1. Politechnika Warszawska
Wydział Geodezji i Kartografii
Studium Podyplomowe „Systemy Informacji Przestrzennej”
Autor pracy dyplomowej
Ewa Gasińska
Tytuł pracy dyplomowej
EXAMPLES OF THE USE OF DIGITAL SURFACE MODEL BASED ON THE MOBILE LASER SCANNING
praca dyplomowa
Opiekun pracy: prof. dr hab. Stanisław Białousz
mgr inż. Piotr Koza
Warszawa 2011

2. I would like to thank you
My close family
Specially I would like to thank you
mgr inż. Aneta Kanclerz for to give me her help, patient and very good notice during create my work.
prof. dr hab. Stanisław Białousz, mgr inż. Piotr Koza
Gispro Sp. Z o.o. Szczecin for access to datas and softwares

3. For my work I used
Microstation v8 2004 ( Terrascan, TerraModeler )
Ewmapa
PosPac MMS 5.3
Global Mapper 12 (trial version)
Riprocess, Pof import 1.3.5 Riegl company
Car and Scanners By Gispro S. z o.o. Szczecin

4. Introduction...................................................................................................................................................................................4
1. MOBILE LASER SCANNING................................................................................................................................................5
1.1 Mobile Mapping System......................................................................................................................................................6
2. POST-PROCESSING................................................................................................................................................................8
2.1 Downloading data rinex from ASG-EUPOS.......................................................................................................................9
2.2 Position and Orientation System Post- processing package mobile mapping Suite ( reference station)...........................13
3. RIPROCESS............................................................................................................................................................................21
3.1. POS DATA, RECORDS, TASKS....................................................................................................................................21
4. EXAMPLES OF THE USE OF DIGITAL SURFACE MODEL (DSMs).............................................................................23
4.1 Digital Terrain Model........................................................................................................................................................25
4.2 ASPECT.............................................................................................................................................................................27
4.3 HYPSOMETRY.................................................................................................................................................................33
4.4 SLOPE...............................................................................................................................................................................40
4.5 PROFILES.........................................................................................................................................................................43
5. Summary..................................................................................................................................................................................54
6. Illustrations..............................................................................................................................................................................55
7. TABLE....................................................................................................................................................................................58
8. References...............................................................................................................................................................................58

5. Introduction
Firstly in my thesis I describe how to receive very accurate Digital Surface Model in the chosen area. The data for
Digital Surface Model must be oriented in time and space. My duty was to give it Georeference by using special softwares
intended to Mobile Laser Scanning and MMS. When Digital Surface Model is ready and oriented by latitude, longitude and
altitude, it is possible to make classification of each part of the environment such us: vegetation, ground, features, buildings
and roads ( water is invisible for scanners).
Secondaly I compare Orthophotomap from photos of Satellite IKONOS and Digital Surface Model from MLS to show
changes of coastline in time. I find Digital Surface Model better to show real coast - line. From the MLS data it is also easily
get Digital Terrain Model. With Digital Terrain Model we can calculate slopes, sunlight of the slopes, hypsometry, we can
draw very accurate ground profiles. In the effect we can show places in the coast that receive more sun in different seasons
and check where dry or wet places in sand dunes are. All these circumstances affect on the plants vegetation process.
Hypsometry shows the accumulation of the sand in the coastline and beaches exposed to the currents that make them narrow.
After storms, we can observe changes. By calculating slopes after winter when ice float to melt we can compare changes in
the coastline. By analyzing steepness and direction of the slope on the coast we can estimate the speed and direction of runoff
from melting snow or rain and also wind or rain erosion treat. By identifying ground profiles it is possible to choose suitable
beach areas for emergency response helicopters. We can compare plants how they spread on the top of the sand dunes in the
desirable region. We can recognize where unsafe areas are, if water - level grows or sea wave will be strong during the storm
or other atmospheric circumstances damages the coast.

