| Land cover and land use data provide important information for environmental management and planning. Traditionally, field surveys of land cover and land use are time consuming and costly and only provide tabular statistics without geographic location information. Satellite remote sensing classification provides a more effective way. However, many of the previous remote sensing classification related studies only focus on land cover inventory and mapping of Level-1 or -2 classes without further cause and effect analysis. In addition, the studies of land use and land cover change dynamics were limited. This research extended land cover classification in TCMA into level-3 classes with percent impervious surface by using multi-seasonal Landsat TM images. Moreover, this research analyzed the land cover changes of TCMA over the past 20 years systematically to develop a comprehensive understanding of land cover dynamic process. An integrated remote sensing and GIS-based analysis methodology is used and emphasized for its capabilities of managing and manipulating large amount spatial data. Specifically, this research firstly describes a three-stage hybrid classification method for regional-scale multi-level land cover mapping and monitoring in the TCMA using multi-seasonal Landsat images of 2000. The overall classification accuracy is 95% for the first level and 80% for the second level. Next, land cover change detection from 1986 to 2002 was performed and illustrated using multi-temporal Landsat remote sensing and GIS spatial analysis. The accuracy of land cover change maps was evaluated using several approaches with overall accuracies ranging from 80 to 90%. The change maps showed that the amount of urban or developed land increased from 23.8 to 33.4% of the total area between 1986 and 2002. The results quantify the overall land cover change patterns in the metropolitan area and can be used to project future transformation patterns, analyze landscape diversity, and examine relationships of development to geographical features such as distance to highways and population density. |
