Understanding Land Change in Amazonia: A Multidisciplinary Research ChallengeGilberto CâmaraDirector National Institute for Space ResearchBrazil

IGERT Colloquim Series, Department of Geography, SUNY Bufallo, February 2007

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 bands (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

The Future of Brazilian Amazon

Why is this an multidisciplinary research challenge?

Amazonia is a key environmental resource

Many different concerns Environment and biodiversity conservation Economic development Native population

Source: Carlos Nobre (INPE)

Can we avoid that this….

Fire...

Source: Carlos Nobre (INPE)

….becomes this?

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)

We might know the past….Estimativa do Desmatamento da Amazônia (INPE)

What’s coming next?

Deforestation...

Source: Carlos Nobre (INPE)

Environmental Modelling in Brasil

GEOMA: “Rede Cooperativa de Modelagem Ambiental” Cooperative Network for Environmental Modelling Established by Ministry of Science and Technology

Long-term objectives Develop models to predict the spatial dynamics of

ecological and socio-economic systems at different geographic scales,

Support policy decision making at local, regional and national levels, by providing decision makers with qualified analytical tools.  

Modelling Complex Problems Application of multidisciplinary knowledge to

produce a model.

If (... ? ) then ...

Desforestation?

What is Computational Modelling?

Design and implementation of computational enviroments for modelling Requires a formal and stable description Implementation allow experimentation

Rôle of computer representation Bring together expertise in different field Make the different conceptions explicit Make sure these conceptions are represented

in the information system

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?

Modelling Land Change in Amazonia How much deforestation is caused by:

Soybeans? Cattle ranching? Small-scale setllers? Wood loggers? Land speculators? A mixture of the above?

Challenge: How do people use space?

Loggers

Competition for Space

Soybeans

Small-scale Farming Ranchers

Source: Dan Nepstad (Woods Hole)

Underlying Factorsdriving proximate causes

Causative interlinkages atproximate/underlying levels

Internal drivers

*If less than 5%of cases,not depicted here.

source:Geist &Lambin

5% 10% 50%

% of the cases

What Drives Tropical Deforestation?

Different agents, different motivations Intensive agriculture (soybeans)

export-based responsive to commodity prices, productivity

and transportation logistics

Extensive cattle-ranching local + export responsive to land prices, sanitary controls and

commodity prices

Large-Scale Agriculture

Agricultural Areas (ha)  1970 1995/1996 %

Legal Amazonia 5,375,16532,932,15

8 513

Brazil33,038,02

799,485,58

0 203Source: IBGE - Agrarian Census

photo source: Edson Sano (EMBRAPA)

Unidade 1992 2001 %Amazônia Legal 29915799 51689061 72,78% Brasil 154,229,303 176,388,726 14,36%Fonte: PAM - IBGE

Cattle in Amazonia and Brazil

Cattle in Amazonia and Brazil

Unidade 1992 2001 %Amazônia Legal 29,915,799 51,689,061 72,78%

Brasil 154,229,303 176,388,726 14,36%

photo source: Edson Sano (EMBRAPA)

Different agents, different motivations Small-scale settlers

Associated to social movements (MST, Church) Responsive to capital availability, land

ownership, and land productivity Can small-scale economy be sustainable?

Wood loggers Primarily local market Responsive to prime wood availability, official

permits, transportation logistics Land speculators

Appropriation of public lands Responsive to land registry controls, law

enforcement

Altamira (Pará) – LANDSAT Image – 22 August 2003

Altamira (Pará) – MODIS Image – 07 May 2004

Imagem Modis de 2004-05-21, com excesso de nuvens

Altamira (Pará) – MODIS Image – 21 May 2004

Altamira (Pará) – MODIS Image – 07 June 2004

6.000 hectares deforested in one month!

Altamira (Pará) – MODIS Image – 22 June 2004

Altamira (Pará) – LANDSAT Image – 07 July 2004

Modelling Land Change in Amazonia

Territory(Geography)

Money(Economy)

Culture(Antropology)

Modelling(GIScience)

Prodes 2003/2004 (INPE, 2005) Estudos Avançados nº 53 (Théry, H.; 2005)

BR-319

BR-174

Cuiabá-Santarém

BR-163

Cuiabá-Porto Velho

BR-364

TransamazônicaBR-230

Belém/Brasília

Current roads Planned roads

“Current and future development axes”

New Frontiers

DeforestationForestNon-forest

Clouds/no data

INPE 2003/2004:

Dynamic areas (current and future)

Intense Pressure

Future expansion

Amazonian new frontier hypothesis (Becker)

“The actual frontiers are different from the 60’s and the 70’s

In the past it was induced by Brazilian government to expand regional economy and population, aiming to integrate Amazônia with the whole country.

Today, induced mostly by private economic interests and concentrated on focus areas in different regions.

