文献类型：期刊文献

英文题名：Deriving China's beach slopes at a national scale: An automated and transferable Fourier-based framework using ICESat-2 data

作者：Xu, Hao[1];Xu, Nan[2,3];Xin, Huichao[1];Ma, Yue[4];Ni, Ming[1];Wang, Yongzhi[5];Li, Gaocong[6];Zhong, Xiaojing[7];Yu, Peng[8];Yuan, Shuai[9];Li, Yuan[10];Chi, Shanhang[10];Zhang, Chi[10];Li, Qingquan[2,3,11]

机构：[1]Hohai Univ, Sch Earth Sci & Engn, Nanjing 211100, Peoples R China;[2]Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Key Lab Geoenvironm Monitoring Great Bay Area, Minist Nat Resources,Guangdong Key Lab Urban Infor, Shenzhen 518060, Peoples R China;[3]Shenzhen Univ, Sch Architecture & Urban Planning, Shenzhen 518060, Peoples R China;[4]Wuhan Univ, Sch Elect Informat, Wuhan 430072, Peoples R China;[5]Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Peoples R China;[6]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China;[7]Jimei Univ, Coll Harbour & Environm Engn, Xiamen 361021, Peoples R China;[8]Xiamen Univ Technol, Coll Comp & Informat Engn, Xiamen 361024, Peoples R China;[9]Univ Hong Kong, Dept Geog, Hong Kong 999077, Peoples R China;[10]Hohai Univ, State Key Lab Water Disaster Prevent, Nanjing 210098, Peoples R China;[11]Guangdong Lab Artificial Intelligence & Digital Ec, Shenzhen 518107, Peoples R China

年份：2026

卷号：146

外文期刊名：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

收录：SCI-EXPANDED(收录号：WOS:001688209200001)、、WOS

基金：This work was funded by the National Natural Science Foundation of China [42571378, 42101343], the Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of the People's Republic of China [KLSMNR-K202309], the Natural Science Foundation of Jiangsu Province [BK20240258], the Innovation Team of the Department of Education of Guangdong Province [2024KCXTD013], the Shenzhen Science and Technology Program [KCXFZ20240903093000002], Scientific Foundation for Youth Scholars of Shenzhen University [868-000001033431], the National Key Research and Development Program of China [2024YFF0617900], and the discipline breakthrough pioneer project of the Ministry of Education of the People's Republic of China.

语种：英文

外文关键词：ICESat-2; Satellite altimeter; Beach; Coast; Sea level rise; Topography

外文摘要：Beach slope is a fundamental morphometric parameter governing coastal dynamics, wave dissipation, and shoreline stability, and serves as a key basis for coastal hazard prediction, wave runup and overtopping assessment, and the design of coastal engineering structures. However, due to the limited spatial coverage and high costs of conventional monitoring techniques, no high-resolution, national-scale dataset of beach slopes has previously been available for China until now. In this study, we develop an automated and transferable framework for deriving beach slopes from ICESat-2 ATL03 photon data and produce the first systematic national-scale dataset of China's sandy beach slopes. The workflow integrates (1) data preprocessing, including shoreline-orthogonal correction of ICESat-2 ground tracks, vertical datum harmonization to EGM2008, and selection of high-confidence photons (confidence >= 2); (2) adaptive photon clustering based on an OPTICS (Ordering Points To Identify the Clustering Structure) algorithm coupled with a moving-window filter for accurate beach photon extraction; and (3) slope inversion using low-order Fourier polynomial delineation of the foreshore combined with robust linear regression. The resulting dataset encompasses 688 along-track profiles covering 243 sandy beaches along China's coastline. Validation against high-precision reference data, including RTK-GNSS surveys and DEM-derived slopes, yielded strong agreement (RMSE = 0.012, R2 = 0.89), demonstrating the reliability of the proposed method across diverse geomorphic settings. Spatially, the results reveal a distinct north-south attenuation pattern, with steeper, more variable slopes in temperate northern coasts and gentler, low-gradient beaches in the tropical south. This novel dataset provides the first quantitative characterization of beach-slope spatial patterns at the national scale, offering critical parameters for storm-surge modeling, coastal-erosion assessment, and sustainable coastal management. Furthermore, leveraging open-accessible, global-scale ICESat-2 data, this framework is designed for easy transferability across coastal regions, offering a scalable solution for worldwide beach monitoring and assessment.