Tracking spatio-temporal dynamics of harmful algal blooms using long-term MODIS observations of Chaohu Lake in China from 2000 to 2021

水华 富营养化 环境科学 降水 中国 营养物 空间分布 遥感 气候学 生态学 地理 气象学 浮游植物 生物 地质学 考古
作者
Ting Zhou,Yan Li,Bo Jiang,Juha M. Alatalo,Chen Li,Cheng Ni
出处
期刊:Ecological Indicators [Elsevier BV]
卷期号:146: 109842-109842 被引量:36
标识
DOI:10.1016/j.ecolind.2022.109842
摘要

Harmful algal blooms (HABs) have long been a critical threat to environmental safety and health, especially in inland lakes with rapidly growing socioeconomic development. Although satellite remote sensing provides an efficient manner to observe floating algae blooms, tracing and capturing the distribution of HABs in water bodies remains challenging for their high dynamics in both spatial and temporal dimensions. This study analyzed the spatio-temporal dynamics of HABs in Chaohu Lake, China, from 2000 to 2021. Daily MODIS remote sensing observations, including 7926 images, were utilized. The Floating Algae Index (FAI) was applied to each image to detect the area and severity of HABs. Results show that the HABs in Chaohu Lake have generally been increasing in the past two decades, with two intermittent decreases in 2016–2017 and 2020–2021, and the duration of HABs tended to be longer over the years. HABs were most severe and frequent in the lake's northwestern area. Temperature and precipitation are two main meteorological factors positively correlated with HABs. The fast-growing socio-economy reflected by population and GDP increase lake eutrophication and HABs. However, environment management policies since 2020 have had a significant and rapid influence in decreasing the lake’s nutrients, which greatly reduced HABs severity in 2020 and 2021. This study's results further indicate that high-frequency remote sensing observations, although with occasional data missing due to cloud cover, can still perform well in describing HABs, as long as the analysis method is appropriately adopted. This finding provides potential significance for utilizing remote sensing products, especially for highly dynamic object observation where high-quality images are lacking.
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