6. 1. MOBILE LASER SCANNING
Mobile laser scanning, what means that it is moving together with a car. Use Light Detection and Ranging (LIDAR
also LADAR), is an optical remote sensing technology that can measure the distance to, or other properties of a target by
illuminating the target with light, often using pulses from a laser. The acronym LADAR (Laser Detection and Ranging) is
often used in military contexts. The term "laser radar" is sometimes used even though LIDAR does not employ microwaves
or radio waves and is not therefore in reality related to radar. [http://en.wikipedia.org/wiki/LIDAR]
Both Riegl 2D and 3D scanners are ideally suited for mobile mapping applications. Nevertheles that scanners are 2D
and 3D, when car is moving give it us clods of points in 3D view. In order to register scan acquired from moving platforms,
such as boats, trains, road, and off-road vehicles, the laser scanner is supplemented by position and attitude sensors, for
example GPS ( Global positioning System) and IMU ( Inertial Measurement Unit). [www.riegl.com]
Digital Remote-Sensing Stations (CST), consist of a Mobile Mapping System and Mobile Laser Scanning
(MMS/MLS). It composed of the 3 Riegl laser scanners on the top of the car Nissan Pathfinder. The name of two scanners is
VZ-400, third is a scanner VQ-250 “profiler”. Vehicle posses Ground Penetrating Radar, for detection of underground
infrastructure and penetrating the surface as well as positioning System GPS/ IMU POS 420 LW of Applanix company. This
system is capable to create a cloud of points with the density 9000 – 10000 points per quadrameter.

7. 1.1 Mobile Mapping System
A mobile mapping system can be defined as a moving platform, upon which multiple sensor/measurement systems
have been integrated to provide three-dimensional near-continuous positioning of both the platform and simultaneously
collected geo-spatial data, with no or limited ground control using single or multiple GPS base stations" [Grejner-Brzezinska,
2002]. It involves the integration and management of different data collection devices along with data processing and
management software to provide the user with the location of spatial features that exist on the earth. Three basic
measurement components comprise most MMS: global positioning system (GPS), inertial measurement unit (IMU) or other
form of dead-reckoning sensor, and an image capture system. Thus, a mobile mapping system that utilizes multiple sensors is
“the product of integrating the concepts of kinematic geodesy and digital photogrammetry, to acquire, store, and process,
measurable quantities that sufficiently describe spatial and/or physical characteristics of a part of the Earth’s surface”
[Mostafa and Schwarz, 2001].
The navigation component consists of two basic systems: GPS and the IMU. The primary function of the GPS receiver
is to provide position information for the imaging sensor at the instant of measurement (exposure), although it is also possible
to use the GPS for platform orientation. In addition, it can also be used to errors that accumulate within the IMU. The GPS
receivers are used in a differential mode with one receiver 3 located on the vehicle and one receiver set up over a known
control point in the survey area. For a more robust solution, two base stations can be occupied simultaneously with GPS. This
will yield two positions for the GPS roving receiver and helps in quality control of the MMS and eliminates systematic errors
of GPS localisation measurements. Carrier phase is generally measured because it provides a much higher accuracy than
pseudo range measurements. From the GPS carrier phase, ranges from the satellite to the receiver along with their range rates
are output from the receiver. These measurements yield position and velocity values for the vehicle with respect to the initial

8. point. Using differencing techniques, the orientation of the system can also be determined. The disadvantages of GPS include
[El - Sheimy, 1996]:
• Possibility of cycle slips 3: it is possible to use multiple GPS receivers on the vehicle to determine the orientation of the
platform and also to provide redundancy/backup to the primary GPS receiver.
• The range and range rates are not truly independent
• The GPS sample rate cannot meet the accuracy requirements for orientation of the system.
As we are all aware, the global positioning system provides us with very accurate positioning data over the entire
surveying mission. Unfortunately, GPS does not work properly in all environments. GPS measurements are susceptible to
poor satellite geometry, cycle slips, satellite outages, and a dynamic lag that may occur during vehicle maneuvers. Situations
where such conditions can occur can be found in urban centers (the so called urban canyon) and along highways where
overpasses and tunnels obstruct the satellite signals.
Moreover, within a MMS, the relationship between the phase center of the antenna needs to be known relative to other
sensors. But, the phase center is not a physically identifiable point, nor is it stable in time or space [Greening et al, 2000]. The
effect is generally dependant on the elevation of the satellite and varies as the satellite signal direction changes. There is also
a problem when the variation of this phase center movement is superimposed on the GPS data for determination of
tropospheric scale factor. Errors in the range of 10 cm have been found in the vertical component.

