UNIVERSITY OF NAIROBI
PREPARATION OF A SUBDIVISION SCHEME PLAN USING
SATELLITE IMAGERY:
Case study: KAYATA FARM
By
Waruguru Samson Karau
F19/2550/2008
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
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ABSTRACT
The project applies remote sensing and GIS techniques to prepare an efficient and effective
subdivision scheme plan for Kayata farm. Satellite images from Google earth, are analyzed using
various softwares to come up with optimum positions for various public and social utilities and
develop a suitable land subdivision scheme plan. The physical planning act cap 286, and also the
physical planning handbook which is a subsidiary of the act were used to decide on the extents of the
riparian reserves, road reserves, area of various social utilities and their location. The main factors
considered in this task were mainly: area of the farm, existing land use, population, the climate and
terrain of the area. The population data was obtained from Kenya National Bureau of Statistics.
DEM was obtained from Oakar services.
From the results it is clear that satellite imagery can be effective in the process of land subdivision
and management. The parcels are equal and accessible from main and access roads. The public
utilities are well distributed throughout the area and existing economic activities like pineapple
growing also maintained. The land now can be more productive and well planned improvements on
land can take shape.
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ACKNOWLEDGEMENTS
I thank the Almighty God for the continued providence and strength throughout my stay in
the University. To my supervisor Mr. B.M.O., I convey my sincere gratitude for support,
guidance and directing me in this course.
The departmental teaching staff has been of great help in terms of offering their time for
consultations, guidance and offering their opinions in different aspects of my project. I
appreciate the support of the non-teaching staff also in offering technical advice, availing
software and administering refresher courses on different study areas.
Oakar services provided me with relevant data and procedures. In a special way, Silas
Afwaba whom I contacted severally and was very instrumental towards completion of the
project.
I appreciate the contribution of one Lawrence Kagwi, a third year in the department of Urban
and Regional Planning, who did a marvelous job in guiding me through the planning aspect
of the project.
Sincere gratitude to my classmates for advice and positive criticism that made the project a
great success.
Am greatly indebted to my family for great support in terms of finances, encouragements,
love and prayers all through. May God bless you.
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DEDICATION
I dedicate this report to my grandparents who have been my guardians all through.
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LIST OF ACRONYMS AND SYMBOLS
DTM- Digital Terrain Model
DEM- Digital Elevation Model
RS- remote sensing
GIS-Geographic Information System
TIFF-Tagged Interface File Format
UTM- Universal Transverse Mercator
AOI- Area of Interest
PPA- Physical Planning Act
PPH- Physical Planning Handbook
KNBS- Kenya National Bureau of Statistics
SoK- Survey of Kenya
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Table of Contents
ABSTRACT ...............................................................................................................................................2
ACKNOWLEDGEMENTS ....................................................................................................................3
DEDICATION ........................................................................................................................................4
LIST OF ACRONYMS AND SYMBOLS .............................................................................................5
LIST OF FIGURES......................................................................................................................................8
Chapter 1 INTRODUCTION .................................................................................................................9
1.1
Overview.................................................................................................................................9
1.2 Statement of the problem ..............................................................................................................9
1.3 Objectives ...................................................................................................................................10
1.4 Scope and limitation of the study ...............................................................................................10
1.5 Organization of the report ...........................................................................................................11
CHAPTER TWO : LITERATURE REVIEW ......................................................................................................12
2.1 Previous work done ....................................................................................................................12
2.3 Planning ......................................................................................................................................13
2.4 Remote sensing ..........................................................................................................................14
2.4.1 Remote Sensing software ....................................................................................................15
2.5 Satellite imagery .........................................................................................................................16
Google earth ....................................................................................................................................17
Uses ..................................................................................................................................................18
Imagery and coordination ..........................................................................................................19
Hardware and software ..............................................................................................................19
2.5.1 Uses of satellite imagery ......................................................................................................21
2.5.2 Resolution and data .............................................................................................................22
2.5.3 GeoEye .................................................................................................................................22
2.5.4 Digital Globe ........................................................................................................................23
2.5.5 Spot Image ...........................................................................................................................23
2.5.6 Rapid Eye .............................................................................................................................23
2.5.7 ImageSat International ........................................................................................................23
2.5.8 Meteosat..............................................................................................................................24
2.5.9 Disadvantages ......................................................................................................................24
2.6 Geographic information system ..................................................................................................25
2.6.1 GIS techniques and technology ...........................................................................................26
2.6.2 Relating information from different sources .......................................................................26
2.6.3 GIS uncertainties ..................................................................................................................26
2.6.4 Projections coordinate systems, and registration ...............................................................27
2.6.5 Data representation.............................................................................................................27
2.6.6 Data capture ........................................................................................................................27
2.6.8 Web mapping.......................................................................................................................29
2.8 Study Area...................................................................................................................................29
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2.8.1 Physical location ..................................................................................................................29
2.8.2 Climatic condition ................................................................................................................31
2.8.3 Population............................................................................................................................31
CHAPTER THREE 3. MATERIALS AND METHODOLOGY..............................................................................33
3.1 Executive summary .....................................................................................................................33
3.2 Material and Methods ................................................................................................................33
3.3 Overview of the Methodology....................................................................................................34
3.3.1 Data Preparation..................................................................................................................34
3.3.2. Data Manipulation ..............................................................................................................36
3.3.3 Land subdivision planning....................................................................................................41
CHAPTER 4 RESULTS AND ANALYSIS ........................................................................................................45
4.1 Coordinate conversion................................................................................................................45
4.2 Cropping of the image. ...............................................................................................................45
4.3 DEM generation ..........................................................................................................................46
4.4 Land use map ..............................................................................................................................46
4.5 Buffer zones ................................................................................................................................47
4.6 Location of the various public utilities........................................................................................48
4.7 Parcel subdivision .......................................................................................................................50
4.8 Subdivision scheme plan.............................................................................................................50
4.9 Land registration database .........................................................................................................51
CHAPTER FIVE Conclusion and Recommendations ..................................................................................52
5.2 Recommendations ......................................................................................................................53
CITED LITERATURE ................................................................................................................................54
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LIST OF FIGURES
FIGURE 2.1: Property planning example...............................................................................................13
FIGURE 2.2: A satellite .........................................................................................................................16
FIGURE 2.3: First television image of the earth ...................................................................................17
FIGURE 2.4: The Globe .........................................................................................................................21
FIG 2.5: Composite image of Kansas, USA ............................................................................................21
FIGURE 2.6: Composite image of the earth at night ............................................................................25
FIGURE 2.7: Data capture ....................................................................................................................27
FIGURE 2.9: Machakos county .............................................................................................................31
FIGURE 2.10: Kayata farm ....................................................................................................................31
FIGURE 3.1: Google earth ....................................................................................................................35
FIGURE 3.2: Elshayal smartweb online software .................................................................................37
FIGURE 3.3: Global mapper .................................................................................................................37
FIGURE 3.4: Coordinate converter.......................................................................................................39
FIGURE 3.5: Parcel division tool ..........................................................................................................44
FIGURE 4.1: Image of area of interest .................................................................................................45
FIGURE 4.2: DEM OF AOI .....................................................................................................................46
FIGURE 4.3: Land use map ...................................................................................................................47
FIGURE 4.4 Buffer zones ......................................................................................................................48
FIGURE 4.5: Public utilities map ..........................................................................................................49
FIGURE 4.6: Subdivision scheme plan..................................................................................................50
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Chapter 1 INTRODUCTION
1.1 Overview
Land is a piece of the earth surface of definite extent including the resources above and
beneath it. It is the main factor of production. Land includes the natural resources found
above or beneath the soil cover which man finds around him and which can be used for
productive use. This resource is fixed in supply i.e. cannot be increased by human effort.
Land is a gift of nature and according to a Nigerian saying “land belongs to a vast multitude
of which many are dead, few are living and countless numbers are not yet born” therefore
land does not belong to anyone, we are just custodians and after sometime the rights are
transferred to someone else. Land is the basic source of wealth and furnishes natural products
which men cannot create. From the creation of universe land has been regarded as the most
valuable asset and form of property.
In earlier forms of land tenure, land belonged to the king, who distributed it among the
people loyal to him. The flow went down to the peasant who owed services to his/her
landlord but nobody owed him any service.
Communism, capitalism, socialism are all system of governance where land rights were not
transferred to individuals but belonged to the crown, community or the local leadership. Due
to population increase and advancement in level of civilization, man consolidated land and
land tenure changed to more effective and efficient way land subdivision plans and
registration systems.
To avoid conflict from the dramatically growing population land matters which are very
emotive especially in Kenya require transparency and accountability. It is the issue of land
that in a great extent contributed to the post-election violence in 2007/2008 elections. The
current greed over land denies the future generation a fair opportunity to own land. Planning
a land subdivision scheme plan is a very important step towards ensuring a fair and open
system of land conveyancing.
The issue of land, its ownership, use and management is a highly emotive one in Kenya and
was one of the key issues that drove the need for a new constitution. Following lengthy
deliberations and a comprehensive public participation process, a new constitution (the
"Constitution") was promulgated on 27 August 2010. The Constitution sets out principles
governing land and also requires all laws relating to land to be revised, consolidated and
rationalized within certain timelines.
1.2 Statement of the problem
Land issues in Kenya have been a source of conflict between communities, families,
individuals and organizations form the time of scramble and partition of Africa. Especially
where the land problem has been politicized and approached with the rule of the jungle i.e.
survival for the fittest.
Also lack of proper planning during subdivision of real property has led to inefficient use of
land resources leading to realization of its maximum potential. Losses like the demolition of
structures in Syokimau will always be incurred due to lack of proper planning and ignorance
of the set planning standards.
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This calls for the need to develop an effective subdivision scheme plan that is fair, corruption
free, and one that conforms to the set planning standards to increase productivity of land.
Such a plan ensures social stability other community and the nation at large.
The use of high resolution satellite imagery together with strict application of appropriate
planning standards ensures that there is equal subdivision, proper planning and identification
of appropriate locations of public utilities. This in turn ensures the maximum utilization of
land.
Through satellite images the boundaries are accurately identified by use of coordinates.
Satellite images also reduce chances of encroachment or intentional shift of boundary in the
interest of specific individuals because it contains evidence of all natural features,
monuments and abuttals. The most recent images are used which are usually updated and
shows the most recent and real state of land. The manipulation and processing of a satellite
imagery and application of planning act results to a line map showing equally subdivided
piece of land, access roads, easements and public utilities in their appropriate locations. Road
and riparian reserves are also catered for.
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1.3 Objectives
The main objective of the project is to use satellite images in subdivision of land and
effective and efficient planning of land development and land improvements. Land is a real
property and is fixed in supply
Its specific objectives are:

