摘要
为了在澎湖水道生成一个高质量的数字水深模型(Digital Bathymetric Model, DBM),分析了不同DBM在不同子区域之间的差异,利用自适应分区域空间加权融合方法构建了一个新的DBM。结果表明,在当前使用最为广泛的DBMs中,SRTM30_PLUS、GEBCO_2021、SRTM15_PLUS这3种DBM质量较好,均方根误差(Root Mean Squared Error, RMSE)为7.931~8.016 m,而ETOPO1、GEBCO_2014和TOPO V19.1质量较差,RMSE为11.439~20.105 m;利用本文研究方法所构建的澎湖水道数字水深模型的RMSE为6.970 m,其精度比GEBCO_2021、SRTM30_PLUS和SRTM15_PLUS模型分别高12%、13%和12%,利用本研究方法所构建的DBM恢复了高精度水深点,保留了地形细节信息。研究结果可为从多个数据集中及时重建和更新大规模海底地形提供参考,并推广应用于其他环境和区域中。
In order to generate a high-quality Digital Bathymetric Model(DBM)for the Penghu Channel,the differences among the DBMs in different sub-regions are analyzed and a new DBM is constructed by using adaptive regional spatial weighted fusion method.The results indicate that among the DBMs used most widely at the present the SPTM30_PLUS,GEBCO_2021 and SRTM15_PLUS are good in quality and have the Root Mean Square Error(RMSE)ranging from 7.931 to 8.016 meters,whereas the ETOPO1,GEBCO_2014 and TOPO V19.1 are poor in quality and have the RMSE ranging from 11.439 to 20.105 meters.The Penghu Channel digital bathymetric model constructed in the present study has a RMSE of 6.970 meters and its accuracy is respectively 12%,13%and 12%higher than that of the GEBCO_2021,SRTM30_PLUS and SRTM15_PLUS.By using the DBM constructed by the method proposed in this study high-precision water depth points are restored and the topographic details are retained.The results of the study can provide a reference for reconstructing and updating timely the large-scale seabed topography from multiple datasets and can be promoted and applied to other environments and regions.
作者
熊桂芳
王波
朱长德
张国栋
郭澍
XIONG Guifang;WANG Bo;ZHU Changde;ZHANG Guodong;GUO Shu(School of Remote Sensing&Geomatics Engineering,Nanjing University of Information Science&Technology,Nanjing 210044,China;Visiontek(Nanjing)Co.,Ltd,Nanjing 210046,China)
出处
《海洋科学进展》
CAS
CSCD
北大核心
2024年第1期149-159,共11页
Advances in Marine Science
基金
江苏省高等学校自然科学研究项目(22KJB170016)。
关键词
多源水深数据
数字水深模型
海图数据
加权融合
multi-source bathymetric data
digital bathymetric models
chart data
weighted fusion
