Challenges in Providing Environmental
& Geospatial Data for modelling
processes in the Amazon Region.
Biodiversity and Land Use and Land
Cover Change for GEOMA Project.
Silvana Amaral
Image Processing Division – DPI
National Institute for Space Research - INPE
Strategies for Open and Permanent Access to Scientific Information in Latin America: Focus on
Health and Environmental Information for Sustainable Development
Atibaia - SP, Brazil
May, 2007
Context

GEOMA: “Rede Cooperativa de Modelagem
Ambiental”




Long-term objectives


2
Cooperative Network for Environmental Modelling
Established by Ministry of Science and Technology
INPE/OBT, INPE/CPTEC, LNCC, INPA, IMPA, MPEG
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.
Context

Processes are scale dependent



Human Dimension
Biodiversity
CENÁRIO BASE – Hot spots de mudança (1997 a 2020)
LUCC
Maior intensidade de mudança nas
novas fronteiras mais conectadas
ao Sudeste e Nordeste
% mudança 1997 a 2020:
ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
0.0 – 0.1
0.1 – 0.2
0.2 – 0.3
0.3 – 0.4
0.4 – 0.5
0.5 – 0.6
0.6 – 0.7
0.7 – 0.8
0.8 – 0.9
0.9 – 1.0
Priority Areas for conservation and sustainable use of the Brazilian Biodiversity -ARPA

3
Data needings - Legal Amazonia scale
Permanent open access to S&T
Challenges in Providing Environmental &
Geospatial Data:

Data Providing

Generation and description
 Governamental Initiatives – MMA, SIPAM, INPE,
etc.

4
Free access in the internet
5
6
7
8
9
10
Permanent open access to S&T
Challenges in Providing Environmental &
Geospatial Data:



Multi-scale
Data generation – scales and quality
Data Availabity
 Space
and time scale and series
 Data format
 Free access / technology
11
Different Scales
ALAP BR 319

ALAP BR 319
Estradas pavimentadas em 2010
Estradas não pavimentadas
Rios principais
Portos
12
Data for regional modeling
Different Scales
Networks and connections
 Regional connectivity


Past and present process of land use occupation
Physical connections of the human settlement and proximity
Arara
Transamazônica
Cachoeira
Kararaô
Koatinemo
Trincheira
Bacajá
Seca do Iriri
Rio Xingu
Araweté /
Ig.Ipiuxuna
Rio Iriri
BR
163
-
Apyterewa
Curuá
São Felix
do Xingu
Novo
Progresso
Baú
Menkragnoti
0
13
200 Km
Kayapó
Different Scales
Altamira
Networks and connections
Field Work
Uruará
Rede Hidrográfica
Rede Estradas
Pistas de pouso
Urban network
Sudoeste
Vila Canopus
Altamira
Vila Caboclo
Canopus
Taboca
Pontalina
Vila dos Crentes
Vila Central
Vila
Central
Primavera
São Félix do Xingu
Porto Estrela
São Félix
Taboca
Tucumã e Ourilândia
Tancredo Neves
Physical network
Belém
Araguaína
Nereu
Carapanã
Sudoeste
Redenção
Minerasul
Tucumã
Ladeira Vermelha
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Metrópole
Cidades
>50.000 hab
Cidades
<50.000 hab
Setores
Urbanos
Localidades
Biodiversity - Detailed Data
Field Work



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BR-319
Madeira - Purus
Areal Videography from Amazonia

GEOMA - May/Jun de 2006 – Intergrated Expedition
INPE, INPA, MPEG,WCS – 1, FUNCATE, CPT, FSP
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http://www.dpi.inpe.br/geoma/videografia/download.php
ZEE - BRAZIL
 “Zoneamento Ecológico Econômico no Brasil”


National Program to support territorial planning and
Environmental Conservation.
Planning, Diagnostic, Prognostic, Implementation
Strategy
Geographical Data Base (Medeiros & Crepani)
INCRA, INPA,
CCSIPAM,
CODEVASF,
Pertobras and
others
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ZEE - Scales
Focus
STRATEGY
(Policy)
TATIC
(Operational)
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Territorial
Administration
Scales
Continental
Federal
1:10.000.000
1:5.000.000
Nacional
Federal
1:2.500.000
1.1000.000
Regional
Federal/Estadual
1:1.000.000
1:250.000
Estadual
Estadual/Municipal
1:250.000 1:100.000
Municipal
Municipal
1:100.000 / 1:50.000
Local
Distrital
1:25.000 / 1:1.000
ZEE – Legal Amazon
Data gathering
Internet
ZEE Brasil
Other sources
ASCII – SPRING
Data Selection
Import
External Tables
TerraLib ASCII Geo
Shapefile
Geotiff
Dgn / partial
Dxf / partial
Shapefile
Arc Info / E00, UNG
Mid / Mif
Tiff – Geotiff / Geojpg
ASCII – SPRING
DBF ASCII tables
Export
Data Selection
Geographical Data Base
SPRING
Data
Verification
Consulting
TerraView
DATA BASE: ~ 40 Gigabytes
520 layers, 23 tables (+600 atributes)
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Geomorphology Data
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Geomorphology and Landsat Data
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Geomorphology and SRTM Data
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Geographic Data Base and Geographic
Information System
Challenge/Commitment: National/ Free Softwares
Spatial
Data
BD
SPRING
SGBD
MySQL
BD
Terralib
Terraview
Client
SPRING
Web Service
TerraPHP
Client
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Client
ZEE – Legal Amazon Scenario:
Systematic and Update Information
MMA-SDS
Source: MMA/SDS
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ZEE – Project Status
1:250.000 products
(2006)
Legend
Working
Concluded
Source: MMA/SDS (2006)
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ZEE - Results

