1

Remote Sensing in BrazilRemote Sensing in Brazil

National Institute for Space ResearchINPE

Leila FonsecaImage Processing Division

INPE

2

Evolution of remote sensing use and Evolution of remote sensing use and applications in Brazilapplications in Brazil

• Up to mid 70´s: all the work which demanded earth information were met by using aerial photogrammetry technology

• 1965-1985: considered the golden period of aerial photogrammetry since this technology was cartelized and was the most expensive in the world

• In this period great projets, which demanded updated mappings and with high international grants came up in Brazil (Coffee plantation survey in the entire São Paulo state)

3

Evolution of remote sensing use and Evolution of remote sensing use and applications in Brazilapplications in Brazil

• After 1985 when the first orbital remote sensing companies were set up, some institutions or companies, which could not afford the aerial photogrammetric products, started to use satellite imagery

• Initially, the research focused on environmental sector such as fire monitoring, deforestation evaluation. All of them used traditional tools

4

Evolution of remote sensing use and Evolution of remote sensing use and applications in Brazilapplications in Brazil

• In the 90´s the use of satellite images increased considerably due to: the quick spreading of such technology; to the high cost of aerial photo technology; spread personal computer image processing tools

• As of 1999 with Landsat-7 launching and 2000 with High resolution IKONOS operation, many applications which were performed using aerial photos, could then be done using satellites images

5

New high resolution satellitesNew high resolution satellites

• Quick Bird (USA): 0.61 meters spatial resolution

• IKONOS (USA): 1.0 meter spatial resolution• EROS (ISRAEL): 1.8 meter spatial

resolution• This technology has made information

acquisition cheaper: the same produts generated using aerial photo (maps in scales 1:2000, 1:5000; urban data) can now be acquired for 1/10 of the price we payed 10 years ago

6

Remote Sensing CompaniesRemote Sensing Companies

• Two groups:– Aerial remote sensing: use photogrammetry

technology (20)• BASE, Aerofoto, Cruzeiro, Aerosul,

Engefoto, Esteio and more 15 smaller ones

– Orbital remote sensing: use satellite imagery technology (40)• IMAGEM, Sensora, Intersat, Geoambiente,

Threetek and more 30 smaller ones

7

Remote Sensing CompaniesRemote Sensing Companies

• Pioneer company: IMAGE (1986)– Leader in Latin America and Brazil (250

persons)

• Other companies were set up in the 90´s due to a great increase in the market

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New TechnologiesNew Technologies

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SPRINGSPRING

• SPRING is a state-of-the-art GIS and remote SPRING is a state-of-the-art GIS and remote sensing image processing system sensing image processing system

• developed at INPE by Division of Image developed at INPE by Division of Image Processing groupProcessing group

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SPRINGSPRING

• provides integration of raster and vector data representations in a single environment

• INPE has invested more than 140 men/year o • used for important projects in Brazil such as:

Multi-temporal evaluation of deforestation in the Amazon; Ecological-Economical Zoning for Brazil; The National Soils Database

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SPRINGSPRING

• A Multi-platform system, including support for Windows95/98/NT, Linux and Solaris

• A widely accessible freeware for the GIS community with a quick learning curve

• new algorithms (spatial analysis)

• Is totally free on http://www.dpi.inpe.br• Training courses: http://www.dpi.inpe/cursos

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CBERSCBERS China-Brazil Earth Resources SatelliteChina-Brazil Earth Resources Satellite

• Spatial program: cooperation between the Brazil and Chine

• CBERS-1 satellite was launched on October 14, 1999, aboard the Chinese rocket Long March 4B, from the Launch Center in Taiyuan, province of Shanxi, approximately 750 kilometers southwest of Beijing

• http://www.inpe.br/programas/cbers/english/index.htm

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CBERSCBERS

• multi-sensor payload with different spatial resolutions and data collecting frequencies

• WFI (Wide Field Imager, swath of 890 km with spatial resolution of 260m):

• CCD (high resolution, 113 km wide strip with 20m spatial resolution)

• IRMSS (Infrared Multispectral Scanner, 120 km swath with the resolution of 80m, 160m in the thermal channel )

• Data collection system (real-time retransmission of environmental data gathered on the ground )

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Image from the WFI 21 Camera - Image from the WFI 21 Camera - 10/21/1999 - Itaipú10/21/1999 - Itaipú

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High resolution CCD image of High resolution CCD image of ManausManaus

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CCD Image of Beijin, ChinaCCD Image of Beijin, China

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Typical Data Collection Platform

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ResultsResults

• participation in the International Space Station due to the experience acquired through the CBERS program

• the establishment of an industrial sector in the space area in Brazil

• Brazil is prepared to get involved in complex and ambitious tasks in the space area

• Studies for the development of more satellites from the CBERS series

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Some important projectsSome important projects

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MONITORING THE BRAZILIAN MONITORING THE BRAZILIAN AMAZON DEFORESTATION AMAZON DEFORESTATION

(PRODES)(PRODES).

