黄河下游滩区是黄河流域的重要组成部分。建立长序列、高精度的黄河下游滩区土地利用/覆被变化数据集，再现其历史轨迹，把握现实格局，对黄河下游滩区高质量发展及其相关问题的科学研究而言，是一项具有重要意义的基础性工作。本文选取1985–2010年36景Landsat5 TM、2015–2020年8景GF-1 WFV和12景Landsat8 OLI遥感影像，采用随机森林、人机交互解译和混淆矩阵精度评价等方法，研制了1985–2020年每5年共8期黄河下游滩区土地利用/覆被变化数据。数据空间分辨率为30m，总体精度均达90%以上，Kappa系数高于0.85。本数据集可为区域生态环境保护、国土空间规划、黄河流域典型区域自然-人文系统耦合过程、机理、测度等研究提供科学数据支撑。
", "ds_source": "本数据集包含1985年、1990年、1995年、2000年、2005年、2010年、2015年、2020年共8期土地利用/覆被变化数据，空间分辨率30m。黄河下游滩区土地利用分类标准包括了5种土地利用/覆被类型，分别为耕地、水体、建设用地、林草地和未利用地。采用的坐标系为WGS_1984UTM zone_50N。数据样本黄河下游滩区2020年土地利用/覆被变化数据。
", "ds_process_way": "黄河下游滩区1985–2020年30 m分辨率LUCC数据的研制，主要以ENVI 5.3和ArcGIS 10.5软件为平台，以Landsat5 TM、Landsat8 OLI和GF-1WFV等多源遥感影像为数据源，采用人机交互的解译方式来完成。主要的操作流程包括：遥感数据预处理、土地利用分类标准确定、解译标志选择及样本库建立、遥感影像解译、精度评价。
", "ds_quality": "数据从遥感影像的收集、遴选和预处理以及解译标志和样本库的选择与建立均符合操作规范，严格控制质量。同时，使用混淆矩阵对生产的数据集进行了精度验证。总体精度和Kappa系数均位于85%以上。数据质量整体效果良好。
", "ds_acq_start_time": "1985-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "黄河下游滩区", "ds_acq_lon_east": 112.85, "ds_acq_lat_south": 34.96666666666667, "ds_acq_lon_west": 119.3, "ds_acq_lat_north": 37.96666666666667, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "open-access", "ds_total_size": 3177713, "ds_files_count": 2, "ds_format": "tiff", "ds_space_res": "30米", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "0ff727cf-a90b-41af-97cc-f67071865d7e.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "b412fac3-1f10-4eb6-9d10-c51bcea30d0c", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2023-09-19 16:26:47", "last_updated": "2025-05-29 11:38:53", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.YRIVER.DB3984.2023", "i18n": { "en": { "title": "Land Use/Cover Change Dataset with 30m Resolution in the Floodplain of the Lower Yellow River from 1985 to 2020", "ds_format": "tiff", "ds_source": "This dataset contains a total of 8 periods of land use/cover change data for the years 1985, 1990, 1995, 2000, 2005, 2010, 2015, and 2020, with a spatial resolution of 30m. The land use classification standard for the floodplain of the lower Yellow River includes five land use/cover types: cultivated land, water bodies, construction land, forest and grassland, and unused land. The coordinate system adopted is WGS_1984 UTM zone_50N. A data sample is provided for the land use/cover change data of the floodplain of the lower Yellow River in 2020.
", "ds_quality": "The data collection, selection, and preprocessing of remote sensing imagery, as well as the selection and establishment of interpretation markers and sample databases, all conform to operational specifications with strict quality control. Additionally, a confusion matrix was used to verify the accuracy of the produced dataset. Both the overall accuracy and Kappa coefficient are above 85%. The overall data quality is good.
", "ds_ref_way": "", "ds_abstract": "The floodplain of the lower Yellow River is an important part of the Yellow River Basin. Establishing a long-term sequence and high-precision land use/cover change dataset for the floodplain of the lower Yellow River，reconstructing its historical trajectory, and grasping the current pattern are fundamental tasks of great significance for the high-quality development of the floodplain of the lower Yellow River and scientific research on related issues. This paper selects 36 scenes of Landsat5 TM imagery from 1985 to 2010, 8 scenes of GF-1 WFV imagery and 12 scenes of Landsat8 OLI imagery from 2015 to 2020. Using methods such as Random Forest, human-computer interaction interpretation, and accuracy evaluation with confusion matrices, we have developed land use/cover change data for the floodplain of the lower Yellow River from 1985 to 2020, with a total of 8 periods every 5 years. The spatial resolution of the data is 30m, with an overall accuracy exceeding 90% and a Kappa coefficient higher than 0.85. This dataset can provide scientific data support for research on regional ecological environmental protection, territorial spatial planning, and the coupling processes, mechanisms, and measurements of natural-human systems in typical regions of the Yellow River Basin.
", "ds_time_res": "年", "ds_acq_place": "Lower Yellow River beach area", "ds_space_res": "30米", "ds_projection": "", "ds_process_way": "The development of the 30m resolution LUCC (Land Use/Cover Change) data for the floodplain of the lower Yellow River from 1985 to 2020 was primarily carried out using ENVI 5.3 and ArcGIS 10.5 software platforms. Multi-source remote sensing imagery, such as Landsat5 TM, Landsat8 OLI, and GF-1 WFV, was used as the data source, and human-computer interaction interpretation methods were employed to complete the task. The main operational procedures included: preprocessing of remote sensing data, determination of land use classification standards, selection of interpretation markers and establishment of sample databases, interpretation of remote sensing imagery, and accuracy assessment.
", "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": [ "土地利用/覆被变化", "随机森林", "栅格数据" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "黄河下游滩区" ], "ds_time_tags": [ 1985, 1986, 1987, 1988, 1989, 1990, 1991, 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": "杨帆", "email": "yangfan@henu.edu.cn", "work_for": "河南大学黄河文明与可持续发展研究中心", "country": "中国" }, { "true_name": "宗丽佳", "email": "", "work_for": "河南大学黄河文明与可持续发展研究中心", "country": "中国" }, { "true_name": "周胜男", "email": "", "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": "杨帆", "email": "yangfan@henu.edu.cn", "work_for": "河南大学黄河文明与可持续发展研究中心", "country": "中国" } ], "ds_managers": [ { "true_name": "杨帆", "email": "yangfan@henu.edu.cn", "work_for": "河南大学黄河文明与可持续发展研究中心", "country": "中国" } ], "category": "生态" }