評估不同數值地型資料於降雨型崩塌作用模式之應用性-以小尺度坡面之崩塌事件為例

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阿孜孜(Azizi Dermawan) 查詢紙本館藏

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遙測科技碩士學位學程

論文名稱

評估不同數值地型資料於降雨型崩塌作用模式之應用性-以小尺度坡面之崩塌事件為例
(Assessing the Applicability of a Physically-based Rainfall-induced Landslide Model at a Hyper-Local Slope with Different Digital Elevation Models)

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摘要(中)

極小尺度(hyper-local)邊坡之崩塌災害之風險評估為一相當重要之課題，由一些過去案例發現，儘管小尺度邊坡之崩塌面積很小，但仍可能致災性極高，特別是崩塌的影響範圍與地方居民高頻度活動的地區(如道路、民居等)重疊時。過去研究使物理型之崩塌模式進行災害預測或評估時，通常需要使用相當高空間解析度的地形參數。過去已有相關研究試驗了使用不同的空間解析度的數值高程模型(Digital Elevation Model, DEM)對崩塌模式產生的影響，並建議使用高解析度的DEM(5-m至10-m)可針對崩塌的發生位置有更準確的預測結果。但是，對於崩塌崩落物(如土石流)於邊坡運動的模擬應用上，DEM適用之解析度範圍仍值得探討。本研究針對印尼東爪哇省，瑪琅區本多薩里村的一個由降雨誘發之極小型之崩塌進行研究，方法為應用一物理型的崩塌-土石流整合模式進行試驗。此試驗為使用不同來源、不同解析度的DEM進行崩塌位置預測以及土石流模擬並進行比較，以了解DEM空間解析度對模式預測所造成之影響。
在本研究中使用了三種不同來源的DEM，包含(1)8公尺之印尼國家標準數值高程模型(8-m ArcDEM)，(2)使用即時動態全球定位系統測量生成的5公尺解析度之數值高程模型(5-m RtkDEM)，以及和(3)由無人飛航照產製之2公尺數值高程模型(2-m UavDEM)。在模式的應用上，本研究於現地採集土壤樣本並進行實驗室實驗以獲得模式所需之土壤物理參數，同時在當地雨量站收集歷史之日雨量記錄。研究結果發現，使用2-m UavDEM可以更準確地預測崩塌位置，而其他DEM則有較明顯的預測錯誤情形。另外，使用2-m UavDEM在模擬研究地點的土石流(崩塌影響範圍)也有較不錯的結果。本研究成果並顯示針對及小尺度邊坡進行邊坡穩定評估的重要性，對於提高當地社區對崩塌災害之認知、防治有其助益，使之能減少山坡社區之崩塌災害風險。

摘要(英)

The risk assessment of landslide hazard over small (hyper-local) slopes is crucial because several cases have revealed that, a small landslide can be fatal despite its small size, especially when the affected area overlaps with the residential area. The prediction of the hyper-local landslide generally requires topographic parameters with high spatial resolution when the physically-based modeling approach is applied. Previous studies have examined different digital elevation models (DEMs) and suggested that finer resolution DEMs (5-m to 10-m) produced more accurate results for predicting the landslide initiation points. However, the suitable DEM resolution used for simulating the damaged area affected by landslide runout (or debris flow) is still questionable. Thus, the comparison of different resolutions of topographic datasets will be examined in this study, for simulating a rainfall-induced landslide and its affected area at a hyper-local slope in Bendosari village, Malang district, East-java, Indonesia.
In this study, three different DEMs were used in an integrated landslides and debris flows modeling; they are (1) the 8-m Indonesian National Standard Digital Elevation Model (ArcDEM), (2) the 5-m DEM generated by real-time kinematic-global positioning system survey (RtkDEM) and (3) the 2-m DEM constructed by the unmanned aerial vehicle mapping (UAV). To perform the model, soil parameters were obtained from the laboratory test, while the daily rainfall records were collected at a local station. The result shows that the shallow landslide is more accurately predicted by the 2-m UavDEM, and it has less overestimation than the 5-m RtkDTM. For 8-m ArcDEM, it also predicts false landslides in the study site. The result indicates the 2-m UavDEM gives a more effective response for simulating the landslide and debris flow in the study site and also shows its significance to promote local community awareness on slope stability for reducing landslide disaster risks.

關鍵字(中)

★ DEM解析度
★ 極小尺度邊坡
★ 崩塌-土石流整合模式
★ 降雨型崩塌
★ 土石流

關鍵字(英)

★ DEM resolution
★ hyper-local slope
★ integrated landslide-debris flow model
★ rainfall-induced landslide
★ debris flow

論文目次

Page
中文摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vii

Chapter 1. Introduction 1
1.1. Background 1
1.2. Problem Statements 4
1.3. Importance and benefits 6
1.4. Structure of the thesis 7
Chapter 2. Literature Review 8
2.1. Landslide modeling 8
2.2. Landslide studies on hyper-local slopes 9
2.3. The application of DEM data 9
2.4. Overview of LEWS’s development 10
Chapter 3. Study Area 12
3.1. The location of study area 12
3.2. InaRISK Map 17
3.3. Documentation of the study area 18
3.4. Workflow of study 20
Chapter 4. The Application of Integrated Landslide and Debris Flow Model 23
4.1. Critical rainfall model 23
4.2. Debris Flow Model 24
4.3. Rainfall Data 25
4.3.1. Available stations 25
4.3.2. Official records 26
4.4. Digital elevation model 27
4.4.1. ArcDEM 27
4.4.2. RtkDEM 27
4.4.3. UavDEM 29
4.5. Flow Algorithm 30
4.6. DEM datasets 32
4.7. Soil physical parameters 34
4.8. Root cohesion 37
Chapter 5. Results 39
5.1. Constructing Digital Elevation Models 39
5.2. Critical rainfall maps 42
Chapter 6. Discussions 49
6.1. The construction of DEM datasets 49
6.2. The application of different DEM datasets 49
6.3. The applicability of topographic data in landslide risk assessment 52
Chapter 7. Conclusions 55
References 56

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指導教授

姜壽浩(Shou-Hao Chiang)

審核日期

2020-8-25

推文