PROF. KIEMA JOHN BOSCO KYALO

By convention, data in the real world is deemed to exist in a continuous or analogue form usually in three dimensional space as discussed in Sect. 2.1. Such data needs to be digitized or made discrete before it can be input and processed by a digital computer. A GIS database can be viewed as an abstraction of reality. To convert object features observed or measured in the real world into the digital realm in a GIS database it is necessary to structure the data appropriately. Four (4) different generic types of primitive object features can be distinguished, namely: point features (0-D), line features (1-D), area features/polygons (2-D), and surface features (3-D). Incidentally, when surface features are captured in a discrete or non-continuous manner, this is then referred to as 2.5D. In general, an object feature is defined by three (3) properties in GIS, namely: position, attributes and relationship with other features referred to as topology.

One of the most fundamental developments in the history of photogrammetry has been the transition from analytical to digital photogrammetry. This was realized in the early 1990s through softcopy-based systems or Digital Photogrammetric Workstations (DPWs). Today, on the one hand, initial applications of digital photogrammetry in performing routine and operational procedures, such as aerial triangulation and map revision, as well as in generating geospatial datasets, including digital elevation models (DEMs) and digital orthophotos, have been essentially standardized. On the other hand, system development in automated feature extraction for diverse geospatial features have been continually improved and refined.

Since time immemorial, natural disasters have continued to plague the history of mankind. They have varied in type, frequency, coverage and severity ranging from earthquakes, landslides, droughts, floods, tornadoes, hurricanes, tsunamis, volcanic eruptions etc. Over the last century, the frequency, severity and impact of natural disasters has increased substantially.

There is no doubt that today, perhaps more than ever before, humanity faces a myriad of complex and demanding challenges. These include natural resource depletion and environmental degradation, food and water insecurity, energy shortages, diminishing biodiversity, increasing losses from natural disasters, and climate change with its associated potentially devastating consequences, such as rising sea levels. These human-induced and natural impacts on the environment need to be well understood in order to develop informed policies, decisions, and remedial measures to mitigate current and future negative impacts. To achieve this, continuous monitoring and management of the environment to acquire data that can be soundly and rigorously analyzed to provide information about its current state and changing patterns, and thereby allow predictions of possible future impacts, are essential. Developing pragmatic and sustainable solutions to address these and many other similar challenges requires the use of geodata and the application of geoinformatics. This book presents the concepts and applications of geoinformatics, a multidisciplinary field that has at its core different technologies that support the acquisition, analysis and visualization of geodata for environmental monitoring and management. We depart from the 4D to the 5D data paradigm, which defines geodata accurately, consistently, rapidly and completely, in order to be useful without any restrictions in space, time or scale to represent a truly global dimension of the digital Earth. The book also features the state-of-the-art discussion of Web-GIS. The concepts and applications of geoinformatics presented in this book will be of benefit to decision-makers across a wide range of fields, including those at environmental agencies, in the emergency services, public health and epidemiology, crime mapping, environmental management agencies, tourist industry, market analysis and e-commerce, or mineral exploration, among many others. The title and subtitle of this textbook convey a distinct message. Monitoring -the passive part in the subtitle - refers to observation and data acquisition, whereas management - the active component - stands for operation and performance. The topic is our environment, which is intimately related to geoinformatics. The overall message is: all the mentioned elements do interact and must not be separated. Hans-Peter B ahr, Prof. Dr.-Ing. Dr.h.c., Karlsruhe Institute of Technology (KIT), Germany.

Environmental Impact Assessment (EIA) is defined by Munn (1979) as the need to identify and predict the impact on the environment and on man’s health and well-being of legislative proposals, policies, programs, projects, and operational procedures, and to interpret and communicate information about the impact.

A natural way to begin this monogram is by posing several pertinent questions. Firstly, what exactly does the term “monitoring” mean. Furthermore, is monitoring synonymous to measuring or observing? And more specifically, what does it mean within an environmental perspective? Monitoring has been defined by James (2003) as observing, detecting, or recording the operation of a system; watching closely for purposes of control; surveillance; keeping track of; checking continually; detecting change.

There exist various definitions to the word pollution depending on one’s jurisdiction and the laws of a particular country. Springer (1977, see references therein) looks at the meaningful concept of defining pollution in international law by posing the questions: “What are you talking about when you are talking about pollution? What is pollution? How would you define it if you are going to remove the concept of damage from it?” These questions are not easily answerable and as Springer (1977) acknowledges, the term pollution is a word whose precise meaning in law, particularly international law, is not easily discerned Springer (1977). It has been used in a wide variety of contexts, from international conventions to pessimistic speeches about the state of the environment, to describe different levels and kinds of man-induced changes in the natural world Springer (1977).

