从大数据中提取的 2009 年至 2019 年中国樱花和银杏叶色物候数据集

IF 3.3 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Geoscience Data Journal Pub Date : 2023-12-10 DOI:10.1002/gdj3.231
Shenghong Wang, Haolong Liu, Xinyue Qin, Junhu Dai, Jun Liu
{"title":"从大数据中提取的 2009 年至 2019 年中国樱花和银杏叶色物候数据集","authors":"Shenghong Wang,&nbsp;Haolong Liu,&nbsp;Xinyue Qin,&nbsp;Junhu Dai,&nbsp;Jun Liu","doi":"10.1002/gdj3.231","DOIUrl":null,"url":null,"abstract":"<p>Ground-based phenological observation data are the most accurate phenological monitoring data currently available. Making effective use of available information on social media to retrieve phenological data is of considerable value in alleviating the lack of phenological data in regions with missing observation sites. In this study, a logistic curve fitting method was developed to extract phenological data on specific species from social media data. After verifying the relationship between the site observation data and the temperature, timing data for two typical phenological phenomena in China, namely cherry blossom flowering in spring and ginkgo leaf coloration in autumn were reconstructed and published. The data availability is from 2010 to 2019 in 176 cities and 2009 to 2018 in 155 cities. This dataset is an effective supplement for existing phenological data, and this method also provides a reference for obtaining phenological data for specific species.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.231","citationCount":"0","resultStr":"{\"title\":\"Cherry blossom and ginkgo leaf coloration phenology dataset of China from 2009 to 2019 extracted from big data\",\"authors\":\"Shenghong Wang,&nbsp;Haolong Liu,&nbsp;Xinyue Qin,&nbsp;Junhu Dai,&nbsp;Jun Liu\",\"doi\":\"10.1002/gdj3.231\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Ground-based phenological observation data are the most accurate phenological monitoring data currently available. Making effective use of available information on social media to retrieve phenological data is of considerable value in alleviating the lack of phenological data in regions with missing observation sites. In this study, a logistic curve fitting method was developed to extract phenological data on specific species from social media data. After verifying the relationship between the site observation data and the temperature, timing data for two typical phenological phenomena in China, namely cherry blossom flowering in spring and ginkgo leaf coloration in autumn were reconstructed and published. The data availability is from 2010 to 2019 in 176 cities and 2009 to 2018 in 155 cities. This dataset is an effective supplement for existing phenological data, and this method also provides a reference for obtaining phenological data for specific species.</p>\",\"PeriodicalId\":54351,\"journal\":{\"name\":\"Geoscience Data Journal\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.3000,\"publicationDate\":\"2023-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.231\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Geoscience Data Journal\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.231\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"GEOSCIENCES, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscience Data Journal","FirstCategoryId":"89","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/gdj3.231","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

地面物候观测数据是目前最准确的物候监测数据。有效利用社交媒体上的可用信息检索物候数据,对于缓解观测点缺失地区物候数据缺乏的问题具有重要价值。本研究开发了一种逻辑曲线拟合方法,从社交媒体数据中提取特定物种的物候数据。在验证了观测点数据与温度之间的关系后,重建并发布了中国两种典型物候现象的时序数据,即春季樱花开花和秋季银杏叶变色。数据可用期为 2010 年至 2019 年的 176 个城市和 2009 年至 2018 年的 155 个城市。该数据集是对现有物候数据的有效补充,该方法也为获取特定物种的物候数据提供了参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Cherry blossom and ginkgo leaf coloration phenology dataset of China from 2009 to 2019 extracted from big data

Ground-based phenological observation data are the most accurate phenological monitoring data currently available. Making effective use of available information on social media to retrieve phenological data is of considerable value in alleviating the lack of phenological data in regions with missing observation sites. In this study, a logistic curve fitting method was developed to extract phenological data on specific species from social media data. After verifying the relationship between the site observation data and the temperature, timing data for two typical phenological phenomena in China, namely cherry blossom flowering in spring and ginkgo leaf coloration in autumn were reconstructed and published. The data availability is from 2010 to 2019 in 176 cities and 2009 to 2018 in 155 cities. This dataset is an effective supplement for existing phenological data, and this method also provides a reference for obtaining phenological data for specific species.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Geoscience Data Journal
Geoscience Data Journal GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
5.90
自引率
9.40%
发文量
35
审稿时长
4 weeks
期刊介绍: Geoscience Data Journal provides an Open Access platform where scientific data can be formally published, in a way that includes scientific peer-review. Thus the dataset creator attains full credit for their efforts, while also improving the scientific record, providing version control for the community and allowing major datasets to be fully described, cited and discovered. An online-only journal, GDJ publishes short data papers cross-linked to – and citing – datasets that have been deposited in approved data centres and awarded DOIs. The journal will also accept articles on data services, and articles which support and inform data publishing best practices. Data is at the heart of science and scientific endeavour. The curation of data and the science associated with it is as important as ever in our understanding of the changing earth system and thereby enabling us to make future predictions. Geoscience Data Journal is working with recognised Data Centres across the globe to develop the future strategy for data publication, the recognition of the value of data and the communication and exploitation of data to the wider science and stakeholder communities.
期刊最新文献
Issue Information Exploring Jalisco's water quality: A comprehensive web tool for limnological and phytoplankton data HSPEI: A 1-km spatial resolution SPEI dataset across the Chinese mainland from 2001 to 2022 High-resolution atmospheric CO2 concentration data simulated in WRF-Chem over East Asia for 10 years The Irish drought impacts database: A 287-year database of drought impacts derived from newspaper archives
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1