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GIS and Remote Sensing in Ocean and Coastal Ecology

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: 31 October 2024 | Viewed by 4875

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Special Issue Editors

Dr. Gang Yang

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Guest Editor

Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315201, China
Interests: coastal remote sensing; remote sensing time-series products temporal reconstruction
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Prof. Dr. Cunjin Xue

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Guest Editor

The Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: marine spatiotemporal data mining; marine GIS
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Dr. Yongze Song

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Guest Editor

School of Design and the Built Environment, Curtin University, Kent Street, Bentley, WA 6102, Australia
Interests: sustainable development; spatial statistics; geospatial methods; urban remote sensing; sustainable infrastructure
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Dr. Xiaoshuang Ma

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Guest Editor

1. School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
2. Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China
Interests: remote sensing image processing and interpretation; remote sensing of environment; synthetic aperture radar; target detection on remote sensing images; image denoising; deep learning; computer vision
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Dr. Jianing Zhen

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Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
Interests: wetland remote sensing; mangrove forests; classicifition; biochemical and biophyscal parameters inversion

Special Issue Information

Dear Colleagues,

We warmly invite you to contribute your research to this new Special Issue, entitled “GIS and Remote Sensing in Ocean and Coastal Ecology”, of the Remote Sensing journal. GIS and remote sensing are vital technologies for exploring ocean and coastal system dynamics. A variety of satellites and sensors provide spatio-temporal data for the monitoring and assessment of day-to-day changes in the ocean and coastal environments. As integrated parts of the Earth’s ecosystem, ocean and coastal areas are immensely important biologically and socially. These areas are under constant threat due to anthropogenic activities of unprecedented resource extraction and changing climatic behaviour. The oceans have varied and complex geometry and physiography; thus, the cognizance of their varied characteristics is essential for identifying any implication over these ecosystems. Remote sensing and geographical information system (GIS) techniques have not only proved effective in analyzing the surface characteristics of the coastal areas, but also hold much importance in identifying the characteristics of the ocean floor, mapping the coastal details, hydrodynamic modelling and coastal ecological process and risk assessment.

We encourage submissions exploring research advancements and applications of modeling systems and coastal monitoring systems to study hydrodynamics, morphodynamics, biodiversity, ecological processes and community succession of the coastal ecosystem; and ocean remote sensing, ocean color monitoring, modeling biomass and the carbon of oceanic ecosystems, biogeochemical process, sea surface temperature (SST) and sea surface salinity, ocean monitoring for oil spills and pollutions, coastal erosion and accretion measurement.

We wholeheartedly appreciate your consideration in submitting manuscripts to this Special Issue, entitled “GIS and Remote Sensing in Ocean and Coastal Ecology”. We also kindly request your assistance in sharing this announcement with esteemed colleagues, encouraging them to contribute their expertise to this important field of study.

Together, let us propel the advancements in ocean and coastal ecology research forward and contribute to a better understanding of the changes in the ocean and coastal environments and their implications for Earth’s system.

Dr. Gang Yang
Prof. Dr. Cunjin Xue
Dr. Yongze Song
Dr. Xiaoshuang Ma
Dr. Jianing Zhen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

coastal ocean modeling
coastal ocean remote sensing
coastal and ocean environment monitoring
coastal ocean forecasting
biodiversity
ecological process and risk assessment

Published Papers (4 papers)

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Research

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19 pages, 14918 KiB

Open AccessArticle

Ocean Colour Atmospheric Correction for Optically Complex Waters under High Solar Zenith Angles: Facilitating Frequent Diurnal Monitoring and Management

by Yongquan Wang, Huizeng Liu, Zhengxin Zhang, Yanru Wang, Demei Zhao, Yu Zhang, Qingquan Li and Guofeng Wu

