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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/81474

    Title: 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
    Authors: 蔡沛芸;Tsai, Pei-Yun
    Contributors: 遙測科技碩士學位學程
    Keywords: 衛星水深反演;近岸水深;遙測;多光譜衛星;Satellite Derived Bathymetry;Multispectral;Remote Sensing;Nearshore Bathymetry
    Date: 2019-07-15
    Issue Date: 2019-09-03 15:56:40 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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。
    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。;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.
    Appears in Collections:[遙測科技碩士學位學程] 博碩士論文

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