (INPE)
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)
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)
Understanding Deforestation in Amazonia
Deforestation...
Source: Carlos Nobre (INPE)
Fire...
Source: Carlos Nobre (INPE)
Fire...
Source: Carlos Nobre (INPE)
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)
Amazon Deforestation 2003
Deforestation 2002/2003 Deforestation until 2002 Fonte: INPE PRODES Digital, 2004.
Scientific Challenges
“Third culture” Modelling of physical phenomena Understanding of human dimensions How to combine man-climate-earth?
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
)
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
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
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?
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
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)
Deforestation in Amazonia
PRODES (Total 1997) = 532.086 km2 PRODES (Total 2001) = 607.957 km2
1 9 7 3
1 9 9 1
1 9 9 9
LBA Flux Towers on Amazonia Source: Carlos Nobre (INPE)
Source: Carlos Nobre (INPE)
Biodiversity...
CBERS Image
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!
Objects
Material world
Space
Actions
Events
“Space is a system of entities and a system of actions” Milton Santos
Spatial Data
Natural Domain Human Domain IMAGES -planes -satellites ENVIRONMENTAL DATA -topography -soils -temperature -hidrography -geology INFRASTRUCTURE -roads -utilities -dams CADASTRAL DATA -parcels -streets -land use CENSUS DATA -Demographics -Economics
FROM DATA TO COMPUTER REPRESENTATION EVENTS / POINT SAMPLES X,Y,Z X,Y,Z X,Y,Z X,Y,Z X,Y,Z SURFACES / REGULAR GRIDS AREA DATA / POLIGONS FLUX DATA / NETWORKS
Remote Sensing
LANDSAT 5 TM image of São Paulo, 1997
Aerial Photos
Favela da maré, Rio de Janeiro - 2001
Choropletic Maps
São Paulo - 96 districts per capita income São Paulo – 270 survey areas per capita income
Trend Surfaces
iex Social Exclusion 1995 Social Exclusion 2002
FLUXES
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....
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?
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?
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?
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…
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
Competition for Space
Soybeans Loggers Competition for Space Small-scale Farming
Source: Dan Nepstad (Woods Hole)
Ranchers
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 (Université Louvain)
Source: LUCC
Modelling and Public Policy
System Ecology Economy Politics External Influences Scenarios Policy Options Decision Maker Desired System State
Modelling Tropical Deforestation
•Análise de tendências •Modelos econômicos Coarse: 100 km x 100 km grid Fine: 25 km x 25 km grid
Factors Affecting Deforestation
Category Demographic Technology Population Density Proportion of urban population Variables Proportion of migrant population (before 1991, from 1991 to 1996) Number of tractors per number of farms Percentage of farms with technical assistance Agrarian strutucture Percentage of small, medium and large properties in terms of area Infra-structure Economy Political Environmental Percentage of small, medium and large properties in terms of number Distance to paved and non-paved roads Distance to urban centers Distance to ports Distance to wood extraction poles Distance to mining activities in operation (*) Connection index to national markets Percentage cover of protected areas (National Forests, Reserves, Presence of INCRA settlements Number of families settled (*) Soils (classes of fertility, texture, slope) Climatic (avarage precipitation, temperature*, relative umidity*)
Coarse resolution: candidate models
MODEL 7: Variables PORC3_AR R² = .86
Description
Percentage of large farms, in terms of area
LOG_DENS PRECIPIT LOG_NR1
Population density (log 10) Avarege precipitation Percentage of small farms, in terms of number (log 10) DIST_EST LOG2_FER PORC1_UC Distance to roads Percentage of medium fertility soil (log 10) Percantage of Indigenous land
stb
0,27
p-level
0,00 0,38 -0,32 0,29 0,00 0,00 0,00 -0,10 -0,06 -0,06 0,00 0,01
MODEL 4: Variables
0,01
CONEX_ME R² = .83
Description
Connectivity to national markets index
LOG_DENS LOG_NR1 PORC1_AR
LOG_MIG2 Population density (log 10) Percentage of small farms, in terms of number (log 10) Percentage of small farms, in terms of area Percentage of migrant population from 91 to 96 (log 10) LOG2_FER Percentage of medium fertility soil (log 10)
stb
0,26
p-level
0,00 0,41 0,38 -0,37 0,00 0,00 0,00 0,12 -0,06 0,00 0,01
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)
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.
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
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
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
O modelo ambiental
proprietário • renda
E 2 Desmatamento
é um possui
E 1 E 3
como?
•
f
(‘floresta’, trator) ‘solo exposto’ desmata trator
E 4
X • g(‘floresta’, trator ) ‘pasto’ espaço • cobertura • uso • tipo de solo • custo • capacidade • depreciação • posição 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
A estrutura do espaço é heterogênea U U U
Ambientes definidos de forma recorrente É possível construir modelos
multiescalas
Porções distintas do espaço podem ter escalas diferentes
Ambiente Computacional de Modelagem: TerraLib
Realidade Moore GPM GPM+Lote 1988 1991 Geoinfo (Aguiar, 2003), Submetido GIScience (Câmara et al, 2004)
Deforestation 2002/2003 Deforestation until 2002 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.
Conjectures and Refutations on Third Culture...
Amazon Deforestation Models: Challenging the Roads Approach Only 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.
How Ethical is Science Judgment?
From: Brian White
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.
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”)
The Road Ahead: Smart Sensors
SMART DUST Autonomous sensing and communication in a cubic millimeter Sources: Silvio Meira and Univ Berkeley, SmartDust project
Limits for Models
Quantum Gravity Social and Economic Systems Particle Physics Living Systems Global Change Chemical Reactions Applied Sciences Solar System Dynamics
Complexity of the phenomenon
Meteorology source: John Barrow
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)
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