{ "created": "2021-09-24 09:57:21", "updated": "2026-05-04 18:58:46", "id": "74adbd25-3c08-4932-ab70-4f634a3d11c1", "version": 5, "ds_topic": null, "title_cn": "渭河流域MODIS MCD12Q1土地利用（LC500米）数据集（2001-2019年）", "title_en": "Weihe River Basin MODIS MCD12Q1 Land Use (LC500 m) Dataset (2001-2019)", "ds_abstract": "

  该土地覆盖数据集中包含了17个主要土地覆盖类型，根据国际地圈生物圈计划（IGBP），其中包括11个自然植被类型，3个土地开发和镶嵌的地类和3个非草木土地类型定义类。\n

\n

  IGBP的全球植被分类方案数据类型最为丰富，因此采用第一个数据集分类方案(LC_Type1)的数据。数据类型是8位无符号整型，有效值范围为1-17,255是填充值。\n

\n

  利用MRT工具及Python语言代码，进行批量拼接、投影转换、裁剪等处理，生成2001-2019年渭河流域MODIS MCD12Q1 土地利用类型数据。本数据集空间分辨率为500米，时间分辨率为每年合成。", "ds_source": "

\n

  数据来源于nasa官网（https://ladsweb.modaps.eosdis.nasa.gov/search/）。\n

\n

  该数据集的数据源为MCD12.v6版本数据，涉及分幅影像网格为h26v05和h27v05，空间分辨率为500米，每年合成。

", "ds_process_way": "

  运行MRT工具，生成MCD12Q1数据拼接和投影转换的prm文件，输出投影为Albers投影,空间分辨率为500米。\n

\n

  利用MATLAB 语言程序生成调用MRT工具的批处理文件，并运行。\n

\n

  利用渭河流域矢量边界，采用Python批量裁剪等步骤，最后采用GeoTIFF格式输出保存。\n

\n

  IGBP分类方案的信息提取主要技术是监督决策树分类。", "ds_quality": "

  该数据集数据质量与MCD12Q1质量一致。", "ds_acq_start_time": "2001-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "渭河流域", "ds_acq_lon_east": 110.27444444444444, "ds_acq_lat_south": 33.69611111111111, "ds_acq_lon_west": 103.9713888888889, "ds_acq_lat_north": 37.40833333333333, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 1975876, "ds_files_count": 3, "ds_format": "tif", "ds_space_res": "500", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "74adbd25-3c08-4932-ab70-4f634a3d11c1.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0d5fea4e-6fd7-4c70-b28b-3f91204c579a", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-09-28 17:40:25", "last_updated": "2025-05-29 11:35:54", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.WRiver.2021.13", "i18n": { "en": { "title": "Weihe River Basin MODIS MCD12Q1 Land Use (LC500 m) Dataset (2001-2019)", "ds_format": "tif", "ds_source": "

  Data from the official nasa website (https://ladsweb.modaps.eosdis.nasa.gov/search/).\n

  The data source for this dataset is MCD version 12.v6 data, involving split-image grids h26v05 and h27v05, with a spatial resolution of 500 meters, synthesized annually.

", "ds_quality": "

  The data quality of this dataset is consistent with the quality of MCD12Q1.", "ds_ref_way": "", "ds_abstract": "

  This land cover dataset contains 17 major land cover types, which according to the International Geosphere-Biosphere Program (IGBP) include 11 natural vegetation types, 3 land classes for land development and mosaic, and 3 non-herbaceous land type definition classes.\n

  The IGBP's Global Vegetation Classification Scheme has the richest data types, so data from the first dataset classification scheme (LC_Type1) were used. The data type is an 8-bit unsigned integer with valid values ranging from 1-17,255 being the padding value.\n

\n

  Using MRT tool and Python language code, batch splicing, projection transformation, cropping and other processing are performed to generate MODIS MCD12Q1 land use type data for the Weihe River Basin from 2001-2019. The spatial resolution of this dataset is 500 meters, and the temporal resolution is synthesized for each year.

", "ds_time_res": "年", "ds_acq_place": "Weihe River Basin", "ds_space_res": "500", "ds_projection": "", "ds_process_way": "

  The MRT tool was run to generate prm files for MCD12Q1 data splicing and projection conversion, and the output projection was the Albers projection with a spatial resolution of 500 meters.\n

\n

  The batch file to call the MRT tool is generated using the MATLAB language program and run.\n

\n

  Using the vector boundaries of the Weihe River Basin, steps such as batch cropping in Python are used, and finally the output is saved in GeoTIFF format.\n

\n

  The main technique for information extraction for the IGBP classification scheme is supervised decision tree classification.", "ds_ref_instruction": "" } }, "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": [ "渭河流域", "MCD12Q1" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "渭河流域" ], "ds_time_tags": [ 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "张耀南", "email": "yaonan@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": "李红星", "email": "lihongxing@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": "敏玉芳", "email": "myf@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": "基础地理" }