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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 l.jpg

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


The problem multiscale spatial dynamic modeling l.jpg

Deforestation Map – 2000 Framework for Integrated LUCC Modeling

(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


Slide3 l.jpg

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



Realistic environmental change studies requires multiple scale models l.jpg
Realistic environmental change studies requires multiple scale models

(Source: Turner II, 2000)


Spatial dynamic modeling l.jpg

Spatial Dynamic Modeling scale models

What we can do?


Simulate physical processes l.jpg

Simulate Physical Processes scale models

- rain drainage in a terrain -


Slide8 l.jpg

Espinhaço Range scale models

O Brasil “from the space”2000


Slide9 l.jpg

Minas Gerais State scale models“from the space”

2000


Slide11 l.jpg

Point of View scale models


Slide12 l.jpg

Lobo’s Range scale models

Itacolomido Itambé Peak


Slide13 l.jpg

Lobo’s Range scale models

Itacolomido Itambé Peak

9 km

9 km


Slide14 l.jpg

rain scale models

rain

rain

Itacolomi do Itambé

Peak

Lobo’s Range

N


Slide15 l.jpg

Picture direction scale models

Itacolomido Itambé Peak

Lobo’s Range


Slide17 l.jpg

Simulation scale models

Result

(36 min.)


Research goal l.jpg
Research Goal scale models

  • 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 l.jpg
Cyclical Model Development Process scale models

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


Terrame architecture applications l.jpg

TerraLib Enviromental scale modelsModeling Framework

C++ Signal Processing librarys

C++ Mathematicallibrarys

C++ Statisticallibrarys

TerraME architecture & applications

RondôniaModel

DinamicaModel

TROLLModel

CLUEModel

TerraME Language

TerraME Compiler

TerraME Virtual Machine

TerraLib



Content l.jpg
Content scale models

  • 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 l.jpg
State of the Art on scale modelsModels 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 idea a earth s environment l.jpg
TerraME Idea scale modelsA 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.


Slide25 l.jpg

Nested-CA Model of Computation scale models

Space function is non-homogeneous


A scale model l.jpg

A Scale Model scale models

- basic concepts -


The scale concept l.jpg
The Scale Concept scale models

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 l.jpg

TerraME Spatial Model scale models

Proximal spaces

Actions at distance

Non-stationary neighborhoods


Terralib cellular space l.jpg

Cellular Spaces scale models

  • 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.


The terrame spatial model l.jpg

GIS scale models

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


Loading data l.jpg

GIS scale models

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 l.jpg

TerraME Behavioral Model scale models

Discrete and continuous

Knowledge based

Sequential and Parallel

Process Trajectory


Terrame automata l.jpg
TerraME automata scale models


Rain automaton l.jpg
Rain Automaton scale models

Raining

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

Global


Rain automaton in terrame l.jpg
Rain Automaton in TerraME scale models

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

}

}

}


Hidrologic balance automaton l.jpg

( scale modelssoilWater > infCap) ?

WET

DRY

(soilWater <= infCap) ?

Hidrologic Balance Automaton

Local

overflow = (soilWater – infCap);

soilWater = infCap;

sendToNeighbour( overflow );


Slide37 l.jpg

Simulation scale modelsoutcome


Terrame temporal model l.jpg

TerraME Temporal Model scale models

Asynchronous Processes

Multiple Temporal Resolutions


The terrame timer l.jpg

1. Get first pair scale models2. 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 l.jpg
TerraME scale modelsTimer Object


Terrame event and message objects l.jpg
TerraME scale modelsEvent and Message objects


Neighborhood based rules time l.jpg

Temporal scale modelsinconsistency

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


Runtime rule activity l.jpg

t scale modelsn

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);



Multiple scales l.jpg

Multiple scales scale models

Basic concepts


Multiple scale approach l.jpg
Multiple Scale Approach scale models

Cellular Spaces

Discrete-Event Schedulers

GlobalAutomata

LocalAutomata


Multiple scale model construction l.jpg
Multiple scale model construction scale models

Using nested scales


Space structure is non homogeneous l.jpg
Space structure is non-homogeneous scale models

Nested scales

Multiscale models can be developed

Diverse space partitions can have different scales



Spatial dynamic modeling50 l.jpg

Spatial Dynamic Modeling scale models

What we can do?


Simulate human environment interactions l.jpg

Simulate Human-Environment Interactions scale models

- Land use and Land Cover Changes -


The clue model in terrame l.jpg

The CLUE model in TerraME scale models

INPE & Wageningen University

A data-driven LUCC model


Spatially explicit lucc models have a common structure l.jpg

1980 scale models

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 1997 inpe prodes 1997 data combined with ibge agricultural census 1996 l.jpg
Deforestation pattern in 1997 scale modelsINPE/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 l.jpg
Applying CLUE model to Brazilian Amazon scale models

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 l.jpg
Model Outcome scale models


The rod nia lucc modeling case of study l.jpg

The Rodônia LUCC modeling scale models - case of study -

A theory-driven model


Introduction rond nia modeling exercise study a rea l.jpg

Deforestation Map – 2000 scale models

(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).


Actors and patterns l.jpg

Model hypothesis: scale models

  • 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


Model overview l.jpg

Deforestation Rate Distribution from 1985 to 2000 - scale modelsLand 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


Model implementation in terrame l.jpg

Legend scale models

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 l.jpg
Deforestation Rate Distribution Module scale models

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 and rules l.jpg
Allocation Module: different factors and scale modelsrules

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 l.jpg
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 l.jpg
Simulation neighborhoods Results

1985 to 1997


Why use terrame l.jpg

Why use TerraME? neighborhoods


Terrame advantages drawbacks l.jpg
TerraME Advantages & Drawbacks neighborhoods

  • Expressividade

  • Legibilidade

  • Data Aquisition & Result Analyses

  • Performance


Research contribution l.jpg
Research Contribution neighborhoods

  • 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 l.jpg

Obrigado... neighborhoods

Questions?


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