Remote Sensing for Marine Management

Fingas, Merv

doi:10.1016/b978-0-12-805052-1.00005-x

Cited by 22 publications

(11 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of continuous discharge over time, as it was the case in the loss of control of the Deepwater Horizon, determining the discharge rate may be more difficult. Another challenge is to estimate the amount of oil collected from the sea surface [15,16].…”

Section: Previous Researchmentioning

confidence: 99%

Optimisation Methodology for Skimmer Device Selection for Removal of the Marine Oil Pollution

Đorđević

Šabalja

Mohović

et al. 2022

JMSE

View full text Add to dashboard Cite

This paper emphasised the importance of proper handling and selection of specialised skimmer equipment for accidental pollution with oils. The research was motivated by the lack of similar papers that offer an extensive and recent literature analysis related to the use of skimmers, with the ultimate aim to present the methodology for proper selection of appropriate pollution equipment. Knowledge on the equipment characteristics such as capacity, selectivity, speed, and pump power of the skimmer is essential for efficient and fast collection of spills. The study aimed to calculate the oil collection rate of an individual skimmer device with different pumps. The classification of skimmers, pumps, and collecting tanks was made according to their main features. The results showed the correlation between pump capacity and the skimmers. Due to the complexity of mechanical equipment and various devices, it was crucial to identify and connect the skimmer with the proper pump. For these reasons, the authors proposed a model which determines the optimal combination for thirteen skimmers and eleven pumps. The model can help shorten the time of collecting oil from the sea surface and provide a more efficient and smoother response in the events of accidental marine pollution.

show abstract

Section: Previous Researchmentioning

confidence: 99%

Optimisation Methodology for Skimmer Device Selection for Removal of the Marine Oil Pollution

Đorđević

Šabalja

Mohović

et al. 2022

JMSE

View full text Add to dashboard Cite

show abstract

“…Strong and Elliot., 2017 [16] provided guidance on remote sensing techniques that can facilitate the extrapolation process by structuring RS data effectively across multiple spatial scales in order to undertake a correct assessment for integrated marine management. Fingas, 2019 [20] presented an overview of the use of optical satellites and sensors used for ocean management and that provide a mapping of several parameters.…”

Section: Remote Sensing Applications That Could Be Used For Dapsi(w)rmentioning

confidence: 99%

“…The new Millennium era of data collection through earth observation technologies and sensors is becoming more widely used for monitoring sectors related to the coastal and marine environment [16,[18][19][20][21][22][23][24][25][26][27]. As these data are collected routinely, they could provide a basis for synoptic-scale analysis covering a larger area over a time series.…”

Section: Introductionmentioning

confidence: 99%

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

et al. 2020

View full text Add to dashboard Cite

Coastal and marine management require the evaluation of multiple environmental threats and issues. However, there are gaps in the necessary data and poor access or dissemination of existing data in many countries around the world. This research identifies how remote sensing can contribute to filling these gaps so that environmental agencies, such as the United Nations Environmental Programme, European Environmental Agency, and International Union for Conservation of Nature, can better implement environmental directives in a cost-effective manner. Remote sensing (RS) techniques generally allow for uniform data collection, with common acquisition and reporting methods, across large areas. Furthermore, these datasets are sometimes open-source, mainly when governments finance satellite missions. Some of these data can be used in holistic, coastal and marine environmental management frameworks, such as the DAPSI(W)R(M) framework (Drivers–Activities–Pressures–State changes–Impacts (on Welfare)–Responses (as Measures), an updated version of Drivers–Pressures–State–Impact–Responses. The framework is a useful and holistic problem-structuring framework that can be used to assess the causes, consequences, and responses to change in the marine environment. Six broad classifications of remote data collection technologies are reviewed for their potential contribution to integrated marine management, including Satellite-based Remote Sensing, Aerial Remote Sensing, Unmanned Aerial Vehicles, Unmanned Surface Vehicles, Unmanned Underwater Vehicles, and Static Sensors. A significant outcome of this study is practical inputs into each component of the DAPSI(W)R(M) framework. The RS applications are not expected to be all-inclusive; rather, they provide insight into the current use of the framework as a foundation for developing further holistic resource technologies for management strategies in the future. A significant outcome of this research will deliver practical insights for integrated coastal and marine management and demonstrate the usefulness of RS to support the implementation of environmental goals, descriptors, targets, and policies, such as the Water Framework Directive, Marine Strategy Framework Directive, Ocean Health Index, and United Nations Sustainable Development Goals. Additionally, the opportunities and challenges of these technologies are discussed.

show abstract

“…The traditional experimental method of mapping seagrass distribution requires large-scale field investigations, which are time consuming and cost-effective (McKenzie et al, 2001;Krause-Jensen et al, 2004). In recent years, due to the development of remote sensing technology, a mushrooming number of data and methods have been applied to marine predictive modeling, such as satellite data, unmanned aerial vehicles (UAV), acoustic surveys, and Geographic Information Systems (GIS; Picart et al, 2014;Ouellette and Getinet, 2016;Fingas, 2019;Belkin, 2021).…”

Section: Introductionmentioning

confidence: 99%

Mapping seagrass habitats of potential suitability using a hybrid machine learning model

Zhao²,

Liu³

et al. 2023

Front. Ecol. Evol.

View full text Add to dashboard Cite

Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines in situ sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems.

show abstract

Remote Sensing for Marine Management

Cited by 22 publications

References 71 publications

Optimisation Methodology for Skimmer Device Selection for Removal of the Marine Oil Pollution

Optimisation Methodology for Skimmer Device Selection for Removal of the Marine Oil Pollution

Contribution of Remote Sensing Technologies to a Holistic Coastal and Marine Environmental Management Framework: A Review

Mapping seagrass habitats of potential suitability using a hybrid machine learning model

Contact Info

Product

Resources

About