Remote Sensing of Urbanization and Environmental Impacts

This thesis aims to establish analytical frameworks to map urban growth patterns with spaceborne remote sensing data and to evaluate environmental impacts through Landscape Metrics and Ecosystem Services. Urbanization patterns at regional scale were evaluated in China's largest urban agglomerations and at metropolitan scale in Shanghai, Stockholm and Beijing using medium resolution optical satellite data. High-resolution data was used to investigate changes in Shanghai’s urban core. The images were co-registered and mosaicked. Tasseled Cap transformations and texture features were used to increase class separabilities prior to pixel-based Random Forest and SVM classifications. Urban land cover in Shanghai and Beijing were derived through object-based SVM classification in KTH-SEG. After post-classification refinements, urbanization indices, Ecosystem Services and Landscape Metrics were used to quantify and characterize environmental impact. Urban growth was observed in all studies. China's urban agglomerations showed most prominent urbanization trends. Stockholm’s urban extent increased only little with minor environmental implications. On a regional/metropolitan scale, urban expansion progressed predominately at the expense of agriculture. Investigating urbanization patterns at higher detail revealed trends that counteracted negative urbanization effects in Shanghai's core and Beijing's urban-rural fringe. Beijing's growth resulted in Ecosystem Services losses through landscape structural changes, i.e. service area decreases, edge contamination or fragmentation. Methodological frameworks to characterize urbanization trends at different scales based on remotely sensed data were developed. For detailed urban analyses high-resolution data are recommended whereas medium-resolution data at metropolitan/regional scales is suggested. The Ecosystem Service concept was extended with Landscape Metrics to create a more differentiated picture of urbanization effects.​QC 20160205