GENERATION OF DEM AND CLUTTER DATA FROM SATELLITE IMAGES FOR USE IN RADIO NETWORK PLANNING

UNIVERSITY OF NAIROBI

GENERATION OF DEM AND CLUTTER DATA FROM SATELLITE IMAGES FOR USE IN RADIO NETWORK PLANNING

By MARK MWANGI NDONGA F19/1919/2007

A project report submitted to the Department of Geospatial and Space Technology in partial fulfillment of the requirements for the award of the degree of:

Bachelor of Science in Geospatial Engineering

April 2013

Abstract

This project deals with the use of Very High Resolution stereo satellite imagery to generate a digital elevation model of Nairobi CBD and its environs for use in radio network planning. Photogrammetric methods are employed in creating the 3D grid that represents the surface of the earth. Extraction of land use patterns and obstacle analysis is also covered as well as the accuracy assessment from secondary higher quality data. This is followed by the use of RF planning software in a GIS environment to model expected propagation characteristics of the Radio waves and the subsequent adjustments of the siting of the base transceiver stations.

The result is a high quality DSM, DEM, Ortho-photo, Clutter data classes and an obstacle heights grid, which are used to create prediction maps for the hypothetical radio network. Propagation prediction maps are produced and show the best sites to place base transceiver stations for the widest and most reliable coverage.

Radio network planners demand high accuracy Geodata to help in their planning and optimization operations in order to deliver high quality service to their clients. Said data needs to be timely, accurate and of sufficient resolution.

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Dedication I dedicate this report to my family for their enormous support throughout my education and all my endeavors. I could not have done this without you.

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Acknowledgements

I would like to acknowledge the immense help accorded to me by Mr. Eric Nyadimo the CEO and staff of Oakar services Ltd in the assistance with data, software and expertise.

I would also like to thank Mary Gwena, Fred Onyango, and Dr. Siriba for guidance and sound advice.

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Table of Contents Dedication .................................................................................................................... ii Acknowledgements ..................................................................................................... iii Table of Contents ........................................................................................................ iv List of Abbreviations .................................................................................................. vi List of Figures ............................................................................................................ vii 1: CHAPTER ONE: INTRODUCTION...................................................................... 1 1.1: Background ....................................................................................................... 1 1.2: Problem statement ............................................................................................ 6 1.3: Objectives ......................................................................................................... 6 1.4: Justification ....................................................................................................... 6 1.5: Organization ..................................................................................................... 7 2: CHAPTER TWO: LITERATURE REVIEW .......................................................... 8 2.1: History of Radio communications .................................................................... 8 2.2: Radio planning .................................................................................................. 9 2.2.1 Atoll & Cellular Expert............................................................................. 11 2.3: Data requirements/acquisition ........................................................................ 12 2.3.1: Satellite Imagery ...................................................................................... 12 2.3.2: GCP collection ......................................................................................... 13 3: CHAPTER THREE: TECHNIQUES AND METHODS USED........................... 16 3.1: Study Area ...................................................................................................... 16 3.2: Equipment and data used. ............................................................................... 18 3.3: Methodology ................................................................................................... 19 3.4: GCP Collection ............................................................................................... 20 3.5: Imagery ........................................................................................................... 23 3.6: Leica Photogrammetry Suite .......................................................................... 25 3.7: RF Coverage Prediction .................................................................................. 32 4: CHAPTER FOUR: RESULTS & ANALYSIS ..................................................... 37 4.1: Orthophoto ...................................................................................................... 37 4.2: Digital Surface Model (DSM) ........................................................................ 37 4.3: Digital Elevation Model (DEM) ..................................................................... 39 4.4: Point cloud ...................................................................................................... 40 4.5: Building heights .............................................................................................. 40

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4.6: Prediction maps .............................................................................................. 41 5: CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS .................... 44 5.1: Conclusion ...................................................................................................... 44 5.2 Recommendations ............................................................................................ 45 6: CHAPTER SIX: REFERENCES .......................................................................... 46 Appendix 1. ................................................................................................................ 47

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List of Abbreviations DEM-Digital Elevation Model DSM-Digital Surface Model UMTS-Universal Mobile telecommunication system LIDAR- Light Detection and Ranging GCP-Ground control point WIMAX-Worldwide Interoperability for Microwave Access BTS- Base Transceiver Station

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List of Figures 

Fig 1. Telecommunications spectrum usage

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Fig 2. Difference between DSM and DTM

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Fig 3. Electromagnetic spectrum

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Fig 4. Cells in a cellular network

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Fig 5. Example of coverage prediction

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Fig 6. Example of coverage prediction using Cellular Expert

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Fig 7 Processing steps for the DEM generation from stereo Ikonos images and evaluation with Lidar elevation data.

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Fig 8. Extent of study area.

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Fig 9 Methodology employed in project.

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Fig10. Distribution of GCPs

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Fig 11. GPS post processing

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Fig 12. Point measuring tool

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Fig 13. Refinement Summary

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Fig 14. Strategy parameters used.

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Fig 15. eATE strategy parameters

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Fig 16. Surface differencing results

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Fig 17. Reclassified surface

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Fig 18. Land cover types

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Fig 19.Geodata input into Atoll

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Fig 20.Prediction template used in Atoll

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Fig 21. Locations of sites chosen for coverage prediction

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Fig 22. DSM produced by eATE

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Fig 23 Reference DSM

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Fig 24. DTM quality Image

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Fig 25. Building heights/cluster heights layer

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Fig 26. Effective service area for mobile internet vii

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Fig 27. Coverage from transmitter signal strength

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Fig 28. Overlapping coverage zone.

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1: CHAPTER ONE: INTRODUCTION 1.1: Background RF (Radio Frequency) Planning is the process of assigning frequencies, transmitter locations and parameters of a wireless communications system to provide sufficient coverage and capacity for the services required. The RF plan of a cellular communication system has two objectives: coverage and capacity. Coverage relates to the geographical footprint within the system that has sufficient RF signal strength to provide for a call/data session. Capacity relates to the capability of the system to sustain a given number of subscribers. Capacity and coverage are interrelated. To improve coverage, capacity has to be compromised, while to improve capacity, coverage will have to be compromised. ( Laiho, et al., 2006) A regulatory body that acts as the custodian of a country’s frequency allocation by the International Telecommunication Union (ITU), which is the global coordinator of radio spectrum use, usually centrally controls the allocation of frequency to the various players and operators in the industry. In Kenya, this role is currently served by the Communications Commission of Kenya (CCK), which regulates all aspects of Radio frequency allocations and use in the telecommunication and broadcast industry.