In this section, we discuss the quantitative and qualitative data that could be collected using GNSS satellites, and in so doing, attempt to answer the question “what can GNSS satellites deliver that is of use to environmental monitoring?” The observed parameters necessary for environmental monitoring vary, depending upon the indicators being assessed.

Like in many other disciplines, there is no universally accepted definition of the term photogrammetry. The Manual of Photogrammetry (2003) defines photogrammetry as the art, science, and technology of obtaining reliable information about physical objects and the environment through processes of recording, measuring, and interpreting photographic images and patterns of electromagnetic (EM) radiant energy and other phenomena. Notably, the extracted information could be of a geometric, physical, semantic or even temporal nature, although in many photogrammetric applications the geometric information is more relevant. Other popular definitions of this non-contact discipline are given e.g., in Moffit and Mikhail (1980),Wolf (1980),Kraus (1994), Schenk (2005) etc. In a very broad sense, and from a network design point of view, (Fraser 2000) reckons that a photogrammetric system is one that meets the following basic requirements:

Remote sensing is defined as the art, science and technology through which the characteristics of object features/targets either on, above or even below the earth’s surface are identified, measured and analyzed without direct contact existing between the sensors and the targets or events being observed, see e.g., (Jensen 2009; Lillesand et al. 2010; Richards 1994; Murai 1999) etc.

Although the environment has remained at the forefront of scientific interest for well over four decades (e.g., Lein (2012)), it is not until this decade that remote sensing of the environment using geodetic methods started gaining momentum. This has largely been fuelled by the launching and modernization of satellites that enable the environment to be measured, mapped, and modelled.

Understanding the characteristics of and possibilities in using geodata is premised on proper comprehension of the underlying concepts of space, time and scale, contextualized within the Earth’s framework. Although these concepts are used in everyday parlance, often without much afterthought, they are not trivial at all. For instance, looking back throughout the entire history of mankind, the concepts of space and time have been the subject of animated philosophical, religious and scientific debates. In this section, we attempt to present a background of each of these dimensions of geodata, both independently and collectively, as well as highlight their relevance in influencing the character of geodata.

Once digitized and edited GIS data are stored in a spatial database. Evidently, the quality of the decisions made from a GIS will depend on the quality of the data contained in the database. A spatial database is defined as a pool of integrated and structured geospatial data, which is a model of reality, and from which data may be retrieved to provide useful information to users. Hence, a spatial database is comprised of inter-related geospatial data that is maintained efficiently and which is shareable between different GIS applications.

The Global Positioning System or GPS is the oldest and most widely used GNSS system, and as such will be extensively discussed in this and the next chapter. The development of GPS satellites dates from the 1960s.

The interpretation and analysis of remote sensing imagery involves the identification and/or measurement of various targets or objects in an image in order to extract useful information about them. More specifically, this seeks to extract qualitative (thematic) and quantitative (metric) information from remote sensing data. Qualitative information provides descriptive data about earth surface features like structure, characteristics, quality, condition, relationship of and between objects.

Precisely because of the expensive cost of GIS data capture and the fact that the procedures involved in this are also fairly time consuming, the sources for GIS data should always be carefully analyzed before selection in order to suit specific GIS application(s). There are many possible sources for GIS data available today. The criteria for assessing the most appropriate sources for GIS data include firstly, collecting only the necessary data and secondly, for cost effectiveness, accepting the minimum data quality that will get the specific GIS job to be successfully accomplished. Moreover, where geospatial data needs to be integrated, it is important that the various sources be critically examined for compatibility.

Land provides the base upon which social, cultural and economic activities are undertaken and as such is of significant importance in environmental monitoring. Social, cultural and economic activities have to be planned and managed in such a way that the sustainable use of land resources is enhanced. Sustainable land use ensures that economic and socio-cultural activities do not benefit at the expense of the environment (see Sect.28.5). Monitoring of changes in land through indicators could help in policy formulation and management issues for the betterment of the environment. Some of the vital indicators for land management include vegetation, soil quality and health, biosolids and waste disposed on land, land evaluation, land use planning, contaminated land, integrity of the food supply chain, mine closure completion criteria, and catchment management, in particular water balance, salinity, eutrophication, and riparian/wetland vegetation. This Chapter presents the possibility of using geoinformatics to enhance the monitoring of some of these indicators.

Fresh water is one of the basic necessities without which human beings cannot survive since water is key to the sustainability of all kinds of lifeforms. Water has multiple uses namely; nutritional, domestic, recreational, navigational, waste disposal and ecological as it is a habitat for living and non-living organisms (biodiversity) etc. And, because it is indispensable to different sectors including manufacturing, agriculture, fisheries, wildlife survival, tourism and hydroelectric power generation, it is a vital factor of economic production. For many countries, most freshwater endowments encompass surface waters, groundwater, wetlands and glaciers.

