Monitoring Algal Blooms in Small Lakes Using Drones: A Case Study in Southern Illinois

IF 0.9 Q4 WATER RESOURCES
Di Wu, Ruopu Li, Jia Liu, Nafeesa Khan
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引用次数: 1

Abstract

Harmful Algal Blooms (HABs) persist in many water bodies around the world and pose adverse health and economic impacts to the affected communities. Small Unmanned Aerial Vehicles (UAVs) have recently been applied as a cost-effective tool for HABs monitoring. In this study, HABs in two small lakes in Southern Illinois (Carbondale Reservoir and the Campus Lake of Southern Illinois University) were monitored using UAVs and biomass concentrations in lake waters. By analyzing vegetation indices derived from multispectral UAV images and chlorophyll-a concentrations in the two lakes, statistical regression models were established for each waterbody. The model relates spectral characteristics of the lake water to its algae biomass. It was found that normalized difference vegetation index (NDVI) and blue-to-green band ratio are the best-fit indices to the variation in chlorophyll-a in Carbondale Reservoir and the Campus Lake, respectively. The findings in this study can be used for monitoring HABs using UAVs in these lakes in the future.

Abstract Image

使用无人机监测小湖藻类水华:以伊利诺伊州南部为例
有害藻类水华(HABs)在世界各地的许多水体中持续存在,并对受影响的社区的健康和经济造成不利影响。小型无人机(UAV)最近被用作一种成本效益高的HABs监测工具。在这项研究中,使用无人机和湖水中的生物量浓度监测了伊利诺伊州南部两个小湖(Carbondale水库和南伊利诺伊大学校园湖)的赤潮。通过分析来自多光谱无人机图像的植被指数和两个湖泊的叶绿素a浓度,建立了每个水体的统计回归模型。该模型将湖水的光谱特征与其藻类生物量联系起来。研究发现,归一化差异植被指数(NDVI)和蓝绿带比率分别是Carbondale水库和Campus湖叶绿素a变化的最佳拟合指标。这项研究的发现可用于未来在这些湖泊中使用无人机监测赤潮。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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