基于MODIS为数据源的多指标沙漠化信息提取方法，以MOD09A1和MOD11A2为数据源，分别利用MOD09A1产品反演NDVI、MSAVI、ALBEDO、FVC和利用MOD11A2产品计算LST，并结合植被指数和陆面温度计算TVDI，在人工目视解译的基础上，选择不同类型沙漠化的样本数据，选择以上六个参数通过机器学习，利用决策树分类方法获得2000-2019年年尺度时间序列沙漠化动态特征。
\n由于沙漠化土地参考基准背景存在明显空间差异，而本数据集未对整个研究区作进一步小区划分，故本数据集给出的沙漠化土地面积与目视解译结果存在明显差距，但可用于研究区沙漠化土地变化趋势分析等研究。
\n各年度栅格数据像元数值具体含义如下 ：
\n0代表非沙漠化土地；
\n1代表轻度沙漠化土地；
\n2代表中度沙漠化土地；
\n3代表重度沙漠化土地；
\n4代表严重沙漠化土地。", "ds_source": "
基于landsat8多光谱数据，依据1:100000比例尺标准制作。", "ds_process_way": "
基于MODIS为数据源的多指标沙漠化信息提取方法，以MOD09A1和MOD11A2为数据源，分别利用MOD09A1产品反演NDVI、MSAVI、ALBEDO、FVC和利用MOD11A2产品计算LST，并结合植被指数和陆面温度计算TVDI，在人工目视解译的基础上，选择不同类型沙漠化的样本数据，选择以上六个参数通过机器学习，利用决策树分类方法获得2000-2019年年尺度时间序列沙漠化动态特征。", "ds_quality": "
数据质量良好", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2019-12-31 00:00:00", "ds_acq_place": "北方半干旱区", "ds_acq_lon_east": 127.0, "ds_acq_lat_south": 35.5, "ds_acq_lon_west": 104.0, "ds_acq_lat_north": 50.5, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 13726119, "ds_files_count": 2, "ds_format": "tif", "ds_space_res": "1000", "ds_time_res": "年", "ds_coordinate": "WGS84", "ds_projection": "", "ds_thumbnail": "51e55bd9-d757-45cf-8df0-31457dc0fd52.jpg", "ds_thumb_from": 2, "ds_ref_way": "宋翔, 郭坚, 刘树林, 廖杰，中国北方半干旱区沙漠化序列（2000-2019年），国家冰川冻土沙漠科学数据中心(www.ncdc.ac.cn)，2022，doi：10.12072/ncdc.ZDYF.db1683.2022", "paper_ref_way": "", "ds_ref_instruction": "用户在使用数据时请在正文中明确声明数据的来源，并在参考文献部分引用本元数据提供的引用方式。", "ds_from_station": null, "organization_id": "8534e8f7-cbd5-4771-81d6-d524ffde0065", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "10.12072/ncdc.ZDYF.db1683.2022", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2022-04-18 11:33:44", "last_updated": "2022-04-18 11:33:44", "protected": false, "protected_to": "2024-04-18 00:00:00", "lang": "zh", "cstr": "11738.11.ncdc.ZDYF.db1683.2022", "i18n": { "en": { "title": "Desertification Sequence in semi-arid areas of northern China (2000-2019)", "ds_format": "", "ds_source": "
Based on Landsat 8 multispectral data, it is made according to 1:100000 scale standard.", "ds_quality": "
Good data quality", "ds_ref_way": "In order to protect the rights and interests of the platform's scientific and technological resources, expand the services of the platform center and enhance the application potential of scientific and technological resources, resource users are requested to clearly indicate the resource source and resource author in the research results generated by the use of resources (including published papers, treatises, data products, unpublished research reports, data products, etc.).", "ds_abstract": "
Based on the multi index desertification information extraction method with MODIS as the data source, taking mod09a1 and mod11a2 as the data sources, retrieve NDVI, MSAVI, albedo and FVC with mod09a1 products respectively, calculate LST with mod11a2 products, calculate TVDI with vegetation index and land surface temperature, and select the sample data of different types of desertification on the basis of artificial visual interpretation, By selecting the above six parameters, the dynamic characteristics of desertification in annual scale time series from 2000 to 2019 are obtained by machine learning and decision tree classification method.
\nBecause there are obvious spatial differences in the reference background of desertification land, and this data set does not further divide the whole study area, there is an obvious gap between the desertification land area given in this data set and the visual interpretation results, but it can be used for the analysis of desertification land change trend in the study area.
\nThe specific meanings of pixel values of grid data in each year are as follows:
\n0 represents non desertification land
\n1 represents slightly desertified land
\n2 represents moderately desertified land
\n3 represents heavily desertified land
\n4 represents seriously desertified land.
", "ds_time_res": "年", "ds_acq_place": "Northern semi-arid region", "ds_space_res": "1000", "ds_projection": "", "ds_process_way": "Based on the multi index desertification information extraction method with MODIS as the data source, taking mod09a1 and mod11a2 as the data sources, retrieve NDVI, MSAVI, albedo and FVC with mod09a1 products respectively, calculate LST with mod11a2 products, calculate TVDI with vegetation index and land surface temperature, and select the sample data of different types of desertification on the basis of artificial visual interpretation, By selecting the above six parameters, the dynamic characteristics of desertification in annual scale time series from 2000 to 2019 are obtained by machine learning and decision tree classification method.", "ds_ref_instruction": " \r\nWhen users use data, please clearly state the source of data in the body and quote the reference method provided by this metadata in the References section." } }, "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": [ "沙漠化", "半干旱", "荒漠化" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北方半干旱区" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 ], "ds_contributors": [ { "true_name": "宋翔", "email": "songxiang@lzb.ac.cn", "work_for": "中国科学院大气物理研究所", "country": "中国" }, { "true_name": "郭坚", "email": "guojian@lzb.ac.cn", "work_for": "中国科学院寒区旱区环境与工程研究所", "country": "中国" }, { "true_name": "刘树林", "email": "liusl@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": "廖杰", "email": "liaojie@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": "宋翔", "email": "songxiang@lzb.ac.cn", "work_for": "中国科学院大气物理研究所", "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": "沙漠与荒漠化" }