9. Greening et al [2000] also draw upon personal experiences with problems involving solving for integer ambiguities.
Over the years, they have found situations where it appears that the integer ambiguities have been solved but in reality may
be incorrect, which could introduce errors up to the 30 cm level. Generally this affects the vertical component more. For
accurate (centimeter level) results, the integer ambiguities must be known for all satellites and maintained, or quickly resolve,
during the mission. Comparison of forward and reverse processing of GPS can be used to find this problem but this does not
always work. The problem with the integer ambiguity is exacerbated by other environmental conditions such as sudden
PDOP ( I described PDOP on the other page) changes that could occur in aircraft banking operations, multipath, baseline
distances, radio frequency (RF) interference, and phase center variations. From experience and under reasonably good
conditions, “the positional accuracy (1σ) relative to the ground station(s) may vary from 5 to 30 cm, in any component (X, Y,
Z), even during a single mission completed in a short time period” [Greening et al,2000].
[http://www.ferris.edu/faculty/burtchr/sure382/lessons/Lesson_4.pdf]
2. POST-PROCESSING
Post-processing enhancement accuracy with Global Positioning System that uses a network of fixed, ground-based
reference stations to broadcast the difference between the positions indicated by the satellite systems and the known fixed
positions. In the post-processing we can see the real time trajectory (Figure 2) and the post-processed (Figure 3) trajectory
used in my project.
Post-processing is used in Differential GPS to obtain precise positions of unknown points by relating them to known
points such as survey markers. The GPS measurements are usually stored in computer memory in the GPS receivers, and are
subsequently transferred to a computer running the GPS post-processing software. The software computes baselines using

10. simultaneous measurement data from two or more GPS receivers. The baselines represent a three-dimensional line drawn
between the two points occupied by each pair of GPS antennas. The post-processed measurements allow more precise
positioning, because most GPS errors affect each receiver nearly equally, and therefore can be cancelled out in the
calculations. Differential GPS measurements can also be computed in real-time by some GPS receivers if they receive a
correction signal using a separate radio receiver, for example in Real Time Kinematic (RTK) surveying or navigation. Real Time
Kinematic (RTK) satellite navigation is a technique used in land survey and in hydrographic survey based on the use of carrier phase
measurements of the GPS, GLONASS and/or Galileo signals where a single reference station provides the real-time corrections,
providing up to centimeter-level accuracy. [http://en.wikipedia.org/wiki/Differential_GPS_post-processing#Post_processing]
2.1 Downloading data rinex from ASG-EUPOS
One of the three main segments of the ASG-EUPOS system is the receiving segment (ground control). Its role is to collect
observational data GNSS (Global Navigation Satellite System) satellites and transfer them in real time into the Calculation
Centre (Figure 4). The segment consists of Global Navigation Satellite System reference stations evenly distributed over the
area of Poland and neighboring countries. According to the EUPOS standard during the construction of the receiving segment
the following assumptions were made:
• the mean distance between stations is 70km,
• existing EPN and IGS stations have been incorporated into the network of reference stations,
• coordinates of the stations will be determined in the ETRS89 system and national systems,
• only precise dual-frequency GNSS receivers have been used in the reference stations.

11. • the locations of installation of the reference stations were chosen such, that to ensure convenient conditions for GNSS
satellite observation.
Presently this segment is composed of the following set of reference stations:
• 84 stations with the GPS module,
• 14 stations with the GPS/GLONASS ( Global Orienting Satellite System) module,
• 22 foreign stations.
The total number of stations incorporated into the system should not exceed 130. The national reference stations are
mostly located on buildings of public administration of the voivodeship and district levels, research institutes and education
buildings. [http://www.asgeupos.pl/index.php?wpg_type=syst_descr ]
The ASG-EUPOS System is composed of three essential segments: the segment of reference stations, the segment of
managing and the segment of users.
Enabling of differential corrections in real-time is designed in the system (NAVGEO, NAVGIS and CODGIS services),
rendering data accessible and calculating in post processing-mode (services: POSGEO and POSGEO D), arranging of the
technical service and also supplying the geodesy and cartography documentation centers with GNSS equipment. Enabling of
corrections will take place on the Internet and GSM (GPRS). Enabling the observations and post processing calculations’
results are realized by the Internet and on CDROM (ASG-PL/EUPOS, 2005; Graszka,2007).