• http://www.inpe.br/Informacoes_Eventos/amazonia.htm

• The largest forest monitoring project in the world

• Started in 1989 and provides annual deforestation rates

• a comprehensive survey based on LANDSAT-TM imagery requires at least 229 scenes to cover the region for one single year

• Coordenador: Dr. João Roberto dos Santos

• budget until 2003: $ 1,7 million

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Analogic PRODES

Data base:• extension and deforestation rate• type of vegetation and increment

TAXA

MÉD

IA D

O D

ESFL

OR

ESTA

MEN

TO

BR

UTO

(km

² / a

no)

0

5000

10000

15000

20000

25000

30000

77/7

8

80 85 88

88/8

9

89/9

0

90/9

1

91/9

2

92/9

3**

93/9

4**

94/9

5

95/9

6

96/9

7

97/9

8

O,54

0,48

0,37

0,30

0,40

0,40

0,81 0,51

0,37

0,48

0,37Taxa Média do

Desflorestamento Bruto ( %/ ano)* Taxa Média da Década

0,47

98/9

9***

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PRODES DIGITALPRODES DIGITAL

EDIÇÃO DA CLASSIFICAÇÃO

CONVERSÃO RASTER --> VETOR

AGREGADO IMAGEMRGB

ARQUIVODIGITAL

E ST IM AT I VA DO DE SFLOR E STAM E N TO B R UT O DA A M A ZÔN IA AT É 1997, E I NC R E M EN T O DE 1998, A PA RT I R DE T É C N IC A S DE P R OCE S SA M EN T O DIG ITA L

NOTA DE CRÉDITOM apeam ento da extens ão do d esflorestam ento bruto da A m azônia brasile ira ocorrido até o ano de 1997 e inc rem ento do desflorestam ento re lativ o à 1998, u tilizando técnic as de segm entação de im agens som bra, derivadas do m odelo de m istura e spectra l e class ificaç ão não supervisionada por regiões, im plem entadas no Sistem a de P roc essam ento de In form ações G eorreferenciadas). P ara m anter e asseg urar a c oerên cia com os dados h is tóricos do pro je to PR O DE S , considerou-s e, com o re ferência , os dados da in terpretação do u ltim o overlay, re la tivos à ex tensão do desflorestam ento até o ano de 1997 .

S PR ING (

MINISTÉRIO DA CIÊNCIA E TECNOLOGIA

Coordenação Geral de Observação da Terra - OBT Programa Institucional Amazônia - AMZ Divisão de Sensoriamento Remoto - DSR Divisão de Processamento de Imagem - DPI

INPE - Instituto Nacional de Pesquisas Espaciais

DIAGRAMA DA ARTICULAÇÃO DAS IMAGENS TM-LANDSAT

CENA WRS 231/067D ata : 0 7 /0 7 /9 7D ata : 1 2 /0 9 /9 8

SIST E M A D E PR O JE ÇÃ O U T MD A T U M , H O R I Z O N T A L S A D - 6 9

E S CA LA 1 : 2 5 0 .0 0 0

L E G E N D A

TemaÁrea (km )2

PRODES DIGITAL

FLOR ESTA-97

DES FLO.-97

DES FLO.-98

ÁGUA

NUV EM

TOTAL DOS TE MAS

NÃO-FLOR.

16.547

10.252

195

47

2

27.738

Image Processing Tecnhique: Linear Mixing Model

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Mapping of flooded area by Mapping of flooded area by brazilian hydroelectric resevoirs brazilian hydroelectric resevoirs

using satellite imagesusing satellite images

ANEEL - ANEEL - National Electric Energy Agency INPE – National Institute for Space INPE – National Institute for Space

ResearchResearchWMO – World Meteorologic Organization WMO – World Meteorologic Organization

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Serra da Mesa resevoirSerra da Mesa resevoir

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ObjectiveObjective

• Provide na uniform data base on the area flooded by hydroelectric resevoirs to support a fair financial compensation of municipalities

• 124 resevoirs were mapped in 3 months• Over 90 TM-5 images were processed and

incorporated into GIS (SPRING)• Normalized water index was used to define the

boundary between water and land• Manual editing was needed to account for erros

related to macrophytes and islands• Accuracy of the methodology was assessed by

comparing topographic maps available for the more recent resevoirs

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ConclusionConclusion

• The methodoly was efficient (low cost and computationally quick)

• The results were quickly implemented in public policies

• The way the projet was set up allowed public administrator having direct acess to the data

• Fexible: allow updating at any time• The results showed that the official area

flooded by resevoirs was smaller than the real area

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Remote Sensing for water Remote Sensing for water management in arid and management in arid and

semi-arid areas: semi-arid areas: The Brazilian ExperienceThe Brazilian Experience

Dra Evlyn (INPE)

www.obt.inpe.br (URLib: evlyn water)

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Remote sensing applicationsRemote sensing applications

• Desertification assessment• Crop irrigation monitoring• Aquaculture zoning• Cartography (X band SAR interferometry)

• Reservoir management

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Brazilian arid and semi-arid regionBrazilian arid and semi-arid region

• Big area: 880 000 km2 to 1000 000 km2

• 10 states• 1257 municipalities• 20 million inhabitants• 18 650 artesian wells• 12 190 active artesian wells• 490 000 ha of irrigated land

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Brazilian agencies using RS for water Brazilian agencies using RS for water managementmanagement

– Water Resources Secretariat at the Ministry of Environment,Water Resources and Legal Amazon (MMA)

– Semi-arid Research Unity (CPATSA) of the Brazilian Agricultural Research Corporation (EMBRAPA)

– National Water Agency (ANA

– Brazilian Geological Survey (CPRM) at the Ministry of Mines and Energy (MME)

– National Electric Energy Agency (ANEEL) – MME

– S.Francisco River Basin Development Company (CODEVASF)

– Ceara Meteorology and Water Resources Institute (FUNCEME)

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Concluding remarksConcluding remarks

• The use of remote sensing techniques for water resources management in Brazil is far behind of other applications. – lack of cloud free remote sensing data at a time

frequency compatible to the water resource monitoring needs;

– lack of human resources capable of coupling with the complexity of remote sensing applications to water resources.

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PROARCO: Fire monitoringPROARCO: Fire monitoring http://www.dpi.inpe.br/proarco/

April/1998

Roraima - 1998

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Data integration analysisData integration analysis

Data integration analysis

Meteorologic

Vegetation

Cartography data

Heat points