from satellite imagery to geo-information 2... · from satellite imagery to geo-information :...

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Présentation CASIT A/AIT 2003 1

From satellite imagery to geo-information :

Principle of Remote Sensing and image processing

overview of methods & applications

Michaël Michaël TONONTONONGDTAGDTA


Geographic Information System (GIS) definition

ESRI definition (1993)« An organised collection of computer hardware, software,geographic data, and personnel designed to efficiently capture, store, update, manipulate, analyse, and display all forms of geographically referenced information. »

A system of hardware, software, and procedures designed tosupport the capture, management, manipulation, analysis, modellingand display of spatially-referenced data for solving complex planningand management problems. (David Cowen, 1989)

What is a GIS ?

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IntroductionInterest of Remote Sensing images

• Various Measurements: OPTICAL / RADARVarious bands (Visible, IR, Thermal ...)

• Various Scales : Global (Whole Earth)Regional (Several countries)NationalLocal (a few kilometres)

• Various precision levels: Low resolution (1 to 5 km per pixel)High resolution (10 to 30 m)Very High resolution (approx. 1m)

• Various Repetitivities: Time between 2 acquisitions :from 1/2 hour to more than 20 days

VariousApplications !

Geology - Agriculture - MeteorologyCartography - OceanographyEnvironmental and resource monitoringUrban and land management...


SATELLITE IMAGES : DEFINITION

What is a satellite image ?What is a satellite image ?

Sensor

Source of energy

Atmosphere

Ground cover

Ground receiving

station

DIGITAL ANALYSIS

VISUAL ANALYSIS

Final user

(E. Chuvieco,1990)

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The electromagnetic waves carry the informationThe electromagnetic waves carry the information1 - Basic Concepts of Remote Sensing

OPTICAL RADARThe Electromagneticspectrum

Wavelengthλ

(m)

10-13 10-11 10-9 10-7

Frequency(Hz)

1021 1019 1017 1015 1013 1011 109

Gamma rays X rays UltravioletVisible light

Infrared Microwaves Radio waves

10-5 10-3 10-1

0.4 0.5 0.6 0.7v iolet blue green y ellow red

λ(µm)

Electromagnetic Spectrumcontinuous

Spectral Bandsdiscontinuous≠


• VISIBLE : 0.4 - 0.8 µm• Solar Reflection - Surface characteristics

• INFRA RED : 0.8 - 14 µm• 0.8 - 1.3 µm : Near Infra Red (NIR) Solar Reflection• 1.3 - 3 µm : Moyen Infra Rouge (MIR) Réflection and

Emission (little)• 3 - 5 µm et 8 - 14 µm : Thermal Infra Red (IRT): Emission• 5 - 8 µm : Atmospheric Absorption

• MICROWAVE : 3 mm - 30 cm (100 - 1 GHz)• Surface and Volume scattering

1 - Basic Concepts of Remote Sensing

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1 - Basic Concepts of Remote Sensing

PASSIVESENSOR

ACTIVESENSOR

SUN

DiffractionBackscattering• Microwaves

REFLECTION THERMAL EMISSION(Thermal IR)

clouds

Atmosphericabsorption

DiffuseRadiation

Direct radiation

Reflectedradiation• Visible• Near IR

clouds

(RADAR)

BACKSCATTERING


Atmospheric vertical Transmittance in the Visible and Infra-Red Spectra

0

20

40

60

80

10003 05 1 5 10 15

200 500 1000 5000 10 000 15 000 20 000

20 (µm)

20

0

40

60

80

100

tran

smis

sion

'Abso

rption

H2O CO2 N2O O3 O2

(10 A = 1 nm = 10-3µm = 10-9 m)°

%

%

Atmospheric windowsInfra RedVisibleUV

1 - Basic Concepts of RS: Atmospheric effects

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aiar

ai ar=

Surface reflection Specular reflection

Incident wave

Reflected wave

Smooth Surface

Scattered waves

Rough surface

Diffuse reflection

Reflected wave

Lambertian surface

Perfectly diffuse

2 - Optical Remote Sensing: Light Reflection


Spectral signatures of natural surfaces

Rocks and Soils : reflectance affected by : minerals, surface alteration, texture, structure, water

content...