In the past, before the advent of mobile telephony, the nature of the frequencies and technology used in the broadcast sector allowed for minimal planning in the siting of the transmitter.

The propagation characteristics of lower frequency radio waves (See Fig. 1) and the uni-directional nature of the communication by broadcast companies allowed the use of a few transmitters to cover a large part of the population and require less bandwidth to do so. The transmitters were usually constructed atop vantage points such as hills or ridges for the farthest reach.

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Fig 1: Spectrum usage (Techcentral, 2013)

The advent of mobile telephony and its subsequent popularity rendered the old model of establishing the radio networks obsolete due to the demands of the new networks. More bandwidth, reuse of frequency, bi-directional communication, low latency and high availability require a more robust network that considers the topography of the region.

Currently, the mobile phone industry is one of the fastest growing industries in the world. All over the world growing numbers of mobile phones require more extensive coverage areas. In coverage area planning locations and number of network sites are optimized. Coverage area planning requires special format of geographical data. Topography and morphography are the most important geographical data required. Topography contains information about elevations in the planning area. Different land cover types according to their wave propagation abilities define morphography. Topography data is usually in the form of Digital Terrain Models and Digital Elevation Models, which may be derived from Digital Surface Models. DSM refers to Digital Surface Model. It is a representation of a portion of the earth’s surface on a 2 dimensional grid. This is an elevation model that takes the surface height inclusive of everything on it. It takes the tops of trees, buildings etc. and includes them in the model. This is usually a remote sensing product from either aerial photos or satellite imaging. It is not very useful in RF planning since the surface doesn’t accurately predict the effects on the radio propagation and interference.

DEM refers to Digital Elevation Model. It is an ordered array of numbers that represents the distribution of elevation values above an arbitrary datum in a landscape (Meijerink et al., 1994) The height values are of the ground and do not

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take into consideration the objects affixed on the surface of the ground e.g. Buildings, trees, power lines etc. It is also sometimes called a “bare earth model”.

Fig 2: Difference between DSM and DTM (Wikipedia, 2013)

DTM is a representation of terrain information using discrete sampled digital values, like slope, aspect, etc. Sometimes also called a TIN (Triangular Irregular Network), which is made of a series of triangular polygons. The triangles are 3D dimensional in that each node will have a different x, y and z. Each triangle becomes one face of the terrain surface.

We can thus see that DEMs and DTMs are similar and are sometimes used interchangeably depending on the data provider or user. DTMs are however more inclined to the emphasis of distinct features on the earth’s surface while DEMs are a more generic term.

Clutter maps/data, also referred to as morphology or land-use maps, and is used in all of today's state-of-the-art radio frequency (RF) propagation tools to model path loss, signal attenuation and frequency re-use. Different land use patterns and ground characteristics affect the signal propagation differently and cannot simply be labeled as obstructions. Land use maps fall under this category e.g. Industrial Areas, residential zones, and office and business zones. These different land uses guide the 3

planners to allocate appropriate resources to high demand areas and less in say croplands.

DEM data is vital in the planning of Radio networks over a large geographical area in order to predict the interference and propagation patterns of the RF signals. Various obstacles above the ground surface including buildings, trees affect the signals, and the land use and land cover characteristics patterns e.g. water bodies, open grassland affect the signal propagation.

Specialized software (For example Cellular Expert, Atoll, Mentum Planet and various internet and web based tools) are used for this purpose in a GIS environment to model the behavior of the very specific equipment that is used by the RF operators and is thus able to accurately model the expected propagation characteristics and interference of the of the said radio waves.

This greatly aids in planning prior to even visiting the region and allows for the determination of the optimum locations of the Base transmitter stations and receivers. Prior knowledge of the optimum locations prepares the installers before commencement of negotiations with the property owners as to the rates of rent particularly in the cases of urban deployment where there is no free space for allocation.

Acquisition of the geodata used in RF planning has been prohibitively expensive due to the methods used to acquire the raw data. Ground surveys have been the most predominantly used methods of obtaining elevation data. Traverse networks, differential GPS networks and leveling are the methods employed and they are laborious and expensive. Digitization from existing topographic maps is also used especially for rural areas but the age of the maps renders the morphological data on the maps unusable for urban or recently urbanized areas.

With the large areas covered by RF the cost involved in covering expansive areas using traditional ground survey methods can be overwhelming. There has been a

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trend towards the use of remote sensed imagery for the generation of clutter data and DEM datasets of large areas. Aerial photographs, stereo satellite images, Shuttle Radar Topography Mission and Light Detection and Ranging data are the most widely used in this regard.

LIDAR (Light Detection And Ranging, also LADAR) is an optical remote sensing technology that can measure the distance to, or other properties of, targets by illuminating the target with laser light and analyzing the backscattered light.

The Shuttle Radar Topography Mission (SRTM) obtained elevation data on a nearglobal scale to generate the most complete high-resolution digital topographic database of Earth. SRTM consisted of a specially modified radar system that flew onboard the Space Shuttle Endeavour during an 11-day mission in February of 2000. SRTM is an international project spearheaded by the National GeospatialIntelligence Agency (NGA) and the National Aeronautics and Space Administration (NASA). (California Institute of Technology, 2013)

Stereo Satellite Imagery is supplied by satellite operators on missions such as ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) and the Ikonos earth observation mission.

IKONOS is a commercial earth observation satellite, and was the first to collect publicly available high-resolution imagery at 1- and 4-meter resolution. It offers multispectral (MS) and panchromatic (PAN) imagery. The IKONOS launch was called by John E. Pike “one of the most significant developments in the history of the space age”. IKONOS imagery began being sold on January 1, 2000. (Digital Globe, 2013)

ASTER is an imaging instrument onboard Terra, the flagship satellite of NASA's Earth Observing System (EOS) launched in December 1999. ASTER is a cooperative effort between NASA, Japan's Ministry of Economy, Trade and Industry

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(METI), and Japan Space Systems (J-space systems). (Jet propulsion Laboratory, 2013)

Freely or cheaply available global DEM (Mostly from SRTM and ASTER) is of low resolution (90 m – 20m) and thus not suitable for dense urban areas or areas that have had significant changes in morphological features in the recent past for example, new buildings coming up. It is also usually outdated and does not offer strategic advantage to RF planners. 1.2: Problem statement Given the shortcomings of the freely available DEM data, it is important to find a way of deriving accurate up-to-date DEM data and fast enough to facilitate RF planning.

