Deep Learning-Based Algal Bloom Identification Method from Remote Sensing Images—Take China’s Chaohu Lake as an Example

Zhu, S. Y.; Wu, Yinglei; Ma, Xiaoshuang

doi:10.3390/su15054545

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Combined with visual interpretation, the images above the water level threshold can be eliminated, which contributes to the selection of the VPF threshold and the extraction of AV. Y = 7.9740X + 8.5254 (8) where Y is the water level (m) on the dam of Yuqiao Reservoir, and X is the flooding area (km 2 ) in Figure 1c.…”

Section: Separating Abs and Avmentioning

confidence: 99%

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Mapping Algal Blooms in Aquatic Ecosystems Using Long-Term Landsat Data: A Case Study of Yuqiao Reservoir from 1984–2022

Liu,

Ding,

Han

et al. 2023

Remote Sensing

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Water eutrophication poses a dual threat to ecological and human well-being. Gaining insight into the intricate dynamics of phytoplankton bloom phenology holds paramount importance in comprehending the complexities of aquatic ecosystems. Remote sensing technologies have gained attention for mapping algal blooms (ABs) effectively, but distinguishing them from aquatic vegetation (AV) remains challenging due to their similar spectral characteristics. To address this issue, we propose a meticulous three-step methodology for AB mapping employing long-term Landsat imagery. Initially, a multi-index decision tree model (DTM) is deployed to identify the vegetation signal (VS) encompassing both AV and ABs. Subsequently, the annual maximum growth range of AV is precisely delineated using vegetation presence frequency (VPF) in conjunction with normal and low water level imagery. Lastly, ABs are accurately extracted by inversely intersecting VS and AV. The performance of our approach is thoroughly validated using the interclass correlation coefficient (ICC) based on a Gaofen-2 Panchromatic Multi-spectral (GF-2 PMS) image, demonstrating strong consistency with notable values of 0.822 longitudinally, 0.771 latitudinally, and 0.797 overall. The method is applied to Landsat images from 1984 to 2022 to quantify the spatial distribution and temporal variations of ABs in Yuqiao Reservoir—a significant national water body spanning a vast area of 135 km2 in China. Our findings reveal a pervasive and uneven dispersion of ABs, predominantly concentrated in the northern sector. Notably, the intensity of ABs experienced an initial surge from 1984 to 2008, followed by a subsequent decline from 2014 to 2022. Importantly, anthropogenic activities, such as fish cage culture, alongside pollution stemming from nearby industrial and agricultural sources, exert a profound influence on the dynamics of water eutrophication. Fortunately, governmental initiatives focused on water purification exhibit commendable efficacy in mitigating the ecological burden on reservoirs and upholding water quality. The methodological framework presented in this study boasts remarkable precision in AB extraction and exhibits considerable potential in addressing the needs of aquatic ecosystems.

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Section: Separating Abs and Avmentioning

confidence: 99%

“…The occurrence of algal blooms (ABs) caused by phytoplankton is a crucial characteristic of water eutrophication [7]. It is essential to develop accurate and efficient methods for mapping ABs to research eutrophication [8].…”

Section: Introductionmentioning

confidence: 99%

Mapping Algal Blooms in Aquatic Ecosystems Using Long-Term Landsat Data: A Case Study of Yuqiao Reservoir from 1984–2022

Liu,

Ding,

Han

et al. 2023

Remote Sensing

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Appraisal and Identification of Algal Bloom Region, Prevention and Management Approaches

Sharma,

Mona

2024

Environmental Science and Engineering

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Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

Colkesen,

Ozturk,

Altuntas

2024

Stoch Environ Res Risk Assess

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One of the main threats to freshwater resources is pollution from anthropogenic activities such as rapid urbanization and excessive agricultural nutrient runoff. Remote sensing technologies have been effectively used in monitoring and mapping rapid changes in the marine environment and assessing the overall health of freshwater ecosystems. The main goal of this study is to comparatively evaluate the performance of index-based and classification-based approaches in mapping dense floating algal blooms observed in Lake Burdur using Sentinel-2 imagery. For index-based mapping, algae-specific indices, namely the Floating Algae Index (FAI), Adjusted Floating Algae Index, Surface Algal Blooms Index (SABI), and Algal Blooms Detection Index (ABDI), were used. At the same time, pixel- and object-based Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Long Short-Term Memory Network (LSTM) were utilized for classification-based algal mapping. For this purpose, seven Sentinel-2 images, selected through time series analysis performed on the Google Earth Engine platform, were used as the primary dataset in the application. The results show that high-density floating algae formations can be detected over 99% by both indices and classification-based approaches, whereas pixel-based classification is more successful in mapping low-density algal blooms. When two-class thematic maps representing water and floating algae classes were considered, the maps produced by index-based FAI using an appropriate threshold value and the classification-based RF algorithm reached an overall accuracy of over 99%. The highest algae density in the lake was observed on July 13, 2021, and was determined to be effective in ~ 45 km2 of the lake’s surface.

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Deep Learning-Based Algal Bloom Identification Method from Remote Sensing Images—Take China’s Chaohu Lake as an Example

Cited by 7 publications

References 29 publications

Mapping Algal Blooms in Aquatic Ecosystems Using Long-Term Landsat Data: A Case Study of Yuqiao Reservoir from 1984–2022

Mapping Algal Blooms in Aquatic Ecosystems Using Long-Term Landsat Data: A Case Study of Yuqiao Reservoir from 1984–2022

Appraisal and Identification of Algal Bloom Region, Prevention and Management Approaches

Comparative evaluation of performances of algae indices, pixel- and object-based machine learning algorithms in mapping floating algal blooms using Sentinel-2 imagery

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