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:

IN

PE

/OB

T

1 9 9 9C

ourt

esy:

IN

PE

/OB

T

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:bwhite@aaas.org> > Date: 09/02/04 09:55:22 >TO: laurancew@tivoli.si.edu <

mailto:laurancew@tivoli.si.edu> >

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