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
Présentation CASIT A/AIT 2003 2
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...
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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≠
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• 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
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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
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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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Présentation CASIT A/AIT 2003 11
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
2 - Optical Remote Sensing: Spectral signatures
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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)
2 - Optical Remote Sensing: Spectral signatures
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Factors influencing the spectral signature
Heigth of the sun(date, time) Atmospheric conditions Relief (shadow)
Relief (slope) Phenology, disease Environment
2 - Optical Remote Sensing: Spectral signatures
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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)
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ROWS
COLUMNS
PIXELPIXEL== Picture ElementPicture Element
Digital image = grid system
Rows and columnsdefine individual
cells (pixels)
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PIXEL: • coordinates• digital count(s)
Value = Digital Count (DC)Usually coded on 8 bits
28 = 256 possible valuesValues range between 0 and 255
4 - Concept of Digital Image
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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)
4 - Concept of Digital Image
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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.
4 - Concept of Digital Image
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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)
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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.
4 - Concept of Digital Image
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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
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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
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5 - Display and Processing
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
5 - Display and Processing
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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
5 - Display and Processing
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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
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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
5 - Display and Processing
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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)
5 - Display and Processing
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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
5 - Display and Processing
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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
5 - Display and Processing
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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)
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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»
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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
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3 - Radar Remote Sensing
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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3 - Radar Remote Sensing
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
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3 - Radar Remote Sensing
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
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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)
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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
Benefits of satellite imagery for users (2)
Synoptic & homogeneous viewing oflarge areas / territories
Synoptic & homogeneous viewing oflarge areas / territories
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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)
Benefits of satellite imagery for users (3)
Repetitivity of data acquisition (revisit capability of satellites)
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• Toulouse airport (France) between 1984 and 1993
Multitemporal images
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Benefits of satellite imagery for users (4)
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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Benefits of satellite imagery for users (5)
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
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Easy registration of images for datafusion & multitemporal studies (usingsame type of sensor, ex: SPOT + TMor SPOT PAN + SPOT XS)
Benefits of satellite imagery for users (6)
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
Benefits of satellite imagery for users (7)
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Benefits of satellite imagery for users (8)
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
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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)
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RASTER LAYERS DERIVED FROM A DEMSlope Aspect Relief (insolation)
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3D VIEW
Haïti
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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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How to extract geoinformation from satellite data ?
Classification
Colour composite Classified image
SPOT Image, 06/96, Montauban
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How to extract geoinformation from satellite data ?
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)
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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)
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Field of application : GEOLOGY
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Field of applicationTouristic space maps
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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)
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SEA STUDIES
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ERSSPOT
Monitoring natural disasters (floods)
SPOT and ERS merging
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Environment : deforestation
SPOT
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EROS A1
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• 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