12. The calculation module for realizing services of precise positioning in post processing mode have been installed in
management centers and web-enabled to users. Within the same login session the user is allowed to send his GPS
observational files (in RINEX format) for processing. This module is working in automatic mode, with the possibility of the
operators’ intervention in case of a need for manual calculations. The data necessary for calculations (RINEX files from the
nearest reference stations or virtual reference stations) within POSGEO D (calculating in post processing-mode) service will
be available the same way. [http://www.asgeupos.pl/webpg/graph/publ/00010_EuropeanJurnalofNavigation_v5_nbr4.pdf]
Receiver Independent Exchange Format ( Rinex) is data interchange format for raw satellite navigation system data.
This allows the user to post-process the received data to produce a more accurate ( cm) solution — usually with other data
unknown to the original receiver, such as better models of the atmospheric conditions at time of measurement.
[http://en.wikipedia.org/wiki/RINEX]
In my calculations I used reference station situated in Kamień Pomorski ( Figure 1). This Station was the nearest to the
coast where we took measurement by mobile laser scanning. This station gets in signals from Navstar GPS Satellite.

13. Figure 1. Reference station situated in Kamień Pomorski

14. 2.2 Position and Orientation System Post- processing
package mobile mapping Suite ( reference station)
For post-procesing I used POSPac Mobile Mapping Suite is Applanix's next generation, industry-leading software for
Direct Georeferencing of mobile mapping sensors using GNSS and inertial technology. Optimized for all environments and
platforms (air, land, and marine), and compatible with a variety of mapping sensors, this smart software solution achieves
both maximum accuracy and maximum efficiency for Direct Georeferencing ( Figure 4).
[http://www.applanix.com/products/land/pospac-mms.html]
The Position and Orientation System Post-Processing Package ( POSPac tm ) Mobile mapping Suite ( MMS tm) is the
latest post-processing software package from Applanix tm Corporation tailored to airborne, land, and marine vehicular
applications.
POSPac MMS is designed with a powerful user- friendly interface to maximize data quality and efficiency. A complete
post-processing solution toolbox, it includes POS ( Position and Orientation System ) real-time data and Base Station import,
IN-Fusion tm technology which provides a deeper level of sensor integration and error modeling, Applanix SmartBase tm
data processing generated from a network of reference stations, Global Navigation Satellite System ( GNSS) integrated
navigation data post – processing, direct exterior orientation generation with calibration and quality control of digital imagery
and other photogrammetry tools such as Lidar and Sar.

15. Applanix Position and Orientation System ( POS) products correlate sensory data derived from various sources such as
Gobal Navigation Satellite System ( GNSS), gyros and accelerometers to provide an extremely accurate post-processed
solution.
PosPac MMS is a simple-to-use, integrated suite of software utilities designed to import and process raw data obtained
using Applanix POS computing equipment. The new IN- Fusion Tm technology- based Applanix SmartBase tm ( ASb)
processing introduces an efficient and accurate aided inertial solution based on a network of GNSS reference stations set up
with the help of a built – in database search tool. SmartBase Processing combines the most reliable coordinates obtained from
the Quality check run with the inertial data a single Kalman Filter; hence the performance of results is more qualified. In
addition to SmartBase and the new tightly- integrated Single Base Station processing mode, POSpac MMS provides
differential and precise point positioning ( PPP) GNSS based processing solutions.
POSPac MMS software tools identify and compensate for sensory and environmental errors, compute an optimally accurate
blended navigation solution and provide the means to view the results in a variety of formats. The sophisticated design of the
POSPac MMS main Graphical Interface Unit ( GUI) displays different types of data including real-time and post – processed
data. All data can be viewed and selected in many different ways including the 2D Plan View, 3D View, and Points
Spreadsheet.
For accuracy, surveys are conducted with an array of sensors and equipment, operating in conjunction with various
devices, each detecting distinct yet complementary information. The combined results, derived from a variety of sources, are
simultaneously displayed in real-time and saved for retrieval.

16. POSPac MMS provides a simple way to import various data into project such as POS data and Base Station. Each data
type is automatically detected. When POS Data is imported into the project, the IMU, GNSS, and additional sensor data are
automatically extracted and ready for post-processing. [http://www.applanix.com/products/land/pospac-mms.html]
Figure 2. Trajectory in real time and post- processing. The red one is in real-time and the green one after post-procesing

17. Figure 3. Trajectory post-processed

18. Figure 4. Accuracy of the RMS (Root Mean Square ) position error for platform.
The result is that North and East is significantly. I achieved accuracy 0.006 m to 0.025m ( X,Y) and 0.03m to
0.035m ( Z ).

