Land change modelling

Gilberto Câmara, Pedro Andrade

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Slides from LANDSAT

Aral Sea 1973 1987 2000

images: USGS

Modelling Human-Environment Interactions

How do we decide on the use of natural resources?

What are the conditions favoring success in resource mgnt?

Can we anticipate changes resulting from human decisions?

TerraME: Computational environment for developing human-environment models

Cell Spaces

www.terrame.org

T. Carneiro, P. Andrade, G. Câmara, A. Monteiro, R. Pereira, “TerraME: an extensible toolbox for modeling nature-society interactions” (under review).

What models are needed to describe human actions?

Modelling human-environment interactions

Modelling and Public Policy

System

EcologyEconomyPolitics

Scenarios DecisionMaker

Desired System

State

ExternalInfluences

Policy Options

Models need to be Calibrated and “Validated”

tp - 20 tp - 10

tp

Calibration Validationtp + 10

ScenarioScenario

Source: Cláudia Almeida

Clocks, clouds or ants?

Clocks: deterministic equations

Clouds: statistical distributions

Ants: emerging behaviour

Statistics: Humans as clouds

Establishes statistical relationship with variables that are related to the phenomena under study

Basic hypothesis: stationary processes

y=a0 + a1x1 + a2x2 + ... +aixi +E

Fonte: Verburg et al, Env. Man., Vol. 30, No. 3, pp. 391–405

Statistical-based land use models

Statistics: Assessment of land use drivers

A. Aguiar, G. Câmara, I. Escada, “Spatial statistical analysis of land-use determinants in the Brazilian Amazon”. Ecological Modelling, 209(1-2):169–188, 2007.

G. Espíndola, A. Aguiar, E. Pebesma, G. Câmara, L. Fonseca, “Agricultural land use dynamics in the Brazilian Amazon based on remote sensing and census data”, Applied Geography, 32(2):240-252, 2012.

Land use models are good at allocating change in space. Their problem is: how much change will happen?

Driving factors of change (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*)

source: Aguiar (2006)

Amazônia in 2007 x All Variables

Variables

Transportation (11)

Distance Markets(7)

Demography (3)

Tecnology (2)

Environmental (20)

Public Policy(8)

Market (8)

Agrarian Structure(6)

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

source: Aguiar (2006)

Amazônia in 2007 x All Variables

1)transformações e análises de correlação

de 65 para 31 variáveis

2)seleção do melhor modelo

de 31 para 10 variáveis

3)regressão linear (AIC = -7838.222)

4)regressão espacial (AIC = -16120)

R-squared 0.5918

Beta p-level

log(N_TRATOR_2006 + 1) 0.3959585 <2e-16

sqrt(PSI_ASSENTAMENTOS_CLASSICOS) 0.2379922 <2e-16

FERTIL_ALTA 0.2203347 <2e-16

log(SOJA_2006 + 1) -0.2135840 <2e-16

sqrt(PSI_GPM_SEDE_AMZ) 0.2080290 <2e-16

sqrt(PSI_GPM_CAPITAIS) -0.1998483 <2e-16

sqrt(PSI_PVM_IUMID) 0.1490201 <2e-16

log(DIST_MIN_MAD + 1) -0.1400526 <2e-16

TI_2006 -0.1372373 <2e-16

UC_2006 -0.1160321 <2e-16

8580 Cells

R-squared 0.5148

Beta p-level

FERTIL_ALTA 0.3639212 <2e-16

log(DIST_MIN_MAD + 1) -0.2887361 <2e-16

log(PREC_INV + 1) 0.1689600 <2e-16

TI_2006 -0.1600799 <2e-16

ASSENT_06_NUNFAM 0.1607236 <2e-16

DIST_MIN_PORTOS 0.1604278 <2e-16

FERTIL_MUITOBAIXA 0.1037998 <2e-16

log(DIST_MIN_ROD_PAV + 1) -0.1030752 <2e-16

UC_2006 -0.0995451 <2e-16

R-squared 0.4501

Beta p-level

FERTIL_ALTA 0.2513421 <2e-16

log(DIST_MIN_MAD_96 + 1) -0.2239365 <2e-16

log(DIST_MIN_ROD_PAV_96 + 1) -0.1830264 <2e-16

DIST_MIN_PORTOS 0.1816154 <2e-16

A_UC_1996 -0.1559962 <2e-16

log(POP_RUR_1996 + 1) 0.1365780 <2e-16

A_ASSENT_96_NUNFAM 0.1346037 <2e-16

log(PREC_INV + 1) 0.1238640 <2e-16

A_NU_AGR_MEDIUM_96 -0.1221922 <2e-16

10 Years 25Km

Amazônia x Variables of 1996/2006

26 Variáveis

Statistical-based land use models

Allocation of change combining demand and cell potential at time t(ALLOCATION)

Cell suitability for each land use at time t

(POTENTIAL)

Rate and magnitude of change for each land use at time t

(DEMAND)

Land use at time t-1

Land use map at time t

Time Loop

Top-down constraint

Bottom-up calculation

Feedbacks

Driving factors of land use/cover change QUANTITY (at time t)

Driving factors of land use/cover change LOCATION (at time t)

