海岸高程数据支撑海岸管理、洪水模拟、气候适应规划等诸多领域研究工作。海拔低于平均海平面 10 米的低洼海岸带，受海平面上升、地面沉降与极端天气频发影响，未来将面临极端高水位灾害风险。但目前公开免费的高程数据集精度不足，难以精准量化上述灾害风险。\n
本数据为开源全球海岸数字地形模型 DeltaDTM，该产品水平分辨率 30 米，全域平均绝对误差（MAE）为 0.43 米。DeltaDTM 借助 ICESat-2 与 GEDI 星载激光雷达数据对哥白尼数字高程模型（CopernicusDEM）开展校正处理，具体流程包括：修正 CopernicusDEM 高程偏差、滤波剔除非地形像元、插值填补数据空缺。\n
对比现有各类 DEM 校正回归算法（含机器学习方法），本研究采用的分类校正方案精度更优；现有同类方法最优全域平均绝对误差仅达 0.72 米。综上，DeltaDTM 可为海岸洪水灾害模拟及其他相关研究提供可靠基础数据支撑。", "ds_source": "", "ds_process_way": "", "ds_quality": "", "ds_acq_start_time": "2012-01-01 00:00:00", "ds_acq_end_time": "2024-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": null, "ds_acq_lat_south": null, "ds_acq_lon_west": null, "ds_acq_lat_north": null, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 35957369600, "ds_files_count": 0, "ds_format": "GeoTiff", "ds_space_res": "30m", "ds_time_res": "", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "c554bcc9-536a-4a9a-8ec0-2d547af0268d.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": null, "ds_serv_phone": null, "ds_serv_mail": null, "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 0, "publish_time": "2026-06-22 16:28:50", "last_updated": "2026-06-22 16:28:50", "protected": false, "protected_to": null, "lang": "zh", "cstr": null, "i18n": { "en": { "title": "DeltaDTM v1.1: A global coastal digital terrain model", "ds_format": "GeoTiff", "ds_source": "", "ds_quality": "", "ds_ref_way": "", "ds_abstract": "
Coastal elevation data are essential for a wide variety of applications, such as coastal management, flood modelling, and adaptation planning. Low-lying coastal areas (found below 10 m +Mean Sea Level (MSL)) are at risk of future extreme water levels due to Sea Level Rise (SLR), subsidence and changing extreme weather patterns. However, current freely available elevation data sets are not sufficiently accurate to model these risks. \r\n
We present DeltaDTM, a global coastal Digital Terrain Model (DTM) available in the public domain, with a horizontal spatial resolution of 30 m and a vertical mean absolute error (MAE) of 0.43 m overall. DeltaDTM corrects the CopernicusDEM with space borne lidar from the ICESat-2 and GEDI missions. Specifically, we correct the elevation bias in CopernicusDEM, apply filters to remove non-terrain cells, and fill the gaps using interpolation. \r\n
Notably, our classification approach produces more accurate results than regression methods (including machine learning) recently used by others to correct DEMs, that achieve an overall MAE of 0.72 m at best. We conclude that DeltaDTM will be a valuable resource for coastal flood impact modelling and other applications.", "ds_time_res": "", "ds_acq_place": "global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "recommendation_value": 0, "license_type": "https://creativecommons.org/licenses/by/4.0/", "doi_reg_from": "reg_local", "cstr_reg_from": "reg_local", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "belong_to_nieer": false, "ds_topic_tags": [ "数字高程模型 (DEM)", "数字地形模型 (DTM)", "高程", "GEDI", "ICESat-2", "激光雷达" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024 ], "ds_contributors": [ { "true_name": "Maarten Pronk", "email": "maarten.pronk@deltares.nl", "work_for": "Delft University of Technology, Delft, Netherlands", "country": "荷兰" } ], "ds_meta_authors": [ { "true_name": "Maarten Pronk", "email": "maarten.pronk@deltares.nl", "work_for": "Delft University of Technology, Delft, Netherlands", "country": "荷兰" } ], "ds_managers": [ { "true_name": "Maarten Pronk", "email": "maarten.pronk@deltares.nl", "work_for": "Delft University of Technology, Delft, Netherlands", "country": "荷兰" } ], "category": "水文" }