1. Airborne Laser Scanning and its
Applications
Prof.Dr.S.Anbazhagan
Centre for Geoinformatics & Planetary Studies
Department of Geology
Periyar University
Salem

2.  About Airborne Laser Scanning
 Case study in parts of Elbe river basin
 Selected applications

3. What is Airborne Laser scanning?
Airborne laser scanning (ALS) represents a new and
independent technology for the generation of highly
automated digital terrain and surface models.
ALS development goes back to the 1970’s and 1980’s,
with an early NASA system and other attempts in USA and Canada
The acronym ‘LASER’ stands for ‘Light Amplification by
Simulated Emission of Radiation’
LADAR – LAser Detection And Ranging
LIDAR – Light Detection And Ranging
ALS is member of the LIDAR family
LIDAR includes terrestrial laser scanners, airborne laser
Scanners and even police speed detection equipment

4. ALS data and Accuracy
Laser scanning systems furnish geometric results in terms of
distance, position, altitude and coordinates
Measuring rates 2KHz to 25KHz, go up to 80KHz
Sampling density on the ground range from about 1 point / 20
Sqm area up to 20 points per sq m area.
Vertical accuracy -0.15m & point spacing 1.5m with an
accuracy of 0.15 m

5. Airborne Laser Scanning (ALS) – Active Sensing
Efficient tool for generating accurate DTMs day
or night, especially over large areas of
featureless or densely covered terrain.
All laser systems measure by some means the
distance between the sensor and the
illuminated spot on ground

6. A Typical ALS System
Ranging Unit Scanner
Laser
Control monitoring Flight Footprint
and recording units direction
Swath Width
DGPS IMU

7. Components in ALS system
 A dual frequency GPS receiver mounted in
the aircraft, positions the ALS unit, typically
every 0.5 seconds
 An Inertial Measuring Unit (IMU) records
orientation of the aircraft, 200 times/sec
 Laser distance measurement unit emits up to
50,000 discrete light beams/sec, records the
travel time and calculates the distance to the
ground

8. Laser ranging
Two ranging principles;
The pulsed ranging principle involves by measuring
the phase difference between the transmitted and
the received signal back scattered from the object
surface.
The phase difference method applied with lasers
that continuously emit light are called ‘ continuous
wave’ (CW) lasers.

9. Time-of-flight ranging
Transmitted
amplitude
R
AT
Transmitter
Received
amplitude Receiver
AR
tL
Traveling Time

10. Range (distance between sensor and object)
R = C t/2
C = velocity of light 300000 km/s
t = traveling time of a light pulse
Range resolution ∆R, in cm
∆R = C ∆t/2
∆t = ns, resolution of time measurement
t = ns, time between sending and receiving a pulse (or echo)
1 ns = 30 cm travel, then range = 15 cm
(Baltsavias 1999)

11. Lasers and wavelength
At present semiconductor diode laser and Nd: YAG lasers
(neodymium-doped yttrium aluminum garnet; Nd:Y3Al5O12 )
pumped by semiconductor lasers are used in the ALS system.
It covers optical band range 800 nm – 1600 nm
Most sensitive detectors are available between 800 nm and 1000 nm
First scanners were worked 900 nm. At this wavelength, powerful
pulsed semiconductor laser diodes were available on the market and
on optimum system performance could be expected. However, at this
wavelength, eye safety is still concern.
The TopoSys laser scanner operates at 1535 nm. At this wavelength,
higher energy levels can be used without running the risk to hurt the
eye.

12. Intensity of return laser
 ALS system intensity (or strength) of the return laser
important
 White sand, most of the emitted beam will be reflected
back to the aircraft
 Black bitumen road, much less of the beam will be
reflected back

13. Backscattering properties of target
Reflectivity of various materials for 900 nm wavelengths
______________________________________________________________
Material Reflectivity (%)
______________________________________________________________
Snow 80-90
White masonry 85
Limestone, clay up to 75
Deciduous trees Typ.60
Coniferous trees Typ.30
Carbonate sand (dry) 57
Carbonate sand (wet) 41
Beach sand Typ.50
Concrete, smooth 24
Asphalt with pebbles 17
Lava 8
______________________________________________________________
(Wehr and Lohr, 1999)

14. Maximum Range vs. Target Reflectivity of LMS-Q280i
Riegl web page

15. ALS and Atmospheric condition
 ALS best performance is achieved when the atmosphere
is cool, dry and clear
 IR propagation is severely attenuated by water vapour,
(rain, fog and/or humidity), CO2
 Dust particles and smoke also reduce detection range
 Best result during night, worst during day with bright
sunlight

16. Physical properties in Laser Scanning
 High power
 Short pulses
 High collimation
 Narrow optical spectrum (10 nm bandwidth)
 Narrow optical spectrum has an advantageous,
because narrow optical interference filters can be
mounted in the receiving path to suppress disturbing
background radiation, caused by backscattered
sunlight.

