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Dear Colleagues,

Coastal ecosystems sequester large quantities of carbon (also known as “blue carbon”), thereby providing climate mitigation, ecosystem services, and biodiversity co-benefits. The potential of blue carbon ecosystems in addressing climate change mitigation and resilience was also discussed in the recent IPCC report on Ocean and Cryosphere in a Changing Climate. More recently, at COP 27, the Mangrove Breakthrough alliance was formed, which highlights the need to develop strategies and guidance for countries to include blue carbon in their nationally determined contributions (NDCs), REDD+ (Reducing Emissions from Deforestation and forest Degradation) programs and National Management plans, and therefore secure long-term financing to conserve and revitalize these coastal ecosystems. This recent formal accreditation of blue carbon services, especially towards meeting country-specific climate targets, has led to the proliferation of conservation and restoration projects worldwide, implying the need to spatially and temporally monitor them to assess on-ground outcomes.

Remote sensing approaches provide cost-effective solutions for monitoring changes to the ecosystem functioning of these habitats, contributing to the science of blue carbon conservation and restoration projects. This Special Issue on “remote sensing applications for blue habitat conservation and restoration” addresses the need to synthesize novel ways of applying remote-sensing-based technologies, methods, tools, and knowledge to assess blue carbon ecosystems across the world. In addition to the topics listed below, manuscripts addressing pathways for merging remote sensing approaches with carbon-estimating methods to improve uncertainties in emission factors for coastal areas, and for mapping of blue habitats such as mangroves, salt marshes, benthic habitats, macro-algae, and seagrasses, are also encouraged.

Examples of themes and topics relevant for this Special Issue:

Papers that discuss the complexities in defining and operationalizing the effectiveness of climate mitigation actions in coastal ecosystems (both research and review papers are welcome);
Uncertainties in remote sensing approaches for monitoring carbon in coastal ecosystems;
Impact of sea level rise and coastal flooding for blue carbon estimation and monitoring;
Approaches focusing on lesser-represented blue carbon habitats, such as coral reefs, tidal flats, kelp forests, seagrasses, or benthic algae;
LiDAR and Unmanned Aerial Vehicles (UAVs) in coastal research (application of structure from motion (SfM) techniques);
Applications of hyperspectral and/or high-spatial-resolution sensors in monitoring the structure, function, and ecological services of blue habitats;
Monitoring the effectiveness of coastal restoration projects;
Assessment techniques to monitor country pledges towards sustainable development goals and nationally determined contributions (NDCs).

Prof. Dr. Deepak R. Mishra
Dr. Dina Rasquinha
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Research

19 pages, 9694 KiB

Open AccessArticle

Quantifying Seagrass Density Using Sentinel-2 Data and Machine Learning

by Martin Meister and John J. Qu

Remote Sens. 2024, 16(7), 1165; https://doi.org/10.3390/rs16071165 - 27 Mar 2024

Viewed by 593

Abstract

Seagrasses, rooted aquatic plants growing completely underwater, are extremely important for the coastal ecosystem. They are an important component of the total carbon burial in the ocean, they provide food, shelter, and nursery to many aquatic organisms in coastal ecosystems, and they improve water quality. Due to human activity, seagrass coverage has been rapidly declining, and there is an urgent need to monitor seagrasses consistently. Seagrass coverage has been closely monitored in the Chesapeake Bay since 1970 using air photos and ground samples. These efforts are costly and time-consuming. Many studies have used remote sensing data to identify seagrass bed outlines, but few have mapped seagrass bed density. This study used Sentinel-2 satellite data and machine learning in Google Earth Engine and the Chesapeake Bay Program field data to map seagrass density. We used seagrass density data from the Chincoteague and Sinepuxent Bay to train machine learning algorithms and evaluate their accuracies. Out of the four machine learning models tested (Naive Bayes (NB), Classification and Regression Trees (CART), Support Vector Machine (SVM), and Random Forest (RF)), the RF model outperformed the other three models with overall accuracies of 0.874 and Kappa coefficients of 0.777. The SVM and CART models performed similarly and NB performed the poorest. We tested two different approaches to assess the models’ accuracy. When we used all the available ground samples to train the models, whereby our analysis showed that model performance was associated with seagrass density class, and that higher seagrass density classes had better consumer accuracy, producer accuracy, and F1 scores. However, the association of model performance with seagrass density class disappeared when using the same training data size for each class. Very sparse and dense seagrass classes had replacedhigherbetter accuracies than the sparse and moderate seagrass density classes. This finding suggests that training data impacts machine learning model performance. The uneven training data size for different classes can result in biased assessment results. Selecting proper training data and machine learning models are equally important when using machine learning and remote sensing data to map seagrass density. In summary, this study demonstrates the potential to map seagrass density using satellite data. Full article

(This article belongs to the Special Issue Remote Sensing Applications for Blue Habitat Conservation and Restoration)

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