Remote sensing Techniques Dr. Ange Felix NSANZIYERA

It is generally accepted that urbanization involves the shift in population from rural to urban settlements

1. Methods and Techniques in Urban
and Regional Planning
Academic Year 2018-2019
Methods of spatial data acquisition:
Remote sensing data
Ir Emmanuel NYANDWI
(MSc UPM, MSc GIS4LA)

2. Fundamentals of Remote Sensing
Remotesensingistheacquisitionofdata,remotely
EarthObservation/RemoteSensing(EO/RS)
ForEO,remotelymeansusinginstruments(sensors)carriedby
platforms.
Wethinkintermsofsatellites,butthisdoesn'thavetobethe
case
aircraft,helicopters,...

3. Remote Sensing Definition
Remotesensingisthesmall-orlarge-scaleacquisitionof
informationofanobjectorphenomenon,bytheuseof
recordingorreal-timesensingdevice(s)thatarewireless,or
notinphysicalorintimatecontactwiththeobject.
Remotesensingisthestand-offcollectionthroughtheuseof
avarietyofdevicesforgatheringinformationonagiven
objectorarea.
Examplesinclude:Aircraft,spacecraft,ships,photography
bydigitalcamera,X-Ray,……
Remotesensingmakesitpossibletocollectdataon
dangerousorinaccessibleareas.

4. Remote Sensing: examples
•Not always big/expensive equipment
•Photography (aerial, helicopter…)
•Field-based

5. Remote Sensing: examples
•Platform depends on application
•What information do we want?
•How much detail?
•What type of detail?

6. 6
Why using the satellite RS ?
Sourceofspatialandtemporalinformation:landsurface,
oceans,atmosphere,ice…etc.
Monitoranddevelopunderstandingoftheenvironment;
Informationcanbeaccurate,timely,consistentandlarge
(spatial)scale;
Somehistoricaldata(60s/70s+).
Movetoquantitativeapplications:climatedatasuchas
temperature,atmosphericgases,landsurface,aerosols….
Somecommercialapplications:Weather,agricultural
monitoring,resourcemanagement

7. But….
Remotesensinghasvariousissues
Canbeexpensive;
Canbetechnicallydifficult;
Measuresarenotdirect:Remotesensingmeasuressurrogate
variableslikepercentageofreflectance,brightness
temperature,orbackscatter….

8. Basic Concepts: EM Spectrum

9. Basic concepts (…)
Electromagnetic radiation
Wavelengths, atmospheric windows
Visible/near infrared (optical) (400-700nm / 700-1500 nm)
Thermal infrared (8.5-12.5 m)
Microwave (1mm-1m)
Orbits:
Geostationary orbit (36 000 km altitude);
Polar orbit (200-1000 km altitude)
Spatial resolution: 10s cm (??) -100s km
Determined by altitude of satellite (across track), altitude
and speed (along track), and viewing angle

10. Basic concepts (…)
Temporal resolution
Minutes to days
NOAA (AVHRR), 12 hrs, 1km (1978+)
MODIS Terra/Aqua, 1-2days, 250m++
Landsat TM, 16 days, 30 m (1972+)
SPOT, 26(...) days, 10-20 m (1986+)
Revisit depends on: latitude, sensor FOV, pointing orbit
(inclination, altitude); cloud cover (for optical
instruments).

11. Major Programs
Geostationary (Met satellites)
Meteosat (Europe)
GOES (US)
GMS (Japan)
INSAT (India)
Polar Orbiting
SPOT (France)
NOAA (US)
ERS-1 & 2, Envisat (Europe)
ADEOS, JERS (Japan)
Radarsat (Canada)
EOS/NPOESS, Landsat, NOAA (US)

12. A Remote Sensing System
Energy source
Platforms
Sensors
Data recording and transmission
Ground receiving station
Data processing
Expert interpretation and data users

13. Physical basis
MeasurementofEMradiation:scatteredandreflected
Energysources:sun,earthandartificial
Sourceproperties:varyinintensityandacrossthe
wavelengths
Intrinsicproperties:emission,scattering,absorption
Theyvarywithwavelength;physicalandchemical
properties;andcanalsovarywithviewingangle.

