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Remote Sensing for Monitoring Harmful Algal Blooms

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ocean Remote Sensing".

Deadline for manuscript submissions: closed (15 March 2023) | Viewed by 24309

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Prof. Dr. Junsheng Li

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Guest Editor

International Research Center of Big Data for Sustainable Development Goals, Chinese Academy of Sciences, Beijing 100094, China
Interests: water color remote sensing; harmful algal blooms monitoring; hyperspectral remote sensing

Dr. Gongliang Yu

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CAS Key Laboratory of Algal Biology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China
Interests: water color remote sensing; harmful algal blooms monitoring; hyperspectral remote sensing

Dr. Chen Wang

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Satellite Application Center for Ecology and Environment, Ministry of Ecology and Environment of China, Beijing 100094, China
Interests: coastal wetland remote sensing; water environment remote sensing; harmful algal blooms monitoring

Dr. Yao Liu

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Guest Editor

Land Satellite Remote Sensing Application Center, Ministry of Natural Resources of China, Beijing 100048, China
Interests: hyperspectral remote sensing; water color remote sensing; image classification

Special Issue Information

Dear Colleagues,

The continuous development of the social economy and the intensification of human activities in recent years has resulted in the occurrence of eutrophication in many sea areas and inland waterbodies, with the frequent occurrence of harmful algal blooms. Remote sensing has many advantages in terms of observing harmful algal blooms. Satellite remote sensing can monitor the spatial distribution of large-scale harmful algal blooms, UAV remote sensing can realize high-resolution monitoring of harmful algal blooms under clouds, and public participation of monitoring of harmful algal blooms can be realized through smartphone-based citizen science. In the process of monitoring harmful algal blooms based on remote sensing, there are still some scientific and technical problems that need to be further studied.

This Special Issue aims at presenting studies covering monitoring methods, temporal and spatial variation rules, environmental impact analysis, and methods for the prediction and early warning of harmful algal blooms based on multisource remote sensing technology. Remote sensing technology includes satellite remote sensing, UAV remote sensing, and smartphone-based citizen science, etc., whereas satellite remote sensing includes optical remote sensing satellite, SAR, and thermal infrared, etc. The methods for remote sensing of harmful algal blooms include traditional threshold segmentation, decision tree, and deep learning methods. The analysis of temporal and spatial variation rules and factors influencing harmful algal blooms can be oriented to a certain waterbody or a wide range of water areas. In addition to remote sensing data, meteorological and other auxiliary data can be used in environmental impact analysis and the prediction and early warning of harmful algal blooms.

Suggested themes and article types for submissions, but not limited to:

Methods for monitoring harmful algal blooms based on satellite remote sensing

Methods for monitoring harmful algal blooms based on UAV

Methods for monitoring harmful algal blooms based on citizen science

Methods for monitoring harmful algal blooms based on deep learning

Analysis of temporal and spatial variation of harmful algal blooms

Analysis on factors influencing harmful algal blooms

Analysis of the environmental impact of harmful algal blooms

Early warning and prediction of harmful algal blooms

Prof. Dr. Junsheng Li
Dr. Gongliang Yu
Dr. Chen Wang
Dr. Yao Liu
Guest Editors

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Keywords

remote sensing
UAV
citizen science
harmful algal blooms
eutrophication
water quality
bio-optical properties
natural and anthropogenic factors

Published Papers (9 papers)

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Research

17 pages, 4175 KiB

Open AccessArticle

Assessment of GCOM-C Satellite Imagery in Bloom Detection: A Case Study in the East China Sea

by Chi Feng, Yuanli Zhu, Anglu Shen, Changpeng Li, Qingjun Song, Bangyi Tao and Jiangning Zeng