19. Figure 5. Position Dilution of Precision

20. Dilution of precision ( DOP table 1), or geometric dilution of precision ( GDOP ), is a term used in GPS and
geometrics engineering to specify the additional mulitplicative effect of GPS satellite geometry on GPS precision. DOP can
be expressed as a number of separate measurements. [http://pl.wikipedia.org/wiki/Dulation_of_precision]
Position Dilution of Precision ( PDOP)-coefficient describe relation between measurement error position the user and
error the position satellite ( Figure 5 )
DOP Value Rating Description
1 Ideal This is the highest possible confidence level to be used for applications demanding the highest
possible precision at all times.
1-2 Excellent At this confidence level, positional measurements are considered accurate enough to meet all but
the most sensitive applications.
2-5 Good Represents a level that marks the minimum appropriate for making business decisions. Positional
measurements could be used to make reliable in-route navigation suggestions to the user.
5-10 Moderate Positional measurements could be used for calculations, but the fix quality could still be
improved. A more open view of the sky is recommended.
10-20 Fair Represents a low confidence level. Positional measurements should be discarded or used only to
indicate a very rough estimate of the current location.
>20 Poor At this level, measurements are inaccurate by as much as 300 meters with a 6 meter accurate
device (50 DOP × 6 meters) and should be discarded
Table 1. Meaning of DOP values. [http://pl.wikipedia.org/wiki/Dilution_of_precision ]
The red I sign PDOP for my region. Result is that this accuracy is good and we can make calculations.

21. Figure 6. Number of available satellite

22. 3. RIPROCESS
Riprocess I use to adjust two 3 scans from 3 scanners to receive the same in position like trajectory and next give it
points of clouds projection. To my work I used Gaus Kruger projection 2000 zone 5.
Riprocess is designed for managing, processing, analyzing, and visualizing data acquired with mobile laser scanning
systems (MMS) based on RIEGL Mobile Laser Scanners. Is project-oriented and enables the user to manage all data acquired
and processed within a single project. Data managed include project data, MMS system information data such as mounting
information and calibration data, original laser data such as digitized echo signals from the RIEGL laser scanner position and
orientation data from an IMU/GPS system ( interial measurement unit/global positioning system). Intermediate data files,
search free files for fast data access, and georeferenced point cloud data with additional descriptors for every point.
[www.riegl.com ]
3.1. POS DATA, RECORDS, TASKS
This chapter represents the files created for a project in Ripprocess software. The project itself system section contains
configuration and calibration information for laser scanners, tilt mounts, cameras (not supported yet) and navigation devices
(IMU/GPS). This entire information is saved in the project file – no external files are referenced. For successful data
processing we need at least one "laser device" and one "navigation device".
POS DATA
This section stores references to POF data files (*.pof - files containing the recorded position and orientation of the system).

23. RECORDS
This section stores the acquired data (laser scans, images). One record represents one acquisition (e.g. one drive track from a
car). Every record can contain multiple "las data" objects (scans).
TASKS
Finally you have to define a POP matrix (project's orientation and position). This matrix defines the transformation between
the PRCS (RIEGL internal project coordinate system) and the source coordinate system (respectively Geodetic Datum; e.g.
WGS84, ITRS, ETRS) of the position & orientation data (POS).
The POP matrix is automatically calculated from the entered region of interest. This leads to a PRCS that has its origin in the
center of the defined region of interest. The PRCS will be horizontally leveled, i.e., the X/Y plane of the PRCS ( project
control revision system) will be tangential to the earth surface at the center of the region of interest. For the definition of the
source coordinate system and possible transformations and conversions into other coordinate systems refer to Data
transformation and conversion .[www.riegl.com]

24. 4. EXAMPLES OF THE USE OF DIGITAL SURFACE MODEL (DSMs)
Digital surface models ( Figure 7 ) ( DSMs ) are topographic maps of the earth's surface that provide you with a
geometrically correct reference frame over which other data layers can be draped. In addition to natural terrain features, the
DSM data includes vegetation as well as cultural features such as buildings and roads.
DSMs (Figure 8) are currently used by create 3D view ( Figure 12), augment simulated environments, and conduct
view shade and line-of-site analyses. They can also be used as a comparatively inexpensive means to ensure that cartographic
products such as topographic line maps (Figure 19), or even road maps, have a much higher degree of accuracy than would
otherwise be possible. [http://www.intermap.com/digital-surface-models]
As I mention DSM includes vegetation, cultural features such as buildings and roads. After filtering and clean each this
elements I left a ground ( Figure 9) and by triangulation made DTM ( page 27).