I intend to show it is possible to generate a sufficiently accurate DEM for the use of radio network planning and model a hypothetical radio network using Stereo Satellite images. Clutter data (in my case building height profiles and land cover classes) will also be extracted from the same image pair. 1.3: Objectives The objective of this project is to generate the following: 

DSM and DTM of Nairobi city center and its environs from a stereo pair of high resolution IKONOS Satellite images

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Clutter data sets (Building heights and Land cover classes) for the same region covered by the DTM

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Prediction patterns for the propagation and interference patterns of the RF signals of cellular radio networks.

1.4: Justification Increased proliferation of mobile phone technology has precipitated intensive demand for wireless networks that are robust and scalable. Advancing radio network

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technology necessitates the review of the propagation characteristics due to the difference in the frequencies employed.

There is also a need to review network performance due to changes in the landscape especially in urban areas where the rate of new constructions is staggering. 1.5: Organization The report is organized into 5 chapters. Chapter 1 introduces the reader to the background and objectives of the project. Chapter 2 gives a thorough review of existing research and work done in the field of remote sensed terrain extraction and RF planning. Chapter 3 looks at the methods employed in the project to achieve the objectives. Chapter 4 gives the results and the analysis including quality of the results. Chapter 5 gives the conclusion and recommendations. Chapter 6 outlines the literature cited.

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2: CHAPTER TWO: LITERATURE REVIEW 2.1: History of Radio communications Radio waves are a form of electromagnetic wave just like light or heat and are part of the electromagnetic spectrum. (See Fig.3)

Fig 3: The Electromagnetic Spectrum (Lawrence Berkely National Laboratory, 2013)

Scottish Physicist James Clerk Maxwell first proposed their existence in 1860’s but it was German physicist Heinrich Rudolph Hertz in 1886 who demonstrated that rapid variations of electric current could be projected into space in the form of radio waves similar to how light and heat were transmitted. (Buchwald , 1994) Gugliemo Marconi is credited as being the first to establish feasible radio communication when in 1899 he flashed a wireless signal across the English Channel. This led to the development of the radiotelegraph and later the TV and radio were built to massive public appeal.

Numerous advancements have been realized since then and radio communications has become ubiquitous in the modern world. Examples of modern technologies that employ wireless communications today are; 

Bluetooth

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Wi-Fi

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GPS

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TV/FM radio

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Mobile phones etc.

Mobile telephony is an especially revolutionary and novel application of radio communications what with the enhancement of interpersonal communication and business functionality. The most common mobile phone technology is GSM, which has been adopted worldwide as a standard and to ensure seamless communication using standard hardware. The reuse of allocated spectrum in small geographic areas allows for widespread coverage with almost homogeneous quality of service. Congested or high traffic areas get smaller and smaller cells thus ensuring their level of service is either sustained or improved. This allows an operator to use very few frequencies and supply service to an increasing number of consumers without locking out other operators. 2.2: Radio planning Radio frequency planners specifically in the mobile phone industry have a tough time determining the most optimum places that base transceiver stations can be located and the interference characteristics that plague the network. The models are restricted to the radio characteristics and the reuse of scarce frequency spectrum. The intersections of the coverage areas between the different cell towers are a delicate balancing affair with a lot of planning and modeling involve (See Fig.4) (Nawrocki, et al., 2006).

Fig 4: Cells in a network (KPI Wireless, 2013)

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The planning and optimization of the said networks is increasingly moving towards becoming automated.

Radio frequency planners have very specific requirements for their data and consider it a vital part of the planning process. Little processing is done in house and they rely on GIS/Geospatial firms to supply the data. The requirements are heavily skewed towards temporal resolution as opposed to spatial resolution. The need for spatially accurate data (DEM, clutter, and attribute data) remains however (Makali, 2012).

Fig 5: Example of Coverage prediction (Banzinet.co.za, 2013)

The image above shows an example of a prediction map that is generated from the RF planning tools using the underlying Geodata to model the interaction of the transmitted radio waves and the terrain.

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2.2.1 Atoll & Cellular Expert Atoll is a software solution sold and marketed by Forsk, an independent company providing radio planning and optimization software solutions to the wireless industry since 1987. It provides various tools including profile analysis, cell planning, radio equipment management, coverage prediction etc. (Forsk Ltd, 2013)

The software is used for the extensive planning and cataloguing of equipment, orientation, settings and the subsequent updating and optimization of the various sites of an extensive radio network. It is a comprehensive, standalone software and supports most geospatial file formats including and not limited to .img, .tif, .png etc. It is also the most popular amongst commercial telecommunication companies (Makali, 2012). It is easy to use and friendly to amateur radio planners.

Cellular expert is a similar tool with almost identical functionality but a much more robust toolset. It is less automated than Atoll but with adequate knowledge it is just as powerful if not superior. It works as an extension of the ArcGIS and requires at least a licensed ArcGIS 10 installation to work. This integration with ArcGIS is advantageous to those already working in an ArcGIS Environment but is however limiting for those who work in other GIS environments. The integration however makes it easier for the software to support many more formats and allow for manipulation of larger datasets. An example of the coverage prediction produced by Cellular Expert is shown in Fig. 6

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Fig 6: Example coverage prediction using Cellular Expert

2.3: Data requirements/acquisition 2.3.1: Satellite Imagery When it comes to methods of generating geodata for the use of radio network planning, use of conventional databases generated from existing maps, aerial photographs and digitized features may prove to be inaccurate, unavailable datasets and other inconsistencies on the ground. Use of remote sensing methods have availed a way of creating modern, up to-date databases (Tiihonen, 1997) (Makela & Turkka, 2000).

