Data-intensive
Geoinformatics
Gilberto Câmara
October 2012
Licence: Creative Commons By Attribution Non Commercial Share Alike
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Spatial segregation indexes
Remote sensing image mining
INPE´s strong point: a combination of problem-driven
GI research and engineering
GI software: SPRING and TerraView
Land change modelling
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)
Human actions and global change
Global Change
Where are changes taking place?
How much change is happening?
Who is being impacted by the change?
What is causing change?
photo: A. Reenberg
Human actions and global change
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?”
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)
How much it takes to survey Amazonia?
116-112
30 Tb of data
500.000 lines of code
150 man-years of software dev
200 man-years of interpreters
116-113
166-112
TerraAmazon – open source software
for large-scale land change monitoring
116-112
116-113
166-112
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+)
Modelling
Data discovery
Data
source
Data access
Data
source
Remote
Analysis
Remote
Analysis
Analysis
Data
source
Remote
Analysis
Data discovery: the whole earth catalogue
?
answers
questions
What data exists about
Quixeramobim?
When did this flood happen?
Where do I find data on forest
degradation?
GEOSS
Improving GEOSS with brokers
catalogue
GEOSS
Broker
catalogue
catalogue
catalogue
source: R.Shibasaki
Linking INPE’s data to a semantic search engine
EuroGEOSS broker
Some experiments linking EuroGEOSS broker with INPE’s data base show
potential (credits to Lubia Vinhas)… but there’s much to be done…
Semantic data discovery in Terralib 5+?
Modelling
Data discovery
Data access
Analysis
internet
Data
source
Data
source
Remote
Analysis
Remote
Analysis
Data
source
Remote
Analysis
What do we know we don’t know?
Representing concepts is hard
vulnerability? climate change? poverty?
image: WMO
What do we know we don’t know?
We’re bad at representing meaning
Representing concepts is hard
degradation
deforestation? degradation? disturbance?
Geosemantics: representing concepts is hard
vulnerability
degradation
image: Y.A. Bertrand
Geosemantics: representing concepts is hard
sustainability
resilience
image: Y.A. Bertrand
Human-environmental models need to describe complex
concepts (and store their attributes in a database)
What do we know we don’t know?
images: USGS
Representing change is very hard
What do we know we don’t know?
Describing events and processes is very hard
When did the flood occur?
images: USGS
Slides from LANDSAT
Modelling Human-Environment Interactions
1973 on the use of natural
1987 resources?
Aral
Sea
How
do we decide
2000
What are the conditions favoring success in resource mgnt?
Can we anticipate changes resulting from human decisions?
FAPESP Climate Change Program Workshop 2011
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
Agent-Based Modelling: Computing approaches
to complex systems
Representations
Goal
Communication
Communication
Action
Perception
Environment
source: Nigel Gilbert
Modelling collective spatial actions
Agent
Agent
Space
Space
source: Benenson and Torrens, “Geographic Automata Systems”, IJGIS, 2005
(but many questions remain...)
Agent-Based Modelling: Computing approaches
to complex systems
Representations
Goal
Communication
Communication
Action
Perception
Environment
source: Nigel Gilbert
Institutional analysis in Amazonia
Identify different agents and try to model their actions
Field work
Urban networks
Land change patterns
Land change models
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
Pathways: understanding the tradeoffs
between land use, emissions and biodiversity
(PRODES,
2008)
(Getty Images, 2008)
source: Espindola, 2012
Ações de Mitigação
(NAMAs)
Uso da terra
Red Desmatamento
Amazônia (80%)
Red Desmatamento no
Cerrado (40%)
Agropecuária
Recuperação de Pastos
ILP - Integração Lavoura
Pecuária
Plantio Direto
Fixação Biológica de
Nitrogenio
Energia
Eficiência Energética
Incremento do uso de
biocombustíveis
Expansão da oferta de
energia por Hidroelétricas
Fontes Alternativas (PCH,
Bioeletricidade, eólica)
Outros
Siderurgia – substituir
carvão de desmate por
plantado
Total
Amplitude da redução
2020
2020 (tendencial)
(mi tCO2)
1084 669
669
Proporção de Redução
24,70%
24,70%
564
564
20,90%
20,90%
104
133
83
104
166
104
3,90%
4,90%
3,10%
3,90%
6,10%
3,80%
18
16
22
20
0,70%
0,60%
0,80%
0,70%
16
166
12
20
207
15
0,60%
6,10%
0,40%
0,70%
7,70%
0,60%
48
60
1,80%
2,20%
79
99
2,90%
3,70%
92
26
8
33
10
1,00%
0,30%
1,20%
0,40%
2703
8
975
10
1052
0,30%
36,10%
0,40%
38,90%
627
901
REDD-PAC: land use policy assessment
GLOBIOM, G4M, EPIC, TerraME
TerraLib
Land use data and drivers for Brazil
Globally consistent policy impact
assessment
Model cluster - realistic assumptions
Information infrastructure
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
Land use models are good at
allocating change in space. Their
problem is: how much change will
happen?
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.
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)
MODIS time series describe changes in land use
Land use change by sugarcane expansion
data source: B. Rudorff (LAF/INPE)
Setting up the global research agenda
Conclusions
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)