25. Figure 7.Digital Surface Model

26. Figure 8. Digital Surface Model view from front side (Dziwnów 2009)

27. 4.1 Digital Terrain Model
Digital terrain models (Figure 10) (DTMs) are topographic models of the "bare earth" that enable users to infer terrain
characteristics that may be hidden in the digital surface model (DSM). A DTM has cultural features such as buildings and
roads, and vegetation canopy digitally removed, leaving just the underlying terrain ( Figure 9).
[http://www.intermap.com/digital-terrain-models]
Figure 9. Beach Dziwnów 2009 [Source: University of Szczecin the Faculty of Geosciences, own elaboration]

28. Figure 10. Digital Terrain Model view from front side
( blue: low; purple: high - Dziwnów 2009)

29. 4.2 ASPECT
Based on the clouds of points ( Figure 13) we create Digital Surface Model. This is as I mention above way to make
Digital Terrain Model. Next Digital Terrain Model we use for to check sun angle for each season of the year ( Figure 11).
Figure 11. Orthophotomap from Satellite IKONOS photo by 2004 and signed area where Digital Surface Model and Digital
Terrain Model were created. [Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration]

30. Figure 12. Digital Surface Model, view from the right side

31. Figure 13. Clouds of points, view from the top side
[University of Szczecin the Faculty of Geosciences, own elaboration]

32. The models shows which places in the coast receive more sun in definite time. So we can check where dry or wet
places in sand dunes are, what affects plants and their vegetation process.
The sun azimuth is it 180 degree every day of the year. Despite the sun angle depends on the time of the year and the
latitude. On 21 June sun is the highest then in the rest of season of the year. It is calculated like a (90 - latitude) + 23 degree
27 minutes, which in my region is (90 – 46 degree 9 minutes ) + 23 degree 27 minutes = 67 degrees 18 minutes (Figure 14).
Dark pleases on the picture 14, 15, 16 to signify about lack of the sun.
Figure 14. Digital Terrain Model with Sun Angle 67 degrees 18 minutes

33. On 23 December the sun is nearest the earth to our position. So in this region is (90 – 46 degree and 9 minutes ) -23
degree 27 minutes = 20 degrees 24 minutes ( Figure 15)”.
Figure 15. Digital Terrain Model with Sun Angle 20 degrees 24 minutes

34. Despite on 21 March and 23 September the sun angle with 90 degree is now on the equator. If distance is longer from
the equator we calculate as 90 – 46 degree 9 minuets = 43 degree 85 minutes = 43 degrees 51 minutes (Figure 16).
Figure 16. Digital Terrain Model with Sun Angle 43 degrees 51 minutes
(blue: low; purple: high)

35. 4.3 HYPSOMETRY
Hypsometry is the measurement of land elevation relative to sea level. [http://en.wikipedia.org/wiki/Hypsometry]
Figure 17. Clouds of points view from the top and right side ( Dziwnów 2009)
[Source:University of Szczecin the Faculty of Geosciences, own elaboration]

36. Figure 18. Digital Terrain Model view from the top and right side
( blue: low; purple: high-Dziwnów 2009)

37. Figure 19. Contours for model made by the Digital Terrain Model from 2009 and Orthophotomap from Satellite IKONOS
photo by 2004. Minor contours we can see every 0,5 meters, basic contours every 1 meter and major 1,5 m
[Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration]

38. Figure 20. Orthophotomap from Satellite IKONOS photo 2004, hypsometry measurement from Digital Terrain Model by
2009 [Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration]

39. The Comparison of the Orthophotomap with contours lines created by the Digital Terrain Model ( Figure 18) based on
the clouds of the points ( Figure 17) is good way to checking accumulation sand in the coastline ( Figure 19). Can you see,
sea line changes (Figure 20 ) because the Orthophotomap from Satellite IKONOS photo is by 2004 and Digital Terrain
Model was by 2009.
On Digital Surface Model ( the picture number 22) we can see breakwater from 2009. In the years before there weren’t
any breakwaters. Beach was exposed to the currents which make it narrow ( Figure 20).
Figure 21. Digital Terrain Model view from the top side ( Dziwnów 2009)

40. Figure 22. Clouds of points 2009 and Orthophotomap from Satellite IKONOS photo 2004
[Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration ]

41. After storm, we can see changes. After winter when the melt ice floats we can see changes in the coastline. This pictures
show how accurate is this model if we want recognize changes on the beach, we can see new form. ( Figure 21).
4.4 SLOPE
By the Digital Surface Model (Figure 24) we can examine steepness and direction of the slope on the coast (Figure 26). As
well Determinine the speed and direction of runoff from snow melts, estimate rain run-off and direction. Estimate the soil erosion
treat. To locate possible suitable beach areas for emergency response helicopters.
Figure 23. Slopes on the Digital Terrain Model made Digital Surface Model from 2009 (blue: low; purple: high).

