通过长时间序列了解全球城市化的时空动态对于实现可持续发展目标越来越重要。通过融合多源夜光观测数据创建的统一夜光（NTL）时间序列复合数据为描述和了解全球城市动态提供了长期、一致的夜景记录。在这项研究中，我们利用一致的 NTL 观测数据生成了全球年度城市范围数据集（1992-2020年），并分析了近30年来全球城市动态的时空模式。利用新的分步分区框架，从全球NTL时间序列图像中绘制了与局部高强度人类活动相关的城市化区域。该框架包括三个部分：(1)对NTL信号进行聚类，生成潜在的城市群；(2)确定最佳阈值，划分年度城市范围；(3)检查时间一致性，修正像素级城市动态。我们利用其他城市遥感产品和社会经济数据对得出的全球城市范围（1992-2020年）进行了评估。评估结果表明，该数据集可用于描述与密集人类居住区和高强度社会经济活动相关的空间范围。这项研究的全球城市范围数据集可以提供独特的信息，以捕捉城市化的历史和未来轨迹，了解和解决城市化对粮食安全、生物多样性、气候变化以及公众福祉和健康的影响。
", "ds_source": "使用统一的全球 NTL 数据集（1992-2020 年）作为绘制全球时间序列城市扩展图的主要数据集。其他辅助数据，如水的掩蔽和气体耀斑，用于过滤 NTL 时间序列中记录的与城市化无关的光照。通过NTL分步校准模型进行相互校准的DMSP稳定NTL合成图（1992-2013年）和利用NTL集成方法从VIIRS NTL模拟的扩展DMSP类数据（2014-2020年）是该时间序列数据集的两个主要组成部分。在本研究中，我们从 figshare 储存库下载了近30年的全球稳定NTLs记录，这些记录以GeoTIFF文件格式标记。在开发城市划分方法时，为了略微区分饱和DMSP NTL观测数据的空间变化，使用了基于归一化差异植被指数（NDVI）构建的权重系数，定义为（1-NDVI/100），用于更新全球范围内NTL时间序列图像的DN值。\n
\n其他辅助数据，如水的掩蔽和气体耀斑，用于过滤 NTL 时间序列中记录的与城市化无关的光照。与之前的尝试类似，水掩模被视为从 250 米 MODIS 陆地水掩模数据（MOD44W）中得出的数值大于 50%的 1 公里水百分比汇总图，而气体耀斑掩模则来自 Elvidge 等人。除了之前使用的全球 1 km 二进制城市地图，2018 年空间分辨率为30m 的全球人工不透水面积（GAIA）数据也被处理为密集人工不透水面积的1 km二进制层，为实施下文提到的分步式城市地图绘制方法提供辅助支持。
", "ds_process_way": "基于一个新的分步分区框架，从一致的 NTL 观测时间序列中绘制了全球年度城市扩展图（1992-2020 年）。它包括三个部分：(1) 对NTL信号进行聚类，以生成潜在的城市群；(2) 确定最佳阈值，以划分年度城市范围；(3) 检查时间一致性，以校正像素级城市动态。本研究中根据 NTL 图像得出的 \"城市范围 \"被定义为城市核心区域，即大部分建筑区和部分绿地以及其他具有城市功能的土地利用类型位于核心区域内。全球城市绘图框架的设计基于自上而下的分割和局部划定以及自下而上的合并。
", "ds_quality": "数据质量良好。
", "ds_acq_start_time": "1992-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 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": 358210212, "ds_files_count": 2, "ds_format": ".tif", "ds_space_res": null, "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "dae3cbcf-9e00-489b-a81e-ede4308f1a92.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "a4dd5849-78f2-44c5-b0f1-3450e952b2a2", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2023-12-27 11:20:47", "last_updated": "2025-05-29 11:33:11", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.FIGSHARE.DB4151.2023", "i18n": { "en": { "title": "Global Urban Annual Dataset (1992-2020)", "ds_format": ".tif", "ds_source": "We used the harmonized global NTL dataset (1992–2020) as the primary dataset for mapping the global time-series urban extents.Other ancillary data such as masks of water and gas flare were used to filter out the urbanization-unrelated illuminations recorded in the NTL time series.The DMSP stable NTL composites (1992–2013) inter-calibrated by the NTL stepwise-calibration model and the extended DMSP-like data (2014–2020) simulated from the VIIRS NTLs using the NTL integration approach are two major components of this time-series dataset.In this study, we downloaded the nearly 30-year-long records of global stable NTLs tagged in GeoTIFF file format at the figshare repository.To slightly distinguish the spatial variations of saturated DMSP NTL observations when developing the urban delineating method, a weight coefficient constructed based on the normalized difference vegetation index (NDVI), defined as (1-NDVI/100), was used to update the DN values of NTL time-series imagery at the global scale.\n
\nOther ancillary data such as masks of water and gas flare were used to filter out the urbanization-unrelated illuminations recorded in the NTL time series. Similarly to in previous attempts, water masks were regarded as the aggregated 1 km water percentage maps with values larger than 50 % derived from 250 m MODIS Land Water Mask data (MOD44W), and gas flare masks were obtained from Elvidge. In addition to the global 1 km binary urban maps used previously, the global artificial impervious area (GAIA) data with a spatial resolution of 30 m in 2018 were also processed to a 1 km binary layer of dense artificial impervious area to provide ancillary support for implementing the stepwise urban mapping approach mentioned below.
", "ds_quality": "The data quality is good.
", "ds_ref_way": "", "ds_abstract": "Understanding the spatiotemporal dynamics of global urbanization over a long time series is increasingly important for sustainable development goals. The harmonized nighttime light (NTL) time-series composites created by fusing multi-source NTL observations provide a long and consistent record of the nightscape for characterizing and understanding global urban dynamics. In this study, we generated a global dataset of annual urban extents (1992–2020) using consistent NTL observations and analyzed the spatiotemporal patterns of global urban dynamics over nearly 30 years. The urbanized areas associated with locally high intensity human activities were mapped from the global NTL time-series imagery using a new stepwise-partitioning framework. This framework includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) check of temporal consistency to correct pixel-level urban dynamics.Our resulting global urban extents (1992–2020) were evaluated using other urban remote sensing products and socioeconomic data. The evaluations indicate that this dataset is reliable for characterizing spatial extents associated with intensive human settlement and high-intensity socioeconomic activities. The dataset of global urban extents from this study can provide unique information to capture the historical and future trajectories of urbanization and to understand and tackle urbanization impacts on food security, biodiversity, climate change, and public well-being and health.
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "", "ds_projection": "", "ds_process_way": "We mapped the global annual urban extents (1992–2020) from time series of consistent NTL observations based on a new stepwise-partitioning framework.It includes three components: (1) clustering of NTL signals to generate potential urban clusters, (2) identification of optimal thresholds to delineate annual urban extents, and (3) checking of temporal consistency to correct pixel-level urban dynamics. The “urban extents” derived from NTL imagery in this study were defined as core urban domains, where most built-up areas and partially green spaces and other land-use types with urban functions are inside. The global urban mapping framework was designed based on top-down segmentation and local delineating and bottom-up merging.
", "ds_ref_instruction": "When using data, please clearly state the source of the data in the main text and cite the citation method provided in this metadata in the reference section." } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "城市范围", "NTL", "全球城市动态", "像素级城市动态" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "程昌秀", "email": "chengcx@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "程昌秀", "email": "chengcx@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "程昌秀", "email": "chengcx@bnu.edu.cn", "work_for": "北京师范大学", "country": "中国" } ], "category": "社会经济文化" }