AbstractSatellite remote sensing started to gain prominence as a useful technology for a wide range of non-military applications after the emergence of the space program in the 1960s. The application of remote sensing technology to population and housing data estimation was thus explored by researchers as a quick response mechanism for alleviating the impact of natural disaster, humanitarian emergencies and post-conflict rehabilitations.
The recent advancements established in satellite technology, remote sensing techniques and the availability of wide range of remotely sensed datasets was explored in this research to establish the extent remote sensing techniques can be utilised for population and housing data acquisition at both the micro and macro level in the newest nation of the 21st Century i.e. Timor-Leste. This includes amongst others: the image-processing techniques that can be used to acquire secondary data that are useful for estimating population and housing data; effectiveness of four types of remotely sensed data sets for deriving secondary data that are useful for estimating population and housing data; models and/or algorithms for deriving population and housing data from remotely sensed data sets.
Implicit in the research findings are ‘that population data can be feasibly estimated from secondary data (i.e. buildings/built-up area) generated from QUICKBIRD satellite imagery through object-based classification approach as well as the buildings counts generated from QUICKBIRD, IKONOS and aerial photography’. And ‘the altitudinal location of the imaging sensor, the sensors onboard wavelength detection mechanism, and the choice of image pre-processing, processing, interpretation and analysis techniques; all to a large extent influence the quality of secondary data that can be generated from the remotely sensed datasets’. The built-up area secondary data can be generated from multi-spectral bundle of QUICKBIRD satellite imagery by using the following remote sensing data processing techniques: principal component analysis, pixel-based unsupervised classification, pixel-based supervised classification, object-based image segmentation and classification, and visual image interpretation. Whilst the built-up area secondary data can be generated from multi-spectral bundle of LANDSAT-TM satellite imagery by using the following remote sensing data processing techniques: principal component analysis, pixel-based unsupervised classification and pixel-based supervised classification. Also the built-up area secondary data can be generated from panchromatic bundle of IKONOS satellite imagery by using visual image interpretation technique; and the built-up area secondary data can be generated from black and white (mono-band) aerial photography by using visual image interpretation technique.
Furthermore the geographical information system (GIS) techniques were also applied to the development and demonstration of a user-friendly demographic information system (DIS) that is useful for disseminating demographic information amongst multi-sector data users. A multi-user geo-database or ‘geospatial one-stop-shop’ for new nation building and development that can provide an information infrastructure for bringing all manner of data together geographically to support integrated and multi-sector decision-making at many levels was postulated.
The DIS developed as an intuitive, user friendly and interactive spatial-demographic analysis tool for disseminating useful demographic information amongst multi-sector data users. The user-defined population statistics, housing statistics, population trends and population estimates can be manipulated and displayed in user-defined output format within a DIS.
The inherent limitations of the research are that the research methodologies were applied only to remotely sensed datasets that are acquired from passive sensor platforms, namely QUICKBIRD, IKONOS and LANDSAT-TM satellite imagery, and black & white aerial photography; and the demographic information products are formulated by the author and suggested to the Timor-Leste National Statistics Directorate for implementation.
Note: Thesis contains culturally or commercially sensitive content that requires indefinite restricted access.
|Date of Award||Apr 2007|
|Supervisor||Greg Hill (Supervisor)|