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
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