MSc thesis submitted by Ron Dalumpines to the International Institute for Geo-information Science and Earth Observation in the Netherlands.
The research explores the extraction of urban form/land use information in developing indicators to support TEF analysis using RS and GIS. Remotely sensed imagery provides a global information resource that when compared to traditional methods of data collection has the ability to provide data of an entire area, of areas that are difficult to access, at a greater frequency in acquiring data, reusable for different projects and can be cost efficient. GIS supports handling of spatial data from remotely sensed imagery and integrates it with other images and ancillary data from different sources. Recent RS and GIS applications can handle various spatial analyses and other data manipulation techniques considered useful for data mining, such as indicator extraction and quantification. In this study, urban RS plays a key role in providing thematic classifications (i.e., residential, commercial, institutional, and industrial classes) based on IRS-P6 satellite imagery. Perpixel classification methods, supervised and unsupervised, grey-level co-occurrence matrix texture measures and spatial metrics are explored in the extraction of four urban land uses for indicator quantification. The utility of freely available high-resolution Google Earth images supported by global positioning systems (GPS) are also explored in the process. The utilization of RS and GIS applications is further illustrated in the extraction and quantification of TEF-related indicators, namely, density, proximity, trip distance estimate, and land-use mix.