Advances and limitations of using satellites to monitor cyanobacterial harmful algal blooms

Ogashawara, Igor

doi:10.1590/s2179-975x0619

Cited by 5 publications

(3 citation statements)

References 30 publications

(36 reference statements)

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“…Ecological monitoring is one example, and this is especially true for cyanobacterial harmful algal blooms. In this regard, I. Ogashawara provides a perspective on the use of remote sensing to monitor harmful algal blooms, with a focus on recent advancements, lingering limitations, and challenges (Ogashawara, 2019). Extending beyond monitoring, Ribas et al (2019) explain how challenging it is to identify causal relationships between interventions and aquatic ecosystem responses.…”

mentioning

confidence: 99%

Editorial: Freshwater sustainability and aquatic ecology in a fast-changing world

et al. 2020

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mentioning

confidence: 99%

Editorial: Freshwater sustainability and aquatic ecology in a fast-changing world

et al. 2020

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“…Remote sensing has been widely used in monitoring water bodies, including water quality parameter inversion and cyanobacteria bloom monitoring [15][16][17][18][19][20][21]. Among them, the common water quality parameters that can be accurately estimated are chlorophyll a (chl-a) levels, TSM, color dissolved organic matter (CDOM), and turbidity [22][23][24][25][26][27]. As a suspension of water-based optically active substances, TSM can be effectively monitored using remote sensing technology [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Characteristics of the Total Suspended Matter Concentration in the Hongze Lake during 1984–2019 Based on Landsat Data

Lyu

et al. 2022

Remote Sensing

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The Hongze Lake is the fourth largest freshwater lake in China and an important lake for the East Route of the South-to-North Water Diversion Project. The water quality of the lake affects social development and the lives of residents. To assess the impacts of environmental changes and human activities on the distribution of the total suspended matter (TSM) in the Hongze Lake, we developed an algorithm that utilizes the near-infrared (NIR) band to estimate TSM based on in situ measurements. The algorithm was applied to Landsat images to derive TSM distribution maps from 1984 to 2019, revealing significant inter-annual, seasonal, and spatial variability. The relationship between TSM, precipitation, and wind speed was analyzed, and we found that: (1) The estimation model of TSM concentration in the Hongze Lake constructed for TM and OLI has a high accuracy, and it can be used to jointly monitor TSM concentration in the Hongze Lake for long-term series; (2) From 1984 to 2019, the concentration of TSM in the Hongze Lake showed a trend of first rising and then falling, with the maximum value in 2010 at 100.18 mg/L mainly caused by sand mining activities. Precipitation and wind speed weakly influence the inter-annual variation of TSM concentration; (3) The concentration of TSM in the Hongze Lake in summer is easily affected by flooding in the Huai River, and the concentration of TSM in other seasons is significantly negatively correlated with precipitation; (4) TSM is highest in the Huaihe Bay, followed by the Lihe Bay and Chengzi Bay. The main reason for this is that the input of the Huaihe Bay flows directly into this lake area and is also the main navigation channel. The results of this study are of great significance for the protection and management of the water environment of the Hongze Lake.

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“…Significant efforts have been made to solve the problem based on satellite remote sensing data. Detailed reviews of modern satellite remote sensing methods are given, for example, in [3][4][5]. The methods Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s).…”

Section: Introductionmentioning

confidence: 99%

Ecological Disaster off Kamchatka Peninsula: Satellite Monitoring of Microalgae <i>Karenia</i> Spp. Bloom

Alexanin¹,

Kachur²,

Orlova³

et al. 2022

Preprint

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The environmental disaster in Kamchatka in the autumn of 2020 was caused by an extensive bloom of harmful microalgae of the genus Karenia. A spectral shape algorithm was used to detect algae. The algorithm calibration of in situ species composition data made it possible to identify areas where harmful algae dominated in biomass. Satellite images of chlorophyll-a concentra-tion, turbidity, specific fluorescence, and spectral shape parameter were computed. The images were used to recognize the stages of algal bloom: intensive growth, blooming, and change in the dominant algal species. Cases of an increase in the concentration of harmful substances in the coastal zone due to wind impact were analyzed. The following explanation of events has been offered. After the stage of intensive growth of microalgae, nutrient deficiency stimulated the production of metabolites that have a harmful effect on the environment. The change of the dominant alga species in the second half of September and the past storm contributed to a sharp increase in the concentration of metabolites and dead organic matter in the coastal zone, which caused an ecological disaster. The subsequent mass bloom of alga species of the same genus, and the regular wind impact leading to the concentration of harmful substances in the coastal zone, contributed to the development of this catastrophic phenomena.

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Advances and limitations of using satellites to monitor cyanobacterial harmful algal blooms

Cited by 5 publications

References 30 publications

Editorial: Freshwater sustainability and aquatic ecology in a fast-changing world

Editorial: Freshwater sustainability and aquatic ecology in a fast-changing world

Characteristics of the Total Suspended Matter Concentration in the Hongze Lake during 1984–2019 Based on Landsat Data

Ecological Disaster off Kamchatka Peninsula: Satellite Monitoring of Microalgae <i>Karenia</i> Spp. Bloom

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