Estimation of phytoplankton community composition from satellite data using a fuzzy and probabilistic combination model in mountainous reservoirs: A case of Huating Lake in spring and summer

IF 5.8 2区环境科学与生态学 Q1 ECOLOGY

Ecological Informatics Pub Date : 2025-04-18 DOI:10.1016/j.ecoinf.2025.103153

Yixuan Qiu , Zhongya Fan , Huiyun Feng , Yutao Wang , Dan Li , Wencai Wang , Ruting Huang , Jingang Jiang

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引用次数: 0

Abstract

Although remote sensing has become a common tool for monitoring mountainous reservoirs, studies on the detection of phytoplankton community compositions (PCCs) remain insufficient. Based on satellite and field data, we developed a mathematical model incorporating fuzzy logic and probabilistic methods to directly estimate the biomass of seven different phytoplankton species in Huating Lake. Water surface temperature (WST) and chlorophyll-a concentration ([Chl-a]) were selected as input parameters for this model. The WST data were processed using a single-channel algorithm that combined the brightness temperature conversion model and land surface emissivity algorithm. Inversion of [Chl-a] was conducted using an empirical approach to compare the four models developed for the two sensitive reflectance bands. The [Chl-a] values obtained from these models were significantly correlated with the field data (R > 0.8). The optimal model was selected based on validation results. After obtaining the inversion results for the WST and [Chl-a], we applied a fuzzy probabilistic model to determine the PCCs in Huating Lake from 2013 to 2023. A comparison with the measured data confirmed that this method reliably estimated PCC biomass (R > 0.65). However, the modeling accuracy was not particularly high for Bacillariophyta and Euglenophyta with high biomass. We analyzed the spatial and temporal distribution of PCCs in Huating Lake over 10 years from 2013 to 2023 and found that the results were reasonable. The results demonstrate that the fuzzy probabilistic approach offers a novel methodology for estimating the biomass of seven phytoplankton species. This method facilitates the expansion of remote-sensing technology for monitoring PCC changes in mountainous reservoirs and provides scientific data support for understanding algal bloom mechanisms and developing prevention strategies.

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基于卫星数据的山区水库浮游植物群落组成模糊概率组合模型估算——以华庭湖春夏季为例

虽然遥感已成为山区水库监测的常用工具，但对浮游植物群落组成的检测研究仍然不足。基于卫星和野外数据，建立了模糊逻辑和概率方法相结合的数学模型，直接估算了华亭湖7种不同浮游植物的生物量。该模型以水体表面温度（WST）和叶绿素a浓度（[Chl-a]）为输入参数。采用光温转换模型与地表发射率算法相结合的单通道算法对WST数据进行处理。利用经验方法对[Chl-a]进行反演，比较针对两个敏感反射波段开发的四种模型。从这些模型得到的[Chl-a]值与现场数据显著相关(R >；0.8)。根据验证结果选择最优模型。在获得WST和[Chl-a]的反演结果后，我们应用模糊概率模型确定了2013 - 2023年华庭湖的PCCs。通过与实测数据的比较，证实了该方法对PCC生物量(R >；0.65)。然而，对于高生物量的硅藻和裸藻，建模精度不是特别高。分析了2013 - 2023年10 a间华庭湖PCCs的时空分布特征，结果表明该结果是合理的。结果表明，模糊概率法为估算7种浮游植物生物量提供了一种新的方法。该方法促进了山区水库PCC变化遥感监测技术的扩展，为了解藻华机制和制定预防策略提供了科学数据支持。

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来源期刊

Ecological Informatics 环境科学-生态学

CiteScore

8.30

自引率

11.80%

发文量

346

审稿时长

46 days

期刊介绍： The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change. The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.