Object-based Classification for Detecting Landslides and Vegetation Recovery-A Case at Baolai, Kaohsiung

This study used object-oriented analysis to classify landslides at Baolai village by using Formosat-2 satellite images. We used multiresolution segmentation to generate the blocks and hierarchical logic to classify five types of features. We then classified the landslides and used univariate image differencing to observe the vegetation recovery after 6 years. We used the SHALSTB model to integrate landslide susceptibility maps. This study used the extreme example of 2009 typhoon Morakot, in which precipitation reached 1991.5 mm in 5 days, and selected a 1% sample with the highest modified success rate to produce the highest landslide susceptible area. Both software programs exhibited high overall accuracy and kappa values. Because of boundary confusion, there were some flaws in calculation. From 2009 to 2015, the landslide area decreased 50%. However, the river bank remains unstable because of the ongoing erosion process. The landslide susceptibility maps indicated that the old landslide area was susceptible to landslides in an extreme event; however, we underestimated the landslide area.