Challenges in Providing Environmental & Geospatial Data for modelling processes in the Amazon Region. Biodiversity and Land Use and Land Cover Change for GEOMA Project.

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

Silvana Amaral Image Processing Division – DPI

National Institute for Space Research - INPE

2

Context

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.  

3

Priority Areas for conservation and sustainable use of the Brazilian Biodiversity -ARPA

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principais

0.0 – 0.10.1 – 0.20.2 – 0.30.3 – 0.40.4 – 0.50.5 – 0.60.6 – 0.70.7 – 0.80.8 – 0.90.9 – 1.0

% mudança 1997 a 2020:0.0 – 0.10.1 – 0.20.2 – 0.30.3 – 0.40.4 – 0.50.5 – 0.60.6 – 0.70.7 – 0.80.8 – 0.90.9 – 1.0

% mudança 1997 a 2020:

CENÁRIO BASE – Hot spots de mudança (1997 a 2020)

Maior intensidade de mudança nas novas fronteiras mais conectadas ao Sudeste e Nordeste

Context Processes are scale dependent

Human Dimension Biodiversity LUCC

Data needings - Legal Amazonia scale

4

Permanent open access to S&T

Challenges in Providing Environmental & Geospatial Data:

Data Providing Generation and description Governamental Initiatives – MMA, SIPAM,

INPE, etc.

Free access in the internet

5

6

7

8

9

10

11

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

12

ALAP BR 319

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principaisPortos

ALAP BR 319Estradas pavimentadas em 2010Estradas não pavimentadasRios principaisPortos

SIMULATION – Differences considering the basic scenario

ALAP BR 319Paved roads 2010Unpaved roadsMain rivers

0.0 -0.50Diminishing:0.0 0.10Increasing:

Deforestarion differencesNew integral conservation areasNew sustainable use areas

Amazonia needs planning combining conservation areas and intra-regionals effects of land use pressure.

With every conservation areas, deforestation process stops. New areas appears along Acre andnoth of Manaus

Different Scales

Data for regional modeling

13

Networks and connections Regional connectivity

Past and present process of land use occupation Physical connections of the human settlement and proximity

Rio Xingu

Rio Iriri

Transamazônica

-BR 163

Novo Progresso

São Felix do Xingu

Apyterewa

Kayapó

TrincheiraBacajá

Araweté /Ig.Ipiuxuna

KoatinemoKararaôCachoeira Seca do Iriri

Arara

Menkragnoti

Baú

Curuá

200 Km0

Rio Xingu

Rio Iriri

Transamazônica

-BR 163

Novo Progresso

São Felix do Xingu

Apyterewa

Kayapó

TrincheiraBacajá

Araweté /Ig.Ipiuxuna

KoatinemoKararaôCachoeira Seca do Iriri

Arara

Menkragnoti

Baú

Curuá

200 Km0 200 Km0

Different Scales

14

Rede HidrográficaRede EstradasPistas de pouso

São Félix do XinguTucumã e Ourilândia

Altamira

Uruará

Canopus

VilaCentral

Taboca

Sudoeste

Rede HidrográficaRede EstradasPistas de pouso

São Félix do XinguTucumã e Ourilândia

Altamira

Uruará

Canopus

VilaCentral

Taboca

Sudoeste

Networks and connectionsField Work

Physical network

Tucumã

Sudoeste

Ladeira Vermelha

Minerasul

Carapanã

Belém

São Félix

Araguaína

AltamiraVila Canopus

Nereu

Tancredo Neves

Taboca

Vila dos Crentes

Porto Estrela

Vila Central

Pontalina

Vila Caboclo

Primavera

Redenção

Metrópole Cidades Cidades Setores Localidades>50.000 hab <50.000 hab Urbanos

Tucumã

Sudoeste

Ladeira Vermelha

Minerasul

Carapanã

Belém

São Félix

Araguaína

AltamiraVila Canopus

Nereu

Tancredo Neves

Taboca

Vila dos Crentes

Porto Estrela

Vila Central

Pontalina

Vila Caboclo

Primavera

Redenção

Metrópole Cidades Cidades Setores Localidades>50.000 hab <50.000 hab Urbanos

Urban network

Different Scales

15

Biodiversity - Detailed Data Field Work

BR-319 Madeira - Purus

16

Areal Videography from Amazonia

GEOMA - May/Jun de 2006 – Intergrated Expedition INPE, INPA, MPEG,WCS – 1, FUNCATE, CPT, FSP

