台東太麻里溪集水區地景變遷之研究

Abstract

土地覆蓋變遷分析對於提供集水區土地經營、監測及規劃等資訊是極為重要的。本研究呈現2005～2011年間東台灣太麻里集水區大規模地景變遷與其頻繁自然干擾間的關係。為了解土地覆蓋變遷與自然干擾間之關係，我們採取複合式的地景分析途徑，應用了地景指標、馬可夫鏈模式、改變軌跡及邏輯斯迴歸模式。組成性地景指標分析結果顯示崩塌地面積擴張7.5倍（15.52 km2），而森林面積縮減10.6%（20.90 km2）。空間型態地景指標分析結果顯示森林區塊面積變得較小、形狀不規則及空間上破碎；然而，崩塌地區塊面積變得較大及空間上較為聚集。因此，研究區土地覆蓋變遷主要發生於森林覆蓋的減少和破碎，以及崩塌地數目和面積的增加。在研究期間，地景中最顯著的面積改變是林地轉變為崩塌地和河道，分別約為15.82 km2 和 6.73 km2。就轉移機率而言，人為區塊轉變為林地和河道的機率為最大。透過馬可夫鏈的分析，本研究產生了三種各類土地覆蓋比例未來（2017～2035）的預測狀況。三種預測狀況分別被視為對於下游居民生命及產財具有高、中、低的風險性。 FL、FC及 VR 三個改變軌跡共占整個集水區全部改變面積的75.65%，他們被視為主要的改變趨勢，而且代表太麻里集水區的整體地景（OL）變遷趨勢。改變分析結果顯示地景變遷易發生於畢祿山層地質、鄰近斷層和河道、坡度大於35°及東向坡的條件下。就模式驗證而言，FC模式具有最高的AUC值。然而，RCI指標值顯示變遷機率的預測皆能高度符合實際的改變軌跡。因此，本研究得出的四個邏輯斯迴歸模式有助於地景變遷的預測。

Analysis of land cover changes is fundamental for providing information in relation to watershed land management, monitoring, and planning. This study reveals large-scale land cover transformation in relation to frequent natural disturbances within the Taimali watershed in eastern Taiwan during 2005–2011. To understand land cover changes in relation to natural disturbances, a combined landscape analysis approach using landscape metrics, the Markov chain model, change trajectories, and the logistic regression model is used. Results of composition metrics analysis show that areas of landslides within the region had expanded 7.5 times (by 15.52 km2), but areas of forest had shrunk by 10.6% (20.90 km2). Spatial configuration metrics analysis indicates that patches of forest are becoming smaller, more irregular, and more spatially fragmented, but that landslide patches are expanding and becoming more spatially aggregated. It is considered that land cover changes in the area have occurred mainly through loss and fragmentation of forest cover, and an increase in the number and area of landslides. During 2005–2011, the most noticeable area changes related to transitions from forest cover to landslides (15.82 km2) and channels (6.73 km2). Results show the transition probabilities of human-made patches changing into forest cover and channel corridors are the greatest. Through Markov chain analysis, three future (2017–2035) projections of the proportions of each land cover type are produced. These three predictive statuses are regarded as posing a high, moderate, and low risk, respectively, to life and property downstream. Three change trajectories, FL, FC, and VR, covering 75.65% of the entire changed area, are considered main changes in trend and represent overall landscape (OL) changes in the Taimali watershed. Results of change analysis indicate that the occurrence of landscape change is subject to the geologic condition of Pilushan Formation, the vicinities of faults and rivers, a gradient greater than 35°, and the eastward slope. As for model validation, the FC model has the highest AUC value. RCI values suggest that the prediction of change probabilities could correspond with the actual change trajectories very well. Therefore, the four logistic regression models are useful tools in the landscape change prediction.