The use of satellite imagery for the generation of data is a recent development with the widespread availability of Very High Resolution satellite imagery and other satellite based methods such as Interferometric Synthetic Aperture Radar

(InSAR), which can be used to generate DEMs with a few meters vertical

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accuracy (Makela & Turkka, 2000). With improvement in software and hardware capabilities in the consumer arena, post processing of satellite images has become cheaper for researchers, academics and industry professionals. Several software packages are commercially available for use including Leica Photogrammetric Suite (LPS) from Leica, Envi 5, and PCI Geomatica 10 amongst others that can be used to process the images and generate DEMs (Eckert, 2008). The Ikonos sensor uses the Push broom sensor model, which makes it extremely difficult to process images by using the rigorous physical method that would ordinarily be employed for such a purpose. The method that is most commonly employed is the use of rational polynomial coefficients (RPC). It has been observed that the differences in accuracy when comparing RPC obtained DEMs (With GCPs) as opposed to Rigorous method obtained DEMs are minimal. (Eckert, 2008) (Grodecki, n.d.). . 2.3.2: GCP collection To correct the RPC model, ground control points (X, Y, and Z) are required to strengthen the block adjustment. An optimum number of Ground control points are 7-8 measured with a differential GPS well distributed in the image (Eckert, 2008). Differential GPS measurements depend on the use of corrections computed from overlapping observations of the same satellites during the time of acquisition by two or more GPS high accuracy GPS units. The underlying premise of differential GPS (DGPS) requires that a GPS receiver, known as the base station, be set up on a precisely known location. The base station receiver calculates its position based on satellite signals and compares this location to the known location. The roving GPS receiver applies the difference to the GPS data recorded. In other parts of the world, dense CORS (Continuously Operating Reference Stations) networks allow for near real-time corrections to be sent to the receiver in

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the field thus reducing or eliminating the need of having a GPS base for your measurements. The case is different in Kenya with only 1 CORS within Nairobi County housed at the Regional center for Mapping and Resource Development (www.rcmrd.org). A base therefore needs to be used to make any meaningfully accurate readings. The general procedure of extracting 3D information (in my case a DSM and DEM) is as follows: 

Acquisition of Stereo Very High resolution Satellite imagery with the complimentary ephemeris and altitude data where available.

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Collect a sufficient number of GCPs to correct the stereo model geometry

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Extract elevation parallax by using automatic image matching techniques

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Compute 3-D coordinates using 3-D stereo intersection

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Create and post processes the DSM to extract the DEM and possibly clutter data (Eckert, 2008).

An example of a possible methodology of the process is shown in Fig. 7

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Figure 7: Processing steps for the DEM generation from stereo Ikonos images and its evaluation with Lidar elevation data (Toutin, 2005).

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3: CHAPTER THREE: TECHNIQUES AND METHODS USED

3.1: Study Area Nairobi City is the capital of Kenya and is located at 1° 16’ South and 36° 48’ East. The altitude of the area of study varies from 1520m to 1790m above sea level. It is a metropolitan city with a population of over 3 million and a rapidly expanding urban space with high-rise buildings and informal settlements in the same stride. The pressure on resources is growing and a major resource suffering from this is radio spectrum for communication purposes.

My study area is a section of Nairobi city and its environs within the greater Nairobi County encompassing an area of 8km by 13km as shown in Fig.8.

Single and multi-story buildings, open ground and a small percentage occupied by vegetation mostly low-lying, occupy a large percentage of the area under study. The built up areas vary from industrial, commercial and residential buildings most of which do not surpass a height of 20m.

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Fig 8. The extent of study area

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3.2: Equipment and data used. 

ERDAS IMAGINE 2013 software with the LPS module which has block adjustment, ortho photo creation and DEM extraction capabilities

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Atoll software for the RF prediction mapping and generation of coverage layers and rasters.

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Stereoscopic IKONOS images of Nairobi. The image characteristics are as follows: ◦ Multispectral with an accuracy of 3.2m horizontal and 22m vertical but with GCP inclusion this rises to 2m horizontal and 3m vertical ◦ Image extent is 8km by 13km ◦ 11 bits per pixel ◦ 1 pixel = 1m on the ground

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Laptop computer with an Intel Core i5 processor, 2 GB of RAM, and 250GB storage.

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Differential GPS units for GCP collection.

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GNSS solutions: A GPS post processing software.

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LIDAR acquired reference DEM of part of the area covered.

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3.3: Methodology Figure 9 presents an outline of the methodology used in the workflow of the project.

Stereo Satellite Photos + RPC

LPS

Accurate GCPs

eATE

Block Adjustment

Unsupervised Classification

Ortho-Photo, DEM

High quality DSM

Obstacle Height Grid + DEM

Land cover classes

RF Planning software (Cellular expert/Atoll)

RF Prediction maps

Prediction of optimum BTS Location Fig 9: Methodology employed in the project.

The primary inputs in the workflow are accurately measured ground control points and a pair of satellite images in stereo. The satellite images came with their Rational 19

Polynomial Coefficients that mathematically define the satellite platform in space. The two are taken through two separate processes to generate the DEM & Orthophoto and the DSM. Manipulation of the DSM and DEM yield an obstacle height grid that is used in the RF planning software as clutter heights grid. The clutter heights grid, the DEM and the land cover classes are used as inputs for the RF planning software as representations of the area under study and are used to generate prediction maps. The prediction maps in turn show the best places to place BTSs.

3.4: GCP Collection The GCPs were collected in a well-distributed manner within the image so as to maintain the integrity of the bundle adjustment since empirical models are sensitive to GCP distribution and number. Because entire area covered by the stereo images was to be used, the GCPs were distributed across the whole image area in planimetry and also covering the height differences across the area though these were subtle. Emphasis was however on GCPs closest to the edges of the image in order to avoid any distortions. I only picked GCPs that were on the ground and not on any artificially elevated point such as on top of buildings and such like areas.

Due to changes in the terrain since the acquisition of the images (due to building construction and road infrastructure changes), finding suitable spots on the ground that were accurately identifiable on the image was a challenge but was eventually surmounted.

A set of differential GPS units was used to collect the information. These were a Promark 800 unit manufactured by Spectra International used as a rover while the base was a Promark 500 unit manufactured by the same company.

Here are the specifications for the Promark 800 unit Product specifications 

Constellation: GPS/GLONASS/GALILEO/SBAS

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Frequency: L1/L2/L5

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Channels: 120

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Update Rate: 0.05 sec

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Data format: RTCM 3.1, ATOM, CMR (+), NMEA

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Raw data output: Yes

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Real-time Accuracy - RTK mode (HRMS): 1 cm

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Real-time Accuracy - DGPS mode (HRMS): < 30 cm

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Real-time Accuracy - SBAS mode (HRMS): < 50 cm

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Post-Processed Accuracy (HRMS): 0.3 cm + 0.5 ppm

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Time to first fix: 2 sec

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Initialization range: Up to 40 km

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Communications: UHF, GSM/GPRS/3.5G, BT

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Unit size (mm / inches): 228x188x84mm / 9x7.4x3.3in

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Weight: 1.4 kg / 3.1 lb.

