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Computational Support for the Scale Concept: The TerraME Framework for Integrated LUCC Modeling. Authors:Tiago Garcia de Senna Carneiro Dr. Antônio Miguel Vieira Monteiro Dr. Gilberto Câmara IAI-CPTEC Training Institute on Climate, Land Use and Modeling

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Computational Support for the Scale Concept: The TerraME Framework for Integrated LUCC Modeling

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Computational Support for the Scale Concept: The TerraME Framework for Integrated LUCC Modeling

Authors:Tiago Garcia de Senna Carneiro

Dr. Antônio Miguel Vieira Monteiro

Dr. Gilberto Câmara

IAI-CPTEC Training Institute on Climate, Land Use and Modeling

August 13-18, 2006, Cachoeira Paulista, CPTEC, Brazil


Deforestation Map – 2000

(INPE/PRODES Project)

Deforestation

Forest

Non-forest

The problem: multiscale spatial dynamic modeling

Provide computational modeling support for GEOMA research areas:

  • Environmental Physics

  • Wetlands

  • Biodiversity

  • LUCC

  • Population Dynamics

  • Climate

GEOMA network Science and Technology Ministry institutions:

  • LNCC-Laboratório Nacional de Computação Científica

  • MPEG-Museu Paraense Emílio Goeldi

  • INPE-Intituto de Pesquisas Espaciais

  • IDSM-Instituto de Desenvolvimento Sustentável Mamirauá

  • IMPA-Instituto de Matemática Pura e Aplicada

  • CBPF-Centro Brasileiro de Pesquisas Físicas


Main requirement: represent and simulate Amazon region space-time diversity of:

  • Actors

  • Processes

  • Speedy of change

  • Connectivity relations

Matogrosso State

Rondônia State

Mato Grosso State


Behavior is non-homogeneous in space and time


Realistic environmental change studies requires multiple scale models

(Source: Turner II, 2000)


Spatial Dynamic Modeling

What we can do?


Simulate Physical Processes

- rain drainage in a terrain -


Espinhaço Range

O Brasil “from the space”2000


Minas Gerais State “from the space”

2000


Point of View


Lobo’s Range

Itacolomido Itambé Peak


Lobo’s Range

Itacolomido Itambé Peak

9 km

9 km


rain

rain

rain

Itacolomi do Itambé

Peak

Lobo’s Range

N


Picture direction

Itacolomido Itambé Peak

Lobo’s Range


Simulation

Result

(36 min.)


Research Goal

  • To define the mathematical foundation of a model of computation for multiplescaleLUCC modeling, the Nested Cellular Automata (Nested-CA) model.

  • To implement the Nested-CA model of computation in a software architecture that provides support for all phases of the development of a multiple scale spatial dynamic model, the TerraME - Terralib Modeling Environment.


Cyclical Model Development Process

TerraME provides support for all phases of the development of a multiple LUCC model.


TerraLib EnviromentalModeling Framework

C++ Signal Processing librarys

C++ Mathematicallibrarys

C++ Statisticallibrarys

TerraME architecture & applications

RondôniaModel

DinamicaModel

TROLLModel

CLUEModel

TerraME Language

TerraME Compiler

TerraME Virtual Machine

TerraLib


TerraME Runtime Environment


Content

  • The state of the art on models of computation for LUCC modeling

  • The Nested-CA model of computation

  • The TerraME modeling environment

  • LUCC applications

    • A data-driven model for the Brazilian Amazon Region (CLUE model)

    • A theory driven model for the Rondônia state center-north region, Brazil.

  • Why use TerraME

  • Conclusion


State of the Art on Models of Computation for LUCC Modeling

(von Neumann, 1966)

(Minsky, 1967)

(Pedrosa et al, 2003)

(Aguiar et al, 2004)

(Wooldbridge, 1995)

(Straatman et al, 2001)

(Rosenschein and Kaelbling, 1995)

Cellular automata models

Agent based models


TerraME IdeaA Earth’s environment …

can be represented as a synthetic environment…

… where analytical entities (rules) change the space properties in time.

Several interacting entities share the same spatiotemporal structure.


Nested-CA Model of Computation

Space function is non-homogeneous


A Scale Model

- basic concepts -


The Scale Concept

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)


TerraME Spatial Model

Proximal spaces

Actions at distance

Non-stationary neighborhoods


Cellular Spaces

  • Components

    • Grid of georeferenced cells:

      • Unique ID

      • Several attributes

      • Generalized proximity matrix(GPM)

TerraLib Cellular Space

A discrete surface of squared cells. Each cell has one ID and several attributes.


