Earth Observation in
Brasil: Data and
Applications
Ministério da Ciência e Tecnologia
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
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
Remote Sensing Ground
Station
Ministério da Ciência e Tecnologia
Remote Sensing Ground Station: 1973-2005
1973
1978
Cuiabá Ground Station: CBERS, LANDSAT, SPOT, MODIS, RADARSAT
Remote Sensing Ground Station: LANDSAT
Ano
Lanc
LANDSAT-1
(MSS)
1972
LANDSAT-2
(MSS)
1975
LANDSAT-3
(MSS)
1978
LANDSAT-5
(TM)
1984
LANDSAT-7
(ETM)
1999
1970
1975
1980
1985
1990
Complete Availability: All data is stored
1995
2000
2005
Remote Sensing Ground Station: SPOT, ERS,
RADARSAT and MODIS
Ano
Lanc
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
1980
1985
1990
1995
2000
SPOT, ERS, RADARSAT: selective availability
TERRA, AQUA: complete availability
2005
Remote Sensing Ground Station: CBERS
Ano
Lanc
CBERS-1
1999
CBERS-2
2003
CBERS-2B
2006
CBERS-3
2008
CBERS-4
2011
1990
1995
2000
2005
2010
CBERS Program has guaranteed data until 2015
2015
Remote Sensing Ground Station: Current
Situation

In Operation




CBERS-2
LANDSAT-5
AQUA, TERRA (MODIS)
Additional Capability


RADARSAT-1
SPOT-2, SPOT-4
INPE’s Remote Sensing Data Base
MSS
CBERS
TM and ETM+
ERS-1/2
SPOT
RADARSAT
Total
10 TB
38 TB
84 TB
6 TB
2 TB
2 TB
142 TB
MSS - Landsat 1
WRS1 248/62
07/07/1973
Sobradinho (BA) – LANDSAT-1 - 14/11/1973
MSS – Landsat 3 – São Paulo (1977)
LANDSAT-5
May/2003
MODIS R (MIR) G (NIR) B (RED) - Mosaico/AGOSTO/2003
WFI/CBERS - 25/03/2000 – Mato Grosso
CBERS Program: An
Overview
Ministério da Ciência e Tecnologia
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
CBERS Orbit







Sun synchronous
Height: 778 km
Inclination: 98,48 degrees
Period: 100,26 min
Equator crossing time: 10:30 AM
Revisit: 26 days
Distance between adjacent tracks: 107 km
CBERS-2
CBERS-2 Launch
(21 October 2003)
CBERS 1,2, 2B Sensor Configuration
WFI 260 m (890 km)
MSS 80 m (120 km)
CCD 20 m (120 km)
0.4
0.5
0.7
Built by China
0.9
1.1
Built by Brazil
1.5
1.7
2.3
2.5
mm
CBERS-2 CCD, Minas Gerais, Brazil
CBERS-2 Delta do Parnaíba Nov-2003
CBERS-2 CCD Manaus, Brazil, Dec 2003
CBERS-2 Represa de Sobradinho Dez 2003

Imagem CBERS2 in Louisiana,
EUA

On-board data
recorder
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)
0.4
mm
0.5
0.7
Built by China
0.9
1.1
Built by Brazil
1.5
1.7
2.1
2.3
CBERS Ground Station
Acquisition
Planning System
Reception &
Recording System
Quality
Control System
Order
Management System
Catalogue
Browse System
Product
Generation System
CBERS Ground Station in Brazil

Developed by Brazilian company and INPE


User-centered design




Major cost saving
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
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.
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
antenna’s footprint

Distribution in areas within the antenna’s 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
Remote Sensing Data
Centre
Ministério da Ciência e Tecnologia
Remote Sensing Data Centre

MSS-LANDSAT: 1973 - 1983



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.dgi.inpe.br/CatalogoMSS/
Available for free on the web (http://www.obt.inpe.br/CBERS)
MODIS

Being placed on-line (free access) until the end of 2005
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
Production environment
10 min
8 PCs/Linux
FTP area for User
Remote Sensing Research
and Applications
Ministério da Ciência e Tecnologia
Remote Sensing Teaching at INPE

Graduate Program in Remote Sensing (MsC and PhD)


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


300+ graduate students
ALFA project (EU-funded)
On-line material (books, overheads)
R&D 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)
Crop Forecasting Using Remote Sensing
soja
café
milho
cana-de-açúcar
Crop Forecasting
Imagens de satélite
Sistema de informação geográfica
Banco de dados
R&D 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)
PRODUTO S5/TM 4R5G3B
source: Waldir Paradella(INPE)
PRODUTO SPC-SAR (IHS)
Mineral Exploration with Integrated Product
ScanSAR-TM/Amplitude Mag (PGBC)
Grupo Salobo
AP1
Grupo Pojuca
AP9
AP3
AP2
Grupo Grão Pará
AP7
AP8
AP5
AP4
source: Waldir Paradella (INPE)
AP6
R&D in Oceanography and Inner Water

