Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98573
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dc.contributor.authorYing-Tong Linen_US
dc.contributor.authorKuo-Chen Changen_US
dc.contributor.authorCi-Jian Yangen_US
dc.contributor.author林穎東zh_TW
dc.contributor.author張國楨zh_TW
dc.contributor.author楊啟見zh_TW
dc.date2018-02zh_TW
dc.date.accessioned2019-04-18T01:16:30Z-
dc.date.available2019-04-18T01:16:30Z-
dc.identifier.urihttp://hdl.handle.net/11455/98573-
dc.description.abstractThis 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. Key Words: Object-oriented, classification, landslide, Baolai Village, FS.en_US
dc.description.abstract本研究設計半自動化崩塌地判釋系統,對高雄寶來地區崩塌地,進行坡地災害潛勢預估。分別使用 eCognition 軟體和 ENVI 軟體,以階層式分類方式和支持向量機進行自動化判釋,並輔以專家判釋檢核,判釋 崩塌地類型。利用直接相減法,以 2009 年及 2015 年影像的常態化差值植生指標 (NDVI) 運算,得到該崩塌 地六年間增減情況。此外,本研究另以 SHALSTLB 模式,使用 NDVI、坡度、岩性、事件的降雨量,自動模 擬,取模式適配性最高的前 1 %筆資料,繪製崩塌地潛勢圖。 研究發現使用上述兩種軟體進行物件式導向判釋,整體精度均可以超過 90 %,Kappa 值超過 80 %,但兩者在 河道與崩塌地的判釋上,仍出現部分瑕疵,原因可能是地物邊界混淆。從 2009 到 2015 年,崩塌地已縮小 50 %面積,但是在河道攻擊坡仍呈現不穩定,甚至擴大崩塌的趨勢。SHALSTLB 的模擬顯示最有可能發生崩塌 地的位置,多集中在舊崩塌地冠部及坡腳處,但是在崩塌地面積的推估上出現低估現象。zh_TW
dc.language.isozh_TWzh_TW
dc.relation中華水土保持, Volume 49, Issue 2, Page(s) 98-109.zh_TW
dc.subjectObject-orienteden_US
dc.subjectclassificationen_US
dc.subjectlandslideen_US
dc.subjectBaolai Villageen_US
dc.subjectFSen_US
dc.subject物件式導向zh_TW
dc.subject影像判釋zh_TW
dc.subject崩塌地zh_TW
dc.subjectSHALSTABzh_TW
dc.titleObject-based Classification for Detecting Landslides and Vegetation Recovery—A Case at Baolai, Kaohsiungen_US
dc.title利用物件式導向進行崩塌地種類判釋、復育追蹤 —以高雄市寶來地區為例zh_TW
dc.typeJournal Articlezh_TW
item.languageiso639-1zh_TW-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeJournal Article-
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item.grantfulltextopen-
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