Vegetation : related to photosynthetic activity (plantphenology),

plant morphology, leaf shape and watercontent...

Water : low reflectance: most of the radiation is absorbed or transmitted. Reflectance is substantially modified by uspended materials (loams, algae) and depth.

Rocks and Soils : reflectance affected by : minerals, surface alteration, texture, structure, water

content...

Vegetation : related to photosynthetic activity (plantphenology),

plant morphology, leaf shape and watercontent...

Water : low reflectance: most of the radiation is absorbed or transmitted. Reflectance is substantially modified by uspended materials (loams, algae) and depth.

THE REFLECTANCE (%): The ratio of energy reflected by a surface

at a given wavelength

2 - Optical Remote Sensing: Spectral signatures

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blue green red near infrared

Fresh snowDry calcareous soils

Water

Reflectance %

Wavelength

50

20

100

Green Vegetation

TYPICAL SPECTRAL SIGNATURES OF NATURAL SURFACESTYPICAL SPECTRAL SIGNATURES OF NATURAL SURFACES



Leaf pigmentsChlorophyll a (65%), xantophyll (29%), carotene (6%): 0.445µm (blue)

Chlorophyll b: 0.645 µm (red)

The greater the photosynthesisthe lower the reflectance in the visible, the higher the reflectance in the NIR.

The PHOTOSYNTHESIS process uses solar radiation as a source of energy for the fixation of atmosphericCO2

Solar Radiation (visible) is absorbed by the leafpigments

Lower Energy radiation (Near Infra-Red) is emitted

Spectral signature of vegetation2 - Optical Remote Sensing: Spectral signatures

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Red (R)

Green VegetationLow values in R (Absorbed)High Values in NIR

Bare GroundHigh Values in R (reflected)Low values in NIR

Near Infra Red (NIR)



Factors influencing the spectral signature

Heigth of the sun(date, time) Atmospheric conditions Relief (shadow)

Relief (slope) Phenology, disease Environment


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Remote sensing sensors measure the earth's surface reflection,emission or back-scattering in various wavelengths. Themeasurements are compiled into grids of mumbers, the digital images.

4 - Concept of Digital Image

121121 38 110

90 79 33 90

58 64 58 70

151 151 83 104

121121 38 110

90 79 33 90

58 64 58 70

151 151 83 104

121121 38 110

90 79 33 90

58 64 58 70

151 151 83 104

Images are matrices of individualpoints with digital values

concepts of grid, rasterconcept of PIXEL (Picture Element)


ROWS

COLUMNS

PIXELPIXEL== Picture ElementPicture Element

Digital image = grid system

Rows and columnsdefine individual

cells (pixels)

121

90

58

151

123

121

79

64

151

85

38

33

58

83

75

110

90

70

104

96

203

115

66

129

69

PIXEL: • coordinates• digital count(s)

Value = Digital Count (DC)Usually coded on 8 bits

28 = 256 possible valuesValues range between 0 and 255


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Band XS1

XS2

XS3

35

42

125

(EX: SPOT- HRV Image)

The Digital Counts (DC)= digital numbers (DN)

correspond to radiometricvalues.

Multispectral data

Y

X

Spatial location

Spectral location

(coordinates, orrows, columns)



SPATIAL RESOLUTION (Optic systems)

SPATIAL RESOLUTION SPATIAL RESOLUTION ((Optic Optic systemssystems))

80 m Landsat MSS

30 m Landsat TM

20 m SPOT XS

tree

street

grass

house

Each pixel of the image represents a sum of the values of the energy reflectedby the various types of canopy cover in the concerned portion of the surface.

Each pixel of the image represents a sum of the values of the energy reflectedby the various types of canopy cover in the concerned portion of the surface.


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RESOLUTIONSpot 5 (2.5 m) Orthophoto (0.5 m)

Spot 1..4 Panchromatique (10 m) Spot 1..4 Multispectral (20 m)


SPATIAL RESOLUTION• The ground surface represented by one pixel (optical systems). • Smallest distance between 2 differenciable objects (radar systems).