Oceanic processess:




Coastal zone monitoring
Winds by scatterometry
Waves by altimetric radar
Environmetal modeling in coastal zones
Temperature AVHRR
source: João Lorenzetti e José Luiz Stech(INPE)
Radar and Oil
CHLOROPHYLL- 22/01/00
Wetland Extraction from L-Band Data
Ica
Manaus
Santarem
Barbosa et al.
INPE/UCSB
Jurua
source: Evlyn Novo e Cláudio Barbosa
(INPE)
Purus
Tapajós
SAR and Wetlands in Amazônia
Mosaico JERS-1 (banda L-HH)
source: Evlyn Novo e Cláudio Barbosa (INPE)
R&D in Forestry/Ecology



Evaluation of deforestation in Amazonia
Monitoring of fires in savannas and tropical forests
Atlantic tropical forest mapping and monitoring
Understanding Deforestation in Amazonia
Source: Carlos Nobre (INPE)
The forest...
Fire...
Source: Carlos Nobre (INPE)
Amazon Deforestation 2003
Deforestation 2002/2003
Deforestation until 2002
Fonte: INPE PRODES Digital, 2004.
DETER
Real Time Monitoring of Amazon
Deforestation
http://www.obt.inpe.br/deter/
DETER: estrutura
Yearly estimates
Deforestation maps
Recent MODIS/WFI
data
Detection of new deforestation
Web maps
External users
Ground Station
Desmatamentos entre
13/Ago/2003 até 07/Mai/2004
Imagem LandSat5 de
13/Ago/2003
Modis Image Sept/2003
Deforestation 13/Ago/2003 until
07/Mai/2004
Deforestation in
13/Aug/2003 (yellow) +
deforestation from
13/Aug/2003 until
07/mai/2004 (red)
Fifteen days later...
Deforestation on 21/May/2004
Modis Mosaic 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)
Gráficos totalizando desmatamento
por municípios ou estado
Desmatamentos detectados em 07/21 Maio (pontos
em azul) + Queimadas detectadas em 10/11 Jun
GIS Technology R&D
Ministério da Ciência e Tecnologia
SPRING

Open access image processing and GIS software.

Multi-platform (Windows, Linux, Solaris)
 Web: http://www.dpi.inpe.br/spring (32.000 downloads)
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

SPRING: User adoption

Universities



Government institutions


Driving factors: documentation and examples, not price
Graduate and undergrads: Geography, Earth Sciences, Social
Sciences
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
SPRING downloads (Top 20 countries)
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!
TerraLib: Open source GIS library

Data management


Functions


All of data (spatial + attributes) is in
database
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
TerraLib applications

Cadastral Mapping


Public Health


Indicators of social exclusion in innercity areas
Land-use change modelling


Spatial statistical tools for
epidemiology and health services
Social Exclusion


Improving urban management of large
Brazilian cities
Spatio-temporal models of
deforestation in Amazonia
Emergency action planning

Oil refineries and pipelines (Petrobras)
What does it take to do it?

SPRING and TerraLib project


Development and Application Team



Software: 40 senior programmers (10 with PhD)
Applications: 30 PhDs in Earth Sciences plus students
Building a resource base



Major emphasis on “learning-by-doing”
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
Crime Mapping
Environmental Modelling
Ministério da Ciência e Tecnologia
The Future of Brazilian Amazon

Amazonia is a key environmental resource

Environment and biodiversity conservation

Economic development

Native populations
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.
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 (Université Louvain)
Competition for Space
Soybeans
Loggers
Competition for
Space
Small-scale Farming
Ranchers
Modelling Tropical Deforestation
•Análise de tendências
•Modelos econômicos
Coarse: 100 km x 100 km grid
Fine: 25 km x 25 km grid
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.
Ambientes Computacionais para Modelagem
Espaços celulares

Componentes

conjunto de células georeferenciadas

identificador único

vários atributos por células

matriz genérica de proximidade - GPM
superfície discreta de células retangulares multivaloradas possivelmente não contíguas
A estrutura do espaço é heterogênea
U
U
U
Ambientes definidos de forma recorrente
É possível construir modelos multiescalas
Porções distintas do espaço podem ter escalas diferentes
Modelling and Public Policy
External
Influences
System
Ecology
Economy
Politics
Scenarios
Policy
Options
Decision
Maker
Desired
System
State
In Conclusion: Earth Observation in Brazil

Amazonian Rain Forest


Semi-Arid North-East


Crop forecasting and yield estimation are crucial information
needs
Large Urban Settlements


Period droughts affect 25% of Brazilian Population
SouthEast and Central Regions


Human actions are modifying environmental conditions
Increasing intra-urban social exclusion and environmental
vulnerability
Remote Sensing and GIS Technologies are essential
from management of Brazilian territory
Empowering People with Geotechnologies: Th
“White-Box”
results = methods + data+ software



Methods
 Sound theory + local knowledge
Data
 Geospatial data sets as “public good”
Software
 Free Software with adequate functionality