應用多時期Sentinel-1 合成孔徑雷達影像進行崩塌及淹水偵測-以印尼爪哇島Pacitan地區為例

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研究生：	曼黎 Mohammad Daman Huri
論文名稱：	應用多時期Sentinel-1 合成孔徑雷達影像進行崩塌及淹水偵測-以印尼爪哇島Pacitan地區為例 Landslide and Flood Mapping Using Multi-Temporal Sentinel-1 C-band SAR Imagery in Pacitan, East Java, Indonesia
指導教授：	姜壽浩 Gilbert Chiang
口試委員:
學位類別：	碩士 Master
系所名稱：	太空及遙測研究中心 - 遙測科技碩士學位學程 Master of Science Program in Remote Sensing Science and Technology
論文出版年：	2019
畢業學年度：	107
語文別：	英文
論文頁數：	40
中文關鍵詞：	崩塌、淹水、合成孔徑雷達、Cempaka颱風、Sentinel-1
外文關鍵詞：	Landslide, Flood, Synthetic Aperture Radar (SAR),, Sentinel-1, Cempaka Tropical Cyclone
相關次數：	點閱：15 下載：0
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印尼鄰近赤道，受颱風災害頻繁，印尼國家災害管理局於2016年指出2011年至2015年期間在印尼各個地區發生約2,425起土石流災害事件，其中爪哇島(Java Island)由其為甚。2017年11月27日至30日期間，位於爪哇島的南海岸－東爪哇省的Pacitan地區受到Cempaka颱風侵襲，為印尼受災最嚴重的地區之一，發生淹水與多處崩塌。在災害發生時，對於崩塌和淹水地區的緊急偵測，對於災害急救與處置極為重要，而目前利用衛星資料可有效應、且經濟地進行災害偵測的工作。在多種衛星資源中，合成孔徑雷達（SAR）受到雲霧覆蓋的影響較小，此特性應在颱風事件期間或災後的快速偵測中非常有用，然而使用雷達資料同時針對淹水及崩塌進行偵測之研究較少，且淹水地區與崩塌地在雷達迴波訊號強度較低，兩者特性相似，不易進行區判。本研究認為可以利用變遷偵測之概念，使用多時序的雷達影像針對此問題進行突破。操作上，本研究應用多時期之Sentinel-1衛星C波段影像，針對Cempaka颱風事件，同時進行崩塌和淹水區域之偵測試驗。首先分析Sentinel-1的多時序影像中崩塌和淹水區域的後向散射係數之時序變遷特性。其次，基於上述特性利用支援向量機（Support Vector Machine, SVM）對崩塌和淹水區域進行影像分類，並對分類結果進行精度評估。試驗結果發現，整合VV及VH極化資料之6組時序影像(共12個波段組合)有最佳的分類結果，總體精度可達81.42％，kappa係數為0.51，說明本研究提出之方法能有效地同時進行崩塌即淹水的監測。

The National Disaster Management Agency of Indonesia (2016) recorded 2,425 incidents of land movement disaster during 2011 to 2015, with locations occurring in various parts of Indonesia. In the South Coast of Java Island, Pacitan where located in East Java is one of the most heavily damaged area, during the tropical cyclones, Cempaka, from 27 to 30 November 2017, and induced floods in the lowland area and landslides in the mountainous area. For landslide and flood detection, satellite data is effective to be applied for larger area with economic cost. Among many kinds of satellite resources, synthetic aperture radar (SAR) has less limitation operating in cloudy conditions, which is considered a very useful characteristic for landslide and flood rapid mapping during cloudy condition. With applying SAR data, few studies have focused on the detection of flood and landslide at once, considering their similar backscattering characteristics which are normally lower and difficult to be distinguished. However, this study proposed a method which analyzes the multi-temporal SAR backscattering to investigate the difference between flood and landslide in time domain.
This study focuses on availability of Sentinel-1 C-Band SAR imagery to detect the landslide and flooded area for Cempaka event. The time series of Sentinel-1 were pre-processed to analyze the backscatter change over the landslide and flooded area. Then, the SVM (Support Vector Machine) classifier was applied to map landslide and flooded areas. The accuracy assessment shows that the best classification result is obtained when combining both six VV and six VH polarization time-series data (twelve-bands in SVM classification). The overall accuracy achieves 81.42% and kappa coefficient 0.51. The result indicates the applicability of the proposed method for landslide and flood detection.

中文摘要     v
ABSTRACT     vi
ACKNOWLEDGEMENTS     vii
Table of Contents     viii
List of Figures and Illustrations     x
List of Tables     xi
 Chapter 1-Introduction     1
1. Research Background     1
2. Statement of Research Problem     5
3. Research Objective     5
 Chapter 2-Literature Review     6
1. Landslide     6
2. Floods or/and Debris flow     6
3. Synthetic Aperture Radar (SAR)     7
3.1. Brief Description of SAR imagery compared to Optical imagery     7
 Chapter 3-Study Area and Data Collection     9
1. Study Area     9
1.1. General Information     9
2. Data Collection     10
2.1. Sentinel-1 Data     10
2.2. Digital Elevation Model (DEM)     11
2.3. Ancillary Data     11

 Chapter 4-Methods     12
1. Image Pre-Processing     13
2. Multi-Temporal Backscattering Analysis     14
3. Support Vector Machine (SVM) Classification     14
3. Accuracy Assessment     15
 Chapter 5-Results     16
1. Multi-Temporal Backscattering Analysis of Sentinel-C-band SAR Imagery     16
2. Landslide and Flood Mapping with Support Vector Machine     17
2. Accuracy Assessment     20
 Chapter 6-Discussion     23
1. Applicability of VV and VH Sentinel-1 Polarization modes     23
2. Multi-Temporal Backscatter Analysis of Landslide and Flood     23
 Chapter 7-Conclusions and Recommendations     25
1. Conclusions     25
2. Recommendations     26
References     27

                                

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