Analyze the various factors that are key to subdivision of real property.

Identification of appropriate locations and size of various public utilities using various
criteria and as per provisions of the planning act.

Produce a line map showing 2ha parcels that are easily accessible.

Develop a database for subsequent registration of the subdivided parcels
1.4 Scope and limitation of the study
The study is limited to Kayata farm in Kenya, Eastern province, Machakos County,
Matungulu constituency, Matungulu North Ward. The farm is chosen because it’s a vast land
with defined boundaries and belongs to a farmers society. The society may have future plans
to subdivide the land among its members. The study is expected to come up with appropriate
scheme plan with proposed locations for various public buildings and 2ha parcels which
result from the preparation of a subdivision scheme plan of the farm.
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1.5 Organization of the report
The report is organized in 5 chapters. In chapter one the introduction to the study area, the
background information, the problem statement, the main and specific objectives, scope and
limitations of the study and report organization
Chapter two’s scope covers the relevant literature review, the use of satellite imagery,
reasons for land subdivision and the necessity of planning in any land subdivision and land
improvement. Also the methodology used and boundaries of the study area are included.
Chapter three consists of the details of the methodology which includes equipment, sources
of data data preparation and the implementation of the methodology. The digitization of the
land already in use, generation of DEM and contours to show the terrain of the land and final
subdivision are covered in this chapter.
Chapter four presents the project results and analysis. The line map showing the equally
subdivided piece of land is the ultimate result of the project. The subdivision is well planned
showing the roads reserves and proposed locations for various public utilities. The results are
also discussed in this chapter.
Chapter five provides the conclusions and recommendations for future work in the same field
of study.
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CHAPTER TWO : LITERATURE REVIEW
2.1 Previous work done
Land subdivision has been carried out everywhere in the world. This is due to the increase in
the world population and hence high demand for land for settlement and economic activities,
e.g. The Mau forest evictees were resettled on a 400acre land which was subdivided among
160 families. However land subdivision using satellite imagery is not common mostly
because the level of resolution required cannot be achieved by many of the sensors. There is
no previous project done on land subdivision using satellite imagery.
2.2 Land subdivision
Subdividing land is either dividing an allotment of land into two or more separate
allotments or adjusting a boundary between neighbouring allotments. The new allotments
will have their own individual certificates of title. Subdivisions may also be for the purpose
of commercial or industrial development, and the results vary from retail shopping malls
with independently owned out parcels, to industrial parks.
The process of subdividing land can be time consuming, complex and expensive. You can
choose to submit the application yourself but as this is a complex process it is strongly
recommended you get a professional licensed surveyor or conveyancer to:
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determine the planning and development requirements
draft plans
lodge all applications and necessary paperwork
monitor the progress of the application
If you are lodging an application to create five or more allotments, your plan’s accuracy must
be vouched for by a licensed surveyor.
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All applications must be prepared according to the Plan Presentation Guidelines.
FIGURE 2.1: Property planning example
2.3 Planning
Planning (also called forethought) is the process of thinking about and organizing the
activities required to achieve a desired goal.
Planning involves the creation and maintenance of a plan. As such, planning is a fundamental
property of intelligent behavior. This thought process is essential to the creation and
refinement of a plan, or integration of it with other plans; that is, it combines forecasting of
developments with the preparation of scenarios of how to react to them.
An important, albeit often ignored aspect of planning, is the relationship it holds with
forecasting. Forecasting can be described as predicting what the future will look like,
whereas planning predicts what the future should look like. The counterpart to planning is
spontaneous order.
In organizations, planning is a management process, concerned with defining goals for
company's future direction and determining on the missions and resources to achieve those
targets. To meet the goals, managers may develop plans such as a business plan or a
marketing plan. Planning always has a purpose. The purpose may be achievement of certain
goals or targets.
Main characteristics of planning in organizations are:
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Planning increases the efficiency of an organization.
It reduces the risks involved in modern business activities.
It facilitates proper coordination within an organization.
It aids in organizing all available resources.
It gives right direction to the organization.
It is important to maintain a good control.
It helps to achieve objectives of the organization.
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It motivates the personnel of an organization.
It encourages managers' creativity and innovation.
It also helps in decision making.
Public policy planning includes environmental, land use, regional, urban and spatial planning. In
many countries, the operation of a town and country planning system is often referred to as
"planning" and the professionals which operate the system are known as "planners".
2.4 Remote sensing
Remote sensing is the acquisition of information about an object or phenomenon without
making physical contact with the object. In modern usage, the term generally refers to the use
of aerial sensor technologies to detect and classify objects on Earth (both on the surface, and
in the atmosphere and oceans) by means of propagated signals (e.g. electromagnetic radiation
emitted from aircraft or satellites) (Schott, John Robert (2007). Remote sensing: The image
chains approach (2nd ed.). Oxford University Press. p. 1. ISBN 978-0-19-517817-3)
Object-Based Image Analysis (OBIA) is a sub-discipline of GIScience devoted to
partitioning remote sensing (RS) imagery into meaningful image-objects, and assessing their
characteristics through spatial, spectral and temporal scale.
Old data from remote sensing is often valuable because it may provide the only long-term
data for a large extent of geography. At the same time, the data is often complex to interpret,
and bulky to store. Modern systems tend to store the data digitally, often with lossless
compression. The difficulty with this approach is that the data is fragile, the format may be
archaic, and the data may be easy to falsify. One of the best systems for archiving data series
is as computer-generated machine-readable ultrafiche, usually in typefonts such as OCR-B,
or as digitized half-tone images. Ultrafiches survive well in standard libraries, with lifetimes
of several centuries. They can be created, copied, filed and retrieved by automated systems.
They are about as compact as archival magnetic media, and yet can be read by human beings
with minimal, standardized equipment.
The quality of remote sensing data consists of its spatial, spectral, radiometric and temporal
resolutions.
Spatial resolution
The size of a pixel that is recorded in a raster image – typically pixels may correspond to
square areas ranging in side length from 1 to 1,000 metres (3.3 to 3,300 ft).
Spectral resolution
The wavelength width of the different frequency bands recorded – usually, this is related to
the number of frequency bands recorded by the platform. Current Landsat collection is that
of seven bands, including several in the infra-red spectrum, ranging from a spectral
resolution of 0.07 to 2.1 μm. The Hyperion sensor on Earth Observing-1 resolves 220 bands
from 0.4 to 2.5 μm, with a spectral resolution of 0.10 to 0.11 μm per band.
Radiometric resolution
The number of different intensities of radiation the sensor is able to distinguish. Typically,
this ranges from 8 to 14 bits, corresponding to 256 levels of the gray scale and up to 16,384
intensities or "shades" of colour, in each band. It also depends on the instrument noise.
Temporal resolution
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The frequency of flyovers by the satellite or plane, and is only relevant in time-series studies
or those requiring an averaged or mosaic image as in deforesting monitoring. This was first
used by the intelligence community where repeated coverage revealed changes in
infrastructure, the deployment of units or the modification/introduction of equipment. Cloud
cover over a given area or object makes it necessary to repeat the collection of said location.
In addition, images may need to be radiometrically and atmospherically corrected.
Radiometric correction
Gives a scale to the pixel values, e. g. the monochromatic scale of 0 to 255 will be converted
to actual radiance values.
Topographic correction
In the rugged mountains, as a result of terrain, each pixel which receives the effective
illumination varies considerably different. In remote sensing image, the pixel on the shady
slope receives weak illumination and has a low radiance value; in contrast, the pixel on the
sunny slope receives strong illumination and has a high radiance value. For the same objects,
the pixel radiance values on the shady slope must be very different from that on the sunny
slope. Different objects may have the similar radiance values. This spectral information
changes seriously affected remote sensing image information extraction accuracy in the
mountainous area. It became the main obstacle to further application on remote sensing
images. The purpose of topographic correction is to eliminate this effect, recovery true
reflectivity or radiance of objects in horizontal conditions. It is the premise of quantitative
remote sensing application.
Atmospheric correction
Eliminates atmospheric haze by rescaling each frequency band so that its minimum value
