{
    "created": "2024-09-24 11:19:55",
    "updated": "2026-08-07 23:59:26",
    "id": "a47de355-d2fe-4f45-814e-0658f628a6ed",
    "version": 8,
    "ds_topic": null,
    "title_cn": "1977-2017年咸海湖滨30米归一化植被指数数据集",
    "title_en": "",
    "ds_abstract": "<p>本数据为1977年、1987年、1997年、2007年2017年咸海湖滨每年6月-10月的NDVI栅格数据，投影采用UTM投影， WGS-8坐标系，空间分辨率为30m和60m，时间分辨率为月。</p>",
    "ds_source": "<p>原始遥感数据主要来源于美国地质调查局（United States Geological Survey，USGS）Landsat卫星遥感影像。</p>",
    "ds_process_way": "<p>运用ENVI 5.3 Toolbox中的NDVI工具对研究区的影像进行波段计算，公式：NDVI=(NIR-R)/(NIR+R)，其中NIR为近红外波段，R为红光波段。在NDVI工具中选择计算的波段并输出结果，最终得到缓冲区范围内各月的NDVI值。\n运用最大值合成法(Maximum Value Composite, MVC)公式：\nMaxNDVI_i=Max(NDVI_1,NDVI_2,NDVI_3,NDVI_4)，式中: MaxNDVI_i 表示月度NDVI最大值; i为月份，取值为5-10; 〖NDVI〗_1-〖NDVI〗_4分别表示相应月份内原始影像预处理后的NDVI值。获得研究区内当年生长季的NDVI值，并计算单位距离内的NDVI均值，利用ArcGIS10.2.2中的栅格计算器计算生长季各月NDVI的均值。</p>",
    "ds_quality": "<p>1977年的影像为Landsat1-5多光谱扫描仪(MSS)一级数据产品，其空间分辨率大小为60m，时间分辨率为18d。1987、1997、2007年的影像为来自Landsat4和Landsat5专题制图仪(TM)的一级光谱数据产品，空间分辨率为30m，时间分辨率为16d。2017年的影像为来自andsat8 OLI(操作陆地成像仪)和TIRS(热红外传感器)的一级多光谱数据产品，空间分辨率大小为30m，时间分辨率为16d。加工后数据精度空间分辨率同原始数据，时间分辨率为月和年。</p>\n<p><img alt=\"\" src=\"/static/upload/images/20250314124427tupian2.png\" /></p>",
    "ds_acq_start_time": "1977-01-01 00:00:00",
    "ds_acq_end_time": "2017-12-31 00:00:00",
    "ds_acq_place": "咸海",
    "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": "apply-access",
    "ds_total_size": 11607849618,
    "ds_files_count": 1,
    "ds_format": "栅格数据",
    "ds_space_res": "30米，60米",
    "ds_time_res": "月，年",
    "ds_coordinate": "WGS84",
    "ds_projection": "UTM",
    "ds_thumbnail": "a47de355-d2fe-4f45-814e-0658f628a6ed.png",
    "ds_thumb_from": 2,
    "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": "2025-03-22 19:31:52",
    "first_publish_time": null,
    "last_updated": "2025-03-22 19:31:52",
    "protected": false,
    "protected_to": null,
    "lang": "zh",
    "cstr": "33110.11.ariddc.00118",
    "license": null,
    "extra": null,
    "files_shape": [
        {
            "name": "Data.rar",
            "size": 11607849618,
            "is_dir": false
        }
    ],
    "features": null,
    "data_level": 0,
    "i18n": {
        "en": {
            "title": "1977-2017 Data set of normalized vegetation index on the 30-meter shore of the Aral Sea Lake in 2001",
            "ds_abstract": "<p>This data is NDVI grid data of the Aral Sea lake from June to October every year in 1977, 1987, 1997, 2007 and 2017. The projection uses UTM projection, WGS-8 coordinate system, spatial resolutions are 30m and 60m, and temporal resolution is month. </p>",
            "ds_source": "<p>The raw remote sensing data mainly comes from the United States Geological Survey (USGS) Landsat satellite remote sensing images. </p>",
            "ds_process_way": "<p>Use the NDVI tool in the ENVI 5.3 Toolbox to calculate the band of the images in the study area. The formula is NDVI=(NIR-R)/(NIR+R), where NIR is the near-infrared band and R is the red light band. Select the calculated bands in the NDVI tool and output the results, and finally get the NDVI values for each month within the buffer zone.\nUse the Maximum Value Composite (MVC) formula:\nMaxNDVI_i=Max(NDVI_1, NDVI_2, NDVI_3, NDVI_4), where: MaxNDVI_i represents the maximum monthly NDVI value; i is the month, with values ranging from 5 to 10; and [NDVI]_1-[NDVI]_4 respectively represent the NDVI values of raw images after preprocessing in the corresponding month. Obtain the NDVI values during the growing season in the study area, calculate the mean NDVI within a unit distance, and use the grid calculator in ArcGIS10.2.2 to calculate the mean NDVI values for each month in the growing season. </p>",
            "ds_quality": "<p>The 1977 image is a first-level data product of the Landsat1-5 Multispectral Scanner (MSS), with a spatial resolution of 60m and a temporal resolution of 18d. Images from 1987, 1997, and 2007 are first-level spectral data products from the Landsat4 and Landsat5 thematic mappers (TM) with a spatial resolution of 30m and a temporal resolution of 16d. The 2017 image is a first-level multispectral data product from andsat8 OLI(Operational Land Imager) and TIRS(Thermal Infrared Sensor), with a spatial resolution of 30m and a temporal resolution of 16d. The spatial resolution of the processed data accuracy is the same as the original data, and the temporal resolution is months and years. </p>\n<p><img alt=\"\" src=\"/static/upload/images/20250314124427tupian2.png\" /></p>",
            "ds_acq_place": "Aral Sea",
            "ds_format": "raster data",
            "ds_projection": "UTM",
            "ds_space_res": "30 meters, 60 meters",
            "ds_time_res": "month, year"
        }
    },
    "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": [
        "归一化植被指数",
        "NDVI"
    ],
    "ds_subject_tags": [
        "地理学"
    ],
    "ds_class_tags": [],
    "ds_locus_tags": [
        "咸海"
    ],
    "ds_time_tags": [
        1977,
        1987,
        1997,
        2007,
        2017
    ],
    "ds_contributors": [
        "吉力力·阿不都外力",
        "黄粤",
        "郑新军",
        "罗毅",
        "刘铁"
    ],
    "ds_meta_authors": [
        "郑新军",
        "崔梦琪"
    ],
    "ds_managers": [
        "刘铁"
    ],
    "category": "陆地卫星"
}