本数据为结合野外调查的植被类型数据；环境因子数据来源于气候、土壤、地形、植被类型等数据为驱动，采用机器学习方法进行模型构建。数据格式为GeoTIFF，空间分辨率约30 m，投影为WGS_1984_Albers。", "ds_source": "
原始数据：野外调查的植被类型数据；\n
环境变量数据：选取了地形、植被、气候及土壤四大类环境变量作为预测因子。", "ds_process_way": "
数据预处理：对上述所有多源栅格数据进行空间配准与标准化处理。统一投影坐标系为 WGS_1984_Albers，将空间范围裁剪至研究区边界，并采用重采样技术将所有变量的空间分辨率统一降尺度至30 m，格式统一为GeoTIFF，确保多源数据在空间上的严格匹配。利用ArcGIS的多值提取至点（Extract Multi-Values to Points）功能，提取每个样本点对应的环境变量数值，构建“样本-环境特征”高维数据集。\n
随机森林模型构建：采用分层随机抽样法（Stratified Random Sampling），将数据集划分为训练集（70%）和测试集（30%）。基于Python环境下的scikit-learn机器学习库构建随机森林分类模型。", "ds_quality": "
计算混淆矩阵、总体准确率（Overall Accuracy）、精确率（Precision）、召回率（Recall）、F1-Score及Kappa系数。结果显示，模型具有较高的一致性。", "ds_acq_start_time": "2023-08-01 00:00:00", "ds_acq_end_time": "2025-10-31 00:00:00", "ds_acq_place": "大兴安岭西坡额尔古纳地区根河流域", "ds_acq_lon_east": 122.70277777777778, "ds_acq_lat_south": 49.92916666666667, "ds_acq_lon_west": 119.3, "ds_acq_lat_north": 51.282777777777774, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 2827909, "ds_files_count": 3, "ds_format": "*.tif", "ds_space_res": "30m", "ds_time_res": "3年", "ds_coordinate": "WGS84", "ds_projection": "WGS_1984_Albers", "ds_thumbnail": "19ef693f-8d48-4e14-8ba3-24ff2be963db.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "221ebf56-1b0b-4574-972b-1fb6d3cf1be7", "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": 0, "publish_time": "2026-04-01 15:59:55", "last_updated": "2026-05-12 09:16:34", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.NIEER.DB7254.2026", "i18n": { "en": { "title": "30m vegetation type distribution map of the Genhe River Basin, Erguna area, western slope of the Greater Khingan Mountains (2023-2025)", "ds_format": "*.tif", "ds_source": "
Raw data: vegetation type data from field surveys;\r\n
Environmental variable data: Four major categories of environmental variables including terrain, vegetation, climate, and soil were selected as predictive factors.", "ds_quality": "
Calculate confusion matrix, Overall Accuracy, Precision, Recall, F1 Score, and Kappa coefficient. The results show that the model has high consistency.", "ds_ref_way": "", "ds_abstract": "
This data is vegetation type data combined with field surveys; The environmental factor data is driven by climate, soil, terrain, vegetation types, and other data, and the model is constructed using machine learning methods. The data format is GeoTIFF, with a spatial resolution of approximately 30m and a projection of WGS1984_ Albers.", "ds_time_res": "", "ds_acq_place": "Genhe River Basin in the Erguna area on the western slope of the Greater Khingan Range", "ds_space_res": "", "ds_projection": "", "ds_process_way": "
Data preprocessing: Perform spatial registration and standardization on all multi-source raster data mentioned above. The unified projection coordinate system is WGS1984_ Albers, and the spatial range is cropped to the boundary of the study area. The spatial resolution of all variables is uniformly downscaled to 30 m using resampling techniques, and the format is unified as GeoTIFF to ensure strict spatial matching of multi-source data. Using ArcGIS' Extract Multi Values to Points feature, extract the environmental variable values corresponding to each sample point and construct a high-dimensional dataset of \"sample environment features\".\r\n
Random Forest Model Construction: Stratified Random Sampling is used to divide the dataset into a training set (70%) and a testing set (30%). Build a random forest classification model based on the scikit learn machine learning library in Python environment.", "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": [ "多年冻土", "大兴安岭", "根河流域", "植被类型" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "大兴安岭西坡额尔古纳地区根河流域" ], "ds_time_tags": [ 2023, 2024, 2025 ], "ds_contributors": [ { "true_name": "胡国杰", "email": "huguojie123@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "邹德富", "email": "defuzou@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "刘广岳", "email": "liuguangyue@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "肖瑶", "email": "xiaoyao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "杜二计", "email": "duerji@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "赵拥华", "email": "zhaoyonghua@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "肖瑶", "email": "xiaoyao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "ds_managers": [ { "true_name": "肖瑶", "email": "xiaoyao@lzb.ac.cn", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" } ], "category": "冻土" }