Earth Observation in Brasil: Data and Applications

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Earth Observation in Brasil Data and Applications
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INPE - brief description

• National Institute for Space Research
• main civilian organization for space activities
  in Brazil
• staff of 1,800 ( 800 Ms.C. and Ph.D.)
• Areas
• Space Science, Earth Observation, Meteorology and
  Space Engineering

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Earth Observation at INPE

• Remote Sensing Ground Station
• CBERS Chinese Brazilian Satellite
• Remote Sensing Data Centre
• Remote Sensing Research and Applications
• GIS Technology Research and Applications
• Environmental Modelling

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Remote Sensing Ground Station
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Remote Sensing Ground Station 1973-2005
1973
1978
Cuiabá Ground Station CBERS, LANDSAT, SPOT,
MODIS, RADARSAT
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Remote Sensing Ground Station LANDSAT
Ano Lanc 1970 1975 1980 1985 1990 1995 2000 2005
LANDSAT-1 (MSS) 1972
LANDSAT-2 (MSS) 1975
LANDSAT-3 (MSS) 1978
LANDSAT-5 (TM) 1984
LANDSAT-7 (ETM) 1999
Complete Availability All data is stored
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Remote Sensing Ground Station SPOT, ERS,
RADARSAT and MODIS
Ano Lanc 1980 1985 1990 1995 2000 2005
SPOT-1 (HRV) 1986
SPOT-2 (HRV) 1990
SPOT-4 (HRV) 1998
ERS-1 (SAR) 1991
RADARSAT-1 (SAR) 1995
TERRA (MODIS) 1999
AQUA (MODIS) 2002
SPOT, ERS, RADARSAT selective availability
TERRA, AQUA complete availability
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Remote Sensing Ground Station CBERS
Ano Lanc 1990 1995 2000 2005 2010 2015
CBERS-1 1999
CBERS-2 2003
CBERS-2B 2006
CBERS-3 2008
CBERS-4 2011
CBERS Program has guaranteed data until 2015
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Remote Sensing Ground Station Current Situation

• In Operation
• CBERS-2
• LANDSAT-5
• AQUA, TERRA (MODIS)
• Additional Capability
• RADARSAT-1
• SPOT-2, SPOT-4

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INPEs Remote Sensing Data Base

• MSS 10 TB
• CBERS 38 TB
• TM and ETM 84 TB
• ERS-1/2 6 TB
• SPOT 2 TB
• RADARSAT 2 TB
• Total 142 TB

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MSS - Landsat 1 WRS1 248/62 07/07/1973
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• Sobradinho (BA) LANDSAT-1 - 14/11/1973

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MSS Landsat 3 São Paulo (1977)
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LANDSAT-5

• May/2003

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MODIS R (MIR) G (NIR) B (RED) -
Mosaico/AGOSTO/2003

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• WFI/CBERS - 25/03/2000 Mato Grosso

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CBERS Program An Overview
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CBERS China-Brazil Earth Resources Satellite

• Brief History
• Initial agreement signed in July 6th, 1988,
  covering CBERS-1 and 2.
• In 2002, both governments decided to expand the
  initial agreement by including CBERS-3 and 4.
• Program objectives
• Build a family of remote sensing satellites to
  support the needs of users in earth resources
  applications
• Improve the industrial capabilities of space
  technology in Brazil and China

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CBERS Orbit

• Sun synchronous
• Height 778 km
• Inclination 98,48 degrees
• Period 100,26 min
• Equator crossing time 1030 AM
• Revisit 26 days
• Distance between adjacent tracks 107 km

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CBERS-2
CBERS-2 Launch (21 October 2003)
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CBERS 1,2, 2B Sensor Configuration
WFI 260 m (890 km)
MSS 80 m (120 km)
CCD 20 m (120 km)
2.3
0.4
2.5
0.7
1.1
0.9
0.5
1.5
1.7
mm
Built by China
Built by Brazil
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CBERS-2 CCD, Minas Gerais, Brazil
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CBERS-2 Delta do Parnaíba Nov-2003
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CBERS-2 CCD Manaus, Brazil, Dec 2003
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CBERS-2 Represa de Sobradinho Dez 2003
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• Imagem CBERS-2 in Louisiana, EUA
• On-board data recorder

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CBERS 3 4 Sensor Configuration
WFI 73 m (860 km)
MSS 40 m (120 km)
CCD 20 m (120 km)
MUX 10 m (60 km)
PAN 5 m (60 km)
2.1
0.4
2.3
0.7
1.1
0.9
0.5
1.5
1.7
Built by China
Built by Brazil
mm
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CBERS Ground Station
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CBERS Ground Station in Brazil

• Developed by Brazilian company and INPE
• Major cost saving
• User-centered design
• User requests products in a web interface
• Products are generated automatically
• User can download products via FTP
• Efficiency and scalability
• Based on low-cost Linux PCs
• Totally automated, no operator intervention

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Data Policy for CBERS

• The downlink data is open to any country or
  organization.
• The data downlink for CBERS will be carried out
  through a ground station.
• China and Brazil may, in a few special cases,
  upon mutual consultation, decide on the transfer
  of data free of charge.
• The revenues resulting from the distribution of
  CBERS data will be equally shared between China
  and Brazil.

