北极植被多样性空间分布格局是北极生态稳定的重要因素。因此，基于ARC-2024环北极土地覆盖产品（10米空间分辨率）提取50km空间分辨率尺度内地表覆被多样性专题图。该数据集通过空间聚合方法将原始高分辨率土地覆盖栅格数据汇总至50km×50km网格单元，计算各网格内不同土地覆盖类型的绝对覆盖面积及相对面积占比。9类土地覆盖类型包括：低灌木(LS)、直立矮灌木(DS)、草地(GR)、小型低矮草本植物(SF)、湿地(WL)、水域(WA)、苔藓地衣(ML)、裸地(BG)及冰雪(IS)。鉴于建筑用地(BA)在泛北极地区分布面积极为有限，本数据集未纳入该类别。该网格化产品可有效支撑区域尺度植被格局分析、生态系统服务评估及气候变化响应研究，为极地科学研究提供标准化空间统计框架。", "ds_source": "
本网格化数据集以ARC-2024环北极土地覆盖产品为数据源，通过空间聚合算法完成50km×50km尺度的面积统计处理。", "ds_process_way": "
（1）网格体系构建：基于Albers等面积圆锥投影构建覆盖泛北极区域的50km×50km规则网格体系，以确保面积统计的几何精度与可比性。\n
（2）空间叠加分析：将ARC-2024原始10米分辨率土地覆盖栅格数据与网格矢量进行空间叠加运算，逐网格统计各土地覆盖类型的像元数量。\n
（3）面积量算：依据像元空间分辨率（10m×10m=100m²）将像元计数转换为绝对覆盖面积（单位：km²），并计算各类型在网格内的面积占比（单位：%）。\n
（4）属性表构建：生成包含网格唯一标识符、中心点地理坐标、各类型覆盖面积及占比等完整属性信息的矢量数据集。\n
（5）质量控制：剔除陆地面积不足网格总面积10%的边缘网格，并排除建筑用地(BA)类别，以确保统计结果的代表性与区域适用性。", "ds_quality": "
本数据集质量取决于ARC-2024源产品的分类精度及网格化处理的算法精度。源产品经多尺度验证体系评估：点基验证总体精度达87.78%，Kappa系数为0.858；斑块尺度验证表明86.72%的100km×100km网格单元总体精度超过80%，且在不同生物气候亚区（A–E区）均保持稳定的分类性能。网格化处理采用精确空间叠加算法，面积统计相对误差控制在0.01%以内。数据集属性完整性经逐字段校验，确认无空值及异常值。投影转换过程采用高精度重投影算法，几何位置误差小于1个原始像元尺寸（10m）。边缘网格经严格质量筛选，有效保障了统计结果的可靠性与科学性。", "ds_acq_start_time": "2024-05-01 00:00:00", "ds_acq_end_time": "2024-10-31 00:00:00", "ds_acq_place": "泛北极区域", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": 55.79, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 84.08999999999999, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 182857, "ds_files_count": 2, "ds_format": "*.shp", "ds_space_res": "50 km × 50 km", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "Albers Equal Area Conic Projection System", "ds_thumbnail": "b6a73661-ec98-4432-affa-e2680d596e43.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "53943799-d453-4bf2-a141-56c205c1355b", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4967592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 3, "publish_time": "2026-02-12 19:50:21", "last_updated": "2026-05-13 16:19:26", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ARCTIC-CHANGE.DB7126.2026", "i18n": { "en": { "title": "Distribution Map of 50km×50km Grid Coverage Area for 9-Class Land Cover in the Arctic Region, 2024", "ds_format": "*.shp", "ds_source": "
This gridded dataset is derived from the ARC-2024 Circumpolar Arctic Land Cover Product, employing spatial aggregation algorithms to perform area statistical processing at the 50km×50km scale.", "ds_quality": "
The quality of this dataset depends on both the classification accuracy of the ARC-2024 source product and the algorithmic precision of the gridding process. The source product was evaluated through a multi-scale validation system: point-based validation achieved an overall accuracy of 87.78% with a Kappa coefficient of 0.858; patch-scale validation demonstrated that 86.72% of 100km×100km grid cells exceeded 80% overall accuracy, maintaining stable classification performance across different bioclimatic subzones (Zones A–E). The gridding process employed precise spatial overlay algorithms, with relative errors in area statistics controlled within 0.01%. Dataset attribute completeness was verified through field-by-field inspection, confirming the absence of null and anomalous values. The projection transformation process utilized high-precision reprojection algorithms, with geometric positional errors less than one original pixel size (10m). Marginal grids underwent rigorous quality screening, effectively ensuring the reliability and scientific validity of statistical results.", "ds_ref_way": "", "ds_abstract": "