GIS

The TerraME spatial model

The space local properties, constraints, and connectivity can be modeled by:

Each cell has a neighborhood that can be, possibly, different.

- Space is nether isomorphic nor structurally homogeneous.(Couclelis 1997)

- Actions at a distance are considered.(Takeyana 1997), (O’Sullivan 1999)

- a spatial structure: a lattice of cells

- a set of geographic data: each cell has various attributes


GIS

Loading Data

-- Loads the TerraLib cellular space

csCabecaDeBoi = CellularSpace

{

dbType = "ADO",

host = "amazonas",

database = "c:\\cabecaDeBoi.mdb",

user = "",

password = "",

layer = "cellsSerraDoLobo90x90",

theme = "cells",

select = { "altimetria", “soilWater", “infCap" }

}

csCabecaDeBoi:load();

csCabecaDeBoi:loadNeighbourhood(“Moore_SerraDoLobo1985");


TerraME Behavioral Model

Discrete and continuous

Knowledge based

Sequential and Parallel

Process Trajectory


TerraME automata


Rain Automaton

Raining

cell.soilWater = cell.past.soilWater +2;

Global


Rain Automaton in TerraME

agRain = GlobalAutomaton{

it = SpatialIterator{ csCabecaDeBoi, function( cell ) return (cell.altimetria >= 1500); end

},

ControlMode{

id = "working",

Flow{

function(event, agent, cell)

cell.soilWater = cell.past.soilWater + 2;

return 0;

end

}

}

}


(soilWater > infCap) ?

WET

DRY

(soilWater <= infCap) ?

Hidrologic Balance Automaton

Local

overflow = (soilWater – infCap);

soilWater = infCap;

sendToNeighbour( overflow );


Simulationoutcome


TerraME Temporal Model

Asynchronous Processes

Multiple Temporal Resolutions


1. Get first pair 2. Execute the ACTION

1.

Execute an agent over the cellular space regions

2.

3. Timer =EVENT

Save the spatial data

3.

Draw cellular spaces and agents states

1:32:10

1:32:00

1:42:00

1:38:07

Mens. 3

Mens. 1

Mens. 2

Mens.4

4.

Carrie out the comunication between agents

return value

. . .

true

4. timeToHappen += period

The TerraME Timer


TerraME Timer Object


TerraME Event and Message objects


Temporal inconsistency

OK

OK

2º step

1º step

update

Neighborhood based rules & Time

Rule:

if ( all neighbors = 1 ) then 0

General rule form:

cell.soilWater= cell.soilWater + 2;

one copy of the

cellular space

past

present


tn

tn+1

Runtime Rule Activity

rule

count = 0 ;

for i, cell ipairs( csValeDoAnary ) do

end

if ( cell.past.cover == “forest”) then

cell.cover =“deforested”;

count = count + 1 ;

end

cell.synchronize( );

?

print(“Number of deforested cells: ”.. count);


TerraME Synchronization Schemes


Multiple scales

Basic concepts


Multiple Scale Approach

Cellular Spaces

Discrete-Event Schedulers

GlobalAutomata

LocalAutomata


Multiple scale model construction

Using nested scales


Space structure is non-homogeneous

Nested scales

Multiscale models can be developed

Diverse space partitions can have different scales


Multiple Time Resolutions & Extents


Spatial Dynamic Modeling

What we can do?


Simulate Human-Environment Interactions

- Land use and Land Cover Changes -


The CLUE model in TerraME

INPE & Wageningen University

A data-driven LUCC model


1980

1990

GIS

t+1

load

2000

When?

Where?

t ≥ tf

How?

CLUE

Model

Spatially explicit LUCC models have a common structure

How much?

idle

play


Deforestation pattern in 1997INPE/PRODES 1997 data combined with IBGE/Agricultural census 1996

Brazilian Legal Amazon

Federative States

Roads

Source: Ana Paula D. de Aguiar

0% ->

100%deforested


Applying CLUE model to Brazilian Amazon

Legal Amazon level

demand module

scenarios of quantity of

changes in

land use types

grid-based level

spatial analysis

allocation module

‘coarse scale’

multiple regression

models

‘coarse scale’

allocation

100 x 100 km2

cells

‘fine scale’

multiple regression

models

‘fine scale’

allocation

25 x 25 km2

cells

Source: Ana Paula D. de Aguiar


Model Outcome


The Rodônia LUCC modeling - case of study -

A theory-driven model


Deforestation Map – 2000

(INPE/PRODES Project)

Deforestation

Forest

Non-forest

Introduction: Rondônia modeling exercise study area

Federal Government induced colonization area (since the 70s):

  • Small, medium and large farms.

