台灣氣候多雨，且位於環太平洋火山地震帶，每年發生地震的次數頻繁使地表土質鬆動，加上每年颱風及梅雨帶來豐沛雨量，導致山區蝕溝持續發育，往往導致災難。蝕溝的位置與分布在以往因技術的限制所以取得相當困難，而現在則可以透過空載光達的技術取得高解析度地表數值地形（DEM），本研究透過小波分析、Canny算子、蝕溝篩選以及雜訊過濾等步驟，設計出一套客觀且自動化的流程來找出蝕溝的分布。本研究的DEM範圍為曾文水庫集水區北段，拍攝時間為2015年12月，涵蓋範圍約140平方公里，在執行完蝕溝自動化搜索流程後，於2017年1月至現場勘查是否為蝕溝，該自動化流程根據Confusion Matrix的計算結果其精準度(Accuracy)約為65.81%。除了蝕溝自動化搜索，本研究偵測研究區域中的蝕溝發育情形，利用地形濕度因子(TWI)、坡度、坡向、順向坡、土地利用、曲率以及地層岩性等七個因子，再搭配本研究所設計的蝕溝自動搜索流程作為目標屬性，評估各個因子與蝕溝的相關程度，計算出蝕溝發育潛勢圖，該結果可以利用成功率曲線來判斷其精準程度，精準度為66.07%。因台18線為此區域的主要幹道，每年因豪大雨所發生的災害頻仍，所以依據蝕溝發育潛勢來評估台18線道路兩側的蝕溝侵蝕程度，標示出受蝕溝侵蝕的危險路段，則為豪雨時極可能發生土石崩落以及路基掏空的位置。

Recently, the technology of Aerial LiDAR was developed quickly, so we could produce a more accuracy digital elevation model (DEM) with higher accuracy. Based on this high resolution DEM, an automatic and objective process to uncover the distribution of gullies via the following methods : “wavelet analyst”, “Canny edge detector”, “define the gully-shape” and “perform non-linear noises removing”. The 140 〖km〗^2 DEM used in this study is at the northern part of Tseng-Wen Reservoir Catchment in southern Taiwan. The points cloud which formed the DEM was captured at Dec. 2015. After appling the automatic searching model for the DEM, we inspected the results on site at Jan, 2017. Compared the results of the automatic model and the site inspection by Confusion Matrix, the total accuracy of automatic model is 68.88%.

The 18th Taiwan Province highway, we also call Alishan-Motorway, is the trunk major traffic road to access the study area. It is very important for residents and tourists for the traffic mission, but Alishan-Motorway also suffered sever disasters, such as landslide and subgrade-emptieded, during heavy rain almost occurred every year. This study considered that some of the disasters that could related to the growth of gully. Therefore, another purpose in this study is to assess the growth potential of gully. Because the development of gully is triggered by many factors, this study used seven topographic factors as the attributes and the gully-distrubution from automatic gully detection model as the target attribute to map the growth potential of gully. The accuracy of the potential map is 66.07%. Finally, this study search the the area which could be threatened by the vagorous grouth of gully. If the dangered locations are spreated at the upper slope, the landslide might happened, else if the dangered locations should scattered at the lower slope, the the subgrade-emptieded may happen.

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