From pixels to processes detecting the evolution of agents in a landscape
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Department of Geography, SUNY Bufallo, February 2007. From Pixels to Processes: Detecting the Evolution of Agents in a Landscape. Gilberto Câmara Director National Institute for Space Research Brazil. Knowledge gap for spatial data. source: John McDonald (MDA).

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From Pixels to Processes: Detecting the Evolution of Agents in a Landscape

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From pixels to processes detecting the evolution of agents in a landscape

Department of Geography, SUNY Bufallo,

February 2007

From Pixels to Processes: Detecting the Evolution of Agents in a Landscape

Gilberto Câmara

Director

National Institute for Space Research

Brazil


Knowledge gap for spatial data

Knowledge gap for spatial data

source: John McDonald (MDA)


The way remote sensing data is used

The way remote sensing data is used

  • Exctracting information from remote sensing imagery

    • Most applications use the “snapshot” paradigm

  • Recipe analogy

    • Take 1 image (“raw”)

    • “Cook” the image (correction + interpretation)

    • All “salt” (i.e., ancillary data)

    • Serve while hot (on a “GIS plate”)

  • But we have lots of images!

    • Immense data archives (Terabytes of historical images)


The challenge of remote sensing data mining

The challenge of remote sensing data mining

  • How many cutting-edge applications exist for extracting information in large image databases?

  • How much R&D is being invested in spatial data mining in large repositories of EO data?

  • How do we put our image databases to more effective use?


Land remote sensing data mining a giscience view

Land remote sensing data mining: A GIScience view

  • A large remote sensing image database is a collection of snapshots of landscapes, which provide us with a unique opportunity for understanding how, when, and where changes take place in our world.

  • We should search for changes, not search for content

  • Research challenge: How do model land change for data extracted from a land remote sensing database?


From pixels to processes detecting the evolution of agents in a landscape

MSS – Landsat 2 – Manaus(1977)


From pixels to processes detecting the evolution of agents in a landscape

TM – Landsat 5 – Manaus (1987)


From pixels to processes detecting the evolution of agents in a landscape

Can we avoid that this….

Source: Carlos Nobre (INPE)


From pixels to processes detecting the evolution of agents in a landscape

Fire...

….becomes this?

Source: Carlos Nobre (INPE)


Dynamic areas current and future

Dynamic areas (current and future)

New Frontiers

INPE 2003/2004:

Intense Pressure

Deforestation

Forest

Future expansion

Non-forest

Clouds/no data


Modelling land change in amazonia

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?


Agent based models

Agent-based models

  • Recent emphasis on agent-based modeling for simulation of social processes.

  • Simulations can generate patterns similar to real-life situations

  • How about real-life modelling?

  • We need to be able to describe the types of agents that operate in a given landscape.


Extracting land change agents from images

Extracting Land Change Agents from Images

  • Land change agents can be inferred from land change segments extracted from remote sensing imagery.

  • Different agents can be distinguished by their different spatial patterns of land use.

  • This presentation

    • Description of methodology

    • Case studies in Amazonia


Research questions

Research Questions

  • What are the different land use agents present in the database?

  • When did a certain land use agent emerge?

  • What are the dominant land use agents for each region?

  • How do agents emerge and change in time?


Challenge how do people use space

Soybeans

Ranchers

Small-scale Farming

Challenge: How do people use space?

Loggers

Competition for Space

Source: Dan Nepstad (Woods Hole)


What drives tropical deforestation

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

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


From pixels to processes detecting the evolution of agents in a landscape

photo source: Edson Sano (EMBRAPA)

Large-Scale Agriculture


From pixels to processes detecting the evolution of agents in a landscape

photo source: Edson Sano (EMBRAPA)


Different agents different motivations1

Different agents, different motivations

  • Small-scale settlers

    • Associated to social movements

    • 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


Landscape analysis land units associated to agents

Landscape Analysis: Land units associated to agents

Space Partitions in Rondônia

…linking human activities

to the landscape


Agent typology a simple example

Is it enough

to describe Amazonian

land use patterns?