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Display: OLED

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Memory: 128 MB + USB

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Temp Min (°C): -30°C / -22°F

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Temp Max (°C): 55°C / 131°F

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Waterproof: Yes

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Shock & vibration: ETS300 019 & EN60945

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Power (type - lifetime): 4600 mash Li-Ion / > 8 hrs.

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Antenna Type: Internal

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Firmware options: Yes

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Software options: Yes

(Spectra Precision, 2013) The specifications for the Promark 500 unit are similar but with slightly lower accuracy (4mm) in the horizontal and vertical accuracy.

The distribution of the GPS points is shown below in Fig 10 below.

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Fig 10: Distribution of GCPS

A total of 11 points were collected inclusive of the control point. The base of the network was at a control point to the south of the region near the edge of the area of interest. The control was in the Arc 1960 datum and had to convert it to the WGS84 datum that was used for all the other datasets.

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Figure 11 is a screenshot of the post-processed GPS measurements also displaying the achieved accuracy.

Fig 11: GPS post processing

An averaged accuracy of 0.071m for each point was achieved for the overall operation. The least accurate observations were for positions farthest from the base station and this was due to the limited observation times that we afforded to occupy the positions. Recommended occupation time is about 30 minutes at each station but due to time constrains, we only occupied for about 10 minutes to 5 minutes at each station.

The error margin is however well within the requirements for photo control due to the resolution of the image that I used. GCPs of an accuracy of at least 0.5m are required to correct the model for block adjustment when using Ikonos Satellite imagery. (Eckert, 2008)

The fieldwork results are in Appendix 1 of this report. 3.5: Imagery A stereo pair of IKONOS multispectral imagery was used.

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Each of the images covered a roughly 13km by 8km area

The IKONOS images used had the following characteristics at the time of acquisition.

Image 1 Sensor: IKONOS-2 Acquired Nominal GSD Pan Cross Scan: 0.9112195969 meters Pan Along Scan: 0.8647795916 meters MS Cross Scan: 3.6448783875 meters MS Along Scan: 3.4591183662 meters Scan Azimuth: 179.9988028957 degrees Scan Direction: Reverse Panchromatic TDI Mode: 13 Nominal Collection Azimuth: 256.0363 degrees Nominal Collection Elevation: 70.47781 degrees Sun Angle Azimuth: 44.9368 degrees Sun Angle Elevation: 59.66842 degrees Acquisition Date/Time: 2009-07-24 08:10 GMT Percent Cloud Cover: 3

Image 2 Sensor: IKONOS-2 Acquired Nominal GSD Pan Cross Scan: 0.9336703420 meters Pan Along Scan: 1.0087776184 meters MS Cross Scan: 3.7346813679 meters MS Along Scan: 4.0351104736 meters Scan Azimuth: 179.9988028957 degrees Scan Direction: Reverse Panchromatic TDI Mode: 13

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Nominal Collection Azimuth: 334.4686 degrees Nominal Collection Elevation: 62.13950 degrees Sun Angle Azimuth: 45.1964 degrees Sun Angle Elevation: 59.51849 degrees Acquisition Date/Time: 2009-07-24 08:09 GMT Percent Cloud Cover: 0

The images were acquired at around 11:10 AM local time. This is close to noon, which is the best time to collect imagery due to the reduced effect of shadows on image interpretation. There is only 3% cloud cover in image 1 and none in image 2. This presents good image interpretation conditions.

The two images overlap is above 95% and thus suitable for use in block adjustment using aero triangulation in the block adjustment tool of the Leica Photogrammetry Suite of software.

3.6: Leica Photogrammetry Suite The Leica Photogrammetry Suite (LPS) is an add-on module of the ERDAS Imagine software suite. It contains a block adjustment tool, DEM extraction, stereo measurement and ortho-photo resampling options.

Within this suite the block tool, point measurement tool and the eATE (Enhanced Automatic Terrain Extraction tool) were used.

The GCPs that were collected using GPS were identified on each image in the pointmeasuring tool as shown in Figure 12.

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Figure 12: Point measuring tool.

The GCPs collected were accurately placed on the adjacent images using the pointmeasuring tool and additional tie points were generated by use of automatic image matching methods image matching methods.

In the Aerial triangulation the number of iterations was set to 10 and the rational function was refined with a 1st order polynomial. The rational function relates ground point coordinates (X, Y, and Z) to image pixel coordinates (x, y) in the form of ratios of two polynomials: x = P1(X, Y, Z) / P2(X, Y, Z); y = P3(X, Y, Z) / P4(X, Y, Z); The polynomial order determines which function to use to compute dx and dy as: Order 0: dx = a0; dy = b0; Order 1: dx = a0 + a1x + a2y; dy = b0 + b1x + b2y; The 0th order results in a simple shift to both image x and y coordinates. The 1st order is an affine transformation

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Aerial triangulation was performed with theses settings and the resultant unit-weight standard error = 1.1594 pixels. The total Image RMSE was 1.1594 pixels. These are acceptable results and the stereo pair can be considered to be successfully oriented.

Figure 13: Refinement summary

The next step was extracting the terrain and the features on the terrain.

Two methods were used to extract the terrain after block triangulation. The first method involved using the block tool that resides in the classic ATE module. This method uses traditional photogrammetric techniques to draw the relief of an area by use of parallax. It is useful in extracting bare earth profiles but performs poorly for building and feature extraction.

The strategy employed involved smoothing out the ground and filtering out the objects and structures based on object heights and widths.

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Fig 14: Strategy parameters used

Figure 14 shows the strategy parameters used. The high smoothing and the object filter are aimed at removing all the trees, buildings and related objects from the terrain and thus leave the bare ground profile.

The other method used involved the Enhanced Automatic Terrain Extraction (eATE) module of the LPS suite. This module uses per pixel comparison to generate RGBencoded, dense point clouds by correlating points from stereo image pairs. These point clouds are then used to build a high quality DSM of the area covered. Using the point clouds in their native formats would be more desirable due to the manipulation options they afford but with the resolution of the images and limited parallax offered due to distance of the sensor platform, the point clouds were thin and not suitable for analysis.

