{ "created": "2021-11-12 17:05:33", "updated": "2026-05-08 15:08:03", "id": "ff434085-4646-45fd-977f-ceb017247d12", "version": 2, "ds_topic": null, "title_cn": "2000-2020年北京冬奥崇礼赛区积雪稳定性数据集", "title_en": "A Dataset of Snow adaptability in Chongli Area of Winter Olympics Division,2000-2020", "ds_abstract": "

积雪稳定性是描述积雪动态变化的重要指标，且利用积雪日数、积雪初日终日等变量能够模拟水文、气候和全球物质能量平衡。崇礼区作为北京2022年冬奥会冰雪运动主要竞赛场地之一，该地区积雪的变化需引起关注。本数据集是针对崇礼区在长时间序列下的描述积雪稳定性的数据，共享和利用本数据集为解决滑雪场储雪提供基础，也有利于进一步开展崇礼区多年积雪稳定性与气候变化、崇礼区积雪资源管理、积雪灾害监测和预警以及滑雪场建设及极端天气应对等研究。数据集共包括6个数据文件，其中：(1)积雪稳定性多年变化趋势，是Tiff数据，数据量110KB；(2)积雪稳定性变化趋势显著性，是Tiff数据，数据量122KB；(3)积雪日数，是Tiff数据，数据量1.2MB；(4)积雪初日终日，是Tiff数据，数据量190KB；(5)连续积雪天数，是Tiff数据，数据量240KB；(6)积雪期长度，是Tiff数据，数据量142KB

", "ds_source": "

  本数据集采用的是中国2000-2020年积雪面积500米逐日无云产品。该产品MODIS表面反射率来自于美国国家航天局(NOAA)，辅助数据为MOD09GA和MYD09GA的优化NDSI阈值、MODIS土地覆盖分类产品MCD12Q1，参照数据为利用监督分类所得的中国Landsat-5 TM/Landsat8 OLI二值积雪影像。

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  产品数据格式为HDF,空间分辨率为500m，时间分辨率为每日，投影类型为GLL投影，命名方式为NIEER_CGF_MODIS_SCE_**DAILY\n500m_V02.hdf.。

", "ds_process_way": "

   对中国2000-2020年积雪面积500米逐日无云产品进行预处理，提取出崇礼区2000-2020年积雪面积500米逐日无云数据，它是二值数据，其中1为雪，0为非雪，与源数据的分辨率、投影、坐标保持一致，空间分辨率为500m，投影类型为GLL投影，坐标系为CGS-WGS1984。

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   积雪稳定性数据的生产采用趋势分析法，利用python脚本实现，详细说明见文章。

", "ds_quality": "

  产品针对不同土地利用类型用Landsat8 OLI进行精度验证，平均总体精度达到87.88%，漏分误差和错分误差分别为5.2%和6.87%，在一定程度上消除了传统单一阈值划分积雪而造成混合像元无法识别的问题，进一步提高了积雪范围识别的有效性。

", "ds_acq_start_time": "2000-01-01 00:00:00", "ds_acq_end_time": "2020-12-31 00:00:00", "ds_acq_place": "北京冬奥崇礼赛区", "ds_acq_lon_east": 115.56666666666666, "ds_acq_lat_south": 40.766666666666666, "ds_acq_lon_west": 114.26666666666667, "ds_acq_lat_north": 41.266666666666666, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "apply-access", "ds_total_size": 1031783, "ds_files_count": 2, "ds_format": "*.shp, *.tif, *.csv, *.xlsx", "ds_space_res": "500m", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "ff434085-4646-45fd-977f-ceb017247d12.jpg", "ds_thumb_from": 0, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "数据解压之后即为各积雪稳定性变量的数据文件夹，可根据需要选取参数和相应年份，文件中的统计数据与影像数据相对应，可根据研究需要作为补充数据。使用者可以使用ArcGIS查看数据、栅格运算以及生成地图，也可使用python进行数据的读取和趋势分析等操作。本数据集还可以用于开展与气候响应等相关研究。", "ds_from_station": null, "organization_id": "aba68fe5-65d3-41b1-b036-bc274a834b5e", "ds_serv_man": "李红星", "ds_serv_phone": "0931-4667592", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.4510" ], "quality_level": 3, "publish_time": "2021-11-16 16:06:25", "last_updated": "2023-03-06 12:11:56", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.ncdc.isnow.db2530.2022", "i18n": { "en": { "title": "A Dataset of Snow adaptability in Chongli Area of Winter Olympics Division,2000-2020", "ds_format": "", "ds_source": "
                     <pre><code>\n
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   This data set adopts the daily cloudless products with a snow area of 500 meters in China from 2000 to 2020 . The MODIS surface reflectance of the product comes from the National Space Administration (NOAA). The auxiliary data are the optimized NDSI threshold of mod09ga and myd09ga and the MODIS land cover classification product mcd12q1. The reference data is the binary snow image of China Landsat-5 TM / landsat8 oli obtained by supervised classification.\n   The product data format is HDF, the spatial resolution is 500m, the time resolution is daily, the projection type is GLL projection, and the naming method is Nieer_ CGF_ MODIS_ SCE_** DAILY\n500m_ V02.hdf.

