Two large tropical cyclones struck Taiwan in the summer of 2004 and landslides triggered by these events caused not only casualties and housing damage but also produced large volumes of sediment that entered rivers and reservoirs. For reservoir and watershed management it is important to quickly identify the location and areal extent of new landslides for coordinating mitigation efforts. In this study, two automated methods, supervised and unsupervised classification of 10 m multi‐spectral SPOT‐5 imagery, were tested for their ability to identify and map landslide areas before and after the two storm events. A slope map was applied to mask roads, riverbeds and agricultural fields erroneously commissioned as landslides. The automated classification results were compared with manually delineated landslides using SPOT‐5 supermode satellite imagery with a resolution of 2.5 m. Statistical testing and spatial analysis of the mapping results were performed. Finally, the results from all three methods were validated by using 0.35 m orthophotographs. This paper reports the results and discusses the salient differences between the automated and manual methods.

We developed a multi-scale OBIA (object-based image analysis) landslide detection technique to map shallow landslides in the Baichi watershed, Taiwan, after the 2004 Typhoon Aere event. Our semi-automated detection method selected multiple scales through landslide size statistics analysis for successive classification rounds. The detection performance achieved a modified success rate (MSR) of 86.5% with the training dataset and 86% with the validation dataset. This performance level was due to the multi-scale aspect of our methodology, as the MSR for single scale classification was substantially lower, even after spectral difference segmentation, with a maximum of 74%. Our multi-scale technique was capable of detecting landslides of varying sizes, including very small landslides, up to 95 m². The method presented certain limitations: the thresholds we established for classification were specific to the study area, to the landslide type in the study area, and to the spectral characteristics of the satellite image. Because updating site-specific and image-specific classification thresholds is easy with OBIA software, our multi-scale technique is expected to be useful for mapping shallow landslides at watershed level.

Driven by rising income and urban population growth, China has experienced rapid urban expansion since the 1980s. Urbanization can have positive effects on the urban environment; however, improvement of urban environment quality, especially its divergence between relatively developed and undeveloped cities in China, is currently a rather rudimentary and subjective issue. This study analyzed urban environmental benefits among China’s prefectural cities based on their structure of urban land use in 2000 and 2010. First, we divided 347 prefectural cities into two groups, 81 coastal and capital cities in the relatively developed group (RD) and 266 other prefectural cities in the undeveloped group (RP). Then, we defined three areas of urban environmental benefits, including green infrastructure, industrial upgrade, and environmental management, and developed an assessment index system. Results showed that all prefectural cities saw improvement in urban environmental quality in 2000–2010. Although the RD cities had higher income and more population growth, they had less improvement than the RP cities during the same period. We also found that demographic and urban land agglomeration among RD cities restrained green infrastructure expansion, making green infrastructure unsuitable as a permanent solution to environmental improvement. It is therefore urgent for China to promote balanced improvement among the three areas of urban environmental benefits and between the RD and RP cities through regional differentiation policies.