Products:
 Technical
Reports
 Geographical Data Base
 Consulting System

Data Dissemination
(Data Base) – under request
 Internet – MacroZEE Consulting and map server
(Other scales - forthcoming)
 CDROM
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Permanent open access to S&T
Barriers in Providing Environmental & Geospatial Data:

TI capacity building and investments – Data Base and
internet services


Data policy - institutional and personal


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Herbarium of Instituto de Botânica – SP
“What would I receive as profit from making my data free?”
Differences between Advertising, Consulting (Map
Server) & Free data access
A “Model” for providing access
(INPE´s strategy and data policy)
Earth observation data for everyone: the CBERS
experience (Camara, 2007)
 The world is changing rapidly
 Climate Change is here to stay
 Global
land observation is a crucial need for the
world, but its future is uncertain


MODIS is very useful,but has no future
What will happen to LANDSAT?
 Global
land observation systems are a public good
DETER, DETEX – environmental
monitoring systems
 PRODES,
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Altamira (Pará) – LANDSAT Image – 22 August 2003
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Altamira (Pará) – MODIS Image – 07 May 2004
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Imagem Modis de
2004-05-21, com
excesso de nuvens
Altamira (Pará) – MODIS Image – 21 May 2004
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Altamira (Pará) – MODIS Image – 07 June 2004
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Altamira (Pará) – MODIS Image – 22 June 2004
6.000 hectares deforested in one month!
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Altamira (Pará) – LANDSAT Image – 07 July 2004
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Go to the field....
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...and get the bad guys!
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Land Remote Sensing: 20 to 50 meter resolution
2003
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LANDSAT-5
1984
LANDSAT-8
2010?
SPOT4
1998
SPOT5
2002
CBERS-2
2003
CBERS-2B
2007
CBERS-3
2009
IRS-P6
2003
2004
2005
2006
2007
2008
2009
2010
Land Remote Sensing: 50 – 300m resolution
Resolution
MODIS
Terra
Aqua
2001
250 m
1 dia
MERIS
2002
300 m
2 dias
WFI
2003
Cbers-2
Cbers-2B
250 m
4 dias
AWFIS
Irs-P6
2002
70 m
4 dias
AWFI
Cbers-3
2009
70 m
4 dias
AWFI
SSR-1
2009
?
70 m
4 dias
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2004
2005
2006
2007
2008
2009
2010
Strategy for CBERS
How do we obtain support for funding Earth Observation
Missions (300+ million dollar question)?


Our answer: Make all sectors of society use publically funded
EO data...
...by providing EO data for free!

CBERS images received in Brazil are freely available
on the Internet for Brazilian and Latin American users

CBERS images received in China are freely available
on the Internet for Chinese users

A high-quality image processing software (SPRING) is
also available free on the Internet in Brazil
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FTP area for User
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CBERS-2 CCD, Minas Gerais, Brazil
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Government Institutions
23%
Educational Sector
26%
Private Companies
51%
Free CBERS data
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
Enables new business and technology
development

Facilitates trial uses for new clients and
students

Planning new applications and scientific
projects becomes easier

Satellite imagery became a popular and
affordable environmental data for business,
education and scientific research.
Free CBERS data
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
The CBERS data policy has been extremely
well-received by government and society in
Brazil

There is an enormous demand for remote
sensing and environmental data in developing
countries

Free on-line data access can significantly
increase the number of users of earth
observation data
Final Comments

There are Environmental and Geospatial data
available


GEOMA demands data of high quality and several
scales



46
BUT … data quality, metadata, scale has to be considered
AND is providing some basic data
AND is working on publishing and data availability (internet
and Newsletters)
Environmental and Geospatial data as public good can
create demands for services and research, what
promote a positive feed-back for free data access.
Thank you!
[email protected]
Slides and photos also provided by:
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Ana Paula Aguiar
Gilberto Camara
José S. de Medeiros
Mario Cohn-Half
INPE´s team