(usually realized in water bodies) corresponds to a pixel value of 0. The digitizing of data
also make possible to manipulate the data by changing gray-scale values.
2.4.1 Remote Sensing software
Remote Sensing data is processed and analyzed with computer software, known as a remote
sensing application. A large number of proprietary and open source applications exist to
process remote sensing data. Remote Sensing Software packages include:
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TNTmips from Micro Images,
PCI Geomatica made by PCI Geomatics, the leading remote sensing software
package in Canada,
IDRISI from Clark Labs,
Image Analyst from Intergraph,
Remote View made by Overwatch Textron Systems.
Dragon/ips is one of the oldest remote sensing packages still available, and is in some
cases free.
Open source remote sensing software includes:
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OSSIM,
Opticks (software),
Orfeo toolbox
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Others mixing remote sensing and GIS capabilities are: GRASS GIS, ILWIS, and QGIS
2.5 Satellite imagery
Satellite imagery consists of photographs of Earth or other planets made by means of
artificial satellites.
FIGURE 2.2: A satellite
The satellite images were made from pixels. The first crude image taken by the satellite
Explorer 6 shows a sunlit area of the Central Pacific Ocean and its cloud cover. The photo
was taken when the satellite was about 17,000 mi (27,000 km) above the surface of the earth
on August 14, 1959. At the time, the satellite was crossing Mexico.
The first images from space were taken on sub-orbital flights. The U.S-launched V-2 flight
on October 24, 1946 took one image every 1.5 seconds. With an apogee of 65 miles
(105 km), these photos were from five times higher than the previous record, the 13.7 miles
(22 km) by the Explorer II balloon mission in 1935. The first satellite (orbital) photographs
of Earth were made on August 14, 1959 by the U.S. Explorer 6. The first satellite
photographs of the Moon might have been made on October 6, 1959 by the Soviet satellite
Luna 3, on a mission to photograph the far side of the Moon. The Blue Marble photograph
was taken from space in 1972, and has become very popular in the media and among the
public. Also in 1972 the United States started the Landsat program, the largest program for
acquisition of imagery of Earth from space. Landsat Data Continuity Mission, the most
recent Landsat satellite, was launched on 11 February 2013. In 1977, the first real time
satellite imagery was acquired by the USA's KH-11 satellite system.
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FIGURE 2.3: First television image of the earth
The first television image of Earth from space transmitted by the TIROS-1 weather satellite
in 1960.
All satellite images produced by NASA are published by Earth Observatory and are freely
available to the public. Several other countries have satellite imaging programs, and a
collaborative European effort launched the ERS and Envisat satellites carrying various
sensors. There are also private companies that provide commercial satellite imagery. In the
early 21st century satellite imagery became widely available when affordable, easy to use
software with access to satellite imagery databases was offered by several companies and
organizations.
Google earth
Google Earth is a virtual globe, map and geographical information program that was
originally called EarthViewer 3D, and was created by Keyhole, Inc, a Central Intelligence
Agency (CIA) funded company acquired by Google in 2004 (see In-Q-Tel). It maps the Earth
by the superimposition of images obtained from satellite imagery, aerial photography and
GIS 3D globe. It was available under three different licenses, two currently: Google Earth, a
free version with limited function; Google Earth Plus (discontinued), which included
additional features; and Google Earth Pro ($399 per year), which is intended for commercial
use.
The product, re-released as Google Earth in 2005, is currently available for use on personal
computers running Windows 2000 and above, Mac OS X 10.3.9 and above, Linux kernel:
2.6 or later (released on June 12, 2006), and FreeBSD. Google Earth is also available as a
browser plugin which was released on May 28, 2008.[6] It was also made available for mobile
viewers on the iPhone OS on October 28, 2008, as a free download from the App Store, and
is available to Android users as a free app in the Google Play store. In addition to releasing
an updated Keyhole based client, Google also added the imagery from the Earth database to
their web-based mapping software, Google Maps. The release of Google Earth in June 2005
to the public caused a more than tenfold increase in media coverage on virtual globes
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between 2004 and 2005, driving public interest in geospatial technologies and applications.
As of October 2011, Google Earth has been downloaded more than a billion times.
For other parts of the surface of the Earth, 3D images of terrain and buildings are available.
Google Earth uses digital elevation model (DEM) data collected by NASA's Shuttle Radar
Topography Mission (SRTM). This means one can view the whole earth in three dimensions.
Since November 2006, the 3D views of many mountains, including Mount Everest, have
been improved by the use of supplementary DEM data to fill the gaps in SRTM coverage.
Many people use the applications to add their own data, making them available through
various sources, such as the Bulletin Board Systems (BBS) or blogs mentioned in the link
section below. Google Earth is able to show all kinds of images overlaid on the surface of the
earth and is also a Web Map Service client. Google Earth supports managing threedimensional Geospatial data through Keyhole Markup Language (KML).
Uses
Google Earth is useful for many day-to-day and other purposes.
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Google Earth can be used to view areas subjected to widespread disasters if Google
supplies up-to-date images. For example, after the January 12, 2010 Haiti earthquake
images of Haiti were made available on January 17.
With Google's push for the inclusion of Google Earth in the Classroom, teachers are
adopting Google Earth in the classroom for lesson planning, such as teaching students
geographical themes (location, culture, characteristics, human interaction, and
movement) to creating mashups with other web applications such as Wikipedia.One
can explore and place location bookmarks on the Moon, and Mars.
One can also get directions using Google Earth, using variables such as street names,
cities, and establishments. But the addresses must by typed in search field, one can't
simply click on two spots on the map.
Google Earth can also function as a hub of knowledge, pertaining the users location.
By enabling certain options, one can see the location of gas stations, restaurants,
museums, and other public establishments in their area. Google Earth can also dot the
map with links to images, YouTube videos, and Wikipedia articles relevant to the
area being viewed.
One can create custom image overlays for planning trips, hikes on handheld GPS
units
Google Earth can be used to map homes and select a random sample for research in
developing countries.
All of these features are also released by Google Earth Blog.
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Imagery and coordination
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Coordinate System and Projection
o The internal coordinate system of Google Earth is geographic coordinates
(latitude/longitude) on the World Geodetic System of 1984 (WGS84) datum.
o Google Earth shows the earth as it looks from an elevated platform such as an
airplane or orbiting satellite. The projection used to achieve this effect is
called the General Perspective. This is similar to the Orthographic projection,
except that the point of perspective is a finite (near earth) distance rather than
an infinite (deep space) distance.
Baseline resolutions
o Czech Republic: 0.1 – 0.5 m (by Eurosense / Geodis Brno)
o Slovakia: 0.5 m (by Eurosense / Geodis Slovakia)
o Hungary: 2.5 m SPOT Images. Budapest approx. 0.3 m.
o Germany, Switzerland, Netherlands, Denmark, UK, Andorra, Luxembourg,
Liechtenstein, San Marino, Vatican City: 1 m or better
o Balkans: 2.5 m (medium resolution)
o U.S.: 1 m (excludes Alaska & Hawaii)
o Global: Generally 15 m (some areas, such as Antarctica, are in extremely low
resolution), but this depends on the quality of the satellite/aerial photograph
uploaded.
Typical high resolutions
o Europe: 0.3 m, 0.15 m (e.g. Berlin, Zurich, Hamburg), 0.1 m Prague
o U.S.: 1 m, 0.6 m, 0.3 m, 0.15 m (extremely rare; e.g. Cambridge and Google
Campus, or Glendale)
Altitude resolution:
o Surface: varies by country
o Seabed: Not previously applicable, but since the introduction of "Ocean",
elevation data has been introduced (a colorscale approximating sea floor depth
is "printed" on the spherical surface at views from high altitudes).
Age: Images dates vary. The image data can be seen from squares made when
DigitalGlobe Coverage is enabled. The date next to the copyright information is not
the correct image date. Zooming in or out could change the date of the pictures. Most
of the international urban image dates are from 2004 and have not been updated.
However, most US images are kept current. Google announces imagery updates on
their LatLong Blog in form of a quiz, with hints of the updated locations. The
answers are posted some days later in the same blog.
Hardware and software
Google Earth is unlikely to operate on older hardware configurations. The most recent
system requirements update document these minimum configurations:
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Pentium 3, 500 MHz
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256 Megabytes of RAM
400 MB free disk space
Network speed: 128 kbit/s
64 MB DirectX9 and 3D capable graphics card
Resolution of 1024x768, 16-bit High Color – DirectX 9 (to run in Direct X mode)
Windows 2000, Windows XP, Windows Vista, Windows 7, Linux or OS X
The most likely cause of failure is insufficient video RAM: the software is designed to warn
the user if their graphics card is not able to support Earth (this often occurs due to insufficient
Video RAM or buggy graphics card drivers). The next most likely mode of failure is Internet
access speed. Fast broadband Internet (Cable, DSL, T1, etc.) is required.
Linux specifications
Minimum system requirements