14. Data acquisition techniques (1)
Therearetwomaintypesofremotesensing:
passiveremotesensingand
activeremotesensing.
Passivesensorsdetectnaturalradiationthatisemittedor
reflectedbytheobjectorsurroundingareabeingobserved.
Activesensorsemitenergyatanobjectandrecordsthe
energyreflectedbacktothesensor.

15. Data acquisition techniques (2)
RSinstrument(sensor)measuresenergyreceivedsensor
–3usefulareasofthespectrum:
1.Visible/near/midinfrared
oPassivesensors
Solarenergyreflectedbyearthsurface
Determinesurface(spectral)reflectance
oActivesensors
LIDAR-activelaserpulse
timedelay(height)
induceflorescence(chlorophyll)

16. Data acquisition techniques (3)
2.Thermal infrared
Energy measured -temperature of surface and emissivity
3.Microwave
Active sensors:
•Microwave pulse transmitted
•Measure amount scattered back
•Infer scattering
Passive sensors:
•emitted energy at shorter end of microwave spectrum

17. Image formation
Photographic (visible and NIR, recorded on film
Whiskbroom scanner
Visible. NIR, MIR and TIR
Point sensor using rotating mirror, build up
image as mirror scans
Landsat MSS, TM
Pushbroom scanner
mainly visible and NIR
array of sensing elements (line)
simultaneously, build up line by line
SPOT

18. 18
Real aperture radar:
–microwave
–energy emitted across-track
–return time measured (slant range)
–amount of energy (scattering)
Synthetic aperture radar
–microwave
–higher resolution -extended antenna
simulated by forward motion of
platform
–ERS-1, -2 SAR (AMI), Radarsat SAR,
JERS SAR
Image Formation: RADAR

19. Image characteristics
pixel -DN
pixels -2D grid (array)
Rows/columns (or lines / samples)
Dynamic range: difference between lowest and highest DN

20. 20
Example Applications
Visible / NIR / MIR -day only, no cloud cover
Vegetation amount/dynamics
Geological mapping (structure, mineral / petroleum
exploration)
Urban and land use (agric., forestry etc.)
Ocean temperature, phytoplankton blooms
Meteorology (clouds, atmospheric scattering)
Ice sheet dynamics

21. Remote Sensing: Image classification
Classified image

22. Thermal infrared: day and night; rate of heating and cooling
Heat loss (urban)
Air pollution
Mapping temperature
Geology
Forest fire
Meteorology (cloud temperature and height)
Example Applications

23. Example Applications
Activemicrowave:
Littleaffectedbyatmosphericconditions:itoperates
duringthedayandnight
Surfaceroughness(soilerosion)
Watercontent(hydrology)-topfewcentimetres
Vegetation-structure(leaf,branch,trunkproperties)
DigitalElevationModels,deformation,volcanoes,
earthquakesetc.(SARinterferometry).

24. Topographic Mapping Data
GeneratedfromSRTM (ShuttleRADAR
TopographicMapping).

25. Data exchange with GIS
DecisionforrasterorvectorGISorhybridsystems;
Dataquantizationandvolume;
Fullexchangeofgeometry(e.g.regions)andattributetable?
Handlingofcomplexdataformats?