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Policy for CBERS International Data Downlink

• Access fee is charged on a LANDSAT-like basis
• One flat annual fee for complete access to all
  data in the antennas footprint
• Distribution in areas within the antennas
  footprint is the responsibility of the ground
  station operator
• There is no additional charge for image
  distribution
• Ground station operators are encouraged to
  distribute CBERS data free of charge on the Web

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Remote Sensing Data Centre
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Remote Sensing Data Centre

• MSS-LANDSAT 1973 - 1983
• Available for free on the web
• http//www.dgi.inpe.br/CatalogoMSS/
• TM/ETM-LANDSAT 1984 - 2000
• Being placed on-line (free access) until the end
  of 2005
• CBERS-2
• Available for free on the web (http//www.obt.inpe
  .br/CBERS)
• MODIS
• Being placed on-line (free access) until the end
  of 2005

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CBERS Image Distribution in Brazil (1st May to
30st December 2004)
Total number of full CCD scenes distributed (145 Mb/scene) 53,000
Number of institutions and companies 4,500
Number of scenes produced per week 2170
Average time to process a user request 10 min
Production environment 8 PCs/Linux
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FTP area for User
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Remote Sensing Research and Applications
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Remote Sensing Teaching at INPE

• Graduate Program in Remote Sensing (MsC and PhD)
• 300 graduate students
• Short Courses in GIS and Remote Sensing (40 h)
• www.dpi.inpe.br/cursos
• Establishment of a distance learning program in
  GIS and Remote Sensing
• ALFA project (EU-funded)
• On-line material (books, overheads)

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RD in Agriculture

• Perennial crops monitoring (sugarcane, citrus)
• Small grains crop area, yield and monitoring
• Rural insurance issues

AVIRIS Image
Apple monitoring
source José Epiphanio (INPE)
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Crop Forecasting Using Remote Sensing
soja
café
milho
cana-de-açúcar
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Crop Forecasting
Sistema de informação geográfica
Imagens de satélite
Banco de dados
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RD in Geology

• Oil and gas exploration Petrobras/ERSDAC
  (Japan)
• RS and Geophysical data integration for mineral
  exploration and mapping
• Evaluation of new sensors (e.g ASTER, Palsar-1)

source Waldir Paradella(INPE)
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Mineral Exploration with Integrated
ProductScanSAR-TM/Amplitude Mag (PGBC)

source Waldir Paradella (INPE)
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RD in Oceanography and Inner Water

• Oceanic processess
• Coastal zone monitoring
• Winds by scatterometry
• Waves by altimetric radar
• Environmetal modeling in coastal zones

Radar and Oil
Temperature AVHRR
source João Lorenzetti e José Luiz Stech(INPE)
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Wetland Extraction from L-Band Data
Barbosa et al. INPE/UCSB
source Evlyn Novo e Cláudio Barbosa (INPE)
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SAR and Wetlands in Amazônia
Mosaico JERS-1 (banda L-HH)
source Evlyn Novo e Cláudio Barbosa (INPE)
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RD in Forestry/Ecology

• Evaluation of deforestation in Amazonia
• Monitoring of fires in savannas and tropical
  forests
• Atlantic tropical forest mapping and monitoring

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Understanding Deforestation in Amazonia
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The forest...
Source Carlos Nobre (INPE)
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Fire...
Source Carlos Nobre (INPE)
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Amazon Deforestation 2003
Deforestation 2002/2003
Deforestation until 2002
Fonte INPE PRODES Digital, 2004.
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DETER Real Time Monitoring of Amazon
Deforestation http//www.obt.inpe.br/deter/
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DETER estrutura
Recent MODIS/WFI data
Deforestation maps
Ground Station
Yearly estimates
External users
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Desmatamentos entre 13/Ago/2003 até 07/Mai/2004
Imagem LandSat5 de 13/Ago/2003
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Modis Image Sept/2003
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Deforestation 13/Ago/2003 until 07/Mai/2004
Deforestation in 13/Aug/2003 (yellow)
deforestation from 13/Aug/2003 until 07/mai/2004
(red)
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Fifteen days later...
Deforestation on 21/May/2004
Deforestation in 13/Aug/2003 (yellow)
deforestation from 13/Aug/2003 until 07/May/2004
(red) deforestation on 21/May/2004 (orange)
Modis Mosaic on 21/May/2004
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Gráficos totalizando desmatamento por
municípios ou estado
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Desmatamentos detectados em 07/21 Maio (pontos em
azul) Queimadas detectadas em 10/11 Jun
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GIS Technology RD
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SPRING

• Open access image processing and GIS software.
• Multi-platform (Windows, Linux, Solaris)
• Web http//www.dpi.inpe.br/spring (32.000
  downloads)