Remote Sens. 2023, 15(3), 691; https://doi.org/10.3390/rs15030691 - 24 Jan 2023

Cited by 1 | Viewed by 1789

Abstract

The coast of the East China Sea (ECS) is one of the regions most frequently affected by harmful algal blooms in China. Remote sensing monitoring could assist in understanding the mechanism of blooms and their associated environmental changes. Based on imagery from the Second-Generation Global Imager (SGLI) conducted by Global Change Observation Mission-Climate (GCOM-C) (Japan), the accuracy of satellite measurements was initially validated using matched pairs of satellite and ground data relating to the ECS. Additionally, using SGLI data from the coast of the ECS, we compared the applicability of three bloom extraction methods: spectral shape, red tide index, and algal bloom ratio. With an RMSE of less than 25%, satellite data at 490 nm, 565 nm, and 670 nm showed good consistency with locally measured remote sensing reflectance data. However, there was unexpected overestimation at 443 nm of SGLI data. By using a linear correction method, the RMSE at 443 nm was decreased from 27% to 17%. Based on the linear corrected SGLI data, the spectral shape at 490 nm was found to provide the most satisfactory results in separating bloom and non-bloom waters among the three bloom detection methods. In addition, the capability in harmful algae distinguished using SGLI data was discussed. Both of the Bloom Index method and the green-red Spectral Slope method were found to be applicable for phytoplankton classification using SGLI data. Overall, the SGLI data provided by GCOM-C are consistent with local data and can be used to identify bloom water bodies in the ECS, thereby providing new satellite data to support monitoring of bloom changes in the ECS. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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23 pages, 5351 KiB

Open AccessArticle

Innovative Remote Sensing Identification of Cyanobacterial Blooms Inspired from Pseudo Water Color

by Zhen Cao, Yuanyuan Jing, Yuchao Zhang, Lai Lai, Zhaomin Liu and Qiduo Yang

Remote Sens. 2023, 15(1), 215; https://doi.org/10.3390/rs15010215 - 30 Dec 2022

Cited by 4 | Viewed by 2419

Abstract

The identification and monitoring of cyanobacterial blooms (CBs) is critical for ensuring water security. However, traditional methods are time-consuming and labor-intensive and are not ideal for large-scale monitoring. In operational monitoring, the existing remote sensing methods are also not ideal due to complex surface features, unstable models, and poor robustness thresholds. Here, a novel algorithm, the pseudo-Forel-Ule index (P-FUI), is developed and validated to identify cyanobacterial blooms based on Terra MODIS, Landsat-8 OLI, Sentinel-2 MSI, and Sentinel-3 OLCI sensors. First, three parameters of P-FUI, that is, brightness Y, saturation s, and hue angle α, were calculated based on remote sensing reflectance. Then, the robustness thresholds of the parameters were determined by statistical analysis for a frequency distribution histogram. We validated the accuracy of our approach using high-spatial-resolution satellite data with the aid of field investigations. Considerable results were obtained by using water color differences directly. The overall classification accuracy is more than 93.76%, and the user’s accuracy and producer’s accuracy are more than 94.60% and 94.00%, respectively, with a kappa coefficient of 0.91. The identified cyanobacterial blooms’ spatial distribution with high, medium, and low intensity produced consistent results compared to those based on satellite data. Impact factors were also discussed, and the algorithm was shown to be tolerant of perturbations by clouds and high turbidity. This new approach enables operational monitoring of cyanobacterial blooms in eutrophic lakes. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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22 pages, 8610 KiB

Open AccessArticle

Monitoring the Spatio-Temporal Distribution of Ulva prolifera in the Yellow Sea (2020–2022) Based on Satellite Remote Sensing

by Zhuyi Wang, Bowen Fan, Dingfeng Yu, Yanguo Fan, Deyu An and Shunqi Pan

Remote Sens. 2023, 15(1), 157; https://doi.org/10.3390/rs15010157 - 27 Dec 2022

Cited by 3 | Viewed by 1888

Abstract

The green tide caused by Ulva prolifera (U. prolifera) is becoming more severe as climate change and human activity accelerate, endangering tourism, aquaculture, and urban landscapes in coastal cities. In order to understand the spatio-temporal distribution of U. prolifera in response to the green tide disaster, this study used the Haiyang-1C (HY-1C) satellite accompanied by the Sentinel-2 and GaoFen-1 (GF-1) satellites to systematically monitor U. prolifera between 2020 and 2022. The consistency of U. prolifera distribution between the HY-1C and Sentinel-2 satellites, as well as the HY-1C and GF-1 satellites, was first investigated and the determination coefficients (R²) were 0.966 and 0.991, respectively, which supports the feasibility of China’s first ocean water color operational satellite, HY-1C, for U. prolifera monitoring. Therefore, the spatio-temporal distribution of U. prolifera is studied herein, along with the influence range, influence area, and drift paths. From 2020 to 2022, U. prolifera appeared in late May and lasted for 61, 88, and 73 days. Additionally, the in influence area continuously decreased in 2020 and 2022, while it generally increased and then decreased in 2021. It is an interesting phenomenon that when the maximum influence area occurred at the early stage of U. prolifera in both 2020 and 2022, the drift paths tended to move southward after traveling northward. The overall trend of the drift path in 2021 was to head northward. Thus, the study of the dynamic evolution, influence range, influence area, and drift paths of U. prolifera is helpful to promote the systematic development of emergency response mechanisms for U. prolifera. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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20 pages, 11457 KiB