Kernel: 2.4 or later
CPU: Pentium III, 500 MHz
System Memory (RAM): 128 MB
Hard Disk: 400 MB free space
Network Speed: 128 kbit/s
Screen: 1024x768, 16 bit color
Tested and works on the following distributions:















Ubuntu 5.10 or later
SUSE 10.1 or later
Fedora Core 4 or later
Linspire 5.1
Gentoo 2006.0
Debian 3.1/4
Red Hat 9
Slackware 11.0
FreeBSD 6.1/7.0 with Linux Emulation
Arch Linux 0.7.2 Duke
Xandros 3.0.3 Business Edition
Mandriva 2007
Sabayon Linux 3.26
PCLinuxOS 5.0
PC/OS 7.10
20
2.5.1 Uses of satellite imagery
FIGURE 2.4: The Globe
Satellite photography can be used to produce composite images of an entire hemisphere...
FIG 2.5: Composite image of Kansas, USA
...or to map a small area of the Earth, such as this photo of the countryside of Haskell
County, Kansas, United States.
Satellite images have many applications in meteorology, agriculture, geology, forestry,
landscape, biodiversity conservation, regional planning, education, intelligence and warfare.
Images can be in visible colours and in other spectra. There are also elevation maps, usually
made by radar images. Interpretation and analysis of satellite imagery is conducted using
specialized remote sensing applications. Some of the first image enhancement of satellite
photos was conducted by the U.S. Government and its contractors. For example ESL
Incorporated developed some of the earliest two dimensional Fourier transforms applied to
digital image processing to address NASA photos as well as national security applications.
Satellite imagery is also used in seismology and oceanography in deducing changes to land
21
formation, water depth and sea bed, by color caused by earthquakes, volcanoes, and
tsunamis.
2.5.2 Resolution and data
There are four types of resolution when discussing satellite imagery in remote sensing:
spatial, spectral, temporal, and radiometric. Campbell (2002) defines these as follows: spatial resolution is defined as the pixel size of an image representing the size of the surface
area (i.e. m2) being measured on the ground, determined by the sensors' instantaneous field
of view (IFOV); - spectral resolution is defined by the wavelength interval size (discreet
segment of the Electromagnetic Spectrum) and number intervals that the sensor is measuring;
temporal resolution is defined by the amount of time (e.g. days) that passes between imagery
collection periods for a given surface location; and radiometric resolution is defined as the
ability of an imaging system to record many levels of brightness (contrast for example). Radiometric resolution refers to the effective bit-depth of the sensor (number of grayscale
levels) and is typically expressed as 8-bit (0-255), 11-bit (0-2047), 12-bit (0-4095) or 16-bit
(0-65,535). - Geometric resolution refers to the satellite sensor's ability to effectively image a
portion of the Earth's surface in a single pixel and is typically expressed in terms of Ground
Sample Distance, or GSD. GSD is a term containing the overall optical and systemic noise
sources and is useful for comparing how well one sensor can "see" an object on the ground
within a single pixel. For example, the GSD of Landsat is ~30m, which means the smallest
unit that maps to a single pixel within an image is ~30m x 30m. The latest commercial
satellite (GeoEye 1) has a GSD of 0.41 m (effectively 0.5 m due to United States
Government restrictions on civilian imaging).
The resolution of satellite images varies depending on the instrument used and the altitude of
the satellite's orbit. For example, the Landsat archive offers repeated imagery at 30 meter
resolution for the planet, but most of it has not been processed from the raw data. Landsat 7
has an average return period of 16 days. For many smaller areas, images with resolution as
high as 41 cm can be available. Satellite imagery is sometimes supplemented with aerial
photography, which has higher resolution, but is more expensive per square meter. Satellite
imagery can be combined with vector or raster data in a GIS provided that the imagery has
been spatially rectified so that it will properly align with other data sets.
2.5.3 GeoEye
GeoEye's GeoEye-1 satellite was launched September 6, 2008.The GeoEye-1 satellite has the
highest resolution of any commercial imaging system and is able to collect images with a
ground resolution of 0.41 meters (16 inches) in the panchromatic or black and white mode. It
collects multispectral or color imagery at 1.65-meter resolution or about 64 inches, a factor
of two better than existing commercial satellites with four-band multistage imaging
capabilities. While the satellite is able to collect imagery at 0.41 meters, GeoEye's operating
license from the U.S. Government requires re-sampling the imagery to 0.5 meters for all
customers not explicitly granted a waiver by the U.S. Government.
22
2.5.4 Digital Globe
Digital Globe's WorldView-2 satellite provides high resolution commercial satellite imagery
with 0.46 m spatial resolution (panchromatic only). The 0.46 meters resolution of
WorldView-2's panchromatic images allows the satellite to distinguish between objects on
the ground that are at least 46 cm apart. Similarly Digital Globe's Quick Bird satellite
provides 0.6 meter resolution (at NADIR) panchromatic images.
2.5.5 Spot Image
The 3 SPOT satellites in orbit (Spot 2, 4 and 5) provide images with a large choice of
resolutions – from 2.5 m to 1 km. Spot Image also distributes multiresolution data from other
optical satellites, in particular from Formosat-2 (Taiwan) and Kompsat-2 (South Korea) and
from radar satellites (TerraSar-X, ERS, Envisat, Radarsat). Spot Image will also be the
exclusive distributor of data from the forthcoming very-high resolution Pleiades satellites
with a resolution of 0.50 meter or about 20 inches. The first launch is planned for the end of
2011. The company also offers infrastructures for receiving and processing, as well as added
value options.
2.5.6 Rapid Eye
RapidEye's constellation of five satellites, launched in August 2008, contain identical
multispectral sensors which are equally calibrated. Therefore, an image from one satellite
will be equivalent to an image from any of the other four, allowing for a large amount of
imagery to be collected (4 million km² per day), and daily revisit to an area. Each travel on
the same orbital plane at 630 km, and deliver images in 5 meter pixel size. RapidEye satellite
imagery is especially suited for agricultural, environmental, cartographic and disaster
management applications. The company not only offers their imagery, but consults with their
customers to create services and solutions based on analysis of this imagery.
2.5.7 ImageSat International
Earth Resource Observation Satellites, better known as “EROS” satellites, are lightweight,
low earth orbiting, high-resolution satellites designed for fast maneuvering between imaging
targets. In the commercial high-resolution satellite market, EROS is the smallest very high
resolution satellite; it is very agile and thus enables very high performances. The satellites are
deployed in a circular sun-synchronous near polar orbit at an altitude of 510 km (+/- 40 km).
EROS satellites imagery applications are primarily for intelligence, homeland security and
national development purposes but also employed in a wide-range of civilian applications,
including: mapping, border control, infrastructure planning, agricultural monitoring,
environmental monitoring, disaster response, training and simulations, etc.
EROS A – a high resolution satellite with 1.9-1.2M resolution panchromatic was launched
on December 5, 2000.
23
EROS B - the second-generation of Very High Resolution satellites with 70 cm resolution
panchromatic, was launched on April 25, 2006.
2.5.8 Meteosat
The Meteosat-2 geostationary weather satellite began operationally to supply imager data on
16 August 1981. Eumetsat operates the Meteosats since 1987.
- The Meteosat visible and infrared imager (MVIRI), three channels imager: visible, infrared
and water vapour; It operates on the first generation Meteosat, Meteosat-7 being still active.
– The 12-channel Spinning Enhanced Visible and Infrared Imager (SEVIRI) includes similar
channels to those used by MVIRI, providing continuity in climate data over three decades;
Meteosat Second Generation (MSG).
- The Flexible Combined Imager (FCI) on Meteosat Third Generation (MTG) will also
include similar channels, meaning that all three generations will have provided over 60 years
of climate data.
2.5.9 Disadvantages
Because the total area of the land on Earth is so large and because resolution is relatively
high, satellite databases are huge and image processing (creating useful images from the raw
data) is time-consuming. Depending on the sensor used, weather conditions can affect image
quality: for example, it is difficult to obtain images for areas of frequent cloud cover such as
mountain-tops.
Commercial satellite companies do not place their imagery into the public domain and do not