42. Figure 24. Clouds of points, Dziwnów 2009
[Source: University of Szczecin the Faculty of Geosciences, own elaboration]

43. Figure 25. Orthophotomap from Satellite IKONOS photo 2004 and calculate slope based on the Digital Terrain Model
by 2009. Slope is definite by the percent
[Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration ]

44. Figure 26. Slope different view ( Dziwnów 2009)

45. 4.5 PROFILES
In this part I present profiles. This profiles show us spread of the beach, height coast and overgrown by plants sand dunes.
These pictures below are intersections. I made them in different parts of the coast in the Dziwnów region. Right now this
region may looks different because ortophotmap was from 2004 and Digital Surface Model was from 2009. So look at this
ortophotomap for orientation, where we are.
Figure 27. Orthophotomap from Satellite IKONOS photo 2004 and Profiles
[Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration ]

46. Figure 28. Profile 1, zoom on the top of the coast
[Source: University of Szczecin the Faculty of Geosciences, own elaboration]

47. Figure 29. Profile 1 based on the clouds of points
[Source: University of Szczecin the Faculty of Geosciences, own elaboration]

48. Figure 30. Cross- section 1 calculated automatically by the meters.
In this moment cast achieve 5 meters and beach is within 35 meters

49. Figure 31. Profile 2 based on the clouds of points
[Source: University of Szczecin the Faculty of Geosciences, own elaboration ]

50. Figure 32. Cross- section 2 calculated automatically by the meters.
In this moment coast achieve around 7 meters and beach is within 42 meters

51. Figure 33. Profile 3 based on the Clouds of points
[Source: University of Szczecin the Faculty of Geosciences, own elaboration]

52. Figure 34. Cross- section 3 calculated automatically by the meters.
In this moment coast achieve around 6 meters and beach is within 52 meters

53. Figure 35. Profile 4 based on the clouds of points
[Source: University of Szczecin the Faculty of Geosciences, own elaboration ]

54. Figure 36. Cross- section 4 calculated automatically by the meters.
In this moment coast achieve around 4 meters and beach is within 54 meters
We can compare this 4 cross- section ( Figures 30, 32, 34, 36 ) and look at the ortophotomap. In the region where we
made cross section 4 coast is lower but beach wider than on the rest of the 3 cross-sections . Recognizing where are more
unsafe areas if water- level grows or sea wave will be strong in storm or other atmospheric circumstance that damages coast.
As we see in some part of the coast, the cliff is higher. The plants are growing on the top of the coast ( Figures 28, 29, 31, 33,
35). So by this profile we can also compare plants how they spread on the sand dunes in the desirable region.

55. 5. Summary
In conclusion I would like to notice that the measurements by the Mobile Laser Scanning are very accurate. We can
generate Digital Surface Model, Digital Terrain Model, profiles of the coast. The advantage of MLS is that we can quick
measurements objects which are hard to reach. Secondly clouds of points to act as information for vector maps ( e.g. master
map). We can take measurements in the day and in the night. The accuracy measurements is approximate to classical
geodetic surveying, Cloud of points precisely represent objects in 3d, it can be use for planning and designing.
The date give it us also possibility to use clouds of points to create by triangulation method of the precise DTM for lain
with roadside. Measurements steepness the river banks and position the roads, railways. Identifying the conditions of
pavement and measurements size of potholes. Determining narrow loading gauge for over size load transport. We can use for
modeling 3D facade of the buildings. Measurements to map for design purposes. Sketch the map to planning traffic on the
streets.