Remote Sens. 2024, 16(1), 183; https://doi.org/10.3390/rs16010183 - 31 Dec 2023

Viewed by 796

Abstract

Accurate atmospheric correction (AC) is one fundamental and essential step for successful ocean colour remote-sensing applications. Currently, most ACs and the associated ocean colour remote-sensing applications are restricted to solar zenith angles (SZAs) lower than 70°. The ACs under high SZAs present degraded accuracy or even failure problems, rendering the satellite retrievals of water quality parameters more challenging. Additionally, the complexity of the bio-optical properties of the coastal waters and the presence of complex aerosols add to the difficulty of AC. To address this challenge, this study proposed an AC algorithm based on extreme gradient boosting (XGBoost) for optically complex waters under high SZAs. The algorithm presented in this research has been developed using pairs of Geostationary Ocean Colour Imager (GOCI) high-quality noontime remote-sensing reflectance (Rrs) and the Rayleigh-corrected reflectance (ρ_rc) derived from the Ocean Colour–Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART) in the morning (08:55 LT) and at dusk (15:55 LT). The algorithm was further examined using the daily GOCI images acquired in the morning and at dusk, and the hourly (total suspended sediment) TSS concentration was also obtained based on the atmospherically corrected GOCI data. The results showed that: (i) the model produced an accurate fitting performance (R² ≥ 0.90, RMSD ≤ 0.0034 sr⁻¹); (ii) the model had a high validation accuracy with an independent dataset (R² = 0.92–0.97, MAPD = 8.2–26.81% and quality assurance (QA) score = 0.9–1); and (iii) the model successfully retrieved more valid Rrs for GOCI images under high SZAs and enhanced the accuracy and coverage of TSS mapping. This algorithm has great potential to be applied to AC for optically complex waters under high SZAs, thus increasing the frequency of available observations in a day. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Ocean and Coastal Ecology)

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23 pages, 3668 KiB

Open AccessArticle

Evaluating Feature Selection Methods and Machine Learning Algorithms for Mapping Mangrove Forests Using Optical and Synthetic Aperture Radar Data

by Zhen Shen, Jing Miao, Junjie Wang, Demei Zhao, Aowei Tang and Jianing Zhen

Remote Sens. 2023, 15(23), 5621; https://doi.org/10.3390/rs15235621 - 04 Dec 2023

Cited by 1 | Viewed by 1069

Abstract

Mangrove forests, mostly found in the intertidal zone, are among the highest-productivity ecosystems and have great ecological and economic value. The accurate mapping of mangrove forests is essential for the scientific management and restoration of mangrove ecosystems. However, it is still challenging to perform the rapid and accurate information mapping of mangrove forests due to the complexity of mangrove forests themselves and their environments. Utilizing multi-source remote sensing data is an effective approach to address this challenge. Feature extraction and selection, as well as the selection of classification models, are crucial for accurate mangrove mapping using multi-source remote sensing data. This study constructs multi-source feature sets based on optical (Sentinel-2) and SAR (synthetic aperture radar) (C-band: Sentinel-1; L-band: ALOS-2) remote sensing data, aiming to compare the impact of three feature selection methods (RFS, random forest; ERT, extremely randomized tree; MIC, maximal information coefficient) and four machine learning algorithms (DT, decision tree; RF, random forest; XGBoost, extreme gradient boosting; LightGBM, light gradient-boosting machine) on classification accuracy, identify sensitive feature variables that contribute to mangrove mapping, and formulate a classification framework for accurately recognizing mangrove forests. The experimental results demonstrated that using the feature combination selected via the ERT method could obtain higher accuracy with fewer features compared to other methods. Among the feature combinations, the visible bands, shortwave infrared bands, and the vegetation indices constructed from these bands contributed the greatest to the classification accuracy. The classification performance of optical data was significantly better than SAR data in terms of data sources. The combination of optical and SAR data could improve the accuracy of mangrove mapping to a certain extent (0.33% to 4.67%), which is essential for the research of mangrove mapping in a larger area. The XGBoost classification model performed optimally in mangrove mapping, with the highest overall accuracy of 95.00% among all the classification models. The results of the study show that combining optical and SAR remote sensing data with the ERT feature selection method and XGBoost classification model has great potential for accurate mangrove mapping at a regional scale, which is important for mangrove restoration and protection and provides a reliable database for mangrove scientific management. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Ocean and Coastal Ecology)

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17 pages, 16671 KiB

Open AccessArticle

Analysis of Spatiotemporal Variations and Influencing Factors of Sea Ice Extent in the Arctic and Antarctic

by Xiaoyu Sun, Tingting Lv, Qizhen Sun, Zhuoming Ding, Hui Shen, Yi Gao, Yawen He, Min Fu and Chunhua Li

Remote Sens. 2023, 15(23), 5563; https://doi.org/10.3390/rs15235563 - 29 Nov 2023

Viewed by 845

Abstract

The 44 years (1979–2022) of