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系統識別號 U0026-0809201510115000
論文名稱(中文) 多尺度地形粗糙度分析與雷射掃描資料之空間幾何特徵
論文名稱(英文) Geospatial Analysis of Multi-Scale Topographic Roughness and the Morphological Characteristics of LiDAR Data
校院名稱 成功大學
系所名稱(中) 地球科學系
系所名稱(英) Department of Earth Sciences
學年度 103
學期 2
出版年 104
研究生(中文) 楊孟學
研究生(英文) Mon-Shieh Yang
學號 L48971041
學位類別 博士
語文別 英文
論文頁數 75頁
口試委員 指導教授-吳銘志
口試委員-曾義星
口試委員-丁澈士
口試委員-張中白
口試委員-劉進金
口試委員-Bernhard Höfle
中文關鍵字 山崩  碎形維度  半變異元圖  空載光達 
英文關鍵字 Landslides  Fractal dimension  Semivariogram  Airborne LiDAR 
學科別分類
中文摘要 多變的氣候環境及嚴峻的地形,加上頻繁的土砂災害使得臺灣的河川流域經營管理成為一項極具挑戰的工作;尤其災後的沉積物及流水,直接影響河川的動態平衡,甚至導致二次災害的發生,因此河川的管理是尚今一項重要的課題。
高解析度地形資料具有判釋及評估災害型態與規模的能力,能夠提供更為詳細且完整的地表災害。本研究期以高解析度的地形資料提供災害發生前後之河床粗糙度的變異型態。此外,由於傳統調查方式,往往受限於災區交通及天候因素,而無法提供即時且完整的地表災害資訊,因此本研究中藉由遙測資料輔助,分析山崩沖積扇的幾合形態變化,其成果也可延伸應用於災害敏感區的劃設。
另外,本研究亦嘗試分析比較常用的規則網格,以及點雲資料在粗糙度計算及應用的差異;爰此,本研究利用標準樣本資料,分析不同資料格式間的穩定度及其可靠度,經過半變異元圖的分析成果,發現利用規則網格資料進行粗糙度運算,可能會導致成果較點雲資料更為粗糙的結果,也可能會導致在實際應用上較為不穩定的成果。
英文摘要 Unpredictable weather condition and highly complex topography; in addition with frequent land mass disaster, which have made river management a substantial challenge in Taiwan. Especially, the sediment and incoming flow will directly affect the dynamic balance of the river. Even more, to induce the occurrence of secondary disaster. Namely, river management has currently become an important issue for today’s disaster prevention and mitigation.

High-resolution topographic data are capable of describing the morphological features and evaluating the magnitude of a disaster; thus, providing more detailed and more complete information of the disaster on the land surface.

Purposes of this study were to demonstrate the implementation of high resolution topographic data to show the multitemporal variability of river bed morphology through roughness mapping; before and after the disaster. In addition, due to the limitation of weather condition and abruption of transportation in the disaster region, the traditional investigation method would not be able to provide a real-time and complete information of the disaster. Therefore, this study has adopted the aids of remote sensing data to analyze the change of various geometric landform for a colluvium fan; results of the study may be extensively applicable for regional mapping of the vulnerable area.

Besides, this study have also tried to analyze and compare the variability of roughness calculation and application between the commonly used regular grid raster data and the point cloud data. Thus, for a greater understanding of the spatial scale-dependent roughness at different scales and resolutions, semivariograms were adopted to determine the effectiveness of data in representing roughness in this study; the range of semivariograms can be a clear identification that raster data generate “rougher” results compared with point cloud data. In a smooth area, the results demonstrated low similarity among point cloud data, indicating that point cloud data are smoother than raster data.
論文目次 摘 要 I
ABSTRACT II
ACKNOWLEDGEMENTS III
TABLE OF CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES VII
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Research Motivation 4
1.2.1 Riverbed morphology analysis 5
1.2.2 Disaster mapping 5
1.3 Research Objective 5
1.3.1 Multitemporal riverbed roughness mapping 6
1.3.2 Characterization of colluvium fan morphology 6
1.3.3 Characterization of the data structure 7
CHAPTER 2 DIGITAL ELEVATION MODEL AND LASER SCANNING SYSTEM 8
2.1 Digital Elevation Model 8
2.1.1 Irregular data structure 8
2.1.2 Raster data structure 10
2.1.3 Types of digital elevation models 10
2.2 Laser Scanning System Principle 11
2.2.1 Terrestrial laser scanning system 13
2.2.2 Airborne laser scanning system 15
2.2.3 Airborne light detection and ranging for the national mapping program of Taiwan 16
CHAPTER 3 GEOLOGICAL HAZARDS AND GEOMORPHOMETRIC PARAMETERS 18
3.1 Study Area 18
3.2 Topographic Data 19
3.3 Definition of Roughness 20
3.4 Geomorphometric Parameters for Disaster Mapping 21
3.4.1 Surface roughness as a geomorphometric variable 22
3.4.2 Slope gradient index 22
3.4.3 Slope-based roughness index 23
3.4.4 Perimeter-area fractal dimension method 25
3.5 Multitemporal Analysis of Riverbed Roughness 27
3.6 Riverbed Roughness Variation Caused by A Typhoon Event 34
3.7 Colluvium Deposits and Morphology 35
3.8 Summary and Discussion 36
3.9 Limitations of Data Structures 37
CHAPTER 4 RASTER AND POINT CLOUD DATA ON SURFACE ROUGHNESS 39
4.1 Materials 40
4.2 Benchmark Point Cloud Data 41
4.3 Roughness Index for Grid and Point Structures 42
4.3.1 Derivation of roughness 43
4.3.2 Experimental procedure 44
4.4 Box-Counting Fractal Dimension Method 45
4.5 Semivariogram 47
4.5.1 Characteristics of the semivariogram 47
4.5.2 Semivariogram model 48
4.6 Results 49
4.6.1 Fractal dimension of multiscale roughness 49
4.6.2 Spatial autocorrelation of multiscale roughness 55
4.6.3 Spatial properties of the data structure 62
4.7 Summary and Discussion 65
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS 67
5.1 Conclusions 67
5.2 Recommendations 69
BIBLIOGRAPHIES 70
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