56. ILLUSTRATIONS
Figure 1. Reference station situated in Kamień Pomorski..........................................................................................................12
Figure 2. Trajectory in real time and post- processing. The red one is in real-time and the green one after post-procesing.....15
Figure 3. Trajectory post-processed............................................................................................................................................16
Figure 4. Accuracy of the RMS (Root Mean Square ) position error for platform. ...................................................................17
Figure 5. Position Dilution of Precision......................................................................................................................................18
Figure 6. Number of available satellite ......................................................................................................................................20
Figure 7.Digital Surface Model...................................................................................................................................................24
Figure 8. Digital Surface Model view from front side (Dziwnów 2009)....................................................................................25
Figure 9. Beach Dziwnów 2009 [Source: University of Szczecin the Faculty of Geosciences, own elaboration].....................26
Figure 10. Digital Terrain Model view from front side ..............................................................................................................27
Figure 11. Orthophotomap from Satellite IKONOS photo by 2004 and signed area where Digital Surface Model and Digital
Terrain Model were created. [Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration]
.....................................................................................................................................................................................................28

57. Figure 12. Digital Surface Model, view from the right side ......................................................................................................29
Figure 13. Clouds of points, view from the top side...................................................................................................................30
Figure 14. Digital Terrain Model with Sun Angle 67 degrees 18 minutes.................................................................................31
Figure 15. Digital Terrain Model with Sun Angle 20 degrees 24 minutes.................................................................................32
Figure 16. Digital Terrain Model with Sun Angle 43 degrees 51 minutes ................................................................................33
Figure 17. Clouds of points view from the top and right side ( Dziwnów 2009)........................................................................34
Figure 18. Digital Terrain Model view from the top and right side............................................................................................35
Figure 19. Contours for model made by the Digital Terrain Model from 2009 and Orthophotomap from Satellite IKONOS
photo by 2004. Minor contours we can see every 0,5 meters, basic contours every 1 meter and major 1,5 m .........................36
Figure 20. Orthophotomap from Satellite IKONOS photo 2004, hypsometry measurement from Digital Terrain Model by
2009 [Source: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej, own elaboration]....................................37
Figure 21. Digital Terrain Model view from the top side ( Dziwnów 2009)..............................................................................38
Figure 22. Clouds of points 2009 and Orthophotomap from Satellite IKONOS photo 2004 ....................................................39
Figure 23. Slopes on the Digital Terrain Model made Digital Surface Model from 2009 (blue: low; purple: high).................40
Figure 24. Clouds of points, Dziwnów 2009...............................................................................................................................41
Figure 25. Orthophotomap from Satellite IKONOS photo 2004 and calculate slope based on the Digital Terrain Model .......42

58. Figure 26. Slope different view ( Dziwnów 2009)......................................................................................................................43
Figure 27. Orthophotomap from Satellite IKONOS photo 2004 and Profiles ...........................................................................44
Figure 28. Profile 1, zoom on the top of the coast......................................................................................................................45
Figure 29. Profile 1 based on the clouds of points......................................................................................................................46
Figure 30. Cross- section 1 calculated automatically by the meters. .........................................................................................47
Figure 31. Profile 2 based on the clouds of points......................................................................................................................48
Figure 32. Cross- section 2 calculated automatically by the meters...........................................................................................49
Figure 33. Profile 3 based on the Clouds of points.....................................................................................................................50
Figure 34. Cross- section 3 calculated automatically by the meters. .........................................................................................51
Figure 35. Profile 4 based on the clouds of points......................................................................................................................52
Figure 36. Cross- section 4 calculated automatically by the meters...........................................................................................53

59. 7. TABLE
Table 1. Meaning of DOP values. The red I sign PDOP for my region………………………………………………….19
8. References
1. http://en.wikipedia.org/wiki/LIDAR
2. www.riegl.com
3. http://www.ferris.edu/faculty/burtchr/sure382/lessons/Lesson_4.pdf
4. http://en.wikipedia.org/wiki/Differential_GPS_post-processing#Post_processing
5. http://www.asgeupos.pl/index.php?wpg_type=syst_descr
6. http://www.asgeupos.pl/webpg/graph/publ/00010_EuropeanJurnalofNavigation_v5_nbr4.pdf
7. http://en.wikipedia.org/wiki/RINEX
8. www.asgeupos.pl
9. http://www.applanix.com/products/land/pospac-mms.html
10. http://pl.wikipedia.org/wiki/Dulation_of_precision
11. http://www.intermap.com/digital-surface-models

60. 12. http://en.wikipedia.org/wiki/Hypsometry
13. www.gispro.pl