26. Indian Remote Sensing (IRS) satellite
IRS-1C launched in December 1995
IRS1D launched in September 1997
Panchromatic: 0.5-0.75 um
5.8 m GRC, 30 km ground swath
22 day repeat cycle with off-nadir pointability

27. Where to get data?

28. © Digital globe 12/1/10 0.5m resolution

29. Space Imaging IKONOS
Panchromatic (0.45-0.9 um): 1 m
Multispectral: 4 m
Blue (445-516nm),
Green(506-595nm)
Red (632-698nm)
NIR (757-853nm)
11 km swath width
Pointable to 45
o
for daily viewing
For more info go to: http://www.spaceimage.com/index.htm

30. Ikonossample imagery
Multi-spectral
images (4m)
Pan-chromatic
1m

31. OrbView-3
Panchromatic: 1 m
Multispectral (color): 4 m
Pointable: anywhere on globe within 3 days
Additional hyper-spectral sensor
For more info go to: http://www.orbimage.com/index.html

32. Quickbird
DigitalGlobe™ successfully launched its QuickBirdsatellite on
the Boeing Delta II launch vehicle on October 18, 2001.
Panchromatic: 0.61-1m
Multispectral (color): 2.5-4 m
Can increase the resolution system by adjusting orbit in which
the satellite is flown.
The panchromatic resolution can then increase from 1 meter to
61 centimeters and from 4-to 2.5-meter resolution for multi-
spectral.
It operates in a 450-km 98-degree sun-synchronous orbit, with
each orbit taking 93.4 minutes:
http://www.digitalglobe.com/index.shtml

33. Different sensors and resolutions
sensor spatial spectral radiometrictemporal
----------------------------------------------------------------------------------------------------------------
AVHRR 1.1and4KM4or5bands 10bit 12hours
2400Km .58-.68,.725-1.1,3.55-3.93(0-1023)(1day,1night)
10.3-11.3,11.5-12.5(micrometers)
LandsatMSS 80meters 4bands 6bit 16days
185Km .5-.6,.6-.7,.7-.8,.8-1.1 (0-63)
LandsatTM 30meters 7bands 8bit 14days
185Km .45-.52,.52-.6,.63-.69, (0-255)
.76-.9,1.55-1.75,
10.4-12.5,2.08-2.3um
SPOTP 10meters 1band 8bit 26days
60Km .51-.73um (0-255) (2outof
5)
SPOTX 20meters 3bands 8bit 26days
60Km .5-.59,.61-.68,.79-.89um (0-255) (2outof5)
IKONOS 1and4meters 1and4bands 10bit 1-2days
11km .45-.9,.44-.51,.52-.60, (0-1023)
.63-.70,.76-.85

34. Spatial resolution problem
Trade-off pixel size vs. spatial
coverage;
Quantization and data volume;
Data merge from different
sources;
Grid displacement in time;
Information content of different
resolutions;
Raster-vector conversion.

35. Image processing steps
Geometric and radiometric correction;
Atmospheric correction;
Sub-setting, mosaic, enhancement
Geo-coding (map projection, spheroid, units)
Parameter extraction (multivariate statistics, regression
modeling,….)
Post-processing (filtering, grouping, data reduction).
Raster GIS: focal or global operations
Hybrid GIS: zonal and region-based operations, spatial
statistics

36. Raster data or hybrid GIS analysis
Global or focal analysis
Find contiguous pixels
Eliminate data by area
Search for raster layer combinations
Define rules for overlay analysis
Pixel comparisons between images
Zonal operations
Spatial statistics in defined polygon overlays
Descriptive, diversity, proximity, neighborhood etc.
Soilmoistureand
soiltextureoverlay

37. Atmospheric Correction
LANDSAT-TM without and with atmospheric correction

38. Processing level of remote sensing data
Rawdatafromsatellite
Systemcorrected,calibrated,geo-coded,terraincorrected
Atmosphericcorrectionforopticaldata
Thematicevaluations(landuse,NDVI,rainfalletc.)
Mostcommercialdataformatsarereadbysoftware

39. Summary
RemotesensingdataprovidelargeareaofspatialdataforGIS
analysisandmodeling;
Basicthematicproductsareavailable;
Imageprocessingandmodelcouplingisneededtoretrieve
quantitativedata;
Commercialsoftwaresforcombinedevaluationarewidely
available
Datamergeshouldbedonecarefully.

40. Assignment
•Based on google earth engine imageries determine the
population of the planning area