Agent Typology: A simple example

Tropical Deforestation Spatial Patterns: Corridor, Diffuse, Fishbone, Geometric (Lambin, 1997)


Landscape ecology metrics

Landscape Ecology Metrics

  • Patterns and differences are immediately recognized by the eye + brain

  • Landscape Ecology Metrics allow these patterns in space to be described quantitatively

Source: Phil Hurvitz


Fragstats patch metrics

(image from Fragstats manual)

Fragstats (patch metrics)


Some patch metrics

Some patch metrics

  • PARA = perimeter/area ratio

  • SHAPE = perimeter/ (perimeter for a compact region)

  • FRAC = fractal dimension index

  • CIRCLE = circle index (0 for circular, 1 for elongated)

  • CONTIG = average contiguity value

  • GYRATE = radius of gyration


From pixels to processes detecting the evolution of agents in a landscape

1975

1986

1992

Increased

fragmentation

on Rondonia, Brazil


Region growing segmentation

Region-growing segmentation


Remote sensing image mining

Remote sensing image mining


Patterns of tropical deforestation example 1

Patterns of tropical deforestation (example 1)


Patch metrics for example 1

Patch metrics for example 1


Decision tree classifier

Decision tree classifier

  • C4.5 decision tree classifier (Quinlan 1993).

  • Each node matches a non-categorical attribute and each arc to a possible value of that attribute.

  • Each node is associated the numerical attribute which is most informative among the attributes not yet considered in the path from the root.


Decision tree for patterns

Decision tree for patterns

metrics are: perimeter/area ratio (PARA) and fractal dimension (FRAC)


Validation set for decision tree ex 1

Validation set for decision tree (ex 1)

Validation showed 81% correctness


From pixels to processes detecting the evolution of agents in a landscape

  • Incra settlement projects

  • Small, medium and large farms

  • Started in the 70’s

  • Different spatial and temporal patterns

  • Lots size of 25 ha to 100 ha – Farms from 500 ha.

  • Cattle ranching

Case Study 1:Rondônia

Objective: To capture patterns and to

characterize and model land use change

processes

TM/Landsat, 5, 4, 3 (2000)

Prodes (INPE, 2000)

Escada, 2003.


Spatial patterns in the vale do anari

Spatial patterns in the Vale do Anari

irregular, linear, regular


Decision tree for vale do anari

Decision tree for Vale do Anari


From pixels to processes detecting the evolution of agents in a landscape

  • Changes in Incra parcels configuration by (Coy, 1987; Pedlowski e Dale, 1992; Escada 2003):

    • Fragmentation

    • Transference

    • Land concentration


Vale do anari 1982 1985

Vale do Anari – 1982 -1985

REG

Patterns/Typology

IRR: Irregular – Colonist parcels

LIN: Linear – roadside parcels

REG: Regular agregation parcels

Pereira et al, 2005

Escada, 2003


Vale do anari 1985 1988

Vale do Anari – 1985 - 1988

REG

Pereira et al, 2005

Escada, 2003


Vale do anari 1988 1991

Vale do Anari – 1988 - 1991

REG

Pereira et al, 2005

Escada, 2003


Vale do anari 1991 1994

Vale do Anari – 1991 - 1994

Pereira et al, 2005

Escada, 2003


Vale do anari 1994 1997

Vale do Anari – 1994 - 1997

REG

Pereira et al, 2005

Escada, 2003


Vale do anari 1997 2000

Vale do Anari – 1997 - 2000

REG

Pereira et al, 2005

Escada, 2003


Vale do anari 1985 2000

Confirmed by

field work

Vale do Anari – 1985 - 2000

REG

REG

Pereira et al, 2005

Escada, 2003


From pixels to processes detecting the evolution of agents in a landscape

Marked land concentration

Government plan for settling many colonists in the area has failed.

Large farmers have bought the parcels in an illicit way


Case study 2 xingi iriri watershed in the state of par

Case study 2: Xingi-Iriri watershed in the state of Pará


Spatial patterns in the xingu iriri region

Spatial patterns in the Xingu-Iriri region

linear, small irregular, irregular, medium regular, large regular


Decision tree for terra do meio spatial patterns

Decision tree for Terra do Meio spatial patterns


From pixels to processes detecting the evolution of agents in a landscape

Trend towards land concentration

where large farms dominate over small settlements.


Conclusions

Conclusions

  • Pattern classification in maps extracted from images of distinct dates enables associating land change objects to causative agent

  • Pattern classification techniques associated to remote sensing image interpretation are a step forward in understanding and modelling land use change.

  • Next step: develop agent-based models for deforestation in Amazonia


References

References

  • Mining Patterns of Change in Remote Sensing Image Databases.Marcelino Silva, Gilberto Camara, Ricardo Souza, Dalton Valeriano, Isabel Escada.Fifth IEEE International Conference on Data Mining. Houston,TX, USA, November 2005.

  • "Remote Sensing Image Mining: Detecting Agents of Land Use Change in Tropical Forest Areas“

    Marcelino Silva, Gilberto Câmara, Ricardo Souza, Dalton Valeriano, Isabel Escada.

    International Journal of Remote Sensing, under review (manuscript available from the author).


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