John B. Kyalo Kiema

Texture analysis and data fusion in the extraction of topographic objects from satellite imagery

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-occurrence based texture measures, as well as different pixel windows is also investigated. It is observed that best results are obtained with a 3 2 3 pixel window and the texture measure homogeneity. The synthetic texture image derived together with a Landsat Thematic Mapper (TM) imagery are then fused to 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.

Market segmentation using geographic information systems (GIS): A case study of the soft drink industry in Kenya

Purpose – To show how the analytical and visualization capabilities of geographic information systems (GIS) can enhance the communication, understanding and utility of data and information to be used in marketing planning, as compared with their conventional presentation as text and tables.Design/methodology/approach – A digital map of the study area was generated and a set of market zones. A multiple regression model for predicting sale of the product under study was then developed, taking into account sales figures from specific distribution outlets and the demographic and socio‐economic characteristics of the population served by the distribution outlets in the identified market zones. Optimum routes and times between the manufacturing plant and the distribution outlets were derived.Findings – Combining geospatial methods with conventional marketing techniques enables users to visualize the spatial distribution of data in maps, complemented by various statistical graphs and diagrams. This form of prese...

Wavelet Compression and the Automatic Classification of Urban Environments Using High Resolution Multispectral Imagery and Laser Scanning Data

The field of wavelets has opened up new opportunities for the compression of satellite data. This paper examines the influence of data compression on the automatic classification of urban environments. Data from Daedalus airborne scanner imagery is used. Laser scanning altitude data is introduced as an additional channel alongside the spectral channels thus effectively integrating the local height and multispectral information sources. In order to incorporate context information, the feature base is expanded to include both spectral and non-spectral features. A maximum likelihood classification is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multispectral and geometric data sets. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based compression algorithm. The compressed imagery is then classified using the approach described hereabove. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.

ASSESSMENT OF KENYA'S READINESS FOR GEOSPATIAL DATA INFRASTRUCTURE TAKE OFF

Abstract Geospatial Data Infrastructure (GDI) is a concept that is a reality in most developed countries today. This concept is however only just beginning to take a foothold in most developing countries. This paper reports on a study to access Kenyas preparedness for GDI take off by evaluating the achievements made thus far in the basic components of GDI: data, technology, policies, institutional framework and people. It is observed that the relatively lukewarm political support and absence of a long-term strategic vision are serious constraints to GDI diffusion. Similarly, the lack of sustainable funding policies and strategies, coupled with the absence of a concise implementation strategy greatly undermine the development of GDI in Kenya. Most geospatial data sets are still in analogue form, are not regularly updated, and their consistency across organizations still needs to be verified. In addition, the absence of an active GIS professional organization greatly handicaps GDI development in Kenya. Nevertheless, despite this largely negative picture, the GDI status in Kenya compares well with those of most other African countries.

Marine and Coastal Resources

Marine habitats are comprised of zones termed coastal terrestrial, open water, and the ocean bottom until several meters deep. Besides fish, these habitats are home to diverse flora and fauna, with swathes of sandy beaches and sand dunes spread across the globe critical for the survival of many endangered species e.g., turtles, dugongs, migratory birds etc

Fundamentals of GIS

Geographic Information System(GIS) is defined as a special type of information system that is used to input, store, retrieve, process, analyze and visualize geospatial data and information in order to support decision making, see e.g.,Aronoff (1989), Tomlinson (2007), Longley et al. (2005), Konecny (2003), Burrough (1986), Murai (1999) etc.

Environmental Monitoring and Management

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.

Microwave Remote Sensing

Persistent cloud cover, especially within the tropics, offers limited clear views of the Earth’s surface from space. This presents a major impediment to the application of optical remote sensing discussed in Chap. 8 in providing global remote sensing coverage. Moreover, other than thermal sensors, most other optical imaging technologies best operate during day time when there is sufficient sunlight. The microwave region of the EM spectrum represents a principal atmospheric window that can be employed to overcome the above limitations in optical remote sensing. For instance, in view of their much longer wavelengths and contrary to optical sensors, microwaves can easily penetrate through vegetation canopies and even dry soils. In addition, microwave systems offer the user more choice and control over the properties of the incident microwave energy to be applied. Furthermore, they can be operated round the clock even under rainy or poor visibility conditions.

Fundamentals of Photogrammetry

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:

Wavelet compression and data fusion: An investigation into the automatic classification of urban environments using colour photography and laser scanning data

The field of wavelets has opened up new opportunities for the compression of satellite sensory imagery. The paper examines the influence of wavelet compression on the automatic classification of urban environments. Airborne laser scanning data is introduced as an additional channel along-side the spectral channels of colour infrared imagery. This effectively integrates the local height and multi-spectral information sources. To incorporate context information, the feature base is expanded to include both spectral and non-spectral features. A maximum likelihood classification approach is then applied. It is demonstrated that the classification of urban scenes is considerably improved by fusing multi-spectral and geometric data sets. The fused imagery is then systematically compressed (channel by channel) at compression rates ranging from 5 to 100 using a wavelet-based algorithm. The compressed imagery is then classified using the approach described here-above. Analysis of the results obtained indicates that a compression rate of up to 20 can conveniently be employed without adversely affecting the segmentation results.