通用土壤流失公式之覆蓋管理因子與空間資料關係建置

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/71227

Title:	通用土壤流失公式之覆蓋管理因子與空間資料關係建置
Authors:	林亭均;Lin,Ting-Chun
Contributors:	土木工程學系
Keywords:	覆蓋管理因子;通用土壤流失公式;空間分析;Minnaert Correction;半變異元演算法;資料探勘
Date:	2016-08-24
Issue Date:	2016-10-13 12:13:38 (UTC+8)
Publisher:	國立中央大學
Abstract:	世界各地廣泛使用的通用土壤流失公式，為一描述土壤侵蝕過程的數學模型。此公式計算侵蝕造成土壤流失量所使用的六個參數中，覆蓋管理因子 (C-factor) 與土地使用情形有關，其常用數值從0.001至1，達千倍差異，因此對通用土壤流失公式計算影響甚鉅。過去估算覆蓋管理因子的方式包括：現地實驗、以物理參數模擬、使用土地利用類別賦予C值轉換表進行估算，以及以植被指標與覆蓋管理因子之關係方程式計算而得。然而上述之覆蓋管理因子建置方式除資料獲取及更新不易外，其估算合理性也有疑義。故本研究利用衛星影像等相關空間資料，以資料探勘方式，建構一套C值推估模型，進以計算土壤沖蝕造成之流失量。本研究以石門水庫集水區為研究區域，本區域主要土地利用類別為森林。本研究以SPOT-4、SPOT-5衛星影像、10公尺網格數值地形模型，及其他相關空間資料與地質資訊推估2004年至2008年之覆蓋管理因子。SPOT衛星影像已經過簡易幾何校正，及有效減少向陽面、背陽面造成之同物異譜情形之Minnaert correction。然後，本研究以Gray Level Co-occurrence Matrix計算影像之紋理指標以作為資料探勘之條件屬性之一，萃取空間資料特性。接著，本研究以決策樹演算法將資料進行初步資料探勘，並於此步驟中考慮條件屬性的優先採用順序以及決策因子數量的優化，以建置更優化之資料探勘模型。最後，本研究使用隨機森林演算法，利用9種決策因子估算研究區域之覆蓋管理因子。研究結果顯示，資料探勘建置模型推估之覆蓋管理因子之整體精度為73 % - 79%，kappa為0.7-0.758。而根據資料探勘成果，覆蓋管理因子計算成果與2002年土地利用資料之整體一致性 (overall agreement) 為60.5% - 62.4%。依本研究推估之覆蓋管理因子，考慮泥沙遞移率後，所估算之砂土量為 74.032 - 129.756（公噸/公頃-年），對照政府公告之石門水庫泥砂沉積量與清淤量（dredging），本研究估算之成果為一合理且相近之數值。 ;The Universal Soil Loss Equation (USLE) is a widely used mathematical model that describes soil erosion processes. Among six different soil erosion risk factors of the equation, the cover-management factor (C-factor) is related to the land use type. Commonly used values of C-factor are from 0.001 to 1, so C-factor might cause a thousandfold difference in USLE calculation. The traditional methods for estimating C-factors are in situ experiments, soil physical parameters models, look up tables with land use map and regression equations between vegetation indices and C-factors. However, the methods described above have limitations in data acquisitions or updating; they may also result in unreasonable estimations. Thus, this research tries to construct a relationship between C-factor and spatial data with a data mining algorithm. The study area is Shimen reservoir watershed in Taiwan. The major land cover here is forest. The SPOT-4, SPOT-5 images, 10m DEM, a land use map and other spatial data were used in this research. The SPOT images were processed with basic radiometric correction and topographic correction with Minnaert constant, which can reduce the difference of apparent radiance between phototropic and apheliotropic regions. After that, Gray Level Co-occurrence Matrix (GLCM) was used to build statistical feature indicators for data mining. A decision tree classifier was used to rank influential conditional attributes in the preliminary data mining. Then, factor simplification and separation were considered to optimize the model and the random forest classifier was used to analyze 9 simplified factor groups. The overall accuracy of data mining model check points is about 73% - 79% with a kappa value of 0.7-0.758. According to the analysis results, the overall agreement between the look-up table approach and the data mining generated C-factor is about 60.5% - 62.4%. The calculated total soil erosion amount in 2004-2008 according to the data mining results is about 74.032 - 129.756 ton/ha-year with the sediment delivery ratio and correction coefficient. Comparing with the sediment and dredging data published by the Shimen watershed administration authority, the experimental results indicated that using spatial analysis to determine C-factor can produce more reasonable soil erosion estimation.
Appears in Collections:	[Graduate Institute of Civil Engineering] Electronic Thesis & Dissertation

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