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首页> 外文期刊>Continental Shelf Research: A Companion Journal to Deep-Sea Research and Progress in Oceanography >Assessment of coastal geomorphological changes using multi-temporal Satellite-Derived Bathymetry
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Assessment of coastal geomorphological changes using multi-temporal Satellite-Derived Bathymetry

机译:使用多颞卫星衍生的沐浴族评估沿海地貌变化

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The present study demonstrates the usability of Satellite-Derived Bathymetry (SDB) to understand the geomorphological changes that have occurred in a coastal region located along Puducherry, India, where a beach restoration project was taken up in 2017 to arrest the shoreline erosion that is prevalent due to installation of hard structures. In the study, multi-temporal bathymetry data is generated by applying a non-linear machine learning technique of Support Vector Regression (SVR) on Landsat 8 OLI satellite datasets of 30 m resolution. The empirically driven SVR is calibrated and validated using eco-sounder data collected during field measurement campaigns and fairly accurate SDBs with Root Mean Square Errors and Mean Absolute Errors ranging between 0.40-1.07 m and 0.31-0.85 m, respectively are obtained. Subsequently, the derived temporal depth maps are studied to understand the morphological changes that have occurred in this coastal stretch and the results clearly show the development of a beach, north of the pier, followed by the stabilization of the coastline. The outcomes are further validated through an independent ArcGIS- DSAS based shoreline change analysis which suggests similar trends of accretion and erosion as observed through the bathymetry change analysis. The study thus substantiates that the beach restoration step has yielded positive results between 2017 and 2018 and throws light on the significance of SDBs in coastal monitoring, modelling and assessment.
机译:本研究展示了卫星衍生的沐浴浴(SDB)的可用性,了解在沿着印度普金彻,沿岸地区发生的地貌变化,在2017年占据了海滩修复项目,以逮捕普遍存在的海岸线侵蚀由于安装硬结构。在该研究中,通过在30米分辨率为30米的Landsat 8 Oli卫星数据集上应用支持向量回归(SVR)的非线性机器学习技术来产生多时间浴权数据。使用在现场测量活动期间收集的生态发声器数据进行校准并验证经验驱动的SVR,并获得了具有根均方误差的相当准确的SDB,并且分别在0.40-1.07 m和0.31-0.85 m之间的平均误差。随后,研究了衍生的时间深度图,以了解这种沿海延伸中发生的形态变化,结果明确显示了码头北部的海滩的发展,然后稳定了海岸线。通过基于独立的ArcGIS-DSAS的海岸线变化分析进一步验证了结果,这表明通过碱度变化分析观察到的相似性和侵蚀的相似趋势。因此,该研究实质上证实,海滩恢复步骤在2017年和2018年之间产生了积极的结果,并在沿海监测,建模和评估中阐明了SDB的重要性。

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