Paleocanais - Marajó

Dinâmica Terra do Meio

BR 163

Desmatamento PRODES

Gradientes florestais

Soja

http://www.dpi.inpe.br/geoma/videografia/http://www.dpi.inpe.br/geoma/videografia/download.phpdownload.php

17

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

18

ZEE - Scales

Focus Territorial Administration Scales

STRATEGY(Policy)

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

TATIC(Operational)

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

19

ZEE – Legal AmazonData gathering

Data Selection

Import

DataVerification

Geographical Data BaseSPRING

Export

InternetZEE Brasil

Other sources

Dgn / partialDxf / partialShapefile

Arc Info / E00, UNGMid / Mif

Tiff – Geotiff / GeojpgASCII – SPRINGDBF ASCII tables

External TablesTerraLib ASCII Geo

ShapefileASCII – SPRING

Geotiff

ConsultingTerraView

Data Selection

DATA BASE: ~ 40 Gigabytes520 layers, 23 tables (+600 atributes)

20

Geomorphology Data

21

Geomorphology and Landsat Data

22

Geomorphology and SRTM Data

23

Geographic Data Base and Geographic Information System

SGBDSGBDMySQLMySQL

BD SPRING

BD TerralibTerralib

Web Service TerraPHP

Client

Spatial Spatial DataData TerraviewTerraview

SPRINGSPRING

Client

Client

Challenge/Commitment: National/ Free Softwares

24

ZEE – Legal Amazon Scenario: Systematic and Update

InformationMMA-SDS

Source: MMA/SDS

25

ZEE – Project Status

Source: MMA/SDS (2006)

1:250.000 products (2006)

LegendWorkingConcluded

26

ZEE - Results

Products: Technical Reports Geographical Data Base Consulting System

Data Dissemination CDROM (Data Base) – under request Internet – MacroZEE Consulting and map

server(Other scales - forthcoming)

27

Permanent open access to S&T

Barriers in Providing Environmental & Geospatial Data:

TI capacity building and investments – Data Base and internet services Herbarium of Instituto de Botânica – SP

Data policy - institutional and personal “What would I receive as profit from making my data

free?”

Differences between Advertising, Consulting (Map Server) & Free data access

28

A “Model” for providing access

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 public goodgood

PRODES, DETER, DETEX – environmental monitoring systems

(INPE´s strategy and data policy)

29

Altamira (Pará) – LANDSAT Image – 22 August 2003

30

Altamira (Pará) – MODIS Image – 07 May 2004

31

Imagem Modis de 2004-05-21, com excesso de nuvens

Altamira (Pará) – MODIS Image – 21 May 2004

32

Altamira (Pará) – MODIS Image – 07 June 2004

33

6.000 hectares deforested in one month!

Altamira (Pará) – MODIS Image – 22 June 2004

34

Altamira (Pará) – LANDSAT Image – 07 July 2004

35

Go to the field....

36

...and get the bad guys!

37

2003 2004 2005 2006 2007 2008 2009 2010

LANDSAT-5 1984

LANDSAT-8 2010?

SPOT4 1998

SPOT5 2002

CBERS-2 2003

CBERS-2B 2007

CBERS-3 2009

IRS-P6 2003

Land Remote Sensing: 20 to 50 meter resolution

38

Land Remote Sensing: 50 – 300m resolution

Resolution 2004 2005 2006 2007 2008 2009 2010

MODISTerraAqua

2001 250 m1 dia

MERIS 2002 300 m2 dias

WFI Cbers-2 Cbers-2B

2003 250 m4 dias

AWFIS Irs-P6

2002 70 m4 dias

AWFI Cbers-3

2009 70 m 4 dias

AWFISSR-1

2009?

70 m 4 dias

39

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

40

41

42

FTP area for User

43

CBERS-2 CCD, Minas Gerais, Brazil

Government Institutions 23%

Educational Sector 26%

Private Companies 51%

44

Free CBERS data

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.

45

Free CBERS data

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

46

Final Comments There are Environmental and Geospatial data

available BUT … data quality, metadata, scale has to be considered

GEOMA demands data of high quality and several scales 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.

47

Thank you!

Slides and photos also provided by:Ana Paula AguiarGilberto CamaraJosé S. de MedeirosMario Cohn-HalfINPE´s team

silvana@dpi.inpe.br