Integrated Land Use and Land Cover Change

Modeling in Pará

http://www.geoma.lncc.br

Land use and Land Cover Dynamics in São Félix do Xingu-Iriri (PA)

Iriri River

S. F Xingu

Xingu River

Novo Progresso

Evolução do Desmatamento

0

500

1000

1500

2000

2500

3000

3500

1997 2000 2001 2002 2003 2004

Ano

Km

2

Desmatamento acumulado

Taxa Anual

0

100

200

300

400

500

600

700

800

1997/2000* 2000/2001 2001/2002 2002/2003 2003/2004

taxa anual

Reservas Indígenas

Rio Xingu

Rio Iriri

TransamazônicaRio Iriri

Escada et al, 2005 – Estudos Avançados , Nº 54

Annual rate

Accumulated Deforestation

Araújo (2004)Escada et al (2005)Land Appropriation Model

ViolentExpropriation

Primaryoccupation

Landpermits

Illegalregistration

Large farms

Small-medium farms

Legal moneyIllegal money

0

2000

4000

6000

8000

10000

12000

14000

1997 2000 2001 2002 2003 2004

Desm

atam

ento

Acu

mul

ado

- km

2

Água Azul do Norte

Marabá

Ourilândia do Norte

Redenção

São Félix do Xingu

Tucumã

Xinguara

Source: DePará, 2005Cattle ranching and deforestation

Museu Paraense Emílio Goeldi e Embrapa Oriental

Accumulated Deforestation

Escada et al, 2005 – Estudos Avançados , Nº 54

Amount of cattle head

Cattle Ranching Model

F

F+R

P

PD

P+R

RP

Forest

Forest + Relief

Pasture

Degraded Pasture

Pasture + Relief

Recovered Pasture

P, M

Tibornea

. F. Cheiro

Área em disputa(CPT, 2004)

G

Branquinho

Cutia

L. Jaba

Toca doSapo

L. Caraíba

Estrada Canopus

Estrada dos fazendeiros

PPrimaver

a10 km

Source: CPT(2004) , Taravello, R. (2004), Isa (2001) , Geoma(2004), Escada et al (2005)

Pequenos e Médios

Grandes

G

P P

G

G

P

G

G

G

G

G

P

GT

TT

T

Ribeirinhos

P - SmallG, M - Large, MediumR - Riverside

Agents in Terra do Meio

Rain season flux Dry season flux

Population Flux: seasonality

ESECTerra do Meio

Parque Nacionalda Serra do

Pardo - 5% df

Canopus

Fazendeiros

RESEXRiozinho do

Anfrísio

Flona de Altamira

Analysis of public policy: Conservation units in Pará

Prodes 2004 (INPE, 2005)

0 50 km

Escada et al, 2005

Sample of results

Test 2: Without demand or regression regionalization;

Test 8: With demand and regression regionalization (one model for fine scale partition – Arco, Central and Occidental);

Test 13: With demand and regression regionalization (Arco regression model used at Central partition).

Statistics: Humans as cloudsMODEL 7: R² = .86

Variables 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

Statistical analysis of deforestation

Land Change Model (1997-2015)

0% -> 100%

Federative States

Roads

Projected hot spots of deforestation 1997- 2015:

Percentage of changein forest cover from 1997 to 2015:

Regionalizing the demand improves pressure on Central area, butCentral area regressions emphasizes proximity to ports and rivers,due to historical process in the area, and not connectivity to the restof the country.

Impact of the proposed Manaus-Porto Velho road

Rede Temática GEOMASetembro, 2006

Área de estudo – ALAP BR 319 e entorno

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principaisPortos

new road

BASELINE SCENARIO – Hot spots of change (1997 a 2020)

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principais

0.0 – 0.10.1 – 0.20.2 – 0.30.3 – 0.40.4 – 0.50.5 – 0.60.6 – 0.70.7 – 0.80.8 – 0.90.9 – 1.0

% mudança 1997 a 2020:

GOVERNANCE SCENARIO – Differences from baseline scenario

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principais

0.0 -0.50Less:0.0 0.10More:

Differences:Protection areas

Sustainable areas

GIScience and change

Modelling change is essential in our world

We need a vision for extending GIScience to have a research agenda for modeling change

Global Land Project•What are the drivers and

dynamics of variability and change in terrestrial human-environment systems?

•How is the provision of environmental goods and services affected by changes in terrestrial human-environment systems?

•What are the characteristics and dynamics of vulnerability in terrestrial human-environment systems?

Limits for Models

source: John Barrow(after David Ruelle)

Complexity of the phenomenon

Unc

erta

inty

on

basi

c eq

uati

ons

Solar System Dynamics Meteorology

ChemicalReactions

HydrologicalModels

ParticlePhysics

Quantum Gravity

Living Systems

GlobalChange

Social and EconomicSystems