SPATIAL RESOLUTION• The ground surface represented by one pixel (optical systems). • Smallest distance between 2 differenciable objects (radar systems).

TEMPORAL RESOLUTION• Time lag between two possible image acquisitions on the same area.

TEMPORAL RESOLUTION• Time lag between two possible image acquisitions on the same area.

Concepts of Resolution

SPECTRAL RESOLUTION• Size and number of the bands (intervals of wavelengths) measuredby a specific sensor.

SPECTRAL RESOLUTION• Size and number of the bands (intervals of wavelengths) measuredby a specific sensor.

RADIOMETRIC RESOLUTION• Sensitivity of a sensor to the level of the signal received.

RADIOMETRIC RESOLUTION• Sensitivity of a sensor to the level of the signal received.


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Association of a shade of grey or colour with each digital count using an encoding table, Look Up Table

(LUT).

Choice of display levels. Usually less than 256.

Display and processing used to enhance the image legibility only affect “colours” associated to each pixel without modify the digital count.

Several "representations" of the same measurement acording to the aim.

Display subjectivity and operator influence

DISPLAYDISPLAY : ANY PROCESS USED TO TRANSFORM INFORMATION MEASURED BY A SENSOR INTO A DOCUMENT EASY TO READ FOR A HUMAN OBSERVER

5 - Display and Processing


Single Band display0 25512763 191Digital Counts

measured by thesensor

dark light

Look Up Table (LUT) : Digital Counts appear with discontinuous chosen colours.Ex: Vegetation in green

Bare soil in Red/Brown

Grey scale : the lowestdigital count appears in black, the highest in white.

Colour scale : use of thechromatic scale (from violet to red).

3 display methods

121

90

58

151

123

121

79

64

151

85

38

33

58

83

75

110

90

70

104

96

203

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Shades of grey

Example : VEGETATION INDEX

w ater bare soil low high clouds

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Encoding Table: Grey scale

InputData

OutputData

Color or grey level

255254253

206205204

154153152

122121120

6968

2625 24

210

255254253

206205204

154153152

122121120

6968

2625 24

210

WHITEWHITEWHITE

VERY LIGHT GREYVERY LIGHT GREYVERY LIGHT GREY

LIGHT GREYLIGHT GREYLIGHT GREY

GREYGREYGREY

DARK GREYDARK GREY

VERY DARK GREYVERY DARK GREYVERY DARK GREY

BLACK BLACKBLACK 0 50 100 150 200 255

0

50

10

0

150

200

25

5

Output value = grey level displayed

Input value = pixel value



InputData

OutputData

Colour or grey level

255254253

206205204

154153152

122121120

6968

2625 24

210

255254253

206205204

154153152

122121120

6968

2625 24

210

REDREDRED

ORANGE AND REDORANGE AND REDORANGE AND RED

ORANGEORANGEORANGE

YELLOWYELLOWYELLOW

GREENGREEN

BLUEBLUEBLUE

VIOLET VIOLETVIOLET 0 50 100 150 200 255

0

50

10

0

150

200

25

5

Encoding Table: Colour scale

Output value = colour displayed

Input value = pixel value


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min. max.

InputData

OutputData

min. max.

InputData

OutputData

min. max.

InputData

OutputData

Without Enhancement

Equalization

Contrast Enhancement or Contrast Stretch5 - Display and Processing

With Enhancement


Band x

Band y

Band z

MULTI-BAND DISPLAY: Color results from the compositing of 2 or 3 bands

ColorComposite

R

G

B

NOTE : The bands can be individually stretched


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Red

Green Blue

Yellow Magenta

Cyan

RedGreen

Blue

Yellow

MagentaCyan

SUBSTRACTIVE ANALYSISSUBSTRACTIVE ANALYSISADDITIVE COLOUR SYNTHESISADDITIVE COLOUR SYNTHESIS

(Color mixing on the screen) (Ink layers on the paper printouts)