sell their imagery; instead, one must be licensed to use their imagery. Thus, the ability to
legally make derivative products from commercial satellite imagery is minimized.
2.5.10 Moving images
In 2005 the Australian company Astrovision (ASX: HZG) announced plans to launch the
first commercial geostationary satellite in the Asia-Pacific. It is intended to provide true
color, real-time live satellite feeds, with down to 250 metres resolution over the entire AsiaPacific region, from India to Hawaii and Japan to Australia. They were going to provide this
content to users of 3G mobile phones, over Pay TV as a weather channel, and to corporate
and government users.
Unfortunately, the market response to the AstroVision concept fell into the classic chickenegg problem: potential customers were excited by the possibilities offered, but they were
unwilling (or, in government cases, generally unable) to sign contracts for a service that
would not be delivered for 3–4 years (the length of time required to build and launch the
satellite). AstroVision ran low on funds and was forced to shut down the program in 2006.
24
FIGURE 2.6: Composite image of the earth at night
Composite image of Earth at night, as only half of Earth is at night at a given moment.
2.6 Geographic information system
A geographic information system (GIS) is a system designed to capture, store, manipulate,
analyze, manage, and present all types of geographical data. The acronym GIS is sometimes
used for geographical information science or geospatial information studies to refer to the
academic discipline or career of working with geographic information systems. In the
simplest terms, GIS is the merging of cartography, statistical analysis, and database
technology.
A GIS can be thought of as a system—it digitally creates and "manipulates" spatial areas that
may be jurisdictional, purpose, or application-oriented. Generally, a GIS is custom-designed
for an organization. Hence, a GIS developed for an application, jurisdiction, enterprise, or
purpose may not be necessarily interoperable or compatible with a GIS that has been
developed for some other application, jurisdiction, enterprise, or purpose. What goes beyond
a GIS is a spatial data infrastructure, a concept that has no such restrictive boundaries.
In a general sense, the term describes any information system that integrates, stores, edits,
analyzes, shares, and displays geographic information for informing decision making. GIS
applications are tools that allow users to create interactive queries (user-created searches),
analyze spatial information, edit data in maps, and present the results of all these operations.
(Stork, E.J., Sakamoto, S.O., and Cowan, R.M. (1999) "The integration of science
explorations through the use of earth images in middle school curriculum", Proc. IEEE
Trans. Geosci. Remote Sensing 37, 1801–1817) Geographic information science is the
science underlying geographic concepts, applications, and systems.
GIS is a relatively broad term that can refer to a number of different technologies, processes,
and methods. It is attached to many operations and has many applications related to
engineering, planning, management, transport/logistics, insurance, telecommunications, and
business. For that reason, GIS and location intelligence applications can be the foundation for
many location-enabled services that rely on analysis, visualization and dissemination of
results for collaborative decision making.
25
2.6.1 GIS techniques and technology
Modern GIS technologies use digital information, for which various digitized data creation
methods are used. The most common method of data creation is digitization, where a hard
copy map or survey plan is transferred into a digital medium through the use of a CAD
program, and geo-referencing capabilities. With the wide availability of ortho-rectified
imagery (both from satellite and aerial sources), heads-up digitizing is becoming the main
avenue through which geographic data is extracted. Heads-up digitizing involves the tracing
of geographic data directly on top of the aerial imagery instead of by the tradition
2.6.2 Relating information from different sources
GIS uses spatio-temporal (space-time) location as the key index variable for all other
information. Just as a relational database containing text or numbers can relate many
different tables using common key index variables, GIS can relate unrelated information by
using location as the key index variable. The key is the location and/or extent in space-time.
Any variable that can be located spatially, and increasingly also temporally, can be
referenced using a GIS. Locations or extents in Earth space–time may be recorded as
dates/times of occurrence, and x, y, and z coordinates representing, longitude, latitude, and
elevation, respectively. These GIS coordinates may represent other quantified systems of
temporal-spatial reference (for example, film frame number, stream gage station, highway
mile-marker, surveyor benchmark, building address, street intersection, entrance gate, water
depth sounding, POS or CAD drawing origin/units). Units applied to recorded temporalspatial data can vary widely (even when using exactly the same data, see map projections),
but all Earth-based spatial–temporal location and extent references should, ideally, be
relatable to one another and ultimately to a "real" physical location or extent in space–time.
Related by accurate spatial information, an incredible variety of real-world and projected past
or future data can be analyzed, interpreted and represented to facilitate education and
decision making.This key characteristic of GIS has begun to open new avenues of scientific
inquiry into behaviors and patterns of previously considered unrelated real-world
information.
2.6.3 GIS uncertainties
GIS accuracy depends upon source data, and how it is encoded to be data referenced. Land
surveyors have been able to provide a high level of positional accuracy utilizing the GPSderived positions. High-resolution digital terrain and aerial imagery, powerful computers and
Web technology are changing the quality, utility, and expectations of GIS to serve society on
a grand scale, but nevertheless there are other source data that have an impact on overall GIS
accuracy like paper maps, though these may be of limited use in achieving the desired
accuracy since the aging of maps affects their dimensional stability.
In developing a digital topographic data base for a GIS, topographical maps are the main
source of Aerial photography and satellite images are extra sources for collecting data and
identifying attributes which can be mapped in layers over a location facsimile of scale. The
scale of a map and geographical rendering area representation type are very important
aspects since the information content depends mainly on the scale set and resulting
locatability of the map's representations. In order to digitize a map, the map has to be
26
checked within theoretical dimensions, then scanned into a raster format, and resulting raster
data has to be given a theoretical dimension by a rubber sheeting/warping technology
process.
A quantitative analysis of maps brings accuracy issues into focus. The electronic and other
equipment used to make measurements for GIS is far more precise than the machines of
conventional map analysis. All geographical data are inherently inaccurate, and these
inaccuracies will propagate through GIS operations in ways that are difficult to predict.
2.6.4 Projections coordinate systems, and registration
The earth can be represented by various models, each of which may provide a different set of
coordinates (e.g., latitude, longitude, elevation) for any given point on the Earth's surface.
The simplest model is to assume the earth is a perfect sphere. As more measurements of the
earth have accumulated, the models of the earth have become more sophisticated and more
accurate. In fact, there are models called datums that apply to different areas of the earth to
provide increased accuracy, like NAD83 for U.S. measurements, and the World Geodetic
System for worldwide measurements.
2.6.5 Data representation
GIS data represents real objects (such as roads, land use, elevation, trees, waterways, etc.)
with digital data determining the mix. Real objects can be divided into two abstractions:
discrete objects (e.g., a house) and continuous fields (such as rainfall amount, or elevations).
Traditionally, there are two broad methods used to store data in a GIS for both kinds of
abstractions mapping references: raster images and vector. Points, lines, and polygons are the
stuff of mapped location attribute references. A new hybrid method of storing data is that of
identifying point clouds, which combine three-dimensional points with RGB information at
each point, returning a "3D color image". GIS thematic maps then are becoming more and
more realistically visually descriptive of what they set out to show or determine.