Open AccessArticle

Deep Learning-Based Automatic Extraction of Cyanobacterial Blooms from Sentinel-2 MSI Satellite Data

by Kai Yan, Junsheng Li, Huan Zhao, Chen Wang, Danfeng Hong, Yichen Du, Yunchang Mu, Bin Tian, Ya Xie, Ziyao Yin, Fangfang Zhang and Shenglei Wang

Remote Sens. 2022, 14(19), 4763; https://doi.org/10.3390/rs14194763 - 23 Sep 2022

Cited by 11 | Viewed by 2423

Abstract

Cyanobacterial harmful algal blooms (CyanoHABs) in inland water have emerged as a major global environmental challenge. Although satellite remote sensing technology has been widely used to monitor CyanoHABs, there are also some automatic extraction methods of CyanoHABs based on spectral indices (such as gradient mode, fixed threshold, and the Otsu method, etc.), the accuracy is generally not very high. This study developed a high-precision automatic extraction model for CyanoHABs using a deep learning (DL) network based on Sentinel-2 multi-spectral instrument (MSI) data of Chaohu Lake, China. First, we generated the CyanoHABs “ground truth” dataset based on visual interpretation. Thereafter, we trained the CyanoHABs extraction model based on a DL image segmentation network (U-Net) and extracted CyanoHABs. Then, we compared three previous automatic CyanoHABs extraction methods based on spectral index threshold segmentation and evaluated the accuracy of the results. Based on “ground truth”, at the pixel level, the F1 score and relative error (RE) of the DL model extraction results are 0.90 and 3%, respectively, which are better than that of the gradient mode (0.81,40%), the fixed threshold (0.81, 31%), and the Otsu method (0.53, 62%); at CyanoHABs area level, the R² of the scatter fitting between DL model result and the “ground truth” is 0.99, which is also higher than the other three methods (0.90, 0.92, 0.84, respectively). Finally, we produced the annual CyanoHABs frequency map based on DL model results. The frequency map showed that the CyanoHABs on the northwest bank are significantly higher than in the center and east of Chaohu Lake, and the most serious CyanoHABs occurred in 2018 and 2019. Furthermore, CyanoHAB extraction based on this model did not cause cloud misjudgment and exhibited good promotion ability in Taihu Lake, China. Hence, our findings indicate the high potential of the CyanoHABs extraction model based on DL in further high-precision and automatic extraction of CyanoHABs from large-scale water bodies. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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22 pages, 6665 KiB

Open AccessArticle

Thirty-Four-Year Record (1987–2021) of the Spatiotemporal Dynamics of Algal Blooms in Lake Dianchi from Multi-Source Remote Sensing Insights

by Jinge Ma, Feng He, Tianci Qi, Zhe Sun, Ming Shen, Zhigang Cao, Di Meng, Hongtao Duan and Juhua Luo

Remote Sens. 2022, 14(16), 4000; https://doi.org/10.3390/rs14164000 - 17 Aug 2022