17. Position and Orientation System (POS)
The 3D position of a point on the earth surface can be
computed, if the position and orientation (POS) of the laser
system is known with respect to a coordinate system.
Integrated POS consisting of DGPS and an Inertial
Measurement Unit (IMU)
Geocoding of laser scanner measurements requires an
exact synchronization of all systems : IMU, DGPS and
laser scanner data
Time synchronization of better than 10 µs in achieved
operationally by this scheme
Wehr and Lohr (1999)

18. Determination of laser points
After a surveying flight, basically two data sets are available:
the POS data and the laser ranges with the instantaneous
scanning angles.
Data visualization and manual editing is necessary in different
stages of the processing chain.
Major advancements have been achieved by improvement mainly of
the post-processing software and less of the laser scanner
hardware.
Interpolated data can be further processed and analyzed by
commercial software e.g. Scop, Microstation, EASI-PACE, and
ARC/INFO.
Currently, the processing time for a DTM computed from laser
scanner data is typically three times the data acquisition time.

19. Processing steps in ALS data
Typical processing steps for laser scanner data
POS Data Ranges and Scan Calibration Data
(DGPS, IMU) Angels and Mounting Parameters
Laser Points
X, Y, Z in WGS84
or
Lat., Long., H in WGS84
Map Projection
Sorting
Filtering
Rasterizing
and
Thinning Out

20. Case study
Airborne laser scanning and high
resolution satellite data for
Geomorphic
study in Elbe river valley, Germany
(Anbazhagan et al 2005)

21. Objective
Integration of high resolution satellite data with
Airborne Laser Scanner (ALS) data to study the
various geomorphic features in parts of Elbe
river valley, Saxony, Germany.

22. Study area
 Elbebasin in parts of Sächische Schweiz
National park zone is mostly covered by
forest
 Airborne laser scanning data give more
informations on terrain condition covered
under dense forest and enable to interpret
the different types of landforms

23. Data used
 Airborne Laser Scanning (ALS) data was obtained from TopScan
GmbH, Germany
 The density of ALS cloud point is 1 point per 9 sq m area. The
accuracy of height is in between ± 10.8 cm and ± 12.6cm
 IKONOS satellite data acquired on 1st August 2000 is used in the
present study. The satellite data in digital format is obtained from
Hansa Luftbild GmbH, Germany.
 The imagery covered 97 sq km area in parts of Sächsischen
Schweiz region
 IKONOS satellite data comprises of panchromatic band (PAN) with
1m spatial resolution and four multi-spectral bands with 4m spatial
resolution.
 Multi-spectral bands were merged with geometrically rectified PAN
data (1m resolution). The output image has 1m spatial resolution
with 8 bit format

24. Software used
The laser point cloud data were processed with
help of SCOP++ software developed by Institute
of Photogrammetry and Remote sensing,
Technical University, Vienna
IKONOS satellite data & DEM,DSM, DGM done
through Erdas imagine 8.7 image processing
software

25. IKONOS-PAN

26. IKONOS- MULTISPECTRAL

27. IKONOS_ PAN + MSS MERGED

28. IKONOS_High pass filter

29. IKONOS_ Histogram equalisation

30. IKONOS-Elbe river valley

31. Digital Elevation Model generated from Laser point data, Elbe river valley

32. Digital Surface Model (DSM) derived from ALS data

33. ALS Digital Surface Model showing different
landforms

34. Terrace surface and River Terraces in ALS DSM

35. IKONOS FCC superimposed over DSM data

36. IKONOS TCC + DSM DATA

37. IKONOS TCC + DSM DATA

38. ALS Digital Ground Model (DGM), Part of Elbe river basin

39. Airborne Laser Scanning data – Shaded relief, Elbe river basin

40. Geomorphology in parts of Elbe river basin (based on IKONOS and ALS data)

41. AIRBORNE LASER SCANNING: OTHER
APPLICATIONS

42. Digital Elevation Models
DTM (Digital Terrain Model)
DEM (Digital Elevation Model)
DSM (Digital Surface Model)
DCM (Digital Canopy Model)
DGM (Digital Ground Model)
DTM and DSM generation in urban areas, automated building
extraction, generation of 3-D city models for urban planning,
wireless telecommunication, microclimate models,
propagation of noise and pollutants
High accuracy and very dense measurement applications e.g.
flood mapping, DTM generation and volume calculation in
open pit mines, road design and modeling

43. Disaster Management
Rapid mapping and damage assessment
after natural disaster
e.g after hurricanes, earthquakes, landslides
etc.,
NRSC has done ALS survey in Tsunami
affected area

44. Mapping of Corridors
Mapping corridors e.g. roads, railway tracks,
pipelines, waterway landscapes
Mapping of electrical transmissions lines and
towers including ground / tree clearance
DTM generation, especially in forested areas (in
forest road and path planning, drainage, etc)

45. Urban City Modeling
 ALS data has become an important source for
generating high quality 3D urban city modeling.
 3D city model using high resolution IKONOS
imagery and airborne laser scanning data (Tao
and Yasuoka 2002).
 Digital Surface Models (DSM) acquired at
different occasions to successfully detect the
building changes (Murakani et al 1999).

