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
 Remote sensing is the acquisition of data, remotely
 Earth Observation /Remote Sensing (EO/RS)
 For EO, remotely means using instruments (sensors) carried by
platforms.
 We think in terms of satellites, but this doesn't have to be the
case
 aircraft, helicopters, ...

3. Remote Sensing Definition
 Remote sensing is the small- or large-scale acquisition of
information of an object or phenomenon, by the use of
recording or real-time sensing device (s) that are wireless, or
not in physical or intimate contact with the object.
 Remote sensing is the stand-off collection through the use of
a variety of devices for gathering information on a given
object or area.
 Examples include: Aircraft, spacecraft, ships, photography
by digital camera, X-Ray, ……
 Remote sensing makes it possible to collect data on
dangerous or inaccessible areas.

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 ?
 Source of spatial and temporal information: land surface,
oceans, atmosphere, ice…etc.
 Monitor and develop understanding of the environment;
 Information can be accurate, timely, consistent and large
(spatial) scale;
 Some historical data (60s/70s+).
 Move to quantitative applications: climate data such as
temperature, atmospheric gases, land surface, aerosols….
 Some commercial applications: Weather, agricultural
monitoring, resource management

7. But….
 Remote sensing has various issues
 Can be expensive;
 Can be technically difficult;
 Measures are not direct: Remote sensing measures surrogate
variables like percentage of reflectance, brightness
temperature, or backscatter….

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
 Measurement of EM radiation: scattered and reflected
 Energy sources: sun, earth and artificial
 Source properties: vary in intensity and across the
wavelengths
 Intrinsic properties: emission, scattering, absorption
 They vary with wavelength; physical and chemical
properties; and can also vary with viewing angle.

14. Data acquisition techniques (1)
 There are two main types of remote sensing:
 passive remote sensing and
 active remote sensing.
 Passive sensors detect natural radiation that is emitted or
reflected by the object or surrounding area being observed.
 Active sensors emit energy at an object and records the
energy reflected back to the sensor.

15. Data acquisition techniques (2)
 RS instrument (sensor) measures energy received sensor
– 3 useful areas of the spectrum:
1. Visible / near/mid infrared
o Passive sensors
 Solar energy reflected by earth surface
 Determine surface (spectral) reflectance
o Active sensors
 LIDAR - active laser pulse
 time delay (height)
 induce florescence (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
 Active microwave:
 Little affected by atmospheric conditions: it operates
during the day and night
 Surface roughness (soil erosion)
 Water content (hydrology) - top few centimetres
 Vegetation - structure (leaf, branch, trunk properties)
 Digital Elevation Models, deformation, volcanoes,
earthquakes etc. (SAR interferometry).

24. Topographic Mapping Data
Generated from SRTM (Shuttle RADAR
Topographic Mapping).

25. Data exchange with GIS
 Decision for raster or vector GIS or hybrid systems;
 Data quantization and volume;
 Full exchange of geometry (e.g. regions) and attribute table?
 Handling of complex data formats ?

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 45o for daily viewing
 For more info go to: http://www.spaceimage.com/index.htm

30. Ikonos sample 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 QuickBird satellite 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 radiometric temporal
----------------------------------------------------------------------------------------------------------------
AVHRR 1.1 and 4 KM 4 or 5 bands 10 bit 12 hours
2400 Km .58-.68, .725-1.1, 3.55-3.93 (0-1023) (1 day, 1 night)
10.3-11.3, 11.5-12.5 (micrometers)
Landsat MSS 80 meters 4 bands 6 bit 16 days
185 Km .5-.6, .6-.7, .7-.8, .8-1.1 (0-63)
Landsat TM 30 meters 7 bands 8 bit 14 days
185 Km .45-.52, .52-.6, .63-.69, (0-255)
.76-.9, 1.55-1.75,
10.4-12.5, 2.08-2.3 um
SPOT P 10 meters 1 band 8 bit 26 days
60 Km .51-.73 um (0-255) (2 out of
5)
SPOT X 20 meters 3 bands 8 bit 26 days
60 Km .5-.59, .61-.68, .79-.89 um (0-255) (2 out of 5)
IKONOS 1 and 4 meters 1 and 4 bands 10 bit 1-2 days
11 km .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.
Soil moisture and
soil texture overlay

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

38. Processing level of remote sensing data
 Raw data from satellite
 System corrected, calibrated, geo-coded, terrain corrected
 Atmospheric correction for optical data
 Thematic evaluations (land use, NDVI, rainfall etc.)
 Most commercial data formats are read by software

39. Summary
 Remote sensing data provide large area of spatial data for GIS
analysis and modeling;
 Basic thematic products are available;
 Image processing and model coupling is needed to retrieve
quantitative data;
 Commercial softwares for combined evaluation are widely
available
 Data merge should be done carefully.

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