MULTI-BAND PRODUCTS NIR band REDRED band GREENGREEN band BLUE

Example: SPOT XS3, XS2, XS1

REDActive

Vegetation

BLACKWater

LIGHT CYANBare Soil

DARK CYANDry VegetationMature Crops

Standard RGB color composite


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Multi-Band products Colour composite (additive synthesis)

Landsat TM:

Blue bandGreen bandRed bandIR band

Landsat TM:

Blue bandGreen bandRed bandIR band

TruecolorsBlue

GreenRed

TruecolorsBlue

GreenRed

Pseudocolors

BlueGreenRed

Pseudocolors

BlueGreenRed

pseudo colors(veget. green)

Blue Red

Green

pseudo colors(veget. green)

Blue Red

Green

StandardColour

Composite



MULTITEMPORAL DISPLAY WITH ERS1 DATA

ERS images colour composite:3 January 94 (before flooding)12 January 9415 January 94

Flooded areas = dark areas due to specular reflection

Flood extent determined by colour composite interpretation

5 - Display and ProcessingMulti-date radarcolour composite Flooded areas:

Flood management (extent) in Camargue, France

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5 - Display and ProcessingFilters

Filtering modifies the Digital Counts

Filtering takes into account the environment of the pixels

Enhancement changes the image tonal contrast, filteringchanges the spatial contrast (smoothing or stretch).

Spatial contrast is the difference between the Digital Number of a pixel and the Digital Number of its neighbours.

Filtering provides images with modified spatial frequencies.

Filtering is used to improve display or remove bad values

Noise reduction (smoothing)edge detection (enhancement)


5 - Display and ProcessingDigital Filters

SPATIALFREQUENCY

(spatial contrast)

NO FREQUENCY LOW HIGH

SPATIAL FREQUENCY: variation of the Digital Count per space unit.

Radiometric profile: approximation of a spatial frequency spectrum

"Smooth image" : low spatial frequency "Rough image" : high spatialfrequency

255

0pixels

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5 - Display and ProcessingFILTERSHIGHHIGH--PASS FILTERSPASS FILTERS:Emphasize the detailed high frequency components of an image and de-emphasize the more general low frequencyinformation.

LOWLOW--PASS FILTERSPASS FILTERS:Emphasize low frequency features (large areas changes inbrightness) and deemphasizethe high frequency components of an image (local detail). Theyare used to reduce noise andartefacts.

255

0

pixels«Smoothing»«Edge Enhancement»


3 - Radar Remote Sensing

3 main characteristics:

Pulse

Distance (Range)Echo

Imaging RADAR

SAR : Synthetic Aperture RADARSatellite borne imaging RADAR

active : emits a signal in the micro-wave wavelengths sideways towards the surface

receives the fraction of the signal back-scattered by the surface

measures the difference of energy of the signal and the time lag between emission (pulse) and reception (echo)

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Surface Roughness:Back Scattering (function of λ)Increases with roughness

Moisture content :Penetration in the soil decreases,Backscateering increases with moisture

Topography :Viewing geometry affects the signal

3

2

1

θlocθ

i

SAR : Geophysical Characteristics3 - Radar Remote Sensing

RADAR remote sensing is a technique that provides information on the physical characteristics of the earth’s surface, mainly:

roughsurface smooth

surface

moistsoil

back-scattering



Free (liquid) waterSmooth Surface dark

Urban AreaRough Surface bright

Prepared FieldsRough Surface bright

Crop at early stageSmooth Surface dark

Roughness of surface Increase of backscattering

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Dry :14th ofmarch

Wet :20th ofmarch

Fields prepared for summer cropRough -> High Backscatter -> BrightEven Brighter when wet

Young winter barley (smooth)-> Dark, a bit less when wet

Effect of soil roughness and moisture

Courtesy of JRC - MARS-STAT (DG VI) Pilot Project«The use of active micro-wave sensors for rapid area estimation of gricultural crops»

Moisture Increase in back-scatter

AGRICULTURAL INFORMATION SYST EMS UNIT



A

B CD

Imagebrightness

Effect of Topography

Problems of:- Shadows- Foreshortening- Layover

(extreme case of foreshortening,topography is inverted)

resulting image

Shadow

Use of DEMto correct ! Smaller !

