基于Google Earth Engine的2002 - 2021年大沼泽地季节性湿地淹没动态监测与分析

Q3 Social Sciences Human Geographies Pub Date : 2023-02-17 DOI:10.3390/geographies3010010
Ikramul Hasan, Weibo Liu, Chao Xu
{"title":"基于Google Earth Engine的2002 - 2021年大沼泽地季节性湿地淹没动态监测与分析","authors":"Ikramul Hasan, Weibo Liu, Chao Xu","doi":"10.3390/geographies3010010","DOIUrl":null,"url":null,"abstract":"Inundation dynamics coupled with seasonal information is critical to study the wetland environment. Analyses based on remotely sensed data are the most effective means to monitor and investigate wetland inundation dynamics. For the first time, this study deployed an automated thresholding method to quantify and compare the annual inundation characteristics in dry and wet seasons in the Everglades, using Landsat imagery in Google Earth Engine (GEE). This research presents the long-term time series maps from 2002 to 2021, with a comprehensive spatiotemporal depiction of inundation. In this paper, we bridged the research gap of space-time analysis for multi-season inundation dynamics, which is urgently needed for the Everglades wetland. Within a GIS-based framework, we integrated statistical models, such as Mann–Kendall and Sen’s Slope tests, to track the evolutionary trend of seasonal inundation dynamics. The spatiotemporal analyses highlight the significant differences in wet and dry seasons through time and space. The stationary or permanent inundation is more likely to be distributed along the coastal regions (Gulf of Mexico and Florida Bay) of the Everglades, presenting a warning regarding their vulnerability to sea level rise.","PeriodicalId":38507,"journal":{"name":"Human Geographies","volume":"24 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Monitoring and Analyzing the Seasonal Wetland Inundation Dynamics in the Everglades from 2002 to 2021 Using Google Earth Engine\",\"authors\":\"Ikramul Hasan, Weibo Liu, Chao Xu\",\"doi\":\"10.3390/geographies3010010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Inundation dynamics coupled with seasonal information is critical to study the wetland environment. Analyses based on remotely sensed data are the most effective means to monitor and investigate wetland inundation dynamics. For the first time, this study deployed an automated thresholding method to quantify and compare the annual inundation characteristics in dry and wet seasons in the Everglades, using Landsat imagery in Google Earth Engine (GEE). This research presents the long-term time series maps from 2002 to 2021, with a comprehensive spatiotemporal depiction of inundation. In this paper, we bridged the research gap of space-time analysis for multi-season inundation dynamics, which is urgently needed for the Everglades wetland. Within a GIS-based framework, we integrated statistical models, such as Mann–Kendall and Sen’s Slope tests, to track the evolutionary trend of seasonal inundation dynamics. The spatiotemporal analyses highlight the significant differences in wet and dry seasons through time and space. The stationary or permanent inundation is more likely to be distributed along the coastal regions (Gulf of Mexico and Florida Bay) of the Everglades, presenting a warning regarding their vulnerability to sea level rise.\",\"PeriodicalId\":38507,\"journal\":{\"name\":\"Human Geographies\",\"volume\":\"24 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-02-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Human Geographies\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/geographies3010010\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Social Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Human Geographies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/geographies3010010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 1

摘要

与季节信息相结合的淹没动态是研究湿地环境的关键。基于遥感数据的分析是监测和调查湿地淹没动态的最有效手段。本研究首次使用谷歌地球引擎(GEE)中的Landsat图像,采用自动阈值方法量化和比较沼泽地干湿季节的年淹没特征。本研究绘制了2002年至2021年的长期时间序列图,对淹没进行了全面的时空描述。填补了沼泽地湿地迫切需要的多季节淹没动态时空分析的研究空白。在基于gis的框架内,我们整合了统计模型,如Mann-Kendall和Sen 's Slope测试,以跟踪季节性淹没动态的演变趋势。时空分析强调了干湿季节在时间和空间上的显著差异。静止或永久的洪水更有可能分布在大沼泽地的沿海地区(墨西哥湾和佛罗里达湾),这对它们易受海平面上升的影响提出了警告。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Monitoring and Analyzing the Seasonal Wetland Inundation Dynamics in the Everglades from 2002 to 2021 Using Google Earth Engine
Inundation dynamics coupled with seasonal information is critical to study the wetland environment. Analyses based on remotely sensed data are the most effective means to monitor and investigate wetland inundation dynamics. For the first time, this study deployed an automated thresholding method to quantify and compare the annual inundation characteristics in dry and wet seasons in the Everglades, using Landsat imagery in Google Earth Engine (GEE). This research presents the long-term time series maps from 2002 to 2021, with a comprehensive spatiotemporal depiction of inundation. In this paper, we bridged the research gap of space-time analysis for multi-season inundation dynamics, which is urgently needed for the Everglades wetland. Within a GIS-based framework, we integrated statistical models, such as Mann–Kendall and Sen’s Slope tests, to track the evolutionary trend of seasonal inundation dynamics. The spatiotemporal analyses highlight the significant differences in wet and dry seasons through time and space. The stationary or permanent inundation is more likely to be distributed along the coastal regions (Gulf of Mexico and Florida Bay) of the Everglades, presenting a warning regarding their vulnerability to sea level rise.
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
Human Geographies
Human Geographies Social Sciences-Geography, Planning and Development
CiteScore
1.10
自引率
0.00%
发文量
7
审稿时长
8 weeks
期刊最新文献
Residents and Stakeholder Opinions on Township Tourism in Langa, Cape Town, South Africa Spatio-Temporal Dynamics and Physico-Hydrological Trends in Rainfall, Runoff and Land Use in Paraíba Watershed Perspectives on Advanced Technologies in Spatial Data Collection and Analysis Contemporary Challenges in Destination Planning: A Geographical Typology Approach Spatiotemporal Dengue Fever Incidence Associated with Climate in a Brazilian Tropical Region
×
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