Conventional land use change detections with remote sensing use annual remote sensing images because of the limitations of optical sensors that cannot collect data in bad weather and cloudy conditions. This limits its applications in rapidly developing areas which are cloudy, such as the Pearl River Delta in China. These areas also need to detect land use changes in short intervals, such as on a monthly basis, in order to monitor illegal land use changes and prevent irreversible land use changes that may damage the environment. The objective of the thesis is to examine short-interval land use change detection, especially the change from agriculture to built-up areas, using RADARSAT-1 images which can go through clouds. This thesis firstly examines the classification of RADARSAT-1 images with pixel-based and object-based classification methods respectively. Based on the classification results, post-classification change detection method are conducted in order to obtain the detailed information of land use changes for the analysis of short-interval land use change. Land use change detection accuracies can be improved as the number of the RADARSAT-1 images used in land use change detection increased. More images, which represent longer monitoring period, can obtain better results of land use change detection. For short-interval land use changes detection, four time periods is the maximum otherwise the period of monitoring will be too long. Agricultural activities such as planting and harvesting have significant effects on the monitoring of land use changes. In planting and harvesting months, the accuracies of the land use change detection are lower than other months because its land cover is often confused with other land uses, such as water and bare soils. The process of construction can be considered as a three-stage process and a combination of two land uses. However, construction sites are often confused with vegetation and bare soil in RADARSAT-1 images because the values of backscatter coefficients of construction sites and the two land uses are very similar. The land cover changes during the planting and harvest seasons are often confused with the process of construction. It is found that construction sites can be identified with their two stages of low values of backscatter coefficients, which is not found in the pattern curves of backscatter coefficients of other land uses. By the comparison of the accuracies of identifying construction sites using two, three and four RADARSAT-1 images, it is found that using three time periods can get better accuracies which is different from the result of general land use change detection. This thesis does not try to evaluate land use change detection methods or find the best method for monitoring land use changes. Instead, it focused on the analysis of confusions caused by the time periods of land use change detection and seasonal variation of vegetations. The main contributions of this study are as follows: 1) it explores the use of multi-temporal RADARSAT-1 images into the land use change detection to overcome the problems of cloudy conditions, making short-interval land use change detection possible for areas which are often covered by clouds; 2) pixel-based maximum likelihood method and the object-based classification method were compared for their accuracies in land use classification of RADARSAT-1 images; 3) it examines the optimal time periods for land use change detection; and 4) it examines the appropriate number of images that are needed for monitoring land use changes in different seasons in order to obtain the best accuracies. / published_or_final_version / Urban Planning and Design / Doctoral / Doctor of Philosophy

Thesis (Ph. D.)--University of Washington, 1996. / Vita. Includes bibliographical references (leaves [125]-131).

Land use/land cover (LULC) information is essential for a plethora of applications including environmental monitoring and natural resource management. Traditionally, field surveying techniques were the sole source of acquiring such information; however, these methods are labour intensive, costly and time consuming. With the advent of remote sensing, LULC information can be acquired in an economical, less tedious and non-time consuming manner at shorter temporal cycles and over larger areas. The aim of this study was to assess the utility of multispectral remote sensing data and the Random Forest (RF) algorithm to improve accuracy of LULC maps in heterogeneous ecosystems. The first part of this study used moderate resolution SPOT-5 data to compare the performance of the RF algorithm to that of the commonly used Maximum Likelihood (ML) classifier. Results indicated that RF performed significantly better than ML (66.1%) and yielded an overall accuracy of 80.2%. Moreover, RF variable importance measures were able to provide an insight on the bands that played a pivotal role in the classification process. Due to the fact that moderate resolution satellite data was used, both classifiers seemed to experience some difficulties in discriminating amongst classes that exhibited similar spectral responses such as Eucalyptus grandis and Pinus tree plantations, young sugarcane and mature sugarcane, as well as river and ocean water. In that regard, the next section attempted to address this shortfall. The second part of the study used high resolution multispectral data acquired from the WorldView-2 sensor to discriminate amongst six spectrally similar LULC classes using the advanced RF algorithm. Results suggested that the use of WorldView-2 data together with the RF ensemble algorithm is a robust and accurate method for separating classes exhibiting similar spectral responses. The classification process yielded an overall accuracy of 91.23% and also provided valuable insight into WorldView-2 bands that were most suitable for discriminating the LULC categories. Overall, the study concluded that: (i) multispectral remote sensing data is an effective tool for obtaining accurate and timely LULC information, (ii) moderate resolution multispectral data can be used to map broad LULC categories whereas high resolution multispectral data can be used to separate LULC at finer levels of detail, (iii) RF is a robust and effective tool for producing LULC maps that are less prone to error. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