Cited by 18 | Viewed by 2332

Abstract

Lake Dianchi is one of the most eutrophic lakes in China. The decline in water quality and the occurrence of massive algal blooms pose a significant threat to the health and environmental safety of the water ecosystem, making Lake Dianchi a key concern for algal bloom management in China. Obtaining the spatiotemporal dynamics of algal blooms for the longest time possible is crucial to algal bloom management and future prediction. However, it is difficult to acquire a long-term record of algal blooms from a single sensor in order to cover a more extended period of eutrophication in the lake due to the limitation of the spatial and temporal resolution of the sensors. In this study, Landsat and Moderate-Resolution Imaging Spectroradiometer (MODIS) images were combined with the Floating Algae Index (FAI) to reconstruct a unified time series of bloom areas to analyze the algal bloom dynamics in Lake Dianchi in recent decades. Regarding the interannual variation, the bloom area showed an increasing trend from 1987 to 2021, with larger bloom areas in 1991–1992, 2000–2003, 2012–2013, and 2020–2021. In terms of seasonal characteristics, the bloom area was significantly more prominent in the rainy season compared with the dry season during the year. The spatial distribution of the bloom frequency showed a pattern of higher frequencies in the north and lower frequencies in the south. From 2000 to 2021, the initial bloom time and bloom duration showed a trend of delaying and then advancing and decreasing and then increasing, respectively. We analyzed the importance of long-term records of algal blooms and found that the percentage of rainy season images is an essential factor in reconstructing time series based on different sensors. In addition, the relationship between environmental factors and algal blooms was analyzed. The results show that wind speed and air temperature were the main meteorological factors controlling the interannual variation in algal blooms in Lake Dianchi. Water quality factors such as nutrients have less of an influence on the variation in algal blooms because the algal growth demand has been met. Environmental management measures taken by local governments have led to improvements in the lake’s trophic state, and continued strengthening of environmental pollution control is expected to curb the algal blooms in Lake Dianchi. This study provides a long-term record of algal blooms in Lake Dianchi, which provides essential reference information for a comprehensive understanding of the development process of algal blooms in Lake Dianchi and its sustainable development. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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17 pages, 7426 KiB

Open AccessArticle

Distinguishing Algal Blooms from Aquatic Vegetation in Chinese Lakes Using Sentinel 2 Image

by Jing Pu, Kaishan Song, Yunfeng Lv, Ge Liu, Chong Fang, Junbin Hou and Zhidan Wen

Remote Sens. 2022, 14(9), 1988; https://doi.org/10.3390/rs14091988 - 21 Apr 2022

Cited by 17 | Viewed by 2778

Abstract

Algal blooms frequently occur in numerous lakes in China, risking human health and the environment. In contrast, aquatic vegetation contributes to water purification. Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8–84.3%. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. This should have practical relevance for water environment management and governance departments. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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20 pages, 5157 KiB

Open AccessArticle

Long-Term Temporal and Spatial Monitoring of Cladophora Blooms in Qinghai Lake Based on Multi-Source Remote Sensing Images

by Hongyu Duan, Xiaojun Yao, Dahong Zhang, Huian Jin and Qixin Wei

Remote Sens. 2022, 14(4), 853; https://doi.org/10.3390/rs14040853 - 11 Feb 2022

Cited by 14 | Viewed by 2051

Abstract

With climate warming and intensification of human activities, the eco-environmental problems of lakes in middle and high latitudes become increasingly prominent. Qinghai Lake, located in the northeastern of the Tibetan Plateau, is the largest inland saltwater lake in China. Recently, the problem of Cladophora blooms has been widely concerning. In this study, the area of floating Cladophora blooms (hereafter FCBs) in Qinghai Lake from 1986 to 2021 was extracted using Floating Algal Index (FAI) method based on Landsat TM/ETM+/OLI and Sentinel-2 MSI images, and then the intra- and inter-annual variation characteristics and spatial patterns of FCBs were analyzed. The results show that the general change trend of FCBs in Qinghai Lake featured starting in May, expanding rapidly from June to August, and increasing steadily from September to October. From 1986 to 2021, the area of FCBs in Qinghai Lake showed an overall increasing trend in all months, with the largest increase in July at 0.1 km²/a, followed by October at 0.096 km²/a. Spatially speaking, the FCBs area showed a significant increasing trend in the northern Buha River estuary (BRN) and southern Buha River estuary (BRS) regions, a slight increase in the Shaliu River estuary (SR) region, and a decreasing trend in the Quanji River estuary (QR) region and the Heima River estuary (HR) region. The correlation between the meteorological factors and the changes in FCBs was weak, but the increase in flooded pastures in the BRN region (Bird Island) due to rising water levels was definitely responsible for the large-scale increase in FCBs in this region. However, the QB, northeastern bay of Shaliu River estuary (SRB) and HR regions, which also have extensive inundated grassland, did not have the same increase in FCBs area, suggesting that the growth of Cladophora is caused by multiple factors. The complex relationships need to be verified by further research. The current control measures have a certain inhibitory effect on the Cladophora bloom in Qinghai Lake because the FCBs area was significantly smaller in 2017–2020 (5.22 km², 3.32 km², 4.55 km² and 2.49 km²), when salvage work was performed, than in 2016 and 2021 (8.67 km² and 9.14 km²), when no salvage work was performed. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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16 pages, 4745 KiB