2.6.6 Data capture
FIGURE 2.7: Data capture
27
Example of hardware for mapping (GPS and laser rangefinder) and data collection (rugged
computer). The current trend for geographical information system (GIS) is that accurate
mapping and data analysis are completed while in the field. Depicted hardware (field-map
technology) is used mainly for forest inventories, monitoring and mapping.
Data capture—entering information into the system—consumes much of the time of
GIS practitioners. There are a variety of methods used to enter data into a GIS where it is
stored in a digital format.
Existing data printed on paper or PET film maps can be digitized or scanned to produce
digital data. A digitizer produces vector data as an operator traces points, lines, and polygon
boundaries from a map. Scanning a map results in raster data that could be further processed
to produce vector data.
Survey data can be directly entered into a GIS from digital data collection systems on survey
instruments using a technique called coordinate geometry (COGO). Positions from a global
navigation satellite system (GNSS) like Global Positioning System can also be collected and
then imported into a GIS. A current trend in data collection gives users the ability to utilize
field computers with the ability to edit live data using wireless connections or disconnected
editing sessions. This has been enhanced by the availability of low-cost mapping-grade GPS
units with decimeter accuracy in real time. This eliminates the need to post process, import,
and update the data in the office after fieldwork has been collected. This includes the ability
to incorporate positions collected using a laser rangefinder. New technologies also allow
users to create maps as well as analysis directly in the field, making projects more efficient
and mapping more accurate.
Remotely sensed data also plays an important role in data collection and consist of sensors
attached to a platform. Sensors include cameras, digital scanners and LIDAR, while
platforms usually consist of aircraft and satellites. Recently with the development of
Miniature UAVs, aerial data collection is becoming possible at much lower costs, and on a
more frequent basis. For example, the Aeryon Scout was used to map a 50-acre area with a
Ground sample distance of 1 inch (2.54 cm) in only 12 minutes.
The majority of digital data currently comes from photo interpretation of aerial photographs.
Soft-copy workstations are used to digitize features directly from stereo pairs of digital
photographs. These systems allow data to be captured in two and three dimensions, with
elevations measured directly from a stereo pair using principles of photogrammetry. Analog
aerial photos must be scanned before being entered into a soft-copy system, for high-quality
digital cameras this step is skipped.
Satellite remote sensing provides another important source of spatial data. Here satellites use
different sensor packages to passively measure the reflectance from parts of the
electromagnetic spectrum or radio waves that were sent out from an active sensor such as
radar. Remote sensing collects raster data that can be further processed using different bands
to identify objects and classes of interest, such as land cover.
When data is captured, the user should consider if the data should be captured with either a
relative accuracy or absolute accuracy, since this could not only influence how information
will be interpreted but also the cost of data capture.
28
After entering data into a GIS, the data usually requires editing, to remove errors, or further
processing. For vector data it must be made "topologically correct" before it can be used for
some advanced analysis. For example, in a road network, lines must connect with nodes at an
intersection. Errors such as undershoots and overshoots must also be removed. For scanned
maps, blemishes on the source map may need to be removed from the resulting raster. For
example, a fleck of dirt might connect two lines that should not be connected.
Interpolation is the process by which a surface is created, usually a raster dataset, through
the input of data collected at a number of sample points. There are several forms of
interpolation, each which treats the data differently, depending on the properties of the data
set. In comparing interpolation methods, the first consideration should be whether or not the
source data will change (exact or approximate). Next is whether the method is subjective, a
human interpretation, or objective. Then there is the nature of transitions between points: are
they abrupt or gradual. Finally, there is whether a method is global (it uses the entire data set
to form the model), or local where an algorithm is repeated for a small section of terrain.
Interpolation is a justified measurement because of a spatial autocorrelation principle that
recognizes that data collected at any position will have a great similarity to, or influence of
those locations within its immediate vicinity.
Digital elevation models, triangulated irregular networks, edge-finding algorithms, Thiessen
polygons, Fourier analysis, (weighted) moving averages, inverse distance weighting, kriging,
spline, and trend surface analysis are all mathematical methods to produce interpolative data.
2.6.8 Web mapping
In recent years there has been an explosion of mapping applications on the web such as
Google Maps and Bing Maps. These websites give the public access to huge amounts of
geographic data.
Some of them, like Google Maps and Open Layers, expose an API that enables users to
create custom applications. These toolkits commonly offer street maps, aerial/satellite
imagery, geocoding, searches, and routing functionality. Web mapping has also uncovered
the potential of crowdsourcing geodata in projects like Open Street Map, which is a
collaborative project to create a free editable map of the world.
2.8 Study Area
2.8.1 Physical location
Kayata farm is located to the south east of Nairobi between longitudes 37.3026E and
37.3797E and latitudes 1.1057s and 1.1408s in the semi-arid uplands. It extends from the east
side of the Kapiti Plains (1600 m.a.s.l.). Its highest point above sea level is 1269m above sea
level. It is located in Matungulu North ward in Matungulu constituency, Machakos County.
It is tract of land of homogenous character or boundaries. Bordering the fareast is Garissa
road and adjacent to it is Athi River. To the north western is Thika where the pineaaples
produced in Kayata farm are transported to for processing.
29
FIGURE 2.8: Map of Kenya
30
FIGURE 2.9: Machakos county
FIGURE 2.10: Kayata farm
2.8.2 Climatic condition
The climatic conditions of the study area are semi-arid, with mean annual temperature
varying from 15ºC to 25ºC and a total annual rainfall ranging between 400 mm and 800 mm
(NRI, 1990)
The mean annual rainfall at the Kayata farm is 602 mm, distributed over a long (March-May)
and a short (October-December) rainy season, separated by a distinct dry season.
2.8.3 Population
Matungulu North ward where the farm is located has a population of 25,041 according to the
census conducted in 2009. In Kayata farm there are no settlements.
Table 2.1: Population of Matungulu Constituency
Constituency No: 078
County: Machakos
Population: 124,736 (National 2009)
Area: 577.50 Sq. Km
No. of County Assembly
5
Wards:
31
County Assembly Wards
No.
Name
Population
(2009
National
Census)
Area
(Sq.
Km)
Description
1 385 Tala
26,297
34.10
Sengani, Kyaume and Katine Sub–
Locations of Machakos County
2 386 Matungulu North
25,041
168.60
Uamani, Kiboko, Kayata, Kyanzavi and
Kwakumbu Sub–Locations of Machakos
County
40.00
Katheka, Matheini, Kingoti, Kambusu
and Mwatati Sub–Locations of Machakos
County.
246.90
Komarock, Kithuani, Nguluni, Kwangii,
Kithimani, Kalandini,Matuu,
Mukengesya, and Mbuni Sub–Locations
of Machakos County
3 387 Matungulu East
4 388 Matungulu West
21,725
33,935
The land is extensive covering about 1400.25ha. 346.426ha of the land is under pineapples
and there is no much settlement. The land is located in a semi-arid area of the former Eastern
province. This farm has only been used for growing pineapples and no attempts have been
made to subdivide the farm. It belongs to Kayata cooperative society that manages the
pineapple farming .The farm has remained to be under pineapple plantation and the
farmhouse is the main building on that land.
32
CHAPTER THREE 3. MATERIALS AND METHODOLOGY.
3.1 Executive summary
This section deals first with defining the extent of the study. Also the data collected is listed
and criterias used to subdivide the land and to locate various public utilities. Data processing
techniques applied on the image so as to make the final output which is a line map are also
explained in details. The application of the planning act to come up with the optimum
positions for location and areas to be reserved are, is performed at this stage.
3.2 Material and Methods
Hardware used includes:

A personal computer system(laptop) Toshiba with speed of 3 GHz, 2GB of RAM and hard
Disk of 250GB

External Storage: 8GB flash disk

Printer
Software used:

ArcView 3.2

Global Mapper 10
 ArcGIS 10.1
 Microsoft Excel
 Microsoft word
 Elshayal smart web online software
 Global map maker
33
3.3 Overview of the Methodology
The method used in preparation of a subdivision scheme plan was based on physical
planning. Before preparation of the plan all the provisions of the planning act concerning
land subdivision were scrutinized to avoid future disputes. This mainly concerns the location
and area of different public utilities as required in the act. Also the delineation of road and
riparian reserves must be taken care of. The area under use on the farm is also demarcated in
this case referring to the area under pineapples, the area where the farmhouse is located and
the earth roads. Then delineation of the buffer zones for both the roads and the river is the
following task together with determination of the appropriate areas for location of various
public utilities. They are situated and their areas subtracted from the total area. The cropping
of the DEM for the area to show the slope of the terrain is important so that the public
utilities and subdivided parcels don’t fall on hilltops ,or rugged landscape.
3.3.1 Data Preparation
3.3.1.1Topographical Map
It was a digital map from the Geography department dated 1984 which showed the location
of Kayata farm with relation to other features. It was georeferenced in Global mapper
software and used as a base map.
3.3.1.2 Population data
The hardcopy was taken from Kenya National Bureau of Statistics (KNBS). The values were
input in an excel sheet.
3.3.1.3 Images D2313 digital globe
Obtained from Google earth, through the Elshayal smart web online software and they were
georefenced automatically through an inbuilt command in the software. The images were at a
zoom scale of about 630. The portions were joined in the Global mapper software to form a
georeferenced homogenous image. Later the image was exported an geotiff format for it to
be acceptable in softwares like ArcGIS and ArcView.
34
FIGURE 3.1: Google earth
3.2.1.4 Land use map and boundary reconciliation map
Obtained from Boma surveys showing the exact areas covered with pineapples and the division of
the plantations. It also showed the exact boundaries of the farm relative to the plantations inside and
outside the farm.
The boundaries were identified on the image and the pineapple plantations extent noted.
35
Cadastral plans
Were obtained from SoK. Provided the coordinates of the corner points of the farm which
were entered into an excel file. The excel file was loaded into ArcGIS together with the
image. The area of study was clipped from the larger image of the area.
Table 3.1: DATASETS
TYPE
SOURCE
CHARACTERISTIC
Satellite image
Google earth
1m, spatial resolution
Cadastral plans
SoK
1:2500
Population data
KNBS
Excel sheet
Land map and boundary
reconciliation map
Boma surveys
1:1000
3.3.2. Data Manipulation
3.2.2.1 Elshayal in image georefencing
Image georeferencing was done using Elshayal smart web online software that has a google
earth interface such that when snapping image from the Globe presented in Google earth,
they are already rectified. A search for Kayata was conducted and the Google earth search
engine navigated to Kayata. A whole image for the extent was not possible in single snapshot
because the zoom level is so small that digitizing in the softwares was not possible. The
images of the area were obtained in a series of overlapping snapshots at same zoom scale.
The images were stored as separate entities. The pineapple plantations guided in identifying
the area of interest.
36
FIGURE 3.2: Elshayal smartweb online software
3.2.2.2 Global mapper implementation in image mosaicing
From Elshayal the images were georeferenced and it was just a matter of adding them into a
global mapper window where they formed a continuous and homogenous image of the area
that had included the study area and its neighborhood. In this software the image was then
exported as Geotiff.
FIGURE 3.3: Global mapper
Global mapper was also used in the converting the coordinates of the area of interest from Cassini
Soldner to Geographic coordinates in WGS 1984 system.
37
Table 3.2 : Coordinates in Cassini Soldner
Point
Y /Northing (ft)
N
-397559.1
T5
-401357.0
Ax
-409699.3
Inter
-411764.2
Kaiata
-414137.9
33
-409634.6
Iron pin Kc
-409832.1
54 Athi
-401937.0
X(easting)
116752.5
111622.3
115004.7
113447.9
123439.8
128237.7
127880.6
120959.8
This was done the coordinate converter in the tools menu. The input and output coordinate
systems were defined as follows:
Input coordinate system:
Cassini Soldner
DATUM: ARC1960
UNIT: international feet
CENTRAL MERIDIAN: 37.000000
CENTRAL LATITUDE: 0.000000
FALSE EASTING (m):
0
FALSE NORTHING (m):
Output coordinate system:
Geographic (Latitude/Longitude)
DATUM: WGS84
UNIT: arc degrees
The coordinates were typed and with the click of ‘convert to’, converted coordinates were
displayed. This was done for all the corner points of the farm area. Coordinates were copied
into an excel file.
38
FIGURE 3.4: Coordinate converter
3.2.2.3 ArcGIS application in cropping
The image that included the AOI and the neighbourhood was loaded into ArcGIS. Also the
excel file was loaded. Then from catalogue the folder connected was right clicked> new>
shapefile > named extent> polygon> geographic coordinates system, WGS 1984>ok
Then in the menu bar >editor>start editing> extent >ok
Then right click on the excel file on the left and select display x.y data.
On tools bar choose create feature tool and selected the polygon and started digitizing the
corners already displayed on the image.
From the geoprocessing dropdown menu >clip> e dialog b
In the dialog box that appeared:
Input feature was Kayata,
Clip feature was Extent
Output feature was AOI,
Then clicked OK.
………………………………………….
39
Also the DEM of AOI was cropped using the shape file from the larger DEM, using the
same process as above.
3.2.2.4 ArcView implementation in delineation
The image that was now in Geotiff form was loaded into the software. Four themes were
used to digitize and store the various land uses.




Earth roads
Pineapples
Buildings
Athi river
Each new theme was created from> view>new theme>define the feature type>ok.
Also to add features into the theme themes were highlighted and from menu bar>
theme>start editing > uses the digitizing tool to add features. After digitizing the edits were
saved then > theme> stop editing. Then the next theme was edited.
3.2.2.5 ArcGIS application in buffering.
The requirements of the planning act on road reserves is as in the table below:
TABLE 3.3 road reserves
Classification
International trunk roads ,A
National trunk roads B
Primary roads, C
Secondary roads, D
Minor roads &SPR, E
( PPH, 2007)
Reserve in metres
60
40-60
25-30
20
18
In buffering that is under geoprocessing- the buffer was put on roads as follows:
From the geoprocessing menu> buffer, the in the dialog box that appeared,
Input feature was earth road, then in the buffer area was 20 metres
Then on the rivers as follows:
The input feature was Athi river
Buffer area was 60metres as recommended for major rivers
40
3.3.3 Land subdivision planning
3.3.3.1 Determination of appropriate locations for various public utilities.
The following were the criterias used to propose various locations for public utilities
according to the provisions of PPA and government regulations.
Primary schools
Assuming that there will be 40 pupils per class and the classes will be from standard 1-8 and
that the school may want to expand facilities in future, an area of 3.9 ha. may be provided as
a minimum. However, schools are encouraged to build storied buildings for economy. If
accommodation is to be provided for teaching staff another 0.8 hectares should be provided.
TABLE 3.4 school planning
Catchment population Number of primary schools
Area
Walking distance
4000
3.9ha
500m-2km
1
Health centre
PPH provides the following guidelines towards location of health centres
“The preferred location for health services should be easily accessible by an ambulance and
be provided with basic infrastructural services.
. However a minimum of the land requirements derived from empirical observations is made
as follows:”
 National referral hospital ---20 hectares
 Provincial hospital------------ 8 hectares
 District hospital--------------- 8 hectares
 Sub-district hospital---------- 4 hectares
 Health center------------------- 3 hectares
 Sub-health center-------------- 2 hectares
 Nursing Homes---------------- 0.4 hectares
 Veterinary clinic---------------- 0.1 hectare
For the case of Kayata sub-health centre was chosen with the recommended area of 2ha.
Community Centre
A community center will enhance social interaction, networks and offers relaxation from
normal day-to-day activities. This enables the total development of the human being and is
therefore pertinent for overall national development. Every center should have a community
center which will provide the following facilities: Library/Resource center
 Social hall
 VCT center
 Public telephone
 Amphi-theatre (Cultural dances, cinema etc).
No specific area is specified for a community centre, it depends on the size of land being
subdivided.
41
Borehole
“Ground water reserve is equally an important source of water, where hand dug wells and
boreholes are dug for water abstraction. Protection and conservation of this water sources is
critical for sustainable development. Ground water reserves require buffer zones of 100
meter (bore holes). While springs protection will also require a buffer zone of 100meters”
Access road (cul de sac)
An access road must be provided for as required in the PPA. This wil enable access of all the
public utilities and centres.
It must ensure maximum use of land.
Police post.
The requirements of PPA are as follows:
TABLE 3.5 police post area
Police post
Police station
Acreage /size
100m2
3ha
catchment
Neighbourhood
Township
3.3.3.2 Delineation of the remaining bare land using ArcGIS
The project was transferred to ArcView and the remaining bare land was digitized. The area
was irregular and it was given the theme name bare land
This was done through the following process: view> new theme > bare land, polygon, (as
name and feature type respectively
Theme> start editing> chose the polygon feature tool and digitized the areas.
3.3.3.3 Subdivision of the parcel
The total area of the remaining land was 877.245ha. The Planning department has
recommended the following in the physical planning handbook.
“When subdividing rural agricultural land, the minimum size of land subdivisions should be
based on the agro ecological zone. Consideration is given to how much land can support a
family before the minimum acreage is determined. It is recommended that the
following minimum land sizes be maintained according to the agro ecological zones.”(PPH,
June 2007. Revised edition 2011)
42
TABLE 3.6 land subdivision guidelines
Agro-ecological zone
Low potential area
Medium potential
High potential
Minimum land size
4ha
10acres
2ha
5acres
1ha
2.5acres
Kayata farm is medium potential because it is in a semi-arid area
For the purpose of ensuring optimal utilization of land, the layout plan should involve
consideration of settlement in a suitable site in the farm. The haphazard type of land uses
should be avoided.
The remaining bare land was subdivided equally into 2ha parcels using ArcGIS parcel fabric
tool
The shape file was displayed as a map. The steps were as follows:
In the parcel fabric, parcels can be divided by area to create new parcels. Using the parcel
division tool, you can divide parcels using the following area-based division methods:



In equal widths
By proportional area
Into equal areas
Steps:
Before working with the Parcel Division dialog box, it was made sure the editor units were
set correctly.
1.
Clicked the Editor menu on the Editor toolbar and click Options.
2.
Clicked the Units tab on the Editing Options dialog box.
3.
Chose the Direction Type and Direction Units drop-down boxes to set angular units
for the editor.
4.
In an edit session in Arc Map, clicked the Select Parcel Features tool on the Parcel
Editor menu, right-clicked on the parcel to divide, then clicked Parcel Division..
5.
In the Parcel Division dialog box, clicked the Plan button to select a plan or record of
survey from the Plan Directory dialog box for the newly subdivided parcels.
6.
Click the drop-down list under Divide this parcel to select how to divide the parcel:
“In equal area” was the input here
43
FIGURE 3.5: Parcel division tool
44
CHAPTER 4 RESULTS AND ANALYSIS
4.1 Coordinate conversion
The converted coordinates from Cassini Soldner, Arc 1960 datum to Geographic coordinate,
UTM datum were as below:
Table 4.1 : converted coordinates
POINT
N
T5
Ax
Inter
Kaiata
33
Iron pin Kc
54 Athi
Y/NORTHING
-1.09833
-1.1091667
-1.131944
-1.137777778
-1.1441667
-1.1000000
-1.1325
-1.11055556
X/ EASTING
37.320556
37.3063889
37.3158333
37.31138889
37.33888889
37.327
37.35111
37.6783
4.2 Cropping of the image.
The image of the area of study was cropped from the large image. It was as below:
FIGURE 4.1: Image of area of interest
45
4.3 DEM generation
The DEM of the area was cropped from a DEM covering the whole country. This was to give
area was found to be relatively flat; there was no much difference in height. This made
almost the whole portion of the farm that is not under use, suitable for subdivision.
FIGURE 4.2: DEM OF AOI
4.4 Land use map
The land under use and also where the road and the river pass were digitized to eliminate the
areas from the subdivision area. The areas identified were:




Pineapple plantations
Earth roads
Athi river
Buildings
46
The result was a land use map as shown below.
FIGURE 4.3: Land use map
4.5 Buffer zones
Along the river, riparian reserve which was 60metres from the edge of the river was clearly defined
as recommended by the government. Also the road reserve was also demarcated which was 20
metres from the edge of the river.
The buffer zones created areas that could not be included in the subdivision area.
The zones are illustrated below within the study area.
47
FIGURE 4.4 Buffer zones
4.6 Location of the various public utilities.
Using the criteria explained in the methodology, the public utilities were distributed in the
subdivision areas as shown in the figure below in total they were 6 including:






school,
borehole
health Centre
police post,
a cul de sac (access road)
Community centre.
Their area was deducted from the area to be subdivided.
48
FIGURE 4.5: Public utilities map
Their areas were as follows:
Cal de sac-with 21m buffer zone
School –3.9ha
Health centre- 2ha
Community centre- 3.24ha
Police post- 100 square meters
Uncontested government land- 2.45ha
Borehole – 100m buffer
49
4.7 Parcel subdivision
From ArcGIS parcel division functionality the land that was not under use was equally
subdivided into half-acre parcels. The total area that was subdivided was 877.245ha. The
area was in 2sections and resulted into 452 parcels as shown.
FIGURE 4.6: Subdivision scheme plan
4.8 Subdivision scheme plan.
Finally a subdivision scheme plan for Kayata farm was the final result. It shows the land use,
buffer zones, proposed sites for public buildings and the half acre parcels. The area figures
are as follows:
50
4.9 Land registration database
To avoid disputes in the future it was prudent to develop a database containing ownership
information of the individual parcels. After every subdivision, subsequent registration is
recommended for formal transfer of ownership and immediate updating of the land register.
Table 4.2: The database of Kayata farm:
51
CHAPTER FIVE Conclusion and Recommendations
5.1 Conclusion.
 Preparation of subdivision scheme plan is very crucial in any subdivision of property.
All the planning guidelines have to be taken into consideration and also the social
stability of the community around. The land subdivision process can be very complex
and time consuming. It is often very risky and usually requires a significant financial
investment
 A plan makes ensures preservation of road and riparian reserves. The government of
the area where the subdivision has taken place finds easy time when expansion of
road is about to take place as it does not have to demolish structures constructed on
the reserves. This saves time and finances.
 Proper planning of subdivision and subsequent registration of the parcels ensures
fairness and hence social stability. The approach used ensures planning standards of
size of public buildings are adhered to. This leads to provision of quality services
under a well-planned environment. It also Reduces congestion and provides for
maintenance of public resources e.g. rivers and roads.
 The use of satellite imagery in carrying out this task improves the clarity and the
accuracy of this task. One main advantage of using satellite imagery is that they show
the most current situation of the area under scrutiny. The developments that have
occurred over time are updated. They also show the relationship of various
geographical phenomena in terms of location. One can be able to relate between
features.
 Subdivision of land has always been regarded as a very rigorous process involving
use of high technology instruments and fieldwork. Satellite imagery approach offers a
simplified way in that the requirements are less and can be done in a short time. On
the image one can be able to visually identify the monuments on his/her parcel and
also the abuttals. This can be used in the process of boundary reconciliation.
 In conclusion, the results of this task may be considered as a future subdivision
scheme plan for the farm if the owners decide to subdivide the land. Some major
improvements could be achieved if the image acquired is in real time.
52
5.2 Recommendations



The accuracy of satellite imagery in land subdivision can be improved if the changes
that occur in a given locality are updated in real time. This can be done by increasing
the repeat cycle of the already existing sensors, such that one can download a real
time image. More high resolution image sensors should be made accessible and cheap
to the public. As of now only Google Earth images are available in the public
domain.
GIS provides very effective tools to analyze multi-layer data spatially and
quantitatively within the area. The subdivision of land using Gis tools is very efficient
and improves quality of work done. GIS not only provides accurate results but also
provides cost and time effective ways of analysis.
The process can be as short as several months but can be as lengthy as several years
in some of the difficult regulatory markets .The use of satellite imagery combined
with Gis softwares should be regarded as a great milestone towards efficient, cost –
effective and accurate land subdivision. It produces neat and excellent results.
53
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“Kenya, Profile of Agricultural Potential.”
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“The Soils of the Nairobi-Thika-Yatta-Machakos Area.”
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4. Clarke, K. C., 1986. Advances in geographic information systems, computers, environment
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5. a b DOC (1928). "A Standard City Planning Enabling Act." Document no. BH11.
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Stork, E.J., Sakamoto, S.O., and Cowan, R.M. (1999) "The integration of science
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