= foreshortening

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Worldwide multi-sensor & multi-scale coverages available :

• low and high resolution => global to local scales• optical and radar data => cloudy areas

Data archives available since 25 years (over certain countries)

• historical studies

Benefits of satellite imagery for users (1)

Complete worldwide coveragesComplete worldwide coverages


FICHE DE PHOTO-INTERPRETATION N° 13

Date/heure de l'acquis itio n: 24.02.2000/10:08Date de la PI: 08.05.2000Zo ne: Mars eilleCapteur: Ikono s-2P ro je ctio n carto : UTM 31Datum: WG S 8 4Angle d'é léva tio n s o la ire: 3 2,21 329 degrés

DIMENSIONSN° OBJECTIFL(m) l(m) H(m) S(m

2)

NATURE COORDONNEES DES CRIPTION

Bâtea u 1 64 27 19.5 4767 Pa queb ot 69 177 547 9820 5

Orie ntat io n dubâ teau : 56 .407de g

-Plu s ie urs n ive aux :

-Ha uteur che minée :13 .5m-Ha uteur bâ teau :19 .5+13.5 =33 m

N

No rdg éo gra phiqu eNord

ma gné tiqu e

1 ,6 7 gr(20 00 )

ZONE

Poupe Che minée

Châte au

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Global and local scales

Low resolution : Meteosat V High resolution : SPOT (Pretoria)


Image (PAN) de Berlin

Lancement d ’IRS-1C le 28/12/1995 Héliosynchrone(Altitude 900 km)Répétitivité (Cycle) : 24 jours

PAN :0.5 - 0.75 µmRésolution : 5.8 mFauchée : 70 km

LISS-III : B2 : 0.52-0.59 µm B3 : 0.62-0.68 µmB4 : 0.77 0.86 µmB5 : 1.55-1.70 µm Résolution : 23m (B5 à 70 m)Fauchée : 141 km

WiFS : B3 & B4Wide Field SensorRésolution : 188 mFauchée : 774 km

LA HAUTE RESOLUTIONIRS-1C

Opérat

ionne

l

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Thanks to the sizes of images / scenes

& to digital mosaicking techniques

spatial continuity of geo-information

uniform accuracy and precision

suitable for integrated multi-thematic studies& spatial analysis


Synoptic & homogeneous viewing oflarge areas / territories

Synoptic & homogeneous viewing oflarge areas / territories


Up-to-date images available for updating geo-information

Multitemporal images available for monitoring & change detection

Acquisition programming available to meet emergency needs (ex: flood monitoring, impact of an earthquake...)

off-track oblique viewing systems increase revisit capabilities (SPOT example)


Repetitivity of data acquisition (revisit capability of satellites)

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• Toulouse airport (France) between 1984 and 1993

Multitemporal images



Geometric quality and “ flexibility ”of digital imagery

Geometric quality and “ flexibility ”of digital imagery

Digital satellite images can be easily georeferenced andrepresented according to various map projection systems

easy integrationinto GIS data bases

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Existing high resolution sensors (10 to 5 m) are compatible with:

medium scales topo-map standards (1:50 000 to 1:25 000)thematic studies and mapping at larger scalessoon satellite with Very High Resolution (1 m)

KVR-1000


Easy registration of images for datafusion & multitemporal studies (usingsame type of sensor, ex: SPOT + TMor SPOT PAN + SPOT XS)


PAN

XS

Merging P + XS

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Stereo capability (thanks to off-track oblique viewing systems)

Digital Elevation Modelling (DEM / DTM)and 3D applications




Access to bio-physical parameters

ex. surface temperature, vegetation index (NDVI)

input data for modelling (ex: agro-meteorological models)

Images available both in digital & and analog formats(paper products)

flexibility for image analysis(manual and / or digital methods)flexibility for GIS integration

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How to extract geoinformation from satellite data ?