Figure 15 shows the strategy parameters used to generate the most accurate DSM from the IKONOS image set.

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Fig 15: eATE Strategy Parameters

The parameters in figure 15 were arrived at after a lot of trial and error with varying results. The correlator used was the NCC correlator, which is a statistical measure of the similarity of two points in two images. The window size is the size in pixels, of the area used for computing the correlation coefficient between the left and right images. This must be an odd number so that there is a center pixel in the window.

The interpolation method caters for points that have not been interpolated. These interpolated points are written to a file and then used to seed the next-pyramid-layer correlation which increases accuracy and point density. Interpolated terrain points are not written to the output DTM. 29

The coefficient start and end defines the correlation coefficient to use for each pyramid level (Pyramid levels is a type of multi-scale signal representation developed by the computer vision, image processing and signal processing communities, in which a signal or an image is subject to repeated smoothing and subsampling ).

LSQ refinement is an algorithm that uses least squares to refine the correlation to provide improved sub-pixel results. When employed at the last pyramid levels, it provides accurate intersections of the matched rays

The best edge constraint value was 4 as this enhanced the buildings edges and thus made identification easier. The objective was to acquire a DSM that most accurately delineated buildings and associated objects from the rest of the terrain.

Smoothing looks for spikes in elevation and removes those points deemed to be too extreme a change compared to surrounding points to be a valid elevation point. In general, this removed outliers. This was not selected as it would have resulted in removal of the clutter heights as erroneous. Low contrast lowers the feature threshold in low-contrast areas

Next, a Surface Differencing operation was carried out on the two DSMs to obtain the difference between the two surfaces, which would constitute the objects on top of the bare earth.

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Fig 16: Surface differencing results

This differencing was then reclassified using the reclassify tool in the 3D Analyst toolbox of ArcGIS 10.1. In the reclassification, the pixels with a value below 100 were eliminated as they represented the ground and the remainder represented buildings and a few other objects on the ground. These were saved as a separate layer and are an acceptable representation of the buildings in the study area (Figure 17)

Fig 17: Reclassified surface

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A classification of the land cover types was also conducted using unsupervised classification within the ERDAS Imagine Raster toolset. This used pixel values to classify the land cover types into Buildings, Open grassland and vegetation (see Figure 18).

Fig 18: Land cover types.

We thus have the major datasets required by the RF planning software to generate prediction maps and possible locations of siting BTS.

3.7: RF Coverage Prediction The RF planning tool, Atoll was used in the generation of coverage predictions. This was due to its ease of use as compared to Cellular expert, which required a lot of RF technical knowledge and was thus deviating from the primary objectives of the project.

Coverage predictions are raster images that show the relative strength of the radio signal as it travels across the landscape. They are very useful in predicting the

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distance the signal is likely to propagate and places that may get excluded from service due to shadowing or obstruction from buildings and the like.

The environment of the Atoll workflow involves having a workspace that holds the geodata and the Base transceiver stations that will be distributed in the area (See figure 19). We are only concerned with the best sites theoretically to allow the best coverage of the area.

Fig 19: Geographic Data input into Atoll

A default database is generated and populated for use and the DTM and building raster are imported. The database is where all the settings for the proposed network will be saved including options of the antenna type, radio type, what kind of network

33

(UMTS, Cellular, Wimax or Microwave) and the orientation of the coverage and how many sectors per site.

Comprehensive settings and data are entered into the prediction model so as to accurately predict the coverage area according to the data on the ground. Most of these settings are however input into templates that significantly reduce the deep technical knowledge required. The template chosen was that of UMTS networks operating at 2100 MHz this is due to the growing trend of telecom companies building out new 3G networks as they transition from the older lower capacity 2G cellular networks. The template is suitable for urban areas with lots of buildings. The template is shown in Figure 20.

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Fig 20: Prediction template used.

Once this is done, the sites can be located in a manner that will cover the largest population and afford the most economical investment in site construction. The site locations chosen are shown in figure 21.

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Fig 21: Locations of the sites chosen for coverage prediction.

Once the parameters for the antennas, sectors, orientation of the sectors and the prediction model has been set, the predictions are created.

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4: CHAPTER FOUR: RESULTS & ANALYSIS

The results of the procedures detailed in the previous chapter are detailed below and several products were realized. 4.1: Orthophoto An ortho-photo of the study area was realized after the block triangulation aided by GPS collected GCPs. This can be used to create maps or be used as a map itself due to its planimetry corrections. The planimetric accuracy of the Orthophoto was found to be ± 1m in both x and y when point coordinates was compared to the GCPs collected. 4.2: Digital Surface Model (DSM) High quality DSM of the study area was generated using the eATE module and was used to extract the building profiles from the ground surface. The resolution is 1m per pixel. Due to the resolution of the image and the pixel size, Small objects of area of about 20 square meters are undetectable. Most buildings can however be distinguished from the rest of the landscape and extraction is possible as shown in Figure 22. The accuracy of this DSM was checked by comparing to a reference DEM that covers part of the area that was acquired by use of LIDAR data. (See Figure 23)

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Fig 22: DSM produced by eATE

Fig. 23: Reference DSM

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Visual comparisons of height values registered on the reference DEM and the created DSM showed the highest difference in heights measured at the same points was ±2m. 4.3: Digital Elevation Model (DEM) A DEM was generated using the classic ATE block tool of LPS and a quality assessment conducted by having a look at the quality raster produced in the process. Figure 24 is a DTM point status image that correlates the observed point heights with the interpolated height and generates a quality rating for the point.

Fig 24: DTM quality Image

Green pixels means perfect while red would mean suspicious. As we can see the above DEM passed the internal quality test.

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4.4: Point cloud The point cloud was generated as a precursor to the DSM. Due to the size of the study area and the limited resolution of the image at a pixel size of 1m squared, the point cloud was not as dense and detailed as desired. It was not used in the process of extraction of features as it was seen to be introducing errors when automatic classification of the results was used.

Using Manual classification of the point cloud would take too much time and effort to be useful and thus that line of pursuit was abandoned.

4.5: Building heights A building heights grid was produced as detailed in chapter 3 above. The quality and accuracy of this grid is not the best. There are also some missing data areas especially with the low buildings that show on the layer as having no buildings.

