Monitoring Anthropogenic Impact on Some Coastal Water Areas of the Black Sea Using Multispectral Satellite Imagery

Бондур, В. Г.; Vorobyev, V. E.; Замшин, В. В.; Serebryany, A. N.; Latushkin, Alexandr A.; Li, M. E.; Martynov, Oleg; Hurchak, A. P.; Grinchenko, D. V.

doi:10.1134/s0001433818090098

Cited by 11 publications

(7 citation statements)

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“…Первый способ предполагает интерактивное (с участием оператора-дешифровщика) выделение зон, обладающих признаками загрязнений (например, нефтепроявлений или глубинных стоков). Для этого оператором-дешифровщиком применяются такие приѐмы как визуальное дешифрирование, пороговая обработка панхроматического канала или вычисленного индекса цвета [4,[13][14][15], а также кластеризация и контролируемая классификация (см. рис.1,2,3).…”

Section: описание методаunclassified

“…Несмотря на значительную вариабельность оптических аномалий, связанных с различными типами загрязнений акваторий, регистрируемых при космической оптической съѐмке, принципиальная возможность автоматизации процесса поиска и идентификации таких аномалий с приемлемой точностью существует. Например, в работах [13,15] показана типичность и относительная стабильность свойств оптических аномалий, обусловленных всплытием на морскую поверхность сточных вод. В настоящей работе (см.…”

Section: описание методаunclassified

“…Перед вычислением контрастов осреднѐнные спектральные кривые фоновой и загрязнѐнной водной поверхности нормировались на величину отражательной способности в синем канале, что позволило исключить из анализа сильно вариабельную величину интегрального контраста и сосредоточиться на изучении спектральных особенностей регистрируемых оптических аномалий. Отметим, что аналогичные исследования оптических характеристик поверхностных проявлений сточных вод также обнаруживают стабильность спектральных контрастов для такого вида загрязнений [15].…”

Section: пример использования методаunclassified

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Developing the Automatic Water Surface Pollution Search and Identification Method Using Satellite Optical Imagery

Замшин¹,

Kharchenko²,

Khodaeva³

2019

Information Technologies in Remote Sensing of the Earth - RORSE 2018

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Here we describe the results from development and testing of the automatic water surface pollution search and identification method using satellite optical imagery. A search of water surface characterized by the specific shape of spectral reflectance curve distinguishing from typical background curve in visible and near-IR bands was carried out within the test area (Russian Black Sea areas) using satellite data (Landsat-7/8 and Sentinel-2A/B, March 2017-August 2018). When such sites were detected, additional research was carried out, including the difference between the anomaly and background water spectra, spatial shape, size, relation of this area with potential pollution sources etc. The results of this research allowed us to make a conclusion on the origin of the registered anomalies. Possible thematic application of the proposed method by the example of water surface oil pollution search and identification are described here.

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Section: описание методаunclassified

Section: пример использования методаunclassified

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Developing the Automatic Water Surface Pollution Search and Identification Method Using Satellite Optical Imagery

Замшин¹,

Kharchenko²,

Khodaeva³

2019

Information Technologies in Remote Sensing of the Earth - RORSE 2018

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“…A reasonable number of studies have employed satellite imagery to monitor the impact of wastewater discharge in the coastal environment [4,[10][11][12]. Overall, those studies proved the Sentinel-2 (S2) ocean color data to be adequate to monitor sea outfall turbid plume dispersion.…”

Section: Introductionmentioning

confidence: 99%

“…Overall, those studies proved the Sentinel-2 (S2) ocean color data to be adequate to monitor sea outfall turbid plume dispersion. For instance, Bondur et al [11] monitored the coastal area of two cities along the coast of the Black Sea affected by wastewater from the sea outfall's pipe cracks. Trinh et al [12] monitored the biophysical impact of direct sewage discharge in a shallow nearshore environment in California.…”

Section: Introductionmentioning

confidence: 99%

Insights for Sea Outfall Turbid Plume Monitoring with High-Spatial-Resolution Satellite Imagery Application in Portugal

et al. 2023

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Coastal municipalities and industries often discharge poorly treated wastewater into proximate marine and estuarine environments. The urban and/or effluent input can lead to eutrophication and lower water quality, as it holds high concentrations of nutrients and pollutants. One widely applied tool to increase effluent dispersion and direct it away from coastal areas, thus causing fewer impacts on human activities, is sea outfall. In Aveiro, Portugal, the São Jacinto sea outfall construction was completed in 1998; however, limited literature regarding the sea outfall’s monitoring using satellite data is available. The methodology in this study involved collecting four years’ worth (2016–2019) of satellite data to investigate visible traces of the interaction between the S. Jacinto sewage water mass and the Ria de Aveiro lagoon ecosystem using ocean color and spectral analysis, and producing new qualitative data regarding sea outfall plume dispersion monitoring through high-resolution Sentinel-2 imagery. The results showed a clear spectral signature of the sewage water mass, and a seasonal pattern was observed in which the plume was more evident in winter and autumn. Additionally, the coastal longshore current and the Aveiro lagoon’s runoff were able to restrict the marine outfall’s dispersion superficially. Ocean color data were revealed to be a factual and cost-effective tool to monitor the plume water. Finally, an exchange between the marine outfall water mass and Ria de Aveiro lagoon could happen in high tide under northern wind conditions. Therefore, it is important to monitor the water quality to ensure the coastal ecosystem’s good environmental state.

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Detection of water surface natural objects based on the satellite sensing data

Panasenko

Motuz

2021

J. Phys.: Conf. Ser.

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The article shows an application of satellite sensing data method in geoenvironmental monitoring of water surface. It is expected to apply combination of LBP and neural network approaches for detection and identification objects of natural and anthropogenic origin. The applying of satellite images, the implementation and operation of the filtration method and satellite sensing data assimilation in real or near-real time are considered to detect the blooming areas and their coordinates. The research demonstrates the need and possibility to apply neural approach and the theory of deep learning for solving the tasks. The results of computer experiments are presented basing on the images from satellites Resurs-P, WorldView and Landsat over the Azov sea area.

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Monitoring Anthropogenic Impact on Some Coastal Water Areas of the Black Sea Using Multispectral Satellite Imagery

Cited by 11 publications

References 11 publications

Developing the Automatic Water Surface Pollution Search and Identification Method Using Satellite Optical Imagery

Developing the Automatic Water Surface Pollution Search and Identification Method Using Satellite Optical Imagery

Insights for Sea Outfall Turbid Plume Monitoring with High-Spatial-Resolution Satellite Imagery Application in Portugal

Detection of water surface natural objects based on the satellite sensing data

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