Data-intensive Geoinformatics

Gilberto CâmaraOctober 2012

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Spatial segregation indexes Remote sensing image mining

GI software: SPRING and TerraView Land change modelling

INPE´s strong point: a combination of problem-driven GI research and engineering

Data-intensive Geoinformatics = principles and applications of spatial information science to extract information from very large data sets

image: NASA

Enhanced environmental information service provision to users through knowledge platforms: Delivering applied knowledge to support innovative adaptation and mitigation solutions, based on the observations and predictions

Nature, 29 July 2010

Nature, 29 July 2010

Brazil is the world’s current largest experiment on land change and its effects: will it also happen elsewhere?

Today’s questions about Brazil could be tomorrow’s questions for other countries

Where is the food coming from and going to?

graphics: The Economist

source: Global Land Project Science Plan (IGBP)

Global Change

Where are changes taking place?How much change is happening? Who is being impacted by the change?What is causing change?

Human actions and global change

photo: A. Reenberg

Global Change

“Can we improve the architecture of land use information systems to increase their capacity to deal with big geospatial data sets and to provide better information for researchers and decision-makers?”

“Can we develop models and statistical analysis methods that increase our knowledge of what causes land change and our capacity to project scenarios of future change?”

Human actions and global change

photo: A. Reenberg

“By 2020, Brazil will reduce deforestation by 80% relative to 2005.” (pres. Lula in Copenhagen COP-15)

“Deforestation in Brazilian Amazonia is down by a whopping 78% from its recent high in 2004. If Brazil can maintain that progress — and Norway has put a US$1-billion reward on the table as encouragement — it would be the biggest environmental success in decades” (Nature, Rio + 20 editorial)

166-112

116-113

116-112

How much it takes to survey Amazonia?

30 Tb of data500.000 lines of code

150 man-years of software dev200 man-years of interpreters

166-112

116-113

116-112

TerraAmazon – open source software for large-scale land change monitoring

Ribeiro V., Freitas U., Queiroz G., Petinatti M., Abreu E. , “The Amazon Deforestation Monitoring System”. OSGeo Journal 3(1), 2008.

Stage 3 – Multidatabase access (Terralib 5+)

Data source

Data source

Data source

Modelling

Data discovery Data access Analysis

Remote Analysis

Remote Analysis

Remote Analysis

questions

answers

?

Data discovery: the whole earth catalogue

What data exists about Quixeramobim?

When did this flood happen?

Where do I find data on forest degradation?

GEOSS

catalogue

GEOSS Broker catalogue

catalogue

catalogue

Improving GEOSS with brokers

source: R.Shibasaki

Linking INPE’s data to a semantic search engine

Some experiments linking EuroGEOSS broker with INPE’s data base show potential (credits to Lubia Vinhas)… but there’s much to be done…

EuroGEOSS broker

Semantic data discovery in Terralib 5+?

Data source

Data source

Data source

Modelling

Data discovery Data access Analysis

Remote Analysis

Remote Analysis

Remote Analysis

internet

Representing concepts is hard

image: WMO

vulnerability? climate change? poverty?

What do we know we don’t know?

degradation

We’re bad at representing meaning

deforestation? degradation? disturbance?

Representing concepts is hard

What do we know we don’t know?

degradation

Geosemantics: representing concepts is hard

vulnerability

image: Y.A. Bertrand

Human-environmental models need to describe complex concepts (and store their attributes in a database)

sustainability

Geosemantics: representing concepts is hard

image: Y.A. Bertrand

resilience

Representing change is very hard

What do we know we don’t know?

images: USGS

Describing events and processes is very hard

When did the flood occur?

What do we know we don’t know?

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?

Land Use Change in Amazonia: Institutional analysis and modeling at multiple temporal and spatial scales

Gilberto Câmara, Ana Aguiar, Roberto Araújo, Patrícia Pinho, Luciano Dutra, Corina Freitas, Leila Fonseca, Isabel Escada, Silvana Amaral, Pedro Andrade-Neto

FAPESP Climate Change Program Workshop 2011

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(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

Institutional analysis in Amazonia

Identify different agents and try to model their actions

Field work Land change patterns

Land change modelsUrban networks

Modelling collective spatial actions

S. Costa, A. Aguiar, G. Câmara, T. Carneiro, P. Andrade, R. Araújo, “Using institutional arrangements and hybrid automata for regional scale agent-based modelling of land change” (under review), 2012.

Linking remote sensing and census: population models

S. Amaral, A. Gavlak , I. Escada, A. Monteiro, “Using remote sensing and census tract data to improve representation of population spatial distribution: Case studies in the Brazilian Amazon”. Population and Environment, 34(1): 142-170, 2012.

Radar images for land cover classification

Li, G. ; Lu, D.; Moran, E. ; Dutra, L. ; Batistella, M. . A comparative analysis of ALOS PALSAR L-band and RADARSAT-2 C-band data for land-cover classification in a tropical moist region. ISPRS Journal of Photogrammetry and Remote Sensing, 70:26-38, 2012.

REDD+ and Land Use Policy Assessment Centre

(Getty Images, 2008)

(PRODES, 2008)

source: Espindola, 2012

Pathways: understanding the tradeoffs between land use, emissions and biodiversity

Ações de Mitigação (NAMAs) 2020 (tendencial)

Amplitude da redução 2020

Proporção de Redução(mi tCO2)Uso da terra 1084 669 669 24,70% 24,70%Red Desmatamento Amazônia (80%) 564 564 20,90% 20,90%Red Desmatamento no Cerrado (40%) 104 104 3,90% 3,90%Agropecuária 627 133 166 4,90% 6,10%Recuperação de Pastos 83 104 3,10% 3,80%ILP - Integração Lavoura Pecuária 18 22 0,70% 0,80%Plantio Direto 16 20 0,60% 0,70%Fixação Biológica de Nitrogenio 16 20 0,60% 0,70%Energia 901 166 207 6,10% 7,70%Eficiência Energética 12 15 0,40% 0,60%Incremento do uso de biocombustíveis 48 60 1,80% 2,20%Expansão da oferta de energia por Hidroelétricas 79 99 2,90% 3,70%Fontes Alternativas (PCH, Bioeletricidade, eólica) 26 33 1,00% 1,20%Outros 92 8 10 0,30% 0,40%

Siderurgia – substituir carvão de desmate por plantado 8 10 0,30% 0,40%Total 2703 975 1052 36,10% 38,90%

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

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)

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?

Information extraction from image time series

“Remote sensing images provide data for describing landscape dynamics” (Câmara et al., "What´s in An Image?“ COSIT 2001)

data source: B. Rudorff (LAF/INPE)

MODIS time series describe changes in land use

Land use change by sugarcane expansion

Setting up the global research agenda

We need to be part of the community that sets up the scientific agenda for global change

We can develop new technology and models to build enhanced environmental information services (knowledge platforms)

Conclusions