It is however usable within the study area as it shows the general layout of the buildings in the area and the height profiles.

Within the Atoll software the combination of the building heights and the DEM are used as a surface layer and the original DSM is usable here.

The building heights are used as an obstacle height layer separate from the terrain. Radio transceivers are routinely mounted on buildings and thus they are used as a surface for the coverage predictions whose characteristics are different from those of the ground. The transceivers mounted on the ground are raised from the surface to a height of about 30m or higher. Those mounted on buildings are not raised and thus would need different representation on the raster and hence the demarcation between buildings and ground level.

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Fig 25: Building heights / clutter heights layer

4.6: Prediction maps Various prediction maps were produced demonstrating several scenarios of RF signal propagation.

Fig 26: Effective Service Area for mobile Internet

The effective service area (See figure 26) refers to the region that has acceptable levels of service as defined by the internal quality management standards within the

41

organization or as mandated by the regulator. The red regions indicate the areas that fall under effective service for the individual BTSs. Note that there is no overlap of the coverage areas from different transceiver stations. This is because of the canceling out and interference of the signals from the neighboring BTSs that all operate at the same frequency. For simplicity, I set all the antennas to operate at 2100 MHz In the real world, antennas in neighboring cells are set to different frequencies to avoid the interference effect.

Fig 27: Coverage from transmitter signal strength

Fig. 27 shows the signal strength of the individual transmitter antennas. The highest strength is shown in yellow while weakest is in light blue. This graphic illustrates the coverage distance that a given antenna can serve. The no. Of concurrent users that a given cell can support are between 50 and 60.

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Fig 28: Overlapping coverage zones

Figure 28 shows the zones with the most overlap with the green regions showing the highest overlap areas and the purple showing the least overlap with only one antenna serving the area. Due to the high population of the city and its environs, the no. of BTS and cell sites that are required to adequately provide reliable service are considerably higher than indicated in my models. Due to lack of expertise, I avoided going too deeply into the telecommunication engineering aspect of the radio planning and the associated considerations and chose to focus on the Geographic data considerations.

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5: CHAPTER FIVE: CONCLUSION AND RECOMMENDATIONS 5.1: Conclusion

The extraction of geodata for the use in radio network planning has been achieved by application of photogrammetric methods on satellite images. The geographic base is thus usable in the generation of prediction patterns.

All the objectives of the project were met with the extraction of a high quality DSM and DEM. Clutter data sets were also extracted from the stereo satellite images. It is thus possible to use affordable and accessible satellite imagery products to extract geodata of sufficient quality for use in terrestrial radio frequency planning.

The use of satellite images has the potential of replacing traditional survey techniques with the exception of collecting GCPs in areas where the control network is not dense enough. With the improvement in satellite based sensor technology, resolutions and ground sample distances of up to 0.51m are commercially available today. With time this will definitely go higher and will reduce the necessity of requiring aerial photography missions to collect imagery for engineering, planning and judicial use.

Using Satellite imagery can however be limiting in the cases of bad weather where cloud cover is high or if the work is urgent. The revisit time of 3-5 days makes urgent work difficult to carry out especially in fast changing landscape.

Use of aerial photography in parallel with LIDAR systems with GNSS and INS will yield far more accurate and timely data sets for radio network planning amongst other uses. These datasets are however likely to be very expensive due to the cost of producing them. Using satellite imagery and photogrammetric methods has the potential of replacing aerial photography at the macro scale and in good weather.

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5.2 Recommendations Higher resolution imagery is desired seeing as this would allow better feature extraction and higher detail observed in finished product.

It is recommended that automated methods of object height extraction be researched on in order to ease the workflow of image manipulation and knowledge creation.

It is also recommended to establish more CORS around the city in order to reduce the necessity of using a base and rover units in the field, as this is more costly in terms of personnel and time spent. The collection of GCPs is important in providing photo control and faster and more efficient processes of getting photo control reduce the project time of extracting 3D information from stereo satellite imagery.

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6: CHAPTER SIX: REFERENCES Laiho, J., Wacker, A. & Tomáš , N., 2006. Radio Network Planning and Optimisation for UMTS. West Sussex: John Wiley and Sons LTD. Becca International consultants LTD, 2005. Creating and Using Digital Elevation Models, s.l.: s.n. Buchwald , J. Z., 1994. The Creation of Scientific Effects. Chicago: University of Chicago press. California Institute of Technology, 2013. Shuttle Radar Topography Mission. [Online] Available at: http://www2.jpl.nasa.gov/srtm/ Camargo, F. F. et al., 2011. An open source object-based framework to extract lanform classes. Expert systems with Applications, Volume 39, pp. 541-554. Deilami, K. & Hashim, M., 2011. Very High Resolution Optiacal Satellites for DEM Generation: A Review. European Journal of Scientific Research, 49(4), pp. 542-554. Digital Globe, 2013. Digital Globe>Products>Earth Imagery>DigitalGlobe Satellites. [Online] Available at: http://www.geoeye.com/CorpSite/products/earth-imagery/geoeyesatellites.aspx#ikonos Eckert, S., 2008. 3D- building height extraction from Stereo IKONOS Data, s.l.: OPOCE. Forsk Ltd, 2013. Forsk: Radio Planning and Optimization Software. [Online] Available at: http://www.forsk.com/ Grodecki, J., n.d. Ikonos stereo feature Extraction - RPC Approach, s.l.: s.n. Heipke, C., Koch, P. & Lohmann, P., 2002. Analysis of SRTM DTM Methodology and practical results. s.l., s.n., pp. 1-12. Jacobsen, K., n.d. DEM generation from satellite data. [Online] Available at: www.earsel.org/tutorials/Jac_03DEMGhent_red.pdf [Accessed 15 January 2013]. Jet propulsion Laboratory, 2013. Aster. [Online] Available at: http://asterweb.jpl.nasa.gov/) KPI Wireless, 2013. KPI Wireless.com. [Online] Available at: kpiwireless.com Lawrence Berkely National Laboratory, 2013. EM spectrum. [Online] Available at: http://www.lbl.gov/images/MicroWorlds/EMSpec.gif

