基于各种植被生物物理指标一致性的全球土地生产力动态新产品

IF 4.2 3区 地球科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Yuran Cui, Xiaosong Li
{"title":"基于各种植被生物物理指标一致性的全球土地生产力动态新产品","authors":"Yuran Cui, Xiaosong Li","doi":"10.1080/20964471.2021.2018789","DOIUrl":null,"url":null,"abstract":"ABSTRACT Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals (IAEG-SDGs) as key indicators for monitoring SDG 15.3.1. Multiple vegetation parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales. However, there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales. This study proposes a fusion framework that incorporates the trends and consistencies within the four commonly used remote sensing-based vegetation indicators. We analyzed the differences among the four vegetation parameters in different land cover and climate zones, finally producing a new global land productivity dynamics (LPD) product with confidence level degrees. The LPD classes indicated by the four vegetation indicators(VIs) showed that all three levels (low, medium, and high confidence) of increasing area account for 23.99% of the global vegetated area and declining area account for 7.00%. The Increase high-confidence(HC) area accounted for 2.77% of the total area, and the Decline-HC accounted for 0.35% of the total area. This study demonstrates the accuracy of the high-confidence (HC) area for the evaluation of land productivity decline and increase. The “forest” landcover type and “humid” climate zone had the largest increasing and declining area but had the lowest high-confidence proportion. The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target. The “Global Land Productivity Dynamic dataset” is available in the Science Data Bank at http://www.doi.org/10.11922/sciencedb.j00076.00084.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":"65 1","pages":"36 - 53"},"PeriodicalIF":4.2000,"publicationDate":"2022-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"A new global land productivity dynamic product based on the consistency of various vegetation biophysical indicators\",\"authors\":\"Yuran Cui, Xiaosong Li\",\"doi\":\"10.1080/20964471.2021.2018789\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals (IAEG-SDGs) as key indicators for monitoring SDG 15.3.1. Multiple vegetation parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales. However, there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales. This study proposes a fusion framework that incorporates the trends and consistencies within the four commonly used remote sensing-based vegetation indicators. We analyzed the differences among the four vegetation parameters in different land cover and climate zones, finally producing a new global land productivity dynamics (LPD) product with confidence level degrees. The LPD classes indicated by the four vegetation indicators(VIs) showed that all three levels (low, medium, and high confidence) of increasing area account for 23.99% of the global vegetated area and declining area account for 7.00%. The Increase high-confidence(HC) area accounted for 2.77% of the total area, and the Decline-HC accounted for 0.35% of the total area. This study demonstrates the accuracy of the high-confidence (HC) area for the evaluation of land productivity decline and increase. The “forest” landcover type and “humid” climate zone had the largest increasing and declining area but had the lowest high-confidence proportion. The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target. The “Global Land Productivity Dynamic dataset” is available in the Science Data Bank at http://www.doi.org/10.11922/sciencedb.j00076.00084.\",\"PeriodicalId\":8765,\"journal\":{\"name\":\"Big Earth Data\",\"volume\":\"65 1\",\"pages\":\"36 - 53\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2022-01-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Big Earth Data\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1080/20964471.2021.2018789\",\"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/10.1080/20964471.2021.2018789","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: 8

摘要

土地生产力的变化已被可持续发展目标机构间专家组(IAEG-SDGs)认可为监测可持续发展目标15.3.1的关键指标。在不同的土地生产力下降过程和尺度上,光学遥感技术的多种植被参数得到了广泛的应用。然而,对指标选择及其在不同尺度上反映土地生产力下降的有效性尚无共识。本研究提出了一个融合四种常用遥感植被指标趋势和一致性的融合框架。我们分析了不同土地覆被和气候带下4个植被参数之间的差异,最终得到了一个具有置信度的全球土地生产力动态(LPD)新产品。4个植被指标(VIs)表示的LPD等级表明,全球植被面积增加的3个水平(低、中、高置信度)均占23.99%,下降的面积占7.00%。增加高置信度(HC)面积占总面积的2.77%,下降高置信度(HC)面积占总面积的0.35%。研究结果表明,高置信度(HC)区域对土地生产力下降和增长的评价具有较高的准确性。“森林”和“湿润”气候带的增减面积最大,但高置信度比例最低。该数据产品为根据具体应用目标在全球和区域尺度上评估SDG 15.3.1提供了重要的可选参考。“全球土地生产力动态数据集”可在科学数据库中获得,网址为http://www.doi.org/10.11922/sciencedb.j00076.00084。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new global land productivity dynamic product based on the consistency of various vegetation biophysical indicators
ABSTRACT Changes in land productivity have been endorsed by the Inter Agency Expert Group on Sustainable Development Goals (IAEG-SDGs) as key indicators for monitoring SDG 15.3.1. Multiple vegetation parameters from optical remote sensing techniques have been widely utilized across different land productivity decline processes and scales. However, there is no consensus on indicator selection and their effectiveness at representing land productivity declining at different scales. This study proposes a fusion framework that incorporates the trends and consistencies within the four commonly used remote sensing-based vegetation indicators. We analyzed the differences among the four vegetation parameters in different land cover and climate zones, finally producing a new global land productivity dynamics (LPD) product with confidence level degrees. The LPD classes indicated by the four vegetation indicators(VIs) showed that all three levels (low, medium, and high confidence) of increasing area account for 23.99% of the global vegetated area and declining area account for 7.00%. The Increase high-confidence(HC) area accounted for 2.77% of the total area, and the Decline-HC accounted for 0.35% of the total area. This study demonstrates the accuracy of the high-confidence (HC) area for the evaluation of land productivity decline and increase. The “forest” landcover type and “humid” climate zone had the largest increasing and declining area but had the lowest high-confidence proportion. The data product provides an important and optional reference for the assessment of SDG 15.3.1 at global and regional scales according to the specific application target. The “Global Land Productivity Dynamic dataset” is available in the Science Data Bank at http://www.doi.org/10.11922/sciencedb.j00076.00084.
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