Feedback on spatial drivers

sources: P. Verburg, A.P. Aguiar

Statistical-based land use models

Simple Demand

Demand submodel

Difference between years

Potential map

Driving factors

Neural Network

Multivariate Statistics Mathematics

Potential

Transition potential submodel

Potential map

Potential – CLUE like

Transition potential submodel

Protected Areas

Roads

Ports

Deforestation

Subtract from

Deforestation

Transition Matrix (Markov chain)

Global economic model

Trend analysis

Building Scenarios

Demand

Potential map at t

Landscape map at t

Landscape map at t+1

Demand

t+1

Rank-order Stochastic Iterative

Allocationsubmodel

Allocation

Scenario exploration: linking to process knowledge

Cellular databaseconstruction

Exploratory analysisand

selection of subset of variables

Porto Velho-Manaus

BR 163Cuiabá-Santarém

São Felix/Iriri

ApuíHumaitáBoca do Acre

SantarémManaus-Boa Vista

Aripuanã

Scenario exploration

Scenarios for deforestation in Amazonia (2020)

SimAmazonia

Modeling conservation in the Amazon basinSoares Filho et al., Nature, 2006

Simamazonia www.csr.ufmg.br

SimAmazonia

Subregiões do modelo SimAmazonia.

Modeling conservation in the Amazon basinSoares Filho et al., Nature, 2006

SimAmazonia: deforestation scenarios

Business-as-usual Governance

Modeling conservation in the Amazon basinSoares Filho et al., Nature, 2006

How good are statistical models?

Agents as basis for complex systems

Agent: flexible, interacting and autonomous

An agent is any actor within an environment, any entity that can affect itself, the environment and other agents.

Agents: autonomy, flexibility, interaction

Synchronization of fireflies

Bird Flocking

No central authority: Each bird reacts to its neighbour

Not possible to model the flock in a global manner. Need to necessary to simulate the INTERACTION between the individuals

Agen

t

Spa

ce

Space Agent

Benenson and Torrens, “Geographic Automata Systems”, IJGIS, 2005(but many questions remain...)

Modelling collective spatial actions

Agent-Based Modelling: Computing approaches to complex systems

Goal

Environment

Representations

Communication

ActionPerception

Communication

source: Nigel Gilbert

Agen

t

Spa

ce

Space Agent

source: Benenson and Torrens, “Geographic Automata Systems”, IJGIS, 2005

Modelling collective spatial actions

Agent-Based Modeling of Land changeT

heor

etic

al M

odel

s

Em

piri

cal m

odel

s

Agent-based models (ABM) range from theoretical to empirical. Theoretical models use simple generalizable ideas, whereas empirical models require more complexity and case-specific data..

61,000 ha

30,000 ha

50-200 ha

60,000 ha

30,000 ha

50 ha

20,000 ha

20,000 ha

200 ha

Different farm sizes, different actors

Frontier areas

Frontier: an area of changing resource use whose boundaries are continually evolving

Census data for São Félix do Xingu

Evolution of cattle/deforestation in São Félix do Xingu

Different actors and strategies in SFX

Model initialization (estimated farm distribution)

(a) Deforested areas in 1985 (b) Estimated distribution of farms in 1985

Deforestation - 1985

Deforestation maps (PRODES)

The frontier “exploded” between 1985 and 1997

Land concentration in Tucumã

Evolution of land tenure in São Felix

How to place agents in a frontier?

São Felix do Xingu in 1985: farms are there, deforestation not yet started

Division of frontier in large and small farms

Use the history of São Felix do Xingu to make assumption about farms

Extracting patterns from sequences of images

M. Silva, G.Câmara, M.I. Escada, R.C.M. Souza, “Remote Sensing Image Mining: Detecting Agents of Land Use Change in Tropical Forest Areas”. International Journal of Remote Sensing, vol 29 (16): 4803 – 4822, 2008.

Hypothetical land tenure in 1985

 

 

(a) Deforested areas in 1985 (b) Estimated distribution of farms in 1985

 Deforestation - 1985

Patterns of tropical deforestation

Agent states in São Felix do Xingu

Pasture management model

Deforestation: simulated x observed

Frontier evolution in Sao Felix

Consolidated (dark red), pre-frontier (light red), frontier (light green) and post-frontier (dark green).

Both the spatial values and the deforestation totals emerge as a result of the agent’s decisions Model updates the support capacity of the region changes in response to agents’ decision. Agents then sense how geographical space has changed and use this information in their decision-making.

Model results

“Agent-based modeling meets an intuitive desire to explicitly represent human decision making. (…)

However, by doing so, the well-known problems of modeling a highly complex, dynamic spatial environment are compounded by the problems of modeling highly complex, dynamic decision-making. (…)

The question is whether the benefits of that approach to spatial modeling exceed the considerable costs of the added dimensions of complexity introduced into the modeling effort. The answer is far from clear and in, my mind, it is in the negative. But then I am open to being persuaded otherwise ”.

(from “Why I no longer work with agents”, 2001 LUCC ABM Workshop)

Some caution necessary...

Helen Couclelis

GLOBIOM: a global model for projecting how much land change could occur

source: A. Mosnier (IIASA)

GLOBIOM: land use types and products

source: A. Mosnier (IIASA)

REDD-PAC: land use policy assessment

Land use data and drivers for Brazil Model cluster - realistic assumptions

Globally consistent policy impact assessment Information infrastructure

GLOBIOM, G4M, EPIC, TerraME

TerraLib