46. Terrain Mapping
 Landscape modeling using integrated airborne multi-
spectral and laser scanning data (Hill et al 2002).
 Generation of digital surface models and digital elevation
models that can provide information on the
geomorphology of the earth’s surface (Pereira and
Wicherson 1999).
 Merging of high resolution satellite data and airborne laser
scanning data provide information geomorphological
features like cuesta, mesa, escarpments and river terraces
(Anbazhagan et al 2005)

47. Forest Resource Mapping
 ALS technology can provide information about tree
height, crown diameter, tree density, and biomass
estimation.
 Vegetation height is a function of species
composition, climate and site quality, and can be
used for land cover classification.
 Forest structure and biophysical parameters, and
digital elevation models for watershed delineation
and water flows.

48. (A) The unprocessed lidar height surface (i.e., digital surface model, DSM),
(B) elevation surface (i.e., digital terrain model, DTM), and
(C) the estimated vegetation height surface (i.e., digital canopy model, DCM)
resulting from the subtraction of the DTM (B) from the DSM (A).
Clark et al 2004

49. Management of Fluvial zones
 Accurate and updated models of flood plains are critical
for flood plain monitoring and disaster planning.
 Laser data used to generate hydrodynamic model. Such
model is determine the effect of high water levels and of
earth works, such as removal of sand in river areas
(Pereira and Wicherson 1999).
 LIDAR and Photogrammetry data used for monitoring
water elevation and volume changes in riparian
resources within the Grand Canyon region (Davis et al
2002).

50. Coastal zone Management
Highly dynamic coastal zone require constant
updating of baseline survey data. ALS offers a
cost effective method to do this on a routine
basis.
Mapping and monitoring of shore lines, beaches,
tidal flats, dunes, and wetlands.
Measurement of coastal areas, determination of
coastal change and erosion

51. HIGWAY ENGINEERINGPROJECTS

52. LIDAR points

53. DTM

54. DTM from LIDAR +
aerophotgrammetric elements

55. HIGWAY ENGINEERINGPROJECTS
 The product generated by DSM, orthophotos
mosaic and highway geometric project
integration made possible a high quality
visualization of highway project. This product
can be used as much visualization element for
customer project presentation as for public
hearings.

56. Coastal Bathymetric studies
 Bathymetric layers operated same principle as the
topographic lasers, but emit in two wavelength, usually
1064 nm and 532 nm.
 The infrared wavelength is reflected on the water
surface, while the green one penetrates the water and
reflected by bottom surface or other objects in the
water.
 Laser data used for water depth measurement and
monitor the submerged jetty and disposal areas.

57. ALS development
 1995 LIDAR commercial operations 5 world wide
 2001 75 organizations 60 sensor commercial
 2002 120 organizations 75 sensors
 2005 150 – 200 sensors
 Major commercial sensors N.America (50%), Europe (28%) 15% Asia-
pacific mostly Japan
 Remote Sensing 9.1% annual growth rate
 Forecast 2006 – LIDAR, SAR, Hyperspectral data
(Lohani and Flood, 2003)

58. ALS development
$30 - $50 million per year for Lidar data acquisition
growth in the rate 20% - 40%
30% private sector in USA , leading market
energy utilities
35% state/local government
35% federal government

59. Conclusion
 Airborne Laser scanning data is an accurate,
fast and versatile measurement technique,
and open up new exciting area of application.
 Integration of airborne laser data and high
resolution satellite data will give excellent
information on landscape modeling
 Potential integration with imaging sensors is
expected to put airborne data acquisition on
a revolutionary level of system performance

60. References
Abbott,R.H., Penny,M.F., 1975.
Ackermann. F., 1999.
Anbazhagan,S., Trommler.M., Csaplovics.E., 2005
Cunningham,L.L., 1972.
Davis.P.A., et al., 2002.
Haala,N., Brenner,C., Anders,K.H., 1997.
Hill.R.A., and Veitch.N., 2002
Hill et al 2002. Irish.L.J, Lillycrop.W.J. 1999.
Kushwaha.S.P.S., and Behera.M.D., 2002.
Lohani.B., and Flood.M., 2004
Maas, H.G., and Vosselman.G.,1999.
Murakami et al (1999).
Pereira.L.M.G, Weicherson.R.J.1999
Tao.G., Yasuoka.Y.,
Vosselman.G., Suveg.I., 2001
Wehr.A., and Lohr.U., 1999.
Wulder. M., Onge,B., Treitz.P., 2000.

61. Thank you