2010-2019年“一带一路”经济走廊山区绿化指数(SDG15.4.2)数据集

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Jinhu Bian, Ainong Li, Xi Nan, G. Lei, Zhengjia Zhang
{"title":"2010-2019年“一带一路”经济走廊山区绿化指数(SDG15.4.2)数据集","authors":"Jinhu Bian, Ainong Li, Xi Nan, G. Lei, Zhengjia Zhang","doi":"10.1080/20964471.2021.1941571","DOIUrl":null,"url":null,"abstract":"ABSTRACT Mountains are undergoing widespread changes caused by human activities and climate change. Given the importance of mountains, the protection and sustainable development of mountain ecosystems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda. As one of the indicators, the Mountain Green Cover Index (MGCI) datasets can provide consistent and comparable status of green vegetation in mountainous areas, which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time. The production of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales. In this paper, the MGCI datasets with 500-meter spatial resolutions, covering the economic corridors of the Belt and Road Initiative (BRI), were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform. The validation of green vegetation cover with the ground-truth samples indicated that the datasets can achieve an overall accuracy of 94.06%, with well-detailed spatial and temporal variations. The archived datasets include the MGCI of each BRI economic corridor, matched to a geospatial layer denoting the economic corridor boundaries. The essential information of the datasets and their limitations, along with the production flow, were described in this paper. The published geospatial datasets are available at http://www.doi.org/10.11922/sciencedb.1005.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"os-35 1","pages":"77 - 89"},"PeriodicalIF":4.2000,"publicationDate":"2021-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Dataset of the mountain green cover index (SDG15.4.2) over the economic corridors of the Belt and Road Initiative for 2010-2019\",\"authors\":\"Jinhu Bian, Ainong Li, Xi Nan, G. Lei, Zhengjia Zhang\",\"doi\":\"10.1080/20964471.2021.1941571\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Mountains are undergoing widespread changes caused by human activities and climate change. Given the importance of mountains, the protection and sustainable development of mountain ecosystems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda. As one of the indicators, the Mountain Green Cover Index (MGCI) datasets can provide consistent and comparable status of green vegetation in mountainous areas, which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time. The production of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales. In this paper, the MGCI datasets with 500-meter spatial resolutions, covering the economic corridors of the Belt and Road Initiative (BRI), were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform. The validation of green vegetation cover with the ground-truth samples indicated that the datasets can achieve an overall accuracy of 94.06%, with well-detailed spatial and temporal variations. The archived datasets include the MGCI of each BRI economic corridor, matched to a geospatial layer denoting the economic corridor boundaries. The essential information of the datasets and their limitations, along with the production flow, were described in this paper. The published geospatial datasets are available at http://www.doi.org/10.11922/sciencedb.1005.\",\"PeriodicalId\":8765,\"journal\":{\"name\":\"Big Earth Data\",\"volume\":\"os-35 1\",\"pages\":\"77 - 89\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2021-07-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Earth Data\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/20964471.2021.1941571\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Big Earth Data","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1080/20964471.2021.1941571","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 6

摘要

由于人类活动和气候变化,山区正在发生广泛的变化。鉴于山区的重要性,山区生态系统的保护和可持续发展已被列为联合国2030年可持续发展议程的目标。山地绿化指数(Mountain Green Cover Index, MGCI)数据集作为指标之一,能够提供山区绿色植被的一致性和可比性状况,为异质山地生态系统健康制图和监测变化提供支持。因此,迫切需要制作明确的高空间分辨率MGCI数据集,以支持次国家和多时间尺度的保护措施。本文基于所有可用的Landsat-8数据和谷歌Earth Engine云计算平台,开发了2010 - 2019年覆盖“一带一路”经济走廊的500米空间分辨率MGCI数据集。地面真值样本对绿色植被覆盖度的验证表明,数据集的总体精度为94.06%,具有较详细的时空变化。存档的数据集包括每个“一带一路”经济走廊的MGCI,并与表示经济走廊边界的地理空间层相匹配。本文描述了数据集的基本信息和它们的局限性,以及生产流程。已发布的地理空间数据集可在http://www.doi.org/10.11922/sciencedb.1005上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dataset of the mountain green cover index (SDG15.4.2) over the economic corridors of the Belt and Road Initiative for 2010-2019
ABSTRACT Mountains are undergoing widespread changes caused by human activities and climate change. Given the importance of mountains, the protection and sustainable development of mountain ecosystems have been listed as the goals of the United Nations 2030 Sustainable Development Agenda. As one of the indicators, the Mountain Green Cover Index (MGCI) datasets can provide consistent and comparable status of green vegetation in mountainous areas, which can support the mapping of heterogeneous mountain ecosystem health and monitoring changes over time. The production of explicitly high-spatial-resolution MGCI datasets is therefore urgently needed to support the protection measures at subnational and multitemporal scales. In this paper, the MGCI datasets with 500-meter spatial resolutions, covering the economic corridors of the Belt and Road Initiative (BRI), were developed for 2010 to 2019 based on all available Landsat-8 data and the Google Earth Engine cloud computing platform. The validation of green vegetation cover with the ground-truth samples indicated that the datasets can achieve an overall accuracy of 94.06%, with well-detailed spatial and temporal variations. The archived datasets include the MGCI of each BRI economic corridor, matched to a geospatial layer denoting the economic corridor boundaries. The essential information of the datasets and their limitations, along with the production flow, were described in this paper. The published geospatial datasets are available at http://www.doi.org/10.11922/sciencedb.1005.
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
Big Earth Data
Big Earth Data Earth and Planetary Sciences-Computers in Earth Sciences
CiteScore
7.40
自引率
10.00%
发文量
60
审稿时长
10 weeks
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信