城市不透水地表（UIS）和城市绿地（UGS）是描述城市底层环境特征的两个核心组成部分。然而，城市不透水地面（UIS）和城市绿地（UGS）在城市景观中往往是镶嵌在一起的，具有复杂的结构和复合性。硬分类或二元单一类型无法有效地划分空间上明确的城市地表属性。虽然目前已开发出六种空间分辨率为 30 米的全球或国家城市土地利用和土地覆被产品主流数据集，但它们仅提供了单一城市土地类型的二元模式或动态，无法有效划分城市内部土地覆被的定量成分或结构。在此，我们提出一种新的测绘策略，通过协同大数据处理和人工判读的优势，借助地理知识，获取全国范围内城市土地覆被基本类型的多时空和分部信息。
", "ds_source": "大地遥感卫星是对地观测时间最长的卫星系列。本研究选择了陆地卫星专题成像仪（TM）、增强型专题成像仪（ETM+）和陆地成像仪（OLI）在中国的路径范围为 118-149 和行距范围为 23-43 的数据。在绘制 CLUD、Landsat TM、ETM+ 和 OLI 各期图时，利用 2010 年中巴地球资源卫星计划（CBERS）和环景（HJ-1A 和 HJ-1B）卫星图像，生成近红外、红、绿光谱带为红、绿、蓝三色的假彩合成图像。对图像进行了增强处理，以提高视觉判读质量。进行了图像到图像的配准，以控制图像校正误差小于 2 像素（60 米）。2010 年，CBERS-1 和环景（HJ-1A 和 HJ-1B）卫星图像仅用于提取 CLUD 的矢量多边形，这是用 Landsat 图像进行的统一数据处理。", "ds_process_way": "
为了获得精确的 CLUD-Urban 产品，我们根据测绘策略，一般实施了三个步骤。首先，从 CLUD 中提取 2000-2018 年的全国城市边界，CLUD 是在人机数字化环境下采用统一的技术流程和分类系统生成的。城市边界及其扩展的时间序列在精度和数据质量方面具有良好的表现。将 2000 年、2005 年、2010 年、2015 年和 2018 年的全国城市矢量边界转换为分辨率为 30 米的栅格数据，以便进一步处理。其次，利用 GEE 平台中的随机森林算法检索了 30 米分辨率的聚落和植被分数。第三，通过将 CLUD 的城市边界与聚落和植被分数叠加，分别绘制出 30 米分辨率的 UIS 和 UGS 分数）。利用谷歌地球图像样本对城市边界以及 UIS 和 UGS 分数进行了精度评估。在绘制 CLUD-Urban 产品的整个数据处理过程中都进行了质量控制。", "ds_quality": "
将我们的产品与现有的六个主流数据集在质量和精度方面进行了比较。评估结果表明，与其他产品相比，CLUD-Urban 产品在城市边界和城市扩张探测方面具有更高的精度，此外，每个时期都开发了准确的 UIS 和 UGS 分数。2000-2018 年城市边界的总体精度超过 92.65%；UIS 和 UGS 分数的相关系数（R）和均方根误差（RMSE）分别为 0.91 和 0.10（UIS）以及 0.89 和 0.11（UGS）。
", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2018-12-31 00:00:00", "ds_acq_place": "中国", "ds_acq_lon_east": 135.01666666666668, "ds_acq_lat_south": 3.8666666666666667, "ds_acq_lon_west": 73.66666666666667, "ds_acq_lat_north": 53.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 19513204523, "ds_files_count": 344, "ds_format": "tiff", "ds_space_res": "30", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "fadbbaf7-6d8c-461a-9db6-20136d2ec3b6.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2024-06-21 10:18:54", "last_updated": "2026-01-14 10:46:03", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6528.2024", "i18n": { "en": { "title": "30 meter resolution dataset of impervious surface area and green space ratio in Chinese cities (2000-2018)", "ds_format": "tiff", "ds_source": "Vector polygons of urban boundaries in the Chinese Land Use/Cover Dataset (CLUD) derived from satellite imagery for the years 2000, 2005, 2010, 2015, and 2018", "ds_quality": "
We compared our product with six mainstream datasets in terms of quality and accuracy. The evaluation results indicate that compared to other products, the CLUD Urban product has higher accuracy in detecting urban boundaries and urban expansion. In addition, accurate UIS and UGS scores have been developed for each period. The overall accuracy of urban boundaries from 2000 to 2018 exceeded 92.65%; The correlation coefficient (R) and root mean square error (RMSE) of UIS and UGS scores are 0.91 and 0.10 (UIS) and 0.89 and 0.11 (UGS), respectively", "ds_ref_way": "", "ds_abstract": "
Urban Impermeable Surface (UIS) and Urban Green Space (UGS) are the two core components that describe the characteristics of the urban underlying environment. However, urban impervious surfaces (UIS) and urban green spaces (UGS) are often embedded together in urban landscapes, with complex structures and composites. Hard classification or binary single type cannot effectively delineate spatially clear urban surface attributes. Although six mainstream datasets of global or national urban land use and land cover products with a spatial resolution of 30 meters have been developed, they only provide binary patterns or dynamics of a single urban land type and cannot effectively divide the quantitative components or structures of urban land cover. Here, we propose a new surveying and mapping strategy that utilizes the advantages of collaborative big data processing and manual interpretation, leveraging geographic knowledge to obtain multi temporal and segmented information on basic types of urban land cover nationwide
", "ds_time_res": "年", "ds_acq_place": "China", "ds_space_res": "30", "ds_projection": "", "ds_process_way": "Firstly, urban boundary vector polygons for the years 2000, 2005, 2010, 2015, and 2018 were extracted from the China Land Use/Cover Dataset (CLUD) derived from satellite imagery. Secondly, using the Google Earth Engine (GEE) platform, the national settlement and vegetation ratios were retrieved using the Random Forest algorithm and sub-pixel decomposition method. Finally, we developed Chinese UIS and UGS fractional products (CLUD Urban) with 30 meter resolution in 2000, 2005, 2010, 2015, and 2018", "ds_ref_instruction": "" } }, "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": [ "不透水地表", "土地利用", "随机森林算法", "GEE平台" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "中国" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018 ], "ds_contributors": [ { "true_name": "匡文慧", "email": "kuangwh@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "匡文慧", "email": "kuangwh@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "ds_managers": [ { "true_name": "匡文慧", "email": "kuangwh@igsnrr.ac.cn", "work_for": "中国科学院地理科学与资源研究所", "country": "中国" } ], "category": "生态" }