| dc.description.abstract | The importance of accurate and timely information describing the nature and extent of land resources and changes over time is increasing, especially in rapidly growing metropolitan areas. Urban expansion is one of the main reasons responsible for a variety of urban environmental issues like decreased air quality, increased runoff and subsequent flooding, heat island effect, deterioration of water quality, etc. Therefore, it is essential to understand its extent and trend, in order to provide accurate and valuable information for dealing with subsequent issues.
In this work, metropolitan Taipei has been taken as a case study. The urban expansion and land cover change that took place within a span of 20 years i.e. from 1990, to 2010 has been studied, using multi-temporal Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM), and SPOT 5 multispectral data of the area. The post classification change detection algorithm was adopted in this study, since it has the ability of providing difference maps, from which the “From-To” change information can be generated using satellite images acquired at different times and from different sensors.
The overall four-class classification accuracies averaged 90.74 % for the three years, and a multi-date post-classification comparison change detection algorithm was used to determine changes in land cover at two intervals, 1990-2001, and 2001-2010. The maps showed that between 1990 and 2010, the amount of urban or built-up land increased by 16.19 % of the total area, while forest cover decreased by 14.45 %. Rural cover types of agriculture, which also includes grasslands along with water bodies have also declined significantly. The results from the 2020 Cellular Automata Markov (CA_Markov) projection indicated a further increase of 0.5% (3.49 km2) in the urban built-up class category, which is mainly contributed by forested areas. The results quantify the land cover change patterns in the metropolitan area and also demonstrate the potential of multi-temporal Landsat and SPOT data to provide an accurate, and economical means to map, analyze and project changes in land cover over time that can be used as inputs to land management and policy making. | en_US |