The spatial distribution pattern of Arctic vegetation diversity constitutes a crucial factor underpinning the ecological stability of the Arctic region. Therefore, a thematic map of surface cover diversity at a spatial resolution of 50 km was derived from the ARC-2024 Circum-Arctic Land Cover Product (with a 10 m spatial resolution). By employing a spatial aggregation approach, this dataset consolidates the original high-resolution land cover raster data into grid cells of 50 km × 50 km, and calculates both the absolute coverage area and relative proportion of different land cover types within each grid cell. The nine land cover categories include: Low Shrub (LS), Dwarf Shrub (DS), Grassland (GR), Sparse Forb (SF), Wetland (WL), Water Body (WA), Moss and Lichen (ML), Bare Ground (BG), and Ice and Snow (IS). Considering that Built-up Area (BA) has an extremely limited spatial distribution across the pan-Arctic region, this category was not incorporated into the dataset. This gridded product can effectively support regional-scale vegetation pattern analysis, ecosystem service assessment, and climate change response research, thereby providing a standardized spatial statistical framework for polar scientific investigations.", "ds_time_res": "", "ds_acq_place": "Circumpolar Arctic", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
(1) Grid System Construction: A 50km×50km regular grid system covering the Circumpolar Arctic region was established based on the Albers Equal Area Conic Projection to ensure geometric accuracy and comparability of area statistics.\r\n
(2) Spatial Overlay Analysis: Spatial overlay operations were performed between the ARC-2024 original 10-meter resolution land cover raster data and grid vectors, with pixel counts statistically computed for each land cover type per grid cell. \r\n
(3) Area Calculation: Pixel counts were converted to absolute coverage area (unit: km²) based on pixel spatial resolution (10m×10m=100m²), and the proportional coverage of each type within each grid was calculated (unit: %). \r\n
(4) Attribute Table Construction: A vector dataset was generated containing complete attribute information including grid unique identifier, center point geographic coordinates, coverage area and proportion for each land cover type.\r\n
(5) Quality Control: Marginal grids with land area less than 10% of total grid area were excluded, and the Built-up area (BA) class was removed to ensure statistical representativeness and regional applicability.", "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, "ds_topic_tags": [ "泛北极", "土地覆盖", "网格化统计", "空间分布", "覆盖面积" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "泛北极" ], "ds_time_tags": [ 2024 ], "ds_contributors": [ { "true_name": "帅艳民", "email": "shuaiym@zjnu.edu.cn", "work_for": "浙江师范大学", "country": "中国" }, { "true_name": "曲歌", "email": "471920609@stu.lntu.edu.cn", "work_for": "辽宁工程技术大学", "country": "中国" }, { "true_name": "霍思慧", "email": "Huosihui@zjnu.edu.cn", "work_for": "浙江师范大学", "country": "中国" }, { "true_name": "马现伟", "email": "maxianwei_lntu@126.com", "work_for": "辽宁工程技术大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "曲歌", "email": "471920609@stu.lntu.edu.cn", "work_for": "辽宁工程技术大学", "country": "中国" }, { "true_name": "帅艳民", "email": "shuaiym@zjnu.edu.cn", "work_for": "浙江师范大学", "country": "中国" } ], "ds_managers": [ { "true_name": "帅艳民", "email": "shuaiym@zjnu.edu.cn", "work_for": "浙江师范大学", "country": "中国" } ], "category": "基础地理" }