Open AccessArticle

SRSe-Net: Super-Resolution-Based Semantic Segmentation Network for Green Tide Extraction

by Binge Cui, Haoqing Zhang, Wei Jing, Huifang Liu and Jianming Cui

Remote Sens. 2022, 14(3), 710; https://doi.org/10.3390/rs14030710 - 02 Feb 2022

Cited by 17 | Viewed by 2760

Abstract

Due to the phenomenon of mixed pixels in low-resolution remote sensing images, the green tide spectral features with low Enteromorpha coverage are not obvious. Super-resolution technology based on deep learning can supplement more detailed information for subsequent semantic segmentation tasks. In this paper, a novel green tide extraction method for MODIS images based on super-resolution and a deep semantic segmentation network was proposed. Inspired by the idea of transfer learning, a super-resolution model (i.e., WDSR) is first pre-trained with high spatial resolution GF1-WFV images, and then the representations learned in the GF1-WFV image domain are transferred to the MODIS image domain. The improvement of remote sensing image resolution enables us to better distinguish the green tide patches from the surrounding seawater. As a result, a deep semantic segmentation network (SRSe-Net) suitable for large-scale green tide information extraction is proposed. The SRSe-Net introduced the dense connection mechanism on the basis of U-Net and replaces the convolution operations with dense blocks, which effectively obtained the detailed green tide boundary information by strengthening the propagation and reusing features. In addition, the SRSe-Net reducs the pooling layer and adds a bridge module in the final stage of the encoder. The experimental results show that a SRSe-Net can obtain more accurate segmentation results with fewer network parameters. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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20 pages, 9653 KiB

Open AccessArticle

Red Tide Detection Method for HY−1D Coastal Zone Imager Based on U−Net Convolutional Neural Network

by Xin Zhao, Rongjie Liu, Yi Ma, Yanfang Xiao, Jing Ding, Jianqiang Liu and Quanbin Wang

Remote Sens. 2022, 14(1), 88; https://doi.org/10.3390/rs14010088 - 25 Dec 2021

Cited by 16 | Viewed by 4006

Abstract

Existing red tide detection methods have mainly been developed for ocean color satellite data with low spatial resolution and high spectral resolution. Higher spatial resolution satellite images are required for red tides with fine scale and scattered distribution. However, red tide detection methods for ocean color satellite data cannot be directly applied to medium–high spatial resolution satellite data owing to the shortage of red tide responsive bands. Therefore, a new red tide detection method for medium–high spatial resolution satellite data is required. This study proposes the red tide detection U−Net (RDU−Net) model by considering the HY−1D Coastal Zone Imager (HY−1D CZI) as an example. RDU−Net employs the channel attention model to derive the inter−channel relationship of red tide information in order to reduce the influence of the marine environment on red tide detection. Moreover, the boundary and binary cross entropy (BBCE) loss function, which incorporates the boundary loss, is used to obtain clear and accurate red tide boundaries. In addition, a multi−feature dataset including the HY−1D CZI radiance and Normalized Difference Vegetation Index (NDVI) is employed to enhance the spectral difference between red tides and seawater and thus improve the accuracy of red tide detection. Experimental results show that RDU−Net can detect red tides accurately without a precedent threshold. Precision and Recall of 87.47% and 86.62%, respectively, are achieved, while the F1−score and Kappa are 0.87. Compared with the existing method, the F1−score is improved by 0.07–0.21. Furthermore, the proposed method can detect red tides accurately even under interference from clouds and fog, and it shows good performance in the case of red tide edges and scattered distribution areas. Moreover, it shows good applicability and can be successfully applied to other satellite data with high spatial resolution and large bandwidth, such as GF−1 Wide Field of View 2 (WFV2) images. Full article

(This article belongs to the Special Issue Remote Sensing for Monitoring Harmful Algal Blooms)

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