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

Ying Tong Lin, Kuo Chen Chang*, Ci Jian Yang

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
頁(從 - 到)98-109
頁數12
期刊Journal of Chinese Soil and Water Conservation
49
發行號2
DOIs
出版狀態已發佈 - 2018 六月 1

ASJC Scopus subject areas

  • 水科學與技術
  • 岩土工程與工程地質
  • 土壤科學
  • 地表過程

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