利用綜合遙測資訊建置之高程模型觀測近岸地形時序變遷

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姓名

蔡沛芸(Pei-Yun Tsai) 查詢紙本館藏

畢業系所

遙測科技碩士學位學程

論文名稱

利用綜合遙測資訊建置之高程模型觀測近岸地形時序變遷
(Using Integrated Remote Sensing Data to Reconstruct Coastal Elevation Model and Detect Temporal Changes)

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摘要(中)

近岸測深技術能用以蒐集海岸地區受自然與人為影響而產生之變遷資訊。為彌補船測法在水深過淺處之施測限制，本研究測試光學衛星測深演算法之應用，並致力於拓展遙測技術反演近岸水深之應用。
以比爾定律為基礎發展之光學測深技術於過去二十年間日趨成熟，本研究則以現有之光譜測深演算法為基礎，著重於光譜水深反演模型之轉移應用，藉此了解由甲地水深實測值建置之甲地水深反演模型，用於反演乙地之水深之可行性。文中分別以南中國海應用案例測試空間維度上的轉移性，並由墾丁國家公園的後灣漁港案例觀察此應用法在時間維度上轉移之成果。兩項測試共使用四種不同空間解析度的光學衛星影像，包含在南海區應用測試中，由東沙群島(測區一)轉移模型至太平島(測區二)的Sentinel-2和Worldview-2光學衛星影像，及後灣(測區三)應用測試中，重建沿岸時序高程模型的Landsat-7及SPOT光學衛星影像。
為進一步提升反演精度，另有兩項操作測試施作於反演流程中。針對Landsat-7及Sentinel-2等高度再訪率(revisit time)影像，我們將多張影像平均以降低隨機誤差造成的水深誤判;此外，也在南海應用測試中，討論底質分類能否有效降低在複雜底質種類測區因固有反照率(albedo)造成之誤判。
研究成果顯示，南海區應用測試中，在水深10米以淺處，以光達實測水深之方均根誤差(RMSE)介於1.5至3米。底質分類則可以提升0.1至1米的精度。後灣應用測試中，以空拍影像建置之高程模型為驗證值之評估顯示光譜法水深反演成果約有1.6米之誤差。總結來說，本研究中，模型轉移應用、底質分類與多影像平均法等三項操作都具有提升反演精度之潛力，並由測區結果推測能提升反演精度0.1至1米。

摘要(英)

Coastal terrain is an important factor to understand natural and anthropogenic forcing contributed to the coastal processes. However, it has long been difficult to map the elevation in the intertidal and shallow water areas, due primarily to the limitation in vessel navigation. In this study, we test an algorithm of satellite derived bathymetry (SDB), aiming to broaden remote sensing applications in coastal areas. Since the physical principle of SDB based one Beer’s law had been well developed in the last two decades, we focus on the model transferability to see whether a multispectral model used to estimate water depth trained in one particular location can be adopted in another one. There are two application test for evaluate this technical question –the first is Application test in South China Sea is used to evaluate feasibility of model transfer in spatial domain, while Application test in Houwan (Kenting National Park) is to confirm model transferability in temporal domain. A total of 4 series of optical satellite missions are used in this study, including Sentinel-2 and WorldView-2 data for Dongsha and Taiping Island, and Landsat-7 and SPOT data for Houwan coastline.
To increase the accuracy of depth measurements, for Landsat-7 and Sentinel-2 images we average out multiple retrieving results to avoid random noises in each individual image. Besides, different categories of substrates are classified separately to create their own corresponded model, in prevention of estimation bias from the inherent albedo of various substrates.
Results in Application in South China Sea indicates that the retrieval depth is limited to ~10m with a root-mean-square-error (RMSE) between 1.5-3 m, as validated with ground-truth provided by air-borne LiDAR surveys. An operation of substrates classification can increase the accuracy of RMSE between 0.1-1 m. In Application in Houwan, coastal dynamic of Houwan area investigating temporal changes of coastline is achieved, where a RMSE of the digital surface model (DSM) is 1.6 m as overlapped with DSM built by a drone campaign. In conclusion, the test of 3 applications as “model transfer application”, “substrates classification” and “multiple images averaging conduct” show a great potential to improve the quality and application in SDB, whose can achieve RMSE at 0.1-1m in coastal areas.

關鍵字(中)

★ 衛星水深反演
★ 近岸水深
★ 遙測
★ 多光譜衛星

關鍵字(英)

★ Satellite Derived Bathymetry
★ Multispectral
★ Remote Sensing
★ Nearshore Bathymetry

論文目次

1. Introduction and Motivation 1
1.1. Coastal Dynamic 1
1.1.1. Coastal Dynamic Issue with Human Activities 1
1.1.2. Alternative Management Options 2
1.2. SDB Strategies and Objectives 2
1.3. Two Application Test by the Three Study Sites 6
2. Background 7
2.1. Bathymetry Mapping 7
2.1.1. Coastal Elevation Model 7
2.1.2. Shipborne Sonar 8
2.1.3. LiDAR Device 10
2.2. Remote Sensing for SDB 11
2.3. Intertidal Zone 12
2.3.1. Tidal Constituents 12
2.3.2. Zoning of Coastal Area 13
2.4. Study Sites 14
2.4.1. Coast Alteration in South China Sea 16
2.4.2. Coast Alteration in Houwan Bay of Kenting National Park 19
2.5. Satellite Images 19
2.5.1. Imagery for Bathymetry 19
2.5.2. Imagery for Intertidal Zone: 21
2.5.3. Introduction of Satellite Data: 22
3. Methodology 24
3.1. Work Flow 24
3.2. Data Preprocessing 26
3.3. Satellite Derived Bathymetry (SDB) 28
3.4. Improvement of SDB I: Model Transfer Application 29
3.5. Improvement of SDB II: Substrate Classification 29
3.6. Improvement of SDB III: Averaging of Medium Spatial Data 32
3.7. Accuracy Assessment 33
4. Application in South China Sea 35
4.1. Retrieval Methods of South China Sea 35
4.2. Mapping Result of South China Sea 40
4.2.1. Medium Spatial Resolution Images 40
4.2.2. High Spatial Resolution Images 44
5. Application in Houwan Bay of Kenting National Park 49
5.1. Retrieval Methods in Houwan Coastal Area 49
5.1.1. Retrieval Processing of Intertidal Zone in Houwan 57
5.1.2. Retrieval Processing of Bathymetry in Houwan 58
5.1.3. High Resolution Integration Model in Houwan 59
5.2. Mapping Result in Houwan Coastal Area 60
5.2.1. Overall Accuracy 60
5.2.2. Intertidal Zone Elevation 60
5.2.3. Near-shore Bathymetry 62
5.2.4. Integration Model of Recent Period 64
6. Discussion 65
6.1. Source of Errors 65
6.2. Bathymetry Retrieval Assessment 67
6.2.1. Overall Discussion of Bathymetry 67
6.2.2. Comparison of Different Processing Disposition 69
6.3. Intertidal Zone Retrieval Assessment 73
7. Conclusion and Future Work 76
8. Reference 78

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指導教授

曾國欣(Kuo-Hsin Tseng)

審核日期

2019-7-15

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