利用大地遥感卫星图像和植被指数评估塞万湖漂浮水生植被的时空变化

Q4 Social Sciences
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引用次数: 0

摘要

这项研究是 "哥白尼辅助黑海盆地环境监测--PONTOS "项目的一部分,该项目旨在利用从卫星、机载和地面来源获得的地球观测产品,支持和加强黑海盆地地区的环境监测。项目小组在亚美尼亚、希腊、格鲁吉亚和乌克兰的试点地点对环境监测系统进行了评估。目前的研究重点是评估 2009-2015 年期间塞万湖湿地和漂浮植被覆盖率的变化,塞万湖是亚美尼亚最大的淡水源,也是该项目的试点地点之一。监测水生植被的时空变化对于了解湖泊生态系统的生态和社会经济功能至关重要,因此需要标准化的方法。为了识别塞万湖的漂浮水生植被,本研究利用了 2009-2015 年 5 月中旬至 9 月中旬主要生长季节期间获取的 Landsat TM 和 OLI 图像。为加强分类过程,应用了归一化差异植被指数(NDVI)、归一化差异水生植被指数(NDAVI)和归一化差异水指数(NDWI)等植被指数。研究结果表明,可免费获取的中等分辨率 Landsat 和类似卫星图像可有效用于以可重复和连续的方式监测湖泊的时空变化。不过,研究还发现,藻类大量繁殖会严重阻碍从卫星图像中准确检测漂浮植被。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessing Spatio-temporal Changes of Floating Aquatic Vegetation in Lake Sevan Using Landsat Imagery and Vegetation Indices
This research was carried out as part of the "Copernicus assisted environmental monitoring across the Black Sea Basin – PONTOS" project, which aimed to support and enhance environmental monitoring in the Black Sea Basin region by utilizing Earth Observation products obtained from satellite, airborne, and ground sources. The project team evaluated the environmental monitoring system in pilot sites across Armenia, Greece, Georgia, and Ukraine. The current study focused on assessing changes in wetland and floating vegetation cover from 2009-2015 in Lake Sevan, the largest freshwater source in Armenia and one of the project's pilot sites. Monitoring spatio-temporal changes in aquatic vegetation is crucial for understanding the ecological and socioeconomic functions of lake ecosystems, and requires standardized methods. In order to identify floating aquatic vegetation in Lake Sevan, this study utilized Landsat TM and OLI imagery that were acquired during the main growing season from middle May to middle September of the years 2009-2015. To enhance the classification process, vegetation indices such as the Normalized Difference Vegetation Index (NDVI), Normalized Difference Aquatic Vegetation Index (NDAVI), and Normalized Difference Water Index (NDWI) were applied. The findings of this study indicate that medium-resolution Landsat and similar satellite images, which are freely available, can be effectively used to monitor spatiotemporal changes in lakes in a reproducible and continuous manner. However, it was also discovered that algal blooms can significantly hinder the accurate detection of floating vegetation from satellite imagery.
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来源期刊
International Journal of Geoinformatics
International Journal of Geoinformatics Social Sciences-Geography, Planning and Development
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