In order to fully appreciate the contribution of geoinformatics in monitoring climate change caused by increase in temperature, a distinction between weather and climate, on one hand, and climate variability and climate change, on the other hand, is essential. Burroughs (2007) points out that weather is what is happening to the atmosphere at any given time (i.e., what one gets), whereas climate is what would be expected to occur at any given time of the year based on statistics built up over many years (i.e., what one expects).

The Internet and web-based technology has dramatically influenced the access to and dissemination of information among communities, locally and globally. This is no less true in the domain of geographic information systems (GIS) which have traditionally been constrained in terms of information access and the communities that use them. Geospatial data has traditionally been captured and managed within individual and separate organizational databases with access by a limited number of expert users. Now, with the integrated use of the web, not just geospatial data, but also the functionality of GIS can be accessed globally by citizens and non-experts.

Normal 0 false false false MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} This article demonstrates the use of Geographic Information Systems (GIS) in auditing the equity as well as evaluating the priority of infrastructure development. A GIS database is created for the study area integrating different infrastructure types and detailing the budgetary allocation to the different infrastructural projects. Stratified priority weight matrices are developed for each infrastructure type. These are then combined to develop the overall infrastructure priority weight matrix. The analyses performed indicate that the security sector should receive the highest priority, while the roads sector should be granted the least priority in the next financial disbursement. The approach proposed in this study will allow governments, donor agencies and the general citizenry to objectively identify the most deserving projects for funding and rank these accordingly in every fiscal year for a defined area.

Poor co-ordination and inefficient flow of information in the Roads Department, Ministry of Roads & Public Works has been a major contributor to inaccuracy in monitoring, management, planning and subsequent development of the road infrastructure in Kenya. The main objective of this study is to demonstrate the effectiveness of Geographic Information Systems (GIS) in making sound and timely decisions about road pavements to support efficient management of the same. This is achieved by development of a prototype GIS-based pavement management system for the Nairobi Central Business District (CBD) as a case study. The three major results of this study are: (1) a database of spatial and non spatial data that is valuable for pavement management; (2) integration of models for predicting future pavement conditions based on the current conditions; and (3) development of user interfaces for querying and analyzing the database. The system is anticipated to considerably improve the decision-making process involved in managing road pavements.

Poor co-ordination and inefficient flow of information in the Roads Department, Ministry of Roads & Public Works has been a major contributor to inaccuracy in monitoring, management, planning and subsequent development of the road infrastructure in Kenya. The main objective of this study is to demonstrate the effectiveness of Geographic Information Systems (GIS) in making sound and timely decisions about road pavements to support efficient management of the same. This is achieved by development of a prototype GIS-based pavement management system for the Nairobi Central Business District (CBD) as a case study. The three major results of this study are: (1) a database of spatial and non spatial data that is valuable for pavement management; (2) integration of models for predicting future pavement conditions based on the current conditions; and (3) development of user interfaces for querying and analyzing the database. The system is anticipated to considerably improve the decision-making process involved in managing road pavements.

Towers are typical structures that can be found in many urban and rural landscapes the world over. From their basic design, they are usually exposed to severe environmental loads. It is therefore prudent to carry out periodic maintenance that includes checking that they are correctly aligned. This paper describes a method that was used for the re-alignment of a guyed tower in Limuru, Kenya. Angular and distance observations, made from two observation points detected a vertical misalignment that was larger than the acceptable tolerance of An iterative re-alignment procedure was then applied, resulting in an acceptable final misalignment of

This paper describes a procedure developed for automated railway feasibility analysis based on GIS and Multi-Criteria analysis. Good engineering railway alignment requirements are assessed and suitable factors and constraints for feasibility analysis derived. Relevant digital map layers are then prepared and reclassified to meet the factor and constraint requirements. The layers are weighted and combined giving rise to a cost layer. Combination of the source and cost layer generates CostDistance and CostDirection layers. These two layers are combined with the destination to automatically generate four alternative routes each constrained to pass through apriori selected locations along the corridor from which the most optimal one is selected. This study aptly demonstrates that GIS is an indispensable and efficient tool for use in railway route selection.

The analytical and visualization capabilities of Geographic Information Systems (GIS) greatly enhance the communication, understanding and utility of data and information as opposed to when the data is simply presented in form of texts and tables. Combining geospatial methods with conventional marketing techniques enables users to visualize the spatial distribution of data on maps and visualize spatial data in various statistical graphs and diagrams. This may yield more insights into the nature of distributions leading to better understanding of diverse markets. In this study a digital map of the study area is created from which the various market zones are delineated. A multiple regression model for predicting sale of soft drink products is then developed. This takes into account sales figures from specific distribution outlets and demographic and socio-economic characteristics of the population served by the distribution outlets for the identified market zones. The study demonstrates the important role that GIS can play in supporting market segmentation.