", "ds_quality": "
                         <pre><code>\n
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   The accuracy of the product is verified by landsat8 oli for different land use types. The average overall accuracy is 87.88%, and the leakage error and misclassification error are 5.2% and 6.87% respectively. To a certain extent, the problem that mixed pixels cannot be recognized due to the traditional single threshold division of snow is eliminated, and the effectiveness of snow range recognition is further improved.

", "ds_ref_way": "", "ds_abstract": "
 <pre><code>\n
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   Snow cover stability is an important index to describe the dynamic change of snow cover, and the variables such as snow cover days, beginning and end of snow cover can be used to simulate hydrology, climate and global material and energy balance. Chongli district is one of the main competition venues for ice and snow sports in Beijing 2022 Winter Olympic Games. The change of snow cover in this area needs attention.\n   This data set is aimed at the data describing the snow stability in Chongli district under a long time series. Sharing and using this data set not only provides a basis for solving the snow storage in ski resorts, but also helps to further carry out the research on snow stability and climate change in Chongli district for many years, snow resource management in Chongli District, snow disaster monitoring and early warning, ski resort construction and extreme weather response.\n   The data set includes 6 data files, including:\n   (1) The multi-year variation trend of snow cover stability is TIFF data, with a data volume of 110kb;\n   (2) The significant change trend of snow cover stability is TIFF data, with a data volume of 122kb;\n   (3) Snow days, tiff data, 1.2MB;\n   (4) The first and last days of snow cover are TIFF data, with a data volume of 190kb;\n   (5) Continuous snow days are TIFF data, with a data volume of 240KB;\n   (6) The length of snow period is TIFF data, with a data volume of 142KB

", "ds_time_res": "年", "ds_acq_place": "Chongli competition area of Beijing Winter Olympics", "ds_space_res": "500m", "ds_projection": "", "ds_process_way": "
                     <pre><code>\n
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   After preprocessing the daily cloudless products with 500 m snow area in China from 2000 to 2020, the daily cloudless data of 500 m snow area in Chongli district from 2000 to 2020 is extracted. It is binary data, in which 1 is snow and 0 is non snow. It is consistent with the resolution, projection and coordinates of the source data. The spatial resolution is 500 m and the projection type is GLL projection, The coordinate system is cgs-wgs1984.\n   The production of snow stability data adopts trend analysis method and is realized by Python script. See the article for details.

", "ds_ref_instruction": " After the data is decompressed, it is the data folder of each snow stability variable. Parameters and corresponding years can be selected as required. The statistical data in the file corresponds to the image data and can be used as supplementary data according to research needs. Users can use ArcGIS to view data, grid operation and generate maps, or Python to read data and trend analysis. This data set can also be used to carry out research related to climate response." } }, "submit_center_id": "ncdc", "data_level": 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": [ "冬奥赛场", "变化趋势", "积雪稳定性", "MODIS" ], "ds_subject_tags": [ "自然地理学" ], "ds_class_tags": [], "ds_locus_tags": [ "北京", "冬奥赛场", "崇礼赛区" ], "ds_time_tags": [ 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020 ], "ds_contributors": [ { "true_name": "杨雅茹", "email": "", "work_for": "中国科学院西北生态环境资源研究院", "country": "中国" }, { "true_name": "李弘毅", "email": "lihongyi@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": "赵琴", "email": "zhaoqin21@mails.ucas.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": "杨雅茹", "email": "", "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": "李弘毅", "email": "lihongyi@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": "积雪" }