  • Mosaic of land use patterns.

  • Definition of land units and typology of actors based on multi-temporal images (85-00) and colonization projects information (Escada, 2003).

  • Intersects 10 municipalities (~100x200 km).


Model hypothesis:

  • Occupation processes are different for Small and Medium/Large farms.

  • Rate of change is not distributed uniformly in space and time:rate in each land unit is influenced by settlement age and parcel size; for small farms, rate of change in the first years is also influenced by installation credit received.

  • Location of change: For small farms, deforestation has a concentrated pattern that spreads along roads. For large farmers, the pattern is not so clear.

62o 30’ W

62o W

9o S

9o S

9o 30’ S

9o 30’ S

10o S

10o S

10o 30’ S

10o 30’ S

Large farms

50

0

Medium farms

62o 30’ W

62o W

Km

Urban areas

Small farms

Reserves

Actors and patterns


Deforestation Rate Distribution from 1985 to 2000 - Land Units Level:

  • Large/Medium Rate Distribution sub-model

  • Small Farms Distribution sub-model

Allocation of changes - Cellular space level:

  • Large/Medium allocation sub-model

  • Small allocation sub-model

Global study

area rate

in time

Land unit 1 rate t

Land unit 2 rate t

2.500 m (large

and

medium)

500 m (small)

Large farms

Medium farms

Urban areas

Small farms

Reserves

Model overview


Legend

Environment

Agent

Asmall

Rsmall

+

+

...

...

+

Rlarge

+

Alarge

Land Unit1

Deforest Rate Distribution

Land Unit2

(two types of agentes Rsmall and R large)

Rsmall

+

+

Asmall

Deforest Allocation

G

(two types of agentes Asmall and A large)

Land Unitn

Model implementation in TerraME

Each Land Unitis anenvironment, nested in the Rondônia environment.

Global rate

...

Rondônia


Deforestation Rate Distribution Module

Small Units Agent

latency > 6 years

Deforesting

Newly implanted

Deforestation > 80%

Factors affecting rate:

  • Global rate

  • Relation properties density - speedy of change

  • Year of creation

  • Credit in the first years (small)

Year of

creation

Slowing down

Iddle

Deforestation = 100%

Large and Medium Units Agent

Deforesting

Deforestation > 80%

Year of

creation

Slowing down

Iddle

Deforestation = 100%


Allocation Module: different factors andrules

Factors affecting location of changes:

Small Farmers (500 m resolution):

  • Connection to opened areas through roads network

  • Proximity to urban areas

    Medium/Large Farmers (2500 m resolution):

  • Connection to opened areas through roads network

  • Connection to opened areas in the same line of ownerships


Allocation Module: different resolution, variables and neighborhoods

1985

  • Small farms environments:

  • 500 m resolution

  • Categorical variable: deforested or forest

  • One neighborhood relation:

  • connection through roads

  • Large farm environments:

  • 2500 m resolution

  • Continuous variable:

  • % deforested

  • Two alternative neighborhood

  • relations:

  • connection through roads

  • farm limits proximity

1997

1997


Simulation Results

1985 to 1997


Why use TerraME?


TerraME Advantages & Drawbacks

  • Expressividade

  • Legibilidade

  • Data Aquisition & Result Analyses

  • Performance


Research Contribution

  • The Nested-CA is formal model that allows:

    • Multiple scales: different temporal, spatial and behavioral resolutions and extents

    • Non-homogeneous space: multiple actors and process in different space partitions

    • Asynchronous space: different synchronization schemes in different space partitions

    • Non-isotropic space: alternative neighborhood relationships

    • LUCC processes representation: situated, continuous and discrete behavior, spatial iterators to describe spatial trajectories

  • The TerraME provides:

    • High level TerraME programming language.

    • GIS full integration.

    • Calibration and validation tools.

  • Futher work:

    • Visual modeling environment

    • High performance computing


Obrigado...

Questions?


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