The analytical and visualization capabilities of Geographic Information Systems (GIS) greatly enhance the communication, understanding and utility of data and information as opposed to when the data is simply presented in form of texts and tables. Combining geospatial methods with conventional marketing techniques enables users to visualize the spatial distribution of data on maps and visualize spatial data in various statistical graphs and diagrams. This may yield more insights into the nature of distributions leading to better understanding of diverse markets. In this study a digital map of the study area is created from which the various market zones are delineated. A multiple regression model for predicting sale of soft drink products is then developed. This takes into account sales figures from specific distribution outlets and demographic and socio-economic characteristics of the population served by the distribution outlets for the identified market zones. The study demonstrates the important role that GIS can play in supporting market segmentation.

Kenyan athletes have continued to dominate middle and long distance running in the global arena for a very long time now. In this study the use of geoinformation in selecting suitable training sites for endurance running was investigated. The study area chosen was Keiyo district within the Rift Valley province in Kenya, where a considerable number of the top Kenyan athletes reside and train. Different geospatial data sources were used and relevant criteria selected. Geographic Information Systems (GIS) was employed as the basic tool for modeling and analysis. For each of the 11 regions within the study area, weights were allocated to each of the multiple criteria identified. The Analytical Hierarchy Process (AHP) was used to determine the overall suitability-ranking index. From the results and analysis performed the regions of Iten, Tambach, Kaptarakwa and Kapkenda respectively were ranked as suitable areas for High-Low training. Kamwosor, Chepkorio, Iten and Kaptarakwa respectively were identified as ideal regions for altitude training. Nyaru and Metkei were determined to be unsuitable for both High-Low and altitude training regimens. The approach formulated in this study can be applied to other areas to enable athletes and coaches identify suitable training sites.

This paper discusses a novel approach for delineating watersheds by integrating spatial and dynamic modeling techniques. In particular, loose coupling between ArcMap and CatchmentSim software is demonstrated. The demarcated watershed can then form the basis for designing appropriate conservation works. The above procedure was successfully applied for the Kinze dam catchment area in Machakos district in Kenya. This study also reiterates that local communities can only be expected to participate in and fully support conservation works if they are involved throughout the entire conservation design process.

This paper discusses a novel approach for delineating watersheds by integrating spatial and dynamic modeling techniques. In particular, loose coupling between ArcMap and CatchmentSim software is demonstrated. The demarcated watershed can then form the basis for designing appropriate conservation works. The above procedure was successfully applied for the Kinze dam catchment area in Machakos district in Kenya. This study also reiterates that local communities can only be expected to participate in and fully support conservation works if they are involved throughout the entire conservation design process.

Surveying is a profession that finds itself increasingly at major crossroads today. This fact is perhaps best vindicated by the increasingly less number of students who are opting to study surveying as their first-degree choice at university level. The number of postgraduate students is also relatively low. In an effort to address this stark reality as well as re-engineer itself, the Department of Surveying, University of Nairobi is in the process of launching new academic programs at both undergraduate and postgraduate levels. This paper traces developments in the curriculum review process at the Department of Surveying, University of Nairobi that began almost a decade ago. A review of the basic principles in curriculum development is presented. The objectives of the above new programs are outlined together with their respective regulations. The course structures for both the undergraduate and postgraduate programs are then presented. A proposal for implementing the new programs is finally outlined before conclusions are drawn.

This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3x3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat TM imagery are then fused with the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.

This paper examines the importance of integrating different spatial and temporal knowledge representations in order to structure disaster relevant information. Special focus is made here on the human-machine interface. An overview for structuring the knowledge is presented within the framework of a technical information system. The idea of configuring the relevant knowledge with templates for spatial reasoning is extended for collaborative spatial decision making. The architecture of a system for co-operative decision making is then outlined. This provides a means for co-operative disaster management. A central part of this system is the messaging system. The specifications and main characteristics for such a messaging system are presented. An example implementation is then given.

This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3x3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat TM imagery are then fused with the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.

This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3x3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat TM imagery are then fused with the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.

This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3x3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat TM imagery are then fused with the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.

This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3x3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat TM imagery are then fused with the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.

This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3x3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat TM imagery are then fused with the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.

This paper examines the influence of multisensor data fusion on the automatic extraction of topographic objects from SPOT panchromatic imagery. The suitability of various grey level co-occurence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3x3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat TM imagery are then fused with the SPOT data using the additional channel concept. The object feature base is expanded to include both spectral and spatial features. A maximum likelihood classification approach is then applied. It is demonstrated that the segmentation of topographic objects is significantly improved by fusing the multispectral and texture information.