博碩士論文 107322103 詳細資訊




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姓名 徐筱柔(Hsiao-Jou Hsu)  查詢紙本館藏   畢業系所 土木工程學系
論文名稱 利用ICESat-2及Sentinel-2反演南海近岸水深
(Bathymetric Mapping in Shallow Water of the South China Sea by ICESat-2 and Sentinel-2)
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摘要(中) 測深對於海岸的研究、資源的利用或航行探勘都是很重要的一項工作,而傳統上為了取得較高精度的水深,常見的方法是由空載光達或船載回波測深儀測量水深,但是其成本高昂,且受天候、航行安全性及測區易達性等影響及限制,水深資料往往不易取得。近年隨著衛星遙感技術的發展,美國NASA於2018年底發射ICESat-2 (The Ice, Cloud, and Land Elevation Satellite-2)雷射測高衛星,證實可於濁度較低的潛水區作為測深光達,提供穩定的測距資料。ICESat-2搭載532nm綠光波段的高精度雷射測高系統ATLAS (the Advanced Topographic Laser Altimeter System),擁有每秒10 kHz脈衝頻率,以及可同時發射6條綠光雷射光至地表。儘管ICESat-2資料具備高解析力,然而ICESat-2僅提供沿軌道的高程剖面資料,在平行的地面軌道之間留有數據間隙。因此,本研究結合ICESat-2觀測資料及多光譜光學衛星影像,它能夠根據光譜衰減行為得出完整的水深圖。
這項研究的目的是結合ICESat-2資料和Sentinel-2光學衛星影像,反演南海六個島礁和潟湖的淺水深度(深度20 m以內)。研究首先對ICESat-2 ATL03光子點雲資料進行濾波,以找到沿著軌道的水底剖面。並且與現地空載LiDAR的測量結果相驗證,得到均方根誤差(RMSE)在0.20–0.47 m範圍內。接下來,使用三個半經驗函數,即修改的線性/多項式/指數比模型,其核心參數由Sentinel-2影像的綠光和藍光波段之間的對數比組成,以將影像的光譜數據與ICESat-2剖面深度資料相擬合。而在使用經過訓練的模型進行水深反演後,利用擬合最好的2張水深圖成果做加權平均以產製出最後水深圖成果,並使用獨立的ICESat-2光子點雲驗證資料來驗證由Sentinel-2加權平均得出的水深圖精度表現。在這6個島中使用2張最佳影像加權平均的三個模型成果,在0至15公尺水深,RMSE約在0.56 m-0.97 m之內。本研究得出的結論是,透過測高衛星的資料和光學衛星影像的結合可以產製水深圖,其水深精度,在0至5公尺深的水深範圍內大致符合電子航海圖(Electronic Navigational Chart)的CATZOC (the Category Zone of Confidence)中的A2和B類別,而6至14公尺水深精度,則符合CATZOC中的C類別。未來可以預見,ICESat-2將成為監測全世界沿海和淺水地區的有利工具,尤其是在沒有測深數據的地區。
摘要(英) To derive shallow water bathymetry for coastal areas, a common approach is to deploy a scanning airborne bathymetric light detection and ranging (LiDAR) system or a shipborne echosounder for ground surveys. However, recent advancements in satellite remote sensing, including the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) offer new tools for generating satellite derived bathymetry (SDB). The key payload onboard ICESat-2 is the Advanced Topographic Laser Altimeter System (ATLAS), a micro-pulse, photon-counting LiDAR system, simultaneously emitting six separate 532 nm beams at a nearly continuous 10 kHz pulse rate. However, despite its high resolution, the major limitation for bathymetry is that ICESat-2 only provides along-track height profiles, leaving observation gaps between the parallel ground tracks. Merging ICESat-2 observations with optical imagery offering multispectral raster, as demonstrated herein, offers an effective solution for deriving a full two-dimensional scene of water depth in light of the spectral attenuation behavior.
This study aims to combine ICESat-2 and Sentinel-2 optical imageries to derive shallow water bathymetry (depth less than 20 m) at six islands and reefs in the South China Sea. ICESat-2 ATL03 point clouds of georeferenced photons are first filtered to determine the seafloor elevation along the ground track. Results indicate a root-mean-square error (RMSE) of 0.20–0.47 m as compared with independent observations from an airborne LiDAR campaign. Next, three semi-empirical functions, namely the Modified Linear/Polynomial/Exponential Ratio Models with its kernel formed by the log ratio between Sentinel-2’s green and blue bands, are used to fit the spectral data with ICESat-2 height profiles. After water depth mapping using the trained model, independent ICESat-2 point clouds are used to validate the Sentinel-2 derived bathymetry. The RMSE of the three models using the weighted average from the best two images for these six islands are within 0.56 m–0.97 m in 0–15 meter deep. We also demonstrate that a synthesis of satellite laser altimetry and optical remote sensing can produce SDB results that potentially meet the requirement of category A2 and B in Zones of Confidence (ZOC) of the Electronic Navigational Chart (ENC) in 0–5 m deep, and satisfy category C in 6–14 meter deep. It is foreseen that ICESat-2 will be a helpful tool for mapping coastal and shallow waters around the world especially where bathymetric data are unavailable.
關鍵字(中) ★ 海岸水深
★ 電子海圖
★ ZOC
★ 光達
關鍵字(英) ★ Coastal Bathymetry
★ Electronic Navigation Chart
★ Zones of Confidence
★ LiDAR
論文目次 摘要 I
Abstract III
致謝 V
Table of Contents VII
List of Figures IX
List of Tables XII
1. Introduction 1
1.1 Demand of Shallow Water Bathymetry 1
1.2 Potential of Laser Altimetry 2
1.3 Requirement of Navigation Chart 3
1.4 Bathymetric Mapping 4
1.5 Structure of This Study 6
2. Study Area and Workflow 8
2.1 Study Area 8
2.2 Workflow 11
3. Data and Methods 14
3.1 ICESat-2 ATL03 Data 14
3.2 Sentinel-2 MSI Level 1 Data 21
3.3 Semi-empirical Model Training 23
4. Results 25
4.1 Validation of ICESat-2 Sea Surface Height 25
4.2 Validation of ICESat-2 Bathymetry 26
4.3 ICESat-2 Trained Sentinel-2 SDB 28
5. Discussion 37
5.1 Increasing SDB Quality from Redundant Observations 37
5.2 Potential ZOC Category of Sentinel-2 SDB 39
6. Conclusions 44
7. Limitations 45
8. Future work 49
Reference 50
Appendix A 57
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指導教授 曾國欣(Kuo-Hsin Tseng) 審核日期 2020-7-7
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