台灣受地狹人稠、降雨集中加上川短流急等等因素的影響，在國家用水上多仰賴水庫等大型蓄水設施，然而受颱風等強降雨的侵襲，大量泥沙灌入壩中，嚴重縮短了水庫的使用年限。因此若能預估集水區的水土流失量，便能對症下藥，減緩壩底淤泥的增加。本研究區域位於曾文水庫上游之樂野集水區，該區地勢陡峭、邊坡穩定性差、侵蝕作用旺盛。研究目的為結合高光譜影像與RUSLE進行土壤沖蝕情形的評估。
在目前廣泛使用的修正版通用土壤流失公式（RUSLE）中，土壤抗侵蝕能力與土壤粒徑息息相關。因此本研究將RUSLE與高光譜影像做結合，透過現地調查評估研究場址之土壤粒徑與光譜資料相關性，並將其應用至高光譜影像，藉此獲得大範圍且快速的土壤流失量評估。
針對高光譜影像的特徵萃取以及影像降維的部分，本研究採用MNF轉換來進行，同時為了降低濾除雜訊時不同地物特徵造成的影響，利用物件式分類將研究區域劃分成林地、裸露地、建物、河道、農作地等類別，取出裸露地區域作為遮罩以方便後續高光譜影像的處理，並將分類結果用作於RUSLE中覆蓋及管理因子的評估上。隨後利用MTMF偵測土壤純淨像元，並利用現地光譜分析結果，得出像元粒徑並將其計算成土壤沖蝕性指數。最後將各因子進行乘積得出土壤流失量預估模型，將其與現地沖蝕釘資料進行比對，探討高光譜影像結合RUSLE之可行性。
在粒徑上，本研究受高光譜拍攝時間與現地試驗時間的因素，無法直接驗證影像粒徑分析之準確性，但亦利用坡度等間接證據，對合理性進行評估，並得出具有一定可行性的結論。而針對土壤流失上，本研究雖然低估了該研究場址之土壤流失情形，但整體的數值尺度十分相似，仍可作為快速評估沖蝕潛勢的手段之一。

With the climate changing and land development, soil erosion is the growing problem in conservation of soil and water within watershed. This study intends to use Revised Universal Soil Loss Equation (RUSLE) with hyperspectral data to analyze the soil properties rapidly and to estimate the quantity soil loss in the Leye watershed at Zengwun reservoir.
Soil erodibility is an important factor related to geometric mean particle diameter (Dg) of soil while applying to RUSLE. This study analyzes the relationship between soil particle size and spectral data via field survey. On the other hand, the pure soil pixels of the hyperspectral image are extracted by MNF transform and MTMF method. Then the distribution of the soil properties and soil erodibility factor (K) will be calculated by applying the result of the field test onto the soil pixels. Other parameters of RUSLE are calculated with remote sensing data, like DEM (Digital Elevation Model). Finally, the distribution and amount of soil loss can be predicted by integrating all parameters and verified by the ground truth data from field erosion pins test.
According to the results of spectral analysis, Dg of the soil is closely related to spectral reflectivity strength and the field data consists with hyperspectral images. So, the Dg and soil loss of each pixel in the Leye watershed could be estimated quickly. However, the potential soil loss is underestimated and inconsistent with ground-truth data at this work. It is caused by: (1) the RUSLE is a long-time empirical model designed for gentle area, therefore it is unsuitable to this study. (2) the soil data is discrete, so the kriging method is inapplicable in this case. (3) any parameter of RUSLE is influenced by other parameters and resulting a low accuracy consequence.

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