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Makali, L., 2012. Optimization Engineer Orange Kenya [Interview] (5th December 2012). Makela, J. & Turkka, T., 2000. Geographical databases for the use of radio network planning. International archives of Photogrammetry and Remote Sensing, pp. 82-87. Nawrocki, M. J., Dohler, M. & Ahgvami, A. H. eds., 2006. Understanding UMTS Radio network Modelling, Planning and Automated Optimization. s.l.:John Wiley & Sons Ltd. Saldana, M., Aguilar, F., Aguilar, I. & Fernandez, I., 2012. DSM extraction and evaluation from Geoeye-1 Stereo Imagery. Melbourne, s.n., pp. 113-116. Shan, J., Lee, D. S. & Bethel, J. S., 2003. Class-Guided Building extraction from Ikonos Imagery*. Photogrammetric Engineering & Remote Sensing, February, pp. 143-150. Spectra Precision, 2013. Promark 800 Spectra Precision Surveying. [Online] Available at: http://www.spectraprecision.com/products/gnss-surveying/promark800-13724.kjsp Techcentral, 2013. TechCentral : How Africa can reap the dividend. [Online] Available at: http://www.techcentral.co.za/how-africa-can-reap-the-dividend/38056/ Toutin, T., 2005. DTM Generation from Ikonos In-Track Stereo Images Using a 3D physical Model. Photogrammetric Engineering & remote sensing , June, 70(6), pp. 695-702. Wikipedia, 2013. Wikipedia: Digital Elevation Model. [Online] Available at: http://en.wikipedia.org/wiki/File:DTM_DSM.png

Appendix 1. Land Survey Overview

GNSS Solutions (C) 2012 Trimble Navigation Limited. All rights reserved. Spectra Precision is a Division of Trimble Navigation Limited. 4/4/2013 11:08:06 AM www.spectraprecision.com Project Name : Mwangi Project Final Spatial Reference System : UTM/WGS 84/UTM zone 37S Time Zone : (UTC+03:00) Nairobi Linear Units : Meters

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Coordinate System Summary Coordinate system Name : UTM/WGS 84/UTM zone 37S Type : Projected Unit name : Meters Meters per unit : 1 Vertical datum : Ellipsoid Vertical unit :Meters Meters per unit : 1 Datum Name : WGS 84 Ellipsoid Name : WGS 84 Semi-major Axis : 6378137.000 m Inverse Flattening : 298.257223563 DX to WGS84 : 0.0000 m DY to WGS84 : 0.0000 m DY to WGS84 : 0.0000 m RX to WGS84 : -0.000000 " RY to WGS84 : -0.000000 " RZ to WGS84 : -0.000000 " ppm to WGS84 : 0.000000000000 Projection Projection Class : Transverse_Mercator latitude_of_origin 0° 00' 00.00000"N central_meridian 39° 00' 00.00000"E scale_factor 0.999600000000 false_easting 500000.000 m false_northing 10000000.000 m

Control Points 95% Name Components Error BM1 East 260541.281 0.000 North 9853584.290 0.000 Ellips height 1649.395 0.000 Description CP

Status Control Error FIXED FIXED FIXED

Logged Points Name P1

East North Ellips height Description

95% Components Error 256494.212 0.005 9858940.979 0.009 1672.701 0.015 GCP

Status Adjusted Adjusted Adjusted

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P10

East North Ellips height Description

263655.582 9860360.163 1615.205 GCP

0.128 0.080 0.111

Adjusted Adjusted Adjusted

P11

East North Ellips height Description

264457.500 9858600.812 1617.034 GCP

0.096 0.068 0.107

Adjusted Adjusted Adjusted

P2

East North Ellips height Description

256763.068 9854424.875 1679.403 GCP

0.006 0.007 0.011

Adjusted Adjusted Adjusted

P3

East North Ellips height Description

266468.391 9853736.164 1630.183 GCP

0.004 0.008 0.011

Adjusted Adjusted Adjusted

P4

East North Ellips height Description

267651.992 9860690.330 1593.655 GCP

0.110 0.079 0.144

Adjusted Adjusted Adjusted

P5

East North Ellips height Description

259540.994 9860460.512 1654.531 GCP

0.141 0.073 0.136

Adjusted Adjusted Adjusted

P6

East North Ellips height Description

261272.063 9856745.953 1640.207 GCP

0.006 0.009 0.014

Adjusted Adjusted Adjusted

P7

East North Ellips height Description

261351.614 9854766.475 1637.405 GCP

0.006 0.005 0.012

Adjusted Adjusted Adjusted

P9

East North Ellips height Description

264740.852 9856732.524 1626.993 GCP

0.005 0.007 0.011

Adjusted Adjusted Adjusted

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Files Name Start Time Sampling Epochs Size (Kb) Type GBM1XC13.082 13/03/23 14:17:40 10 1838 2155 L1/L2 GPS/WAAS GP1XXA13.082 13/03/23 18:52:00 10 32 58L1/L2 GPS/GLONASS/WAAS GP2XXB13.082 13/03/23 19:18:10 10 32 66L1/L2 GPS/GLONASS/WAAS GP3XXB13.082 13/03/23 15:32:50 10 83 142L1/L2 GPS/GLONASS/WAAS GP4XXB13.082 13/03/23 17:41:40 10 38 67L1/L2 GPS/GLONASS/WAAS GP5XXC13.082 13/03/23 18:35:10 10 34 60L1/L2 GPS/GLONASS/WAAS GP6XXA13.082 13/03/23 16:30:48 6 56 105L1/L2 GPS/GLONASS/WAAS GP7XXC13.082 13/03/23 16:12:06 6 58 113L1/L2 GPS/GLONASS/WAAS GP9XXB13.082 13/03/23 17:01:54 6 53 97L1/L2 GPS/GLONASS/WAAS GP10XB13.082 13/03/23 18:09:30 10 31 54L1/L2 GPS/GLONASS/WAAS GP11XA13.082 13/03/23 17:21:40 10 43 81L1/L2 GPS/GLONASS/WAAS

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Adjusted vectors Vector Length Vector Tau Vector Identifier Length Resid. Components Resid. BM1 - P11 6363.959 0.000 X -2286.762 0.000 13/03/23 17:21:40.00 Y 3185.292 0.000 Z 5012.445 0.000 BM1 - P2 3870.475 13/03/23 19:18:10.00 Z

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No

No

52

BM1 - P7 1433.229 13/03/23 16:12:06.00 Z

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BM1 - P1 6713.269 13/03/23 18:52:00.00 Z

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No

53