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
Ikramul Hasan, Weibo Liu, Chao Xu
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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。
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.
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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
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