本数据集包含1990、2000、2010年3期中亚五国范围的土地利用矢量数据，比例尺1:10万，投影采用GCS_WGS1984。本数据采用1989-1991年度、1999-2000年、2010年前后的TM遥感数据和Google earth数据作为数据源和验证数据；利用ARCGIS、Ecognition软件工具，对TM遥感影像数据进行预处理和自动半自动人工解译分类，并加以地面实地调查和Google earth高精度数据验证。\n土地覆被分类依据《中亚地区土地利用/覆被分类体系》，该体系参考中国科学院地理与资源研究所遥感土地分类系统（刘纪远）和国土资源部立项研究制定《土地利用现状分类》国家标准，采用两级分类系统:一级分为8 类, 主要根据土地资源的利用属性，二级分为 30个类型, 主要根据土地资源的经营特点、利用方式和覆盖特征。\n
", "ds_source": "1989-1991年度、1999-2000年、2010年前后的TM遥感数据和Google earth数据。
", "ds_process_way": "使用ARCGIS、ERDAS软件工具，选取对应年代的TM数据，数据预处理，在Ecognition和Arcgis软件平台上，采用“分割-特征计算-监督分类-人工检查修正”的路线进行分类解译，依据《中亚综合科学考察遥感解译手册》（中亚地区土地利用/覆被分类体系），对遥感影像进行了土地利用/覆被分类和信息的提取，并利用野外考察资料对提取的结果进行了实地验证。
", "ds_quality": "通过地面实地调查和高空间分辨率的遥感数据分类结果对土地覆被数据精心精度评估，为保证评估结果的有效性，除准确合理的参考数据外，还设计出样本大小和采样方法、采样分布等有效的采样方案，方案设计的最终目标是：实现寻求图像采样及实地采样的人工及设备投入与分析结果精度之间的优化平衡，即以最小的经济投入得到较高精度的分析结果。\n整个作业流程均遵守国家相关标准，数字化误差控制在2个像素以内，保证了矢量数据能真实反映地物形态和属性信息，数据精确度高达99%。
", "ds_acq_start_time": "1990-01-01 00:00:00", "ds_acq_end_time": "2010-12-31 00:00:00", "ds_acq_place": "哈萨克斯坦；吉尔吉斯斯坦；塔吉克斯坦；乌兹别克斯坦；土库曼斯坦", "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": "login-access", "ds_total_size": 2477255561, "ds_files_count": 1, "ds_format": "矢量数据", "ds_space_res": "30", "ds_time_res": "10年", "ds_coordinate": "WGS84", "ds_projection": "WGS84", "ds_thumbnail": "44e5cb32-44c5-40e6-a86b-95aa2a8da70c.png", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "a5877b42-96ea-4f13-af7e-246f355413d6", "doi_value": "", "subject_codes": [ "170.45" ], "quality_level": 1, "publish_time": "2025-01-15 10:48:40", "first_publish_time": null, "last_updated": "2025-03-28 18:34:12", "protected": false, "protected_to": null, "lang": "zh", "cstr": "33110.11.ariddc.00259", "license": null, "extra": null, "files_shape": [ { "name": "CC_CA_LUCC_1990-2010-data.rar", "size": 2477255561, "is_dir": false } ], "features": null, "data_level": 0, "i18n": { "en": { "title": "Land use data of 1: 100,000 in the five Central Asian countries in 1990, 2000 and 2010", "ds_abstract": "This dataset contains land use vector data from five Central Asian countries in 1990, 2000 and 2010, with a scale of 1: 100,000. The projection uses GCS_WGS1984. This data uses TM remote sensing data and Google earth data from 1989-1991, 1999-2000, and 2010 as data sources and verification data; using ARCGIS and Ecocognition software tools, TM remote sensing image data are preprocessed and automatically semi-automatically manually interpreted and classified, and verified by ground field surveys and Google earth high-precision data.\nThe land cover classification is based on the \"Central Asia Land Use/Cover Classification System\", which refers to the Remote Sensing Land Classification System of the Institute of Geography and Resources, China Academy of Sciences (Liu Jiyuan) and the Ministry of Land and Resources initiated a project to study and formulate the \"Land Use Status Classification\" national standard. It adopts a two-level classification system: the first level is divided into 8 categories, mainly based on the utilization attributes of land resources, and the second level is divided into 30 types, mainly based on the operating characteristics, utilization methods and coverage characteristics of land resources.\n
", "ds_source": "TM remote sensing data and Google earth data around 1989-1991, 1999-2000, and around 2010.
", "ds_process_way": "Using ARCGIS and ERDAS software tools, TM data corresponding to the age is selected, the data is preprocessed, and on the Ecocognition and Arcgis software platforms, the route of \"segmentation-feature calculation-supervised classification-manual inspection and correction\" is used for classification and interpretation. According to the \"Remote Sensing Interpretation Manual for Comprehensive Scientific Expeditions in Central Asia\"(Central Asia Land Use/Cover Classification System), remote sensing images were used for land use/cover classification and information extraction, and the extracted results were verified on the spot using field inspection data.
", "ds_quality": "The land cover data is carefully and accurately evaluated through ground field surveys and high spatial resolution remote sensing data classification results. In order to ensure the validity of the evaluation results, in addition to accurate and reasonable reference data, the sample size, sampling method, sampling distribution and other effective sampling schemes are also designed. The ultimate goal of the scheme design is: Achieve an optimal balance between labor and equipment investment in image sampling and field sampling and the accuracy of analysis results, that is, obtain higher-precision analysis results with minimum economic investment.\nThe entire operation process complies with relevant national standards, and the digital error is controlled within 2 pixels, ensuring that vector data can truly reflect the shape and attribute information of ground features, and the data accuracy is as high as 99%.
", "ds_acq_place": "Kazakhstan; Kyrgyzstan; Tajikistan; Uzbekistan; Turkmenistan", "ds_format": "vector data", "ds_projection": "WGS84", "ds_space_res": "30", "ds_time_res": "10 years" } }, "license_type": null, "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": [ "土地利用", "土地覆被" ], "ds_subject_tags": [ "地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "哈萨克斯坦", "乌兹别克斯坦", "吉尔吉斯斯坦", "塔吉克斯坦", "土库曼斯坦" ], "ds_time_tags": [ 1990, 2000, 2010 ], "ds_contributors": [ "方晖", "陈曦" ], "ds_meta_authors": [ "李锦" ], "ds_managers": [ "李锦" ], "category": "土地利用/土地覆被" }