1st Brazilian Symposium on Global Enviromental Change,
Rio de Janeiro, March 2007
Modelling Land Change:
The Scientific
Challenges
Gilberto Câmara
Director
National Institute for Space Research
Brazil
What is a Model?

Model = a simplified description of a complex
entity or process
Deforestation
deforest
Farmer
E0
• income
owns
E4
space
• land use
• soil type
Model = entities + relations + attributes + rules
source: Carneiro (2006)
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
Scientific Challenges

“Third culture”
 Modelling
of physical phenomena
 Understanding of human dimensions

How to model human actions?
 What
makes people do certain things?
 Why do people compete or cooperate?
 What are the causative factors of human actions?
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)
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
Territory
(Geography)
Money
(Economy)
Modelling
(GIScience)
Culture
(Antropology)
Dynamic Spatial Models
Forecast
tp - 20
tp - 10
tp
Calibration
source: Cláudia Almeida
Calibration
tp + 10
Challenge: How do people use space?
Soybeans
Loggers
Competition for
Space
Small-scale Farming
Source: Dan Nepstad (Woods Hole)
Ranchers
Dynamic areas (current and future)
Escada et al. (2005)
New Frontiers
INPE 2003/2004:
Intense Pressure
Future expansion
Deforestation
Forest
Non-forest
Clouds/no data
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
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.
Modelling Human Actions: Two
Approaches

Models based on global factors
 Explanation
based on causal models
 “For everything, there is a cause”
 Human_actions = f (factors,....)

Emergent models
 Local
actions lead to global patterns
 Simple interactions between individuals lead to
complex behaviour
 “More is different”
 “The organism is intelligent, its parts are simpleminded”
Emergence: Clocks, Clouds or Ants?

Clocks
 Paradigms:
Newton’s laws (mechanistic, cause-effect
phenomena describe the world)

Clouds
 Stochastic
models
 Theory of chaotic systems

Ants
 The
colony behaves intelligently
 Intelligence is an emergent property
Statistics: Humans as clouds
y=a0 + a1x1 + a2x2 + ... +aixi +E



Establishes statistical relationship with variables
that are related to the phenomena under study
Basic hypothesis: stationary processes
Exemples: CLUE Model (University of
Wageningen)
Statistics: Humans as clouds
MODEL 7:
Variables
source: Aguiar (2006)
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
Área de estudo – ALAP BR 319 e entorno (Aguiar, 2006b)
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
source: Aguiar (2006b)
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 (Aguiar, 2006b)
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
The trouble with statistics

Extrapolation of current measured trends

How do we know if tommorow will be like today?

How do we incorporate feedbacks?
Complex adaptative systems

How come that a city with many inhabitants
functions and exhibits patterns of regularity?

How come that an ecosystem with all its diverse
species functions and exhibits patterns of
regularity?

How can we explain how similar exploration
patterns appear on the Amazon rain forest?
Results of human society such as economies
Source: John Finnigan (CSIRO)
Agents and CA: Humans as ants
Identify different actors and try to model their
actions
Farms
Settlements
10 to 20 anos
Recent
Settlements
(less than 4
years)
Escada, 2003
Old
Settlements
(more than
20 years)
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
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
Agent model using Cellular Automata
1985
Small farms environments:
500 m resolution
Categorical variable:
deforested or forest
One neighborhood relation:
•connection through roads
Large farm environments:
2500 m resolution
1997
Continuous variable:
% deforested
Two alternative neighborhood
relations:
•connection through roads
• farm limits proximity
1997
The trouble with agents

Many agent models focus on proximate causes
 directly
linked to land use changes
 (in the case of deforestation, soil type, distance to
roads, for instance)

What about the underlying driving forces?
 Remote
in space and time
 Operate at higher hierarchical levels
 Macro-economic changes and policy changes
Game theory and mobility




Two players get in a strive can choose shoot or
not shoot their firearms.
If none of them shoots, nothing happens.
If only one shoots, the other player runs away,
and then the winner receives $1.
If both decide to shoot, each group pays $10
due to medical cares.
B shoots
B does not shoot
A shoots
(-10,-10)
(+1,-1)
A does not shoot
(-1,+1)
(0,0)
Game theory and mobility
Three strategies
A - ((10%;; $200; 0)
B - ((50%;; $200; 0)
C - ((100%;; $200;; 0))
Game theory and mobility

What happens when players can move?
If a player loses too
much, he might move to
an adjacent cell
Mobility breaks the Nash equilibrium!
TerraME Runtime Environment
TerraME INTERPRETER
• model syntax semantic checking
• model execution
TerraView
• data acquisition
• data visualization
• data management
• data analysis
LUA interpreter
TerraME framework
data
model
model
TerraME/LUA interface
MODEL DATA
Model
source code
TerraLib
database
data
Eclipse & LUA plugin
• model description
• model highlight syntax
Flexible neighbourhoods
Consolidated area
Emergent area
Scale
Scale is a generic concept that includes the
spatial, temporal, or analytical dimensions used
to measure any phenomenon.
Extent refers to the magnitude of measurement.
Resolution refers to the granularity used in the
measures.
(Gibson et al. 2000)
Multi-scale approach
The trouble with current theories of scale


Conservation of “energy”: national demand is
allocated at local level
No feedbacks are possible: people are guided
from the above
The search for a new theory of scale



Non-conservative: feedbacks are possible
Linking climate change and land change
Future of cities and landscape integrate to the
earth system
The big challenge: a theory of scale
Nested Cellular Automata
U
U
U
Environments can be nested
Multiscale modelling
Space can be modelled in different resolutions
Computational Modelling with Cell
Spaces
Cell Spaces

fonte: Carneiro (2006)
Components

Cell Spaces

Generalizes Proximity Matriz – GPM

Hybrid Automata model

Nested enviroment
Cell Spaces
Cellular Data Base Resolution
2500 m
2.500 m e 500 m
TerraME functionality
TerraME INTERPRETER
• model syntax semantic checking
• model execution
TerraView
• data acquisition
• data visualization
• data management
• data analysis
LUA interpreter
TerraME framework
data
model
model
TerraME/LUA interface
MODEL DATA
Model
source code
fonte: Carneiro (2006)
TerraLib
database
data
Eclipse & LUA plugin
• model description
• model highlight syntax
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?
References




Carneiro, T., 2006. Nested-CA: a foundation for
multiscale modeling of land use and land change., in
PhD Thesis in Computer Science. INPE: São José dos
Campos, Brazil.
Aguiar, A., 2006a. Modeling Land Use Change in the
Brazilian Amazon: Exploring Intra-Regional
Heterogeneity, in PhD Thesis, Remote Sensing
Program, INPE: Sao Jose dos Campos
Escada, M.I.S., 2003. Evolução de Padrões da Terra na
Região Centro-Norte de Rondônia. 2003, INPE: São
José dos Campos. p. 164.
Escada, M.I.S., et al.,2005. Padrões e Processos de
Ocupação nas Novas Fronteiras da Amazônia: O
Interflúvio do Xingu/Iriri Estudos Avançados, 19(54): p.
9-23.