利用MODIS衍生指数和Google Earth引擎平台进行干旱监测

S. Aksoy, Ozge Gorucu, Elif Sertel
{"title":"利用MODIS衍生指数和Google Earth引擎平台进行干旱监测","authors":"S. Aksoy, Ozge Gorucu, Elif Sertel","doi":"10.1109/Agro-Geoinformatics.2019.8820209","DOIUrl":null,"url":null,"abstract":"Drought is one of the frequently observed natural hazard resulting from precipitation deficit and increased evapotranspiration caused by high temperatures. Remote sensing indices are used to analyze spatio-temporal distribution of drought conditions and identify drought severity. In this study, we analyzed the spatio-temporal distribution of drought conditions in Turkey from February 2000 to January 2019 by using different drought indices produced from MODIS satellite data in Google Earth Engine (GEE) platform. Vegetation Health Index (VHI), Normalized Multiband Drought Index(NMDI) and Normalized Difference Drought Index (NDDI) maps in country level for different years and months of the related years were utilized to assess the drought conditions. Time series were also created for some specific locations to deeply analyze the drought conditions in 20-year period. Our results show that MODIS derived drought indices provide useful geospatial information to assess drought conditions in country level. Moreover, GEE platform is very handy and rapid tool to reach related satellite images and conduct remote sensing analysis of huge and long term date efficiently. Geospatial big data could be successfully accessed and processed in this platform not only for drought monitoring but also for other environmental monitoring applications.","PeriodicalId":143731,"journal":{"name":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Drought Monitoring using MODIS derived indices and Google Earth Engine Platform\",\"authors\":\"S. Aksoy, Ozge Gorucu, Elif Sertel\",\"doi\":\"10.1109/Agro-Geoinformatics.2019.8820209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Drought is one of the frequently observed natural hazard resulting from precipitation deficit and increased evapotranspiration caused by high temperatures. Remote sensing indices are used to analyze spatio-temporal distribution of drought conditions and identify drought severity. In this study, we analyzed the spatio-temporal distribution of drought conditions in Turkey from February 2000 to January 2019 by using different drought indices produced from MODIS satellite data in Google Earth Engine (GEE) platform. Vegetation Health Index (VHI), Normalized Multiband Drought Index(NMDI) and Normalized Difference Drought Index (NDDI) maps in country level for different years and months of the related years were utilized to assess the drought conditions. Time series were also created for some specific locations to deeply analyze the drought conditions in 20-year period. Our results show that MODIS derived drought indices provide useful geospatial information to assess drought conditions in country level. Moreover, GEE platform is very handy and rapid tool to reach related satellite images and conduct remote sensing analysis of huge and long term date efficiently. Geospatial big data could be successfully accessed and processed in this platform not only for drought monitoring but also for other environmental monitoring applications.\",\"PeriodicalId\":143731,\"journal\":{\"name\":\"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820209\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/Agro-Geoinformatics.2019.8820209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

干旱是由于高温引起的降水不足和蒸散量增加而引起的常见自然灾害之一。利用遥感指标分析干旱条件的时空分布,识别干旱严重程度。本研究利用谷歌Earth Engine (GEE)平台MODIS卫星数据生成的不同干旱指数,分析了2000年2月至2019年1月土耳其干旱条件的时空分布。利用植被健康指数(VHI)、标准化多波段干旱指数(NMDI)和标准化干旱差异指数(NDDI)在国家层面上不同年份和相关年份的月份进行干旱状况评估。并对部分特定地点建立了时间序列,对20年的干旱状况进行了深入分析。研究结果表明,MODIS干旱指数为国家干旱状况评估提供了有用的地理空间信息。此外,GEE平台是非常方便和快速的工具,可以获得相关卫星图像,并有效地进行大数据和长期数据的遥感分析。在该平台上,地理空间大数据不仅可以用于干旱监测,还可以用于其他环境监测应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Drought Monitoring using MODIS derived indices and Google Earth Engine Platform
Drought is one of the frequently observed natural hazard resulting from precipitation deficit and increased evapotranspiration caused by high temperatures. Remote sensing indices are used to analyze spatio-temporal distribution of drought conditions and identify drought severity. In this study, we analyzed the spatio-temporal distribution of drought conditions in Turkey from February 2000 to January 2019 by using different drought indices produced from MODIS satellite data in Google Earth Engine (GEE) platform. Vegetation Health Index (VHI), Normalized Multiband Drought Index(NMDI) and Normalized Difference Drought Index (NDDI) maps in country level for different years and months of the related years were utilized to assess the drought conditions. Time series were also created for some specific locations to deeply analyze the drought conditions in 20-year period. Our results show that MODIS derived drought indices provide useful geospatial information to assess drought conditions in country level. Moreover, GEE platform is very handy and rapid tool to reach related satellite images and conduct remote sensing analysis of huge and long term date efficiently. Geospatial big data could be successfully accessed and processed in this platform not only for drought monitoring but also for other environmental monitoring applications.
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
自引率
0.00%
发文量
0
×
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