本研究利用物件導向式遙測影像分析方法進行大範圍坡地與河川災害快速判釋，以改善像元式影像分析方法於分類時常有之雜訊問題。研究使用福爾摩沙二號衛星在莫拉克颱風事件前後遙測影像，利用災害前後期影像同時進行均質化分割技術，於兩張影像上產生出相同區塊，以解決不同時期影像之地貌分類邊界問題；以區塊為分析單元，依照人工判釋經驗與階層式的邏輯，加入合適之空間特徵資訊（包括光譜值、坡度等），訂定分類規則流程，由單純至複雜的地貌進行十四種分類，建立出有系統之分類程序，以作為未來災前後影像快速判釋之參考。 應用於研究區之分類結果，進一步與航照判釋及現地踏勘比對，訓練成果由誤差矩陣評估得整體精度達 81.6%；崩壞比由災前的 1.5%增加為 10.5%、河道變遷河段達 307處，災後河道面積相對於災前增加 32%、受影響之開發地達 26%、受影響之房舍達 26% (1179處)、受影響之道路達幾 18% (2281處)。

In this study, object-oriented analysis method was applied to interpret landslide and flood disasters before and after Typhoon Morakot with remote sensing images. It is shown that remote sensing images in wide area can be recognized quickly using this method. Two images before and after the disaster were homogeneously segmented to generate the same blocks in order to solve the boundary problem in feature classification. Based on the rules of artificial interpretation, proper information (ex. spectral values, slopes…etc.) was added to construct hierarchical logic, classifying 14 different kinds of features. The classification results are double checked with aerial photographs and field investigation. The overall accuracy of final training outcome based on the error matrix assessment is about 83.3%. The results show that the landslide ratio increased from 1.5% to 10.5%, blocks features of river channel increased 307 sites, area ratio increasing to 32% compared with the original river channel area, 26% of the developed lands were affected, 26% (1179 sites) of the houses were affected, 18% (2281 sites) of the roads were affected .