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Technology as a social product

• Research system in the developed world
• discourages the production of training material
• There are good books on GIS!
• unfortunately, these books are in English and
  are expensive
• Need for open access of information
• Open access literature in local language
• Brazilian experience
• three-volume set (Introduction to GIS, Spatial
  Analysis, Spatial Databases)
• Application examples using SPRING key factors in
  software adoption

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SPRING User adoption

• Universities
• Driving factors documentation and examples, not
  price
• Graduate and undergrads Geography, Earth
  Sciences, Social Sciences
• Government institutions
• Replace existing US-based commercial solutions
• Agricultural research agency (EMBRAPA)
• Geological Survey (CPRM)
• Census bureau (IBGE)
• Private companies
• Saving of licensing costs
• Local support and training

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SPRING downloads (Top 20 countries)
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Innovation in GIS

• Current generation of GIS
• Built on proprietary architectures
• Interface functions database monolithic
  system
• Geometric data structures archived outside of
  the DBMS
• New generation of spatial information technology
• All data will be handled by the database
  (inclusive images and maps)
• Users can develop customized applications (small
  GIS)
• They need appropriate tools!

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TerraLib Open source GIS library

• Data management
• All of data (spatial attributes) is in database
• Functions
• Spatial statistics, Image Processing, Map Algebra
• Innovation
• Based on state-of-the-art techniques
• Same timing as similar commercial products
• Web-based co-operative development
• http//www.terralib.org

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TerraLib applications

• Cadastral Mapping
• Improving urban management of large Brazilian
  cities
• Public Health
• Spatial statistical tools for epidemiology and
  health services
• Social Exclusion
• Indicators of social exclusion in inner-city
  areas
• Land-use change modelling
• Spatio-temporal models of deforestation in
  Amazonia
• Emergency action planning
• Oil refineries and pipelines (Petrobras)

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What does it take to do it?

• SPRING and TerraLib project
• Major emphasis on learning-by-doing
• Development and Application Team
• Software 40 senior programmers (10 with PhD)
• Applications 30 PhDs in Earth Sciences plus
  students
• Building a resource base
• Graduate Programs in Computer Science and Remote
  Sensing
• SPRING and Terralib 20 PhD thesis and 35 MsC
  dissertations
• Institutional effort
• Requires long-term planning and vision

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Crime Mapping
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Environmental Modelling
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The Future of Brazilian Amazon

• Amazonia is a key environmental resource
• Environment and biodiversity conservation
• Economic development
• Native populations

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Environmental Modelling in Brasil

• GEOMA Rede Cooperativa de Modelagem Ambiental
• Cooperative Network for Environmental Modelling
• Established by Ministry of Science and Technology
• INPE/OBT, INPE/CPTEC, LNCC, INPA, IMPA, MPEG
• Long-term objectives
• Develop computational-mathematical models to
  predict the spatial dynamics of ecological and
  socio-economic systems at different geographic
  scales, within the framework of sustainability
• Support policy decision making at local, regional
  and national levels, by providing decision makers
  with qualified analytical tools.  

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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 5of cases, not depicted here.
sourceGeist Lambin (Université Louvain)
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Competition for Space
Loggers
Competition for Space
Source Dan Nepstad (Woods Hole)
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Modelling Tropical Deforestation
Coarse 100 km x 100 km grid
Fine 25 km x 25 km grid
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Modelling Deforestation in Amazonia

• High coefficients of multiple determination were
  obtained on all models built (R2 from 0.80 to
  0.86).
• The main factors identified were
• Population density
• Connection to national markets
• Climatic conditions
• Indicators related to land distribution between
  large and small farmers.
• The main current agricultural frontier areas, in
  Pará and Amazonas States, where intense
  deforestation processes are taking place now were
  correctly identified as hot-spots of change. 

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Ambientes Computacionais para Modelagem
superfície discreta de células retangulares
multivaloradas possivelmente não contíguas
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A estrutura do espaço é heterogênea
Ambientes definidos de forma recorrente
É possível construir modelos multiescalas
Porções distintas do espaço podem ter escalas
diferentes
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Modelling and Public Policy
External Influences
System Ecology Economy Politics
Desired System State
Decision Maker
Scenarios
Policy Options
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In Conclusion Earth Observation in Brazil

• Amazonian Rain Forest
• Human actions are modifying environmental
  conditions
• Semi-Arid North-East
• Period droughts affect 25 of Brazilian
  Population
• SouthEast and Central Regions
• Crop forecasting and yield estimation are crucial
  information needs
• Large Urban Settlements
• Increasing intra-urban social exclusion and
  environmental vulnerability
• Remote Sensing and GIS Technologies are essential
  from management of Brazilian territory

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Empowering People with Geotechnologies The
White-Box

• results methods data software
• Methods
• Sound theory local knowledge
• Data
• Geospatial data sets as public good
• Software
• Free Software with adequate functionality