Short-interval monitoring of land use changes with RADARSAT-1

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_versionUrban Planning and DesignDoctoralDoctor of Philosoph