what is the future of the brazilian amazon? the challenges of spatial information modelling gilberto...
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What is the Future of the Brazilian Amazon? The Challenges of Spatial Information Modelling Gilberto CâmaraDirector for Earth ObservationNational Institute for Space ResearchBrazil
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
The forest...Source: Carlos Nobre (INPE)
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 m3/s) Over 100 G ton C stored in vegetation and
soil A multitude of ecosystems, biological and
ethnic diversitySource: 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)
© S
ebas
tião
Sal
gado
But there are millions of the beingsAll so well disguised
That no-one asksFrom where such people come
Chico Buarque Source: Carlos Nobre (INPE)
Amazon Deforestation 2003Amazon Deforestation 2003
Fonte: INPE PRODES Digital, 2004.Fonte: INPE PRODES Digital, 2004.
Deforestation 2002/2003Deforestation 2002/2003
Deforestation until 2002Deforestation until 2002
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 ChangeCauses 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 km2PRODES (Total 2001) = 607.957 km2
1 9 7 3
1 9 9 1C
ourt
esy:
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1 9 9 9C
ourt
esy:
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LBA Flux Towers on Amazonia
Source: Carlos Nobre (INPE)
Biodiversity...
Source: Carlos Nobre (INPE)
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 Actions
Space Space
“Space is a system of entities and a system of actions” Milton Santos
Material worldMaterial world EventsEvents
Spatial Data
Natural Domain
HumanDomain
IMAGES
-planes-satellites
ENVIRONMENTALDATA
-topography-soils-temperature-hidrography-geology
CADASTRALDATA
-parcels-streets-land use
CENSUS DATA
-Demographics-Economics
INFRASTRUCTURE
-roads-utilities-dams
EVENTS / POINT SAMPLES
SURFACES / REGULAR GRIDS
AREA DATA / POLIGONS
FLUX DATA / NETWORKS
X,Y,ZX,Y,Z X,Y,Z
X,Y,Z
X,Y,Z
FROM DATA TO COMPUTER REPRESENTATION
Remote SensingRemote Sensing
LANDSAT 5 TM image of São Paulo, 1997
Aerial PhotosAerial 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
Social Exclusion 1995
iex
Trend Surfaces
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
Loggers
Competition for Space
Soybeans
Small-scale Farming Ranchers
Source: Dan Nepstad (Woods Hole)
What Drives Tropical Deforestation?
Underlying Factorsdriving proximate causes
Causative interlinkages atproximate/underlying levels
Internal drivers
*If less than 5%of cases,not depicted here.
source:Geist &Lambin (Université Louvain)
5% 10% 50%
% of the cases
Source: LUCC
Modelling and Public Policy
System
EcologyEconomyPolitics
ScenariosDecisionMaker
Desired System
State
ExternalInfluences
Policy Options
Modelling Tropical Deforestation
Fine: 25 km x 25 km grid
Coarse: 100 km x 100 km grid
•Análise de tendências•Modelos econômicos
Factors Affecting Deforestation
Category VariablesDemographic Population Density
Proportion of urban populationProportion of migrant population (before 1991, from 1991 to 1996)
Technology Number of tractors per number of farmsPercentage of farms with technical assistance
Agrarian strutucture Percentage of small, medium and large properties in terms of areaPercentage of small, medium and large properties in terms of number
Infra-structure Distance to paved and non-paved roadsDistance to urban centersDistance to ports
Economy Distance to wood extraction polesDistance to mining activities in operation (*)Connection index to national markets
Political Percentage cover of protected areas (National Forests, Reserves, Presence of INCRA settlementsNumber of families settled (*)
Environmental Soils (classes of fertility, texture, slope)Climatic (avarage precipitation, temperature*, relative umidity*)
Coarse resolution: candidate models
MODEL 7: R² = .86Variables Description stb p-level
PORC3_ARPercentage of large farms, in terms of area 0,27 0,00
LOG_DENS Population density (log 10) 0,38 0,00
PRECIPIT Avarege precipitation -0,32 0,00
LOG_NR1Percentage of small farms, in terms of number (log 10) 0,29 0,00
DIST_EST Distance to roads -0,10 0,00
LOG2_FER Percentage of medium fertility soil (log 10) -0,06 0,01
PORC1_UC Percantage of Indigenous land -0,06 0,01
MODEL 4: R² = .83Variables Description stb p-level
CONEX_ME Connectivity to national markets index 0,26 0,00
LOG_DENS Population density (log 10) 0,41 0,00
LOG_NR1Percentage of small farms, in terms of number (log 10) 0,38 0,00
PORC1_ARPercentage of small farms, in terms of area -0,37 0,00
LOG_MIG2Percentage of migrant population from 91 to 96 (log 10) 0,12 0,00
LOG2_FER Percentage of medium fertility soil (log 10) -0,06 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 (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.
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
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
E1
E2
E3possui
é um
E4
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
A estrutura do espaço é heterogênea
UU
U
Ambientes definidos de forma recorrente
Porções distintas do espaço podem ter escalas diferentes
É possível construir modelos multiescalas
Ambiente Computacional de Modelagem: TerraLib
GPM+LoteGPM
1991
1988
MooreRealidade
Geoinfo (Aguiar, 2003), Submetido GIScience (Câmara et al, 2004)
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.Fonte: INPE PRODES Digital, 2004.
Deforestation 2002/2003Deforestation 2002/2003
Deforestation until 2002Deforestation until 2002
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.
How Ethical is Science Judgment?
From: Brian White <mailto:[email protected]> > Date: 09/02/04 09:55:22 >TO: [email protected] <
mailto:[email protected]> >
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
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
Sources: Silvio Meira and Univ Berkeley, SmartDust project
SMART DUST Autonomous sensing and communication in a cubic millimeter
Limits for Models
source: John BarrowComplexity of the phenomenon
Un
cert
ain
ty o
n b
asic
eq
uat
ion
s
Solar System DynamicsMeteorology
ChemicalReactions
AppliedSciences
ParticlePhysics
Quantum Gravity
Living Systems
GlobalChange
Social and EconomicSystems
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