From Pixels to Processes: Detecting the Evolution of Agents in a LandscapeGilberto CâmaraDirector National Institute for Space ResearchBrazil

Department of Geography, SUNY Bufallo, February 2007

Knowledge gap for spatial data

source: John McDonald (MDA)

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 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

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?

MSS – Landsat 2 – Manaus(1977)

TM – Landsat 5 – Manaus (1987)

Source: Carlos Nobre (INPE)

Can we avoid that this….

Fire...

Source: Carlos Nobre (INPE)

….becomes this?

New Frontiers

DeforestationForestNon-forest

Clouds/no data

INPE 2003/2004:

Dynamic areas (current and future)

Intense Pressure

Future expansion

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

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 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

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?

Loggers

Competition for Space

Soybeans

Small-scale Farming Ranchers

Source: Dan Nepstad (Woods Hole)

Underlying Factorsdriving proximate causes

Causative interlinkages atproximate/underlying levels

Internal drivers

*If less than 5%of cases,not depicted here.

source:Geist &Lambin

5% 10% 50%

% of the cases

What Drives Tropical Deforestation?

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

Large-Scale Agriculture

Agricultural Areas (ha)  1970 1995/1996 %

Legal Amazonia 5,375,165 32,932,158 513Brazil 33,038,027 99,485,580 203

Source: IBGE - Agrarian Census

photo source: Edson Sano (EMBRAPA)

Unidade 1992 2001 %Amazônia Legal 29915799 51689061 72,78% Brasil 154,229,303 176,388,726 14,36%Fonte: PAM - IBGE

Cattle in Amazonia and Brazil

Cattle in Amazonia and Brazil

Unidade 1992 2001 %

Amazônia Legal 29,915,799 51,689,061 72,78%

Brasil 154,229,303 176,388,726 14,36%

photo source: Edson Sano (EMBRAPA)

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

Space Partitions in Rondônia

…linking human activities to the landscape

Landscape Analysis: Land units associated to agents

Agent Typology: A simple example

Tropical Deforestation Spatial Patterns: Corridor, Diffuse, Fishbone, Geometric

(Lambin, 1997)

Is it enough to describe Amazonian

land use patterns?

23

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

24

Fragstats (patch metrics)

(image from Fragstats manual)

25

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

Increased fragmentation

on Rondonia, Brazil

19861975

1992

Region-growing segmentation

Remote sensing image mining

Patterns of tropical deforestation (example 1)

Patch metrics for example 1

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

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

Validation set for decision tree (ex 1)

Validation showed 81% correctness

Case Study 1:Rondônia

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

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

Objective:Objective: To capture patterns and to characterize and model land use changeprocesses

Escada, 2003.

Spatial patterns in the Vale do Anari

irregular, linear, regular

Land use patterns

Spatial distribution

Clearing size

Actors Main land use Description

Linear (LIN) Roadside Variable Small households

Subsistence agriculture

Settlement parcels less than 50 ha. Deforestation uses linear patterns following government planning.

Irregular (IRR)

Near mainSettlement main

roads

Small (< 50 ha)

Small farmers Cattle ranching and subsistence agriculture

Settlement parcels less than 50 ha. Irregular clearings near roads following settlement parcels.

Regular (REG)

Near mainSettlement main

roads

Medium- large

(> 50 ha)

Midsized and large farms

Cattle ranching Patterns produced by land concentration.

Decision tree for Vale do Anari

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

• Fragmentation• Transference• Land concentration

Vale do Anari – 1982 -1985

Pereira et al, 2005

Escada, 2003

Patterns/TypologyIRR: Irregular – Colonist parcels

LIN: Linear – roadside parcels

REG: Regular agregation parcels

REG

Vale do Anari – 1985 - 1988

Pereira et al, 2005

Escada, 2003

REG

Vale do Anari – 1988 - 1991

Pereira et al, 2005

Escada, 2003

REG

Vale do Anari – 1991 - 1994

Pereira et al, 2005

Escada, 2003

Vale do Anari – 1994 - 1997

Pereira et al, 2005

Escada, 2003

REG

Vale do Anari – 1997 - 2000

Pereira et al, 2005

Escada, 2003

REG

Vale do Anari – 1985 - 2000

Confirmed by field work

Pereira et al, 2005

Escada, 2003

REG

REG

Marked land concentrationGovernment 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 Pará

Spatial patterns in the Xingu-Iriri region

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

Land use patterns

Spatial distribution

Clearing size

Actors Main land use

Description

Linear (LIN)

Roadside Variable Small households

Subsistence agriculture

Roadside clearings, following main roads

Small irregular (SMALL)

Near main settlements and main roads

Small(< 35 ha)

Small farmers

Family labour and cattle ranching

Near main roads and settlements up to 10 Km.

Irregular (IRR)

Near main settlements and main roads

Small (35 – 190 ha)

Small farmers

Cattle ranching

Associated to small family households

Medium Regular(MED)

Isolated or near secondary roads

190 – 900 ha

Medium farmers

Cattle ranching

Associated to medium to large farms

Large Regular(LARGE)

Isolated or at the end of secondary roads

Large(> 900 ha)

Large farmers

Cattle ranching

Isolated, may have airstrips

Decision tree for Terra do Meio spatial patterns

Trend towards land concentrationwhere large farms dominate over small settlements.

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

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).