{
    "created": "2019-10-10 16:29:28",
    "updated": "2026-05-03 01:34:01",
    "id": "cec6065d-d68f-41e0-b2a9-534e60e72e81",
    "version": null,
    "ds_topic": "c6f9fed5-f7bf-4502-a904-d99914875270",
    "title_cn": "中亚1：25万湖泊与河流分布数据（2000年，2010年）",
    "title_en": "1:250000 lake and river distribution data in Central Asia (2000, 2010)",
    "ds_abstract": "<p>本数据包括中亚五国湖泊和河流要素，采用Albers等面积割圆锥投影，以2000年度TM遥感影像，利用ENVI、eCognition软件，通过选取对水体反映强烈的绿波段和近红外波段进行叠加计算，在生成的矢量层下叠加TM数据，通过人工干预修正，提取湖泊和河流要素，生产最后的数据产品，生成新疆与中亚湖泊与河流分布数据。数据属性信息见本数据文档。该数据可作为调查中亚地区湖泊和河流分布概况提供科学数据支持。本数据为矢量数据，数据属性信息包括以下几个字段FID:多边形 feature IDAREA:面积PERIMETER:周长OBJECTID:湖泊河流编号。</p>",
    "ds_source": "<p>中亚1_25万湖泊与河流分布数据（2000年）由中国科学院新疆生态与地理研究所采用全数字化的方式独立完成，主要数据源： 1.  2000年度TM遥感数据 上述数据来自本研究所多年科研成果的积累。</p>",
    "ds_process_way": "<p>利用ENVI、eCognition软件，通过选取对水体反映强烈的绿波段和近红外波段进行叠加计算，在生成的矢量层下叠加TM数据，通过人工干预修正，生产最后的数据产品。</p>",
    "ds_quality": "<p>数据采集按照矢量化标准执行，严格按照TM数据进行修正。</p>",
    "ds_acq_start_time": "2000-01-01 00:00:00",
    "ds_acq_end_time": "2010-01-01 00:00:00",
    "ds_acq_place": "中亚五国",
    "ds_acq_lon_east": 90.0,
    "ds_acq_lat_south": 35.0,
    "ds_acq_lon_west": 45.0,
    "ds_acq_lat_north": 55.0,
    "ds_acq_alt_low": null,
    "ds_acq_alt_high": null,
    "ds_share_type": "apply-access",
    "ds_total_size": 23965099,
    "ds_files_count": 5,
    "ds_format": "矢量",
    "ds_space_res": null,
    "ds_time_res": "年",
    "ds_coordinate": "WGS84",
    "ds_projection": "Albers等面积割圆锥投影",
    "ds_thumbnail": "cec6065d-d68f-41e0-b2a9-534e60e72e81.jpg",
    "ds_thumb_from": 0,
    "ds_ref_way": "李均力，中亚1：25万湖泊与河流分布数据（2000年，2010年），国家特殊环境、特殊功能 观测研究台站共享平台，2018，doi：10.12072/casnw.063.2019.db",
    "paper_ref_way": "",
    "ds_ref_instruction": null,
    "ds_from_station": null,
    "organization_id": "52b7b79b-860c-49a5-9083-9a70cf8bed5a",
    "ds_serv_man": "李锦",
    "ds_serv_phone": "09917827369",
    "ds_serv_mail": "lijin@ms.xjb.ac.cn",
    "doi_value": "",
    "subject_codes": null,
    "quality_level": 3,
    "publish_time": "2020-12-18 16:07:40",
    "last_updated": "2023-05-16 17:35:38",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "11738.11.ncdc.nieer.2020.1470",
    "i18n": {
        "en": {
            "title": "1:250000 lake and river distribution data in Central Asia (2000, 2010)",
            "ds_format": "",
            "ds_source": "<p>1:250000 lake and river distribution data in Central Asia, which was independently completed by Xinjiang Institute of ecology and geography, Chinese Academy of Sciences. The main data sources are as follows: 1. TM remote sensing data of 2000. The above data are from the accumulation of scientific research achievements of the Institute for many years. </p>",
            "ds_quality": "<p>Data collection is performed according to vectorization standards, and corrections are made strictly according to TM data.</p>",
            "ds_ref_way": "\nLi Junli, 1:250000 lake and river distribution data in Central Asia (2000, 2010), national special environment and special function observation and research station sharing platform, 2018, DOI: 10.12072/casnw.063.2019.db",
            "ds_abstract": "<p>This data includes lake and river elements of five Central Asian countries. Using Albers' equal area conic projection, using ENVI and ecognition software, the authors selected the green band and near-infrared band which strongly reflected the water body to carry on the superposition calculation, superposed the TM data under the generated vector layer, and extracted the lake and River elements through manual intervention correction To generate the distribution data of lakes and rivers in Xinjiang and Central Asia. See this data document for data attribute information. The data can be used as scientific data to investigate the distribution of lakes and rivers in Central Asia. This data is vector data, and the data attribute information includes the following fields: polygon feature idarea: area perimeter: objectid: Lake River number. </p>",
            "ds_time_res": "年",
            "ds_acq_place": "Five Central Asian countries",
            "ds_space_res": "",
            "ds_projection": "",
            "ds_process_way": "<p>By using ENVI and eCognition software, the green band and near-infrared band which reflect strong water body are selected for superposition calculation. TM data is superimposed under the generated vector layer, and the final data product is produced by manual intervention correction. </p>",
            "ds_ref_instruction": "\nNone"
        }
    },
    "submit_center_id": "ncdc",
    "data_level": 0,
    "license_type": "CC 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": [
        "自然资源数据库\\水资源",
        "中亚",
        "湖泊",
        "水资源",
        "河流"
    ],
    "ds_subject_tags": [
        "自然地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "中亚"
    ],
    "ds_time_tags": [
        2000,
        2010
    ],
    "ds_contributors": [
        {
            "true_name": "李均力",
            "email": "lijl@ms.xjb.ac.cn",
            "work_for": "中科院新疆生态与地理研究所",
            "country": ""
        }
    ],
    "ds_meta_authors": [
        {
            "true_name": "李锦",
            "email": "lijin@ms.xjb.ac.cn",
            "work_for": "新疆生态与地理研究所",
            "country": "中国"
        }
    ],
    "ds_managers": [
        {
            "true_name": "李锦",
            "email": "lijin@ms.xjb.ac.cn",
            "work_for": "新疆生态与地理研究所",
            "country": "中国"
        },
        {
            "true_name": "李均力",
            "email": "lijl@ms.xjb.ac.cn",
            "work_for": "中科院新疆生态与地理研究所",
            "country": ""
        }
    ],
    "category": "水文"
}