Remote Sensing for Detecting Changes of Land Use in Taipei City, Taiwan
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This study combined statistical analysis and remote sensing techniques to explore the environmental effect of land use and land cover changes in Taipei City, Taiwan. Together with SPOT satellite images from multiple periods (1993, 2003, and 2014), unsupervised Iterative Self-Organizing Data Analysis techniques were used to classify images into three categories: water, vegetation (green area), and non-planting (buildings) areas. An accuracy assessment was conducted to analyze the changes in each administrative region and to explore the differences in the green cover ratios of the various administrative regions through the spatial distribution characteristics of satellite images. We found that if the area of green cover must be increased, the need for green coverage and distribution can be quantified through geospatial analysis to identify preferred sites. In addition to increasing the proportion of green coverage in the city, this approach can effectively mitigate changes in the ambient temperature. The overall accuracy and kappa values of this study were more than 90% and 0.8, respectively, indicating that the image classification results had favorable reliability.
KeywordsLand use and land cover (LULC) Remote sensing Iterative Self-Organizing Data Analysis (ISODATA) Accuracy assessment
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