This study determines and predicts multi-temporal Land-Use-Land-Cover Change (LULC) in a peripheral urban landscape over a 22 year period in relation to the study area‘s greenery. A change detection analysis using post classification Maximum Likelihood algorithm on three multispectral SPOT-4 images was used to determine land-cover transformation. To predict future land coverage, a Land-Cover Change Modeler (LCM) and a Markov Chain were used. Results show that between the year 2000-2006, 2006-2011 and 2000-2011 the study area experienced varied changes in the different LULCs. Built-up areas increased by 10.08%, 3.15% and 13.23% in 2000-2006, 2006-2011, and 2000-2011 respectively. Areas covered by thicket decreased by 0.59% in 2000-2006 but increased by 0.56%, 0.07% in 2006-2011 and 2000-2011 respectively. Forest land-cover increased by 2.59% in 2000-2006, 2.82% in 2006-2011, and 5.41% in 2000-2011. Grassland declined by 8.46% and 2.64% in 2000-2006 and 2000-2011 respectively while degraded grassland declined by 3.62%, 12.45% and 16.07% in 2000-2006, 2006-2011, and 2000-2011 respectively. Projection results indicate a consistent pattern of growth or decline to those experienced between 2000-2011. This study provides insight into LULC patterns within the eThekwini metro area and offers invaluable understanding of the transformation of the urban green spaces. Key words: Land-Use-Land-Cover Change, Change detection, Land-Cover Change Modeler, Markov Chain Process, Land-Cover Change Prediction. / Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2013.

In order to manage the often highly dynamic urban landscapes, it is important to map different themes from time to time. This study made use of Geographical Information System and aerial photographs to determine LULC transformation in the eastern suburbs of Pietermaritzburg in KwaZulu-Natal, South Africa. Land use land cover maps for the eastern suburbs (Copesville, Eastwood, Raisethorpe and Willowton) for the years 1989 to 2009 were generated and transformations based on twelve LULCs determined. Results in this study showed that the most significant increase were in residential (formal and informal) and industrial LULCs while the most significant decrease were recorded in the cultivated and open LULC. Generally, results in this study further show that urban LULC attributed to human influx has been at the expense of internal open green spaces and peripheral cultivated and uncultivated lands. The study concludes that aerial photographs in concert with GIS are valuable tools in mapping rapidly changing urban landscapes. / Thesis (M.Env.Dev.)-University of KwaZulu-Natal, Pietermaritzburg, 2012.

The purpose of this research was to explore the potential of using LANDSAT MSS data in the study of land use/cover patterns and turbidity relationships within the Lake Anna watershed. Two premises of this research are that a relationship exists between land use/cover patterns and turbidity levels, and that LANDSAT MSS data can be used to study this relationship. Turbidity levels within Lake Anna were estimated by the chromaticity technique used by Munday et al and were correlated to two groups of ground-based data -- surface turbidity levels and the product of the Universal Soil Loss Equation (USLE). Estimated turbidity levels correlated moderately well with surface data, but only a slight relationship could be established between land use/cover patterns and estimated turbidity. Possible explanations for these results were grouped into two categories, practical and conceptual problems. Practical problems were defined as data collection problems and included LANDSAT system and data accuracy problems. Conceptual problems were problems based on theoretical issues of using LANDSAT MSS data to study relationships between land use/cover patterns and turbidity levels. Conceptual problems remained even after the practical problems were solved. The accomplishments of this research included the application of chromaticity analysis to small man-made reservoirs, further exploration of the relationship between land use/cover patterns, and turbidity levels, and extension of LANDSAT MSS data in watershed management. Most importantly, this research exposed some of the limitations in using LANDSAT MSS data to study relationships between land use/cover patterns and turbidity levels. / Master of Science