Used as:

base map allowing multiple layers to be registred to a commonsource (particularly suitable on areas where topomaps are old orunavailable)

backdrop image map for information extraction & spatialanalysis

Thematic mapping & GIS-oriented applications usuallyrequire ready-to-use ortho-rectified images


How to extract geoinformation from satellite data ?MORPHOLOGICAL APPROACHderived from photo-interpretation & photogrammetry techniques

visual analysis using a space ortho-rectified imagepaper product

creation of paper thematic mapsthat can be digitized to create digital vectors layers

computer aided photo-interpretation (CAPI)of digital space ortho-rectified images

= interactive image processing techniques enabling theoperator:

to improve feature detectability/interpretability on screento digitize/revise boundaries/polygonsto extract/revise attributes

creation/updating of digital thematic vector layers

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How to extract geoinformation from satellite data ?MORPHOLOGICAL APPROACH (continued)derived from photo-interpretation & photogrammetry techniques

DEM creation from imagestereo pairs (SPOT)using automated correlation=> analogue/digital restitution,=> derived raster layers (slope,shadowing,…)=> 3D views

digital file ready-to-use intoa GIS (3D applications)


RASTER LAYERS DERIVED FROM A DEMSlope Aspect Relief (insolation)

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3D VIEW

Haïti


How to extract geoinformation from satellite data ?CLASSIFICATION (statistical approach)

to classify a digital image into useful categories(themes) for a given application (land cover, etc...)

a pattern-based process that assigns individual pixels tocategories based on spectral properties (variousalgorithms available)

importance of “ ground truth ” and external data to properlyinitialize a classification and check the results (using a GIS)

thematic raster layers that can be vectorized

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Classification

Colour composite Classified image

SPOT Image, 06/96, Montauban



CHANGE DETECTION AND MONITORING

to create data sets representing different moments in timeby using mutitemporal images

to create, from 2 or more reference images acquired atdifferent times, another image that pinpoints areas ofchange

multitemporal data fusionimage differencing techniques

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Summing up satellite imagery inputs to GIS Ortho-rectified images can be used within a GIS as;

base map allowing multiple layers to be registred to acommon source (particularly suitable on areas wheretopomaps are old or unavailable)backdrop image map for extracting information andspatial analysis

DEM creation from satellite image stereo pairs

easy integration of the resulting file into a GIS databasevarious 3D applications within a GIS environment

Thematic layers can be extracted and updated from imageproducts (CAPI, classifications)


Remote sensing in the Geo-information World

Remotely sensed DATA data are converted intoINFORMATION

Inventories• topomaps production and updating (= GIS basemaps)• thematic maps production and updating (= GIS layers)• DEM and derived information (3D)

Monitoring• change detection• early warning• impact studies• modelling

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Remote sensing in the Geo-information World

Satellite imagery is now recognized to bea major information source

Geographic

Multi-thematic / multipurpose

Up-to-date

Reliable

Cost-effective

at various geographic levels and scales (local to global)


Field of application : GEOLOGY

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Field of applicationTouristic space maps


Field of applicationTopographic map updating

(P+XS) SPOT images, 1:50,000. Survey of Kenya / IGN International

1972 edition 1994 1996 edition

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Field of applicationCoastal management

Bissau-Guinea (Bijagos Archipelago)Digital SPOT mosaic (left)Land use map derived from SPOT and overlayed with environment and socio-economic data (above)


SEA STUDIES

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ERSSPOT

Monitoring natural disasters (floods)

SPOT and ERS merging


Environment : deforestation

SPOT

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EROS A1


• Les sites Web des différentes sociétés commerciales :

Earthwatch , QuickBird : www.digitalglobe.comCarterra, Ikonos, IRS : www.spaceimage.comIRS : www.euromap.deOrbview : www.orbimage.comSPOT 5 : www.spotimage.frALOS, ADEOS … : yyy.tksc.nasda.go.jpGEROS : www.ger.comEROS A1 : www.imagesatintl.com

• La presse spécialisée : Space News, Air & Cosmos, GIS-World, ...

• Sites généraux : www.fas.org/spp www-projet.cst.cnes.fr

• Infos sur les lancements : www.flatoday.com/space/next/sked

Les sources d'information

from satellite imagery to geo-information 2... · from satellite imagery to geo-information :...

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