scholarly journals Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

2019 ◽  
Author(s):  
Sidrah Hafeez ◽  
Man Sing Wong ◽  
Sawaid Abbas ◽  
Coco Y. T. Kwok ◽  
Janet Nichol ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Marine Pollution

Monitoring Mediterranean marine pollution using remote sensing and hydrodynamic modelling

2011 ◽  
Author(s):  
Goffredo La Loggia ◽  
Fulvio Capodici ◽  
Giuseppe Ciraolo ◽  
Aldo Drago ◽  
Antonino Maltese
Keyword(s):  
Remote Sensing ◽  
Marine Pollution ◽  
Hydrodynamic Modelling

scholarly journals The Use of Remote Sensing Analysis for Evaluating the Impact of Development Projects in the Yellow Sea Large Marine Ecosystem

2020 ◽  
Vol 12 (9) ◽  
pp. 3628
Author(s):  
Gabriel Sidman ◽  
Sydney Fuhrig ◽  
Geeta Batra
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Yellow Sea ◽  
Marine Pollution ◽  
Marine Ecosystem ◽  
Environmental Indicators ◽  
The Yellow Sea ◽  
Series Data ◽  
Large Marine Ecosystem ◽  
The Impact

Remote sensing has long been valued as a data source for monitoring environmental indicators and detecting trends in ecosystem stress from anthropogenic causes such as deforestation, river dams and air and water pollution. More recently, remote sensing analyses have been applied to evaluate the impacts of environmental projects and programs on reducing environmental stresses. Such evaluation has focused primarily on the change in above-surface vegetation such as forests. This study uses remote sensing ocean color products to evaluate the impact on reducing marine pollution of the Global Environment Facility’s (GEF) portfolio of projects in the Yellow Sea Large Marine Ecosystem. Chlorophyll concentration was derived from satellite images over a time series from the 1990s, when GEF projects began, until the present. Results show a 50% increase in chlorophyll until 2011 followed by a 34% decrease until 2019, showing a potential delayed effect of pollution control efforts. The rich time series data is a major advantage to using geospatial analysis for evaluating the impacts of environmental interventions on marine pollution. However, one drawback to the method is that it provides insights into correlations but cannot attribute the results to any particular cause, such as GEF interventions.

Applications of Satellite Remote Sensing to Marine Pollution Studies

1991 ◽  
Vol 5 (1-2) ◽  
pp. 57-73 ◽  
Author(s):  
P. Kilho Park ◽  
Jane A. Elrod ◽  
Dana R. Kester
Keyword(s):  
Remote Sensing ◽  
Satellite Remote Sensing ◽  
Marine Pollution

scholarly journals Effect of Reduced Anthropogenic Activities on Water Quality in Lake Vembanad, India

2021 ◽  
Vol 13 (9) ◽  
pp. 1631
Author(s):  
Gemma Kulk ◽  
Grinson George ◽  
Anas Abdulaziz ◽  
Nandini Menon ◽  
Varunan Theenathayalan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Sustainable Development ◽  
Sustainable Management ◽  
Marine Pollution ◽  
Anthropogenic Activities ◽  
Landsat 8 ◽  
Anthropogenic Pressures ◽  
Socioeconomic Impacts ◽  
Agriculture Industry

The United Nation’s Sustainable Development Goal Life Below Water (SDG-14) aims to “conserve and sustainably use the oceans, seas, and marine resources for sustainable development”. Within SDG-14, targets 14.1 and 14.2 deal with marine pollution and the adverse impacts of human activities on aquatic systems. Here, we present a remote-sensing-based analysis of short-term changes in the Vembanad-Kol wetland system in the southwest of India. The region has experienced high levels of anthropogenic pressures, including from agriculture, industry, and tourism, leading to adverse ecological and socioeconomic impacts with consequences not only for achieving the targets set out in SDG-14, but also those related to water quality (SDG-6) and health (SDG-3). To move towards the sustainable management of coastal and aquatic ecosystems such as Lake Vembanad, it is important to understand how both natural and anthropogenic processes affect water quality. In 2020, a unique opportunity arose to study water quality in Lake Vembanad during a period when anthropogenic pressures were reduced due to a nationwide lockdown in response to the global pandemic caused by SARS-CoV-2 (25 March–31 May 2020). Using Sentinel-2 and Landsat-8 multi-spectral remote sensing and in situ observations to analyse changes in five different water quality indicators, we show that water quality improved in large areas of Lake Vembanad during the lockdown in 2020, especially in the more central and southern regions, as evidenced by a decrease in total suspended matter, turbidity, and the absorption by coloured dissolved organic matter, all leading to clearer waters as indicated by the Forel-Ule classification of water colour. Further analysis of longer term trends (2013–2020) showed that water quality has been improving over time in the more northern regions of Lake Vembanad independent of the lockdown. The improvement in water quality during the lockdown in April–May 2020 illustrates the importance of addressing anthropogenic activities for the sustainable management of coastal ecosystems and water resources.

scholarly journals Editorial for the Special Issue “Remote Sensing of the Oceans: Blue Economy and Marine Pollution”

2021 ◽  
Vol 13 (8) ◽  
pp. 1522
Author(s):  
Andrea Buono ◽  
Yu Li ◽  
Rafael Lemos Paes
Keyword(s):  
Remote Sensing ◽  
Marine Pollution ◽  
Special Issue ◽  
Blue Economy

Oceans represent an extraordinary source of resources that needs to be preserved while being exploited [...]

scholarly journals Multi-Spectral Ship Detection using Optical, Hyperspectral, and Microwave SAR Remote Sensing for Sustainability of the Coastal Region

2018 ◽  
Author(s):  
Kyung-Ae Park ◽  
Jae-Jin Park ◽  
Jae-Cheol Jang ◽  
Ji-Hyun Lee ◽  
Sangwoo Oh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Marine Pollution ◽  
Coastal Region ◽  
Microwave Remote Sensing ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Ship Detection ◽  
Radar Images ◽  
Ship Management ◽  
Multi Wavelength

As human activities of the countries in the East Asia have been remarkably expanding over recent decades, various problems in relation to ships, such as oil spill and many other coastal marine pollution, are continuously occurring in the coastal region. In order to conserve marine resources and prepare for possible ship accidents in advance, the need for efficient ship management is increasing over time. Multi-satellite, multi-sensor, multi-wavelength or multi-frequency observations make it possible to monitor a variety of vessels in the coastal region. This study presents the results of ship detection methodology applied to multi-spectral satellite images in the seas around Korean Peninsula based on optical, hyperspectral, and microwave remote sensing. To detect ships from hyperspectral images with a few hundreds of spectral channels, spectral matching algorithms are used to investigate similarity between the spectra and in-situ measurements. In the case of SAR (Synthetic Aperture Radar) images, the Constant False Alarm Rate (CFAR) algorithm is used to discriminate the vessels from backscattering coefficients of Sentinel-1 SAR and ALOS-2 PALSAR2 images. The present ship detection methods can be extensively utilized for optical, hyperspectral, and SAR images for comprehensive coastal management purposes toward perpetual sustainability in the future.

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