Transcript Slide 1

IGERT Colloquim Series, Department of
Geography, SUNY Bufallo, February 2007
Understanding Land
Change in Amazonia: A
Multidisciplinary
Research Challenge
Gilberto Câmara
Director
National Institute for Space Research
Brazil
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

LANDSAT/SPOT Receiving and Processing
Station
 in

operation since 1974
China-Brazil Earth Resources Satellite
5

forecast and climate studies
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
Can we avoid that this….
Source: Carlos Nobre (INPE)
Fire...
….becomes this?
Source: Carlos Nobre (INPE)
Amazonia at a glance ... The Natural
System
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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
diversity
Source: Carlos Nobre (INPE)
We might know the past….
Estimativa do Desmatamento da Amazônia (INPE)
What’s coming next?
Source: Carlos N
Deforestation...
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

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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?
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
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
photo source: Edson Sano (EMBRAPA)
Large-Scale Agriculture
Agricultural Areas (ha)
1970
Legal Amazonia
Brazil
1995/1996
%
5,375,165
32,932,158
513
33,038,027
99,485,580
203
Source: IBGE - Agrarian Census
photo source: Edson Sano (EMBRAPA)
Cattle in Amazonia and Brazil
Unidade
Amazônia Legal
Brasil
Fonte: PAM - IBGE
1992
29915799
154,229,303
2001
51689061
176,388,726
%
72,78%
14,36%
Cattle in Amazonia and Brazil
Unidade
Amazônia Legal
Brasil
1992
2001
%
29,915,799
51,689,061
72,78%
154,229,303
176,388,726
14,36%
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
Altamira (Pará) – MODIS Image – 22 June 2004
6.000 hectares deforested in one month!
Altamira (Pará) – LANDSAT Image – 07 July 2004
Modelling Land Change in Amazonia
Territory
(Geography)
Money
(Economy)
Modelling
(GIScience)
Culture
(Antropology)
“Current and future development axes”
BR-174
Transamazônica
BR-230
Belém/Brasília
BR-319
CuiabáSantarém
BR-163
Cuiabá-Porto
Velho
BR-364
Current roads
Planned roads
Prodes 2003/2004 (INPE, 2005)
Estudos Avançados nº 53 (Théry, H.; 2005)
Dynamic areas (current and future)
New Frontiers
INPE 2003/2004:
Intense Pressure
Future expansion
Deforestation
Forest
Non-forest
Clouds/no data
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
Novo Progresso
Xingu River
Transamazônica
Accumulated
Deforestation
Evolução do Desmatamento
Rio Iriri
3500
3000
Rio Xingu
Km2
2500
2000
1500
1000
500
Rio Iriri
0
1997
2000
2001
2002
2003
2004
Ano
Reservas Indígenas
Desmatamento acumulado
Taxa Anual
Annual
rate
800
700
600
500
taxa anual
400
300
200
100
0
1997/2000*
2000/2001
2001/2002
2002/2003
2003/2004
Escada et al, 2005 – Estudos Avançados , Nº 54
Land Appropriation Model
Araújo (2004)
Escada et al (2005)
Primary
occupation
Land
permits
Smallmedium farms
Violent
Expropriation
Illegal
registration
Large
farms
Illegal money
Legal money
Cattle ranching and deforestation
Source: DePará, 2005
Amount of cattle head
Accumulated Deforestation
Desmatamento Acum ulado - km
2
14000
12000
10000
Água Azul do Norte
Marabá
8000
Ourilândia do Norte
Redenção
6000
São Félix do Xingu
Tucumã
Xinguara
4000
2000
0
Museu Paraense Emílio
Goeldi e Embrapa Oriental
1997
2000
2001
2002
2003
2004
Escada et al, 2005 – Estudos Avançados , Nº 54
Cattle Ranching Model
F
F+R
Forest
Forest + Relief
P
PD
Pasture
P+R Pasture + Relief
Degraded Pasture
RP
Recovered Pasture
Agents in Terra do Meio
T
T
G
G
G, M - Large, Medium
Grandes
Toca do
Sapo
G
L. Jaba
P
G
Cutia
G
Branquinho
L. Caraíba
T
. F. Cheiro
P
P
Primavera
10 km
Tibornea
G
G
T
R
- Riverside
Ribeirinhos
Área em disputa
(CPT, 2004)
G
P
P
- Small
Pequenos
e Médios
G
G
P
P, M
Population Flux: seasonality
Rain season flux
Dry season flux
Analysis of public policy: Conservation units in Pará
RESEX
Riozinho do
Anfrísio
ESEC
Terra do Meio
Parque Nacional
da Serra do
Pardo - 5% df
Flona de
Altamira
0
Escada et al, 2005
50 km
Prodes 2004 (INPE, 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 clouds
MODEL 7:
Variables
R² = .86
PORC3_AR
Description
Percentage of large farms, in terms of
area
LOG_DENS
Population density (log 10)
PRECIPIT
stb
p-level
0,27
0,00
0,38
0,00
-0,32
0,00
LOG_NR1
Avarege precipitation
Percentage 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)
Projected hot spots of deforestation 1997- 2015:
Federative States
Regionalizing the demand improves pressure on Central area, but
Central area regressions emphasizes proximity to ports and rivers,
due to historical process in the area, and not connectivity to the rest
of the country.
Roads
Percentage of change
in forest cover from 1997 to
2015:
0% ->
100%
Impact of the proposed
Manaus-Porto Velho road
Rede Temática GEOMA
Setembro, 2006
Área de estudo – ALAP BR 319 e entorno
new road
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
Portos
BASELINE SCENARIO – Hot spots of change (1997 a 2020)
% mudança 1997 a 2020:
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
0.0 – 0.1
0.1 – 0.2
0.2 – 0.3
0.3 – 0.4
0.4 – 0.5
0.5 – 0.6
0.6 – 0.7
0.7 – 0.8
0.8 – 0.9
0.9 – 1.0
GOVERNANCE SCENARIO – Differences from baseline
scenario
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
Protection areas
Sustainable areas
Differences:
Less:
More:
0.0
-0.50
0.0
0.10
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 humanenvironment systems?
• How is the provision of
environmental goods and
services affected by changes
in terrestrial humanenvironment systems?
• What are the characteristics
and dynamics of vulnerability
in terrestrial humanenvironment systems?
Uncertainty on basic equations
Limits for Models
Social and Economic
Systems
Quantum
Gravity
Particle
Physics
Living
Systems
Global
Change
Chemical
Reactions
Hydrological
Models
Solar System Dynamics
Meteorology
Complexity of the phenomenon
source: John Barrow
(after David Ruelle)