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Abstract 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. Acknowledgments We thank the Water Resource Agency Southern Bureau for funding the 2004 Tsengwen Project. We thank Shou‐Hao Chiang for assisting in the validation of landslides using orthophotographs. Thanks also to Simon Dadson, Colin Stark and the anonymous reviewers for their useful comments.
Borghuis et al. (Sun,) studied this question.