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Assessment of coastal geomorphological changes using multi-temporal satellite derived bathymetry
Continental Shelf Research ( IF 2.3 ) Pub Date : 2020-12-01 , DOI: 10.1016/j.csr.2020.104213
Ankita Misra , Balaji Ramakrishnan

Abstract 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.

中文翻译:

使用多时相卫星测深法评估海岸地貌变化

摘要 本研究证明了卫星衍生水深测量 (SDB) 在了解印度本地治里沿海地区发生的地貌变化方面的可用性,该地区于 2017 年开始实施海滩修复项目以阻止海岸线侵蚀。由于安装了硬结构而普遍存在。在这项研究中,通过在 30 m 分辨率的 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生成多时相测深数据。经验驱动的 SVR 使用在现场测量活动期间收集的生态测深仪数据进行校准和验证,并且获得了均方根误差和平均绝对误差范围分别在 0.40-1.07 m 和 0.31-0.85 m 之间的相当准确的 SDB。随后,研究派生的时间深度图以了解发生在这片沿海地带的形态变化,结果清楚地显示了码头以北海滩的发展,随后海岸线趋于稳定。结果通过独立的基于 ArcGIS-DSAS 的海岸线变化分析得到进一步验证,该分析表明与通过测深变化分析观察到的相似的吸积和侵蚀趋势。因此,该研究证实海滩修复步骤在 2017 年至 2018 年间取得了积极成果,并阐明了 SDB 在沿海监测、建模和评估中的重要性。其次是海岸线的稳定。结果通过独立的基于 ArcGIS-DSAS 的海岸线变化分析得到进一步验证,该分析表明与通过测深变化分析观察到的相似的吸积和侵蚀趋势。因此,该研究证实海滩修复步骤在 2017 年至 2018 年间取得了积极成果,并阐明了 SDB 在沿海监测、建模和评估中的重要性。其次是海岸线的稳定。结果通过独立的基于 ArcGIS-DSAS 的海岸线变化分析得到进一步验证,该分析表明与通过测深变化分析观察到的相似的吸积和侵蚀趋势。因此,该研究证实海滩修复步骤在 2017 年至 2018 年间取得了积极成果,并阐明了 SDB 在沿海监测、建模和评估中的重要性。
更新日期:2020-12-01
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