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標題: 集水區崩塌地植生復育影響因子及治理區位優選之研究
Affecting factors of vegetation recovery and the priority of management sites for the watershed landslides
作者: 林于筌
Lin, Yu-Chuan
Contributors: 林昭遠
Chao-Yuan Lin
水土保持學系所
關鍵字: 崩塌地;多變量分析;植生復育率;優先治理
Landslides;Multivariate Analysis;Vegetation Recovery Rate;Management Priority
日期: 2012
Issue Date: 2013-11-07 13:17:50 (UTC+8)
Publisher: 水土保持學系所
摘要: 莫拉克颱風於2009年8月6日至10日為台灣中南部帶來嚴重災害,創紀錄之超強雨勢造成南台灣山區多處村落遭到崩塌而摧毀掩埋、人員傷亡與房屋毀損,並造成土石鬆動以及植生破壞,地形地貌已全然不同。然風災至今已近三年,有必要對崩塌地進行植生復育評估,瞭解崩塌區位植生復育變遷情況,俾供集水區治理區位優選之參考。
本研究利用旗山溪集水區莫拉克風災前後之衛星影像,萃取崩塌區位,並以風災後一年及二年之衛星影像,分別計算崩塌區位兩期之植生復育率。結果顯示風災後一年及二年;旗山溪崩塌地植生復育率分別為6.16%與28.61%,植生復育率提升了22.45%,表示崩塌區位植生有持續復原之趨勢。另以19項集水區地文因子;利用主成分分析,探討集水區地文因子與植生復育率之關係,可歸類為五個主成份軸,五個主成份軸該因數所能解釋的變異數的比例為90.82%。再以K-means群集分析將崩塌地植生復育率分為優、好、普通及差等四類,利用判別分析具顯著影響之集水區地文因子,可得四組之Fisher’s線性區別函數,其分類準確第一年達60%、第二年可達90%;兩年間植生復育差值之分類精準可達77.5%,得知集水區地文因子對崩塌區位之植生復育率有顯著之影響,其解釋能力隨演替時間越久有越好之趨勢。
另將集水區劃分成40個管理分區,以各管理分區之災後兩年以及兩期差值之植生復育率配合 K-means群集分析,篩選出植生復育率較差之10個分區,做為須優先治理之區位,研究成果可供相關單位參考。
Typhoon Morakot dumped torrential rains that caused serious flooding in the central and southern Taiwan. This serious storm had washed out the villages in the mountain and caused large amounts of landslide. Three years passed, the vegetation recovery rate (VRR) of the landslides needs to be evaluated to know the changes of vegetation recovery status for the priority selection of the management sites in a watershed.
SPOT satellite images of the Chishan streams watershed were applied to extract the spatial distribution of landslides and calculate the VRR of the landslides. Results show that the average of VRR is 6.16% for the 1st year and 28.61% for the 2nd year. The increase of 22.45% means that the recovery status is better. Principle component analysis depicts that five principle components can be derived from 19 topographic factors and there is about 98.82% interpretation. Furthermore, four categories (excellent, fine, ordinary and worse) of VRR were classified using K-Means clustering method, which coupled with discriminant analysis can get four sets of Fisher’s Linear Discriminator to understand the effects of topographic factors on the VRR of the watershed landslides. The accuracy can reach up to 60% for the 1st year and 90% for the 2nd year, while the difference of the two phases is 77.5%. It shows that watershed topographic factors can efficiently interpret the VRR of the landslides.
The interested watershed can then be divided into 40 sub-watersheds. Kmeans analysis coupled with the data of VRR derived from two-year post-disaster and/or the difference of two-phase satellite SPOT images are applied to screen the worst VRR category for the use of management priority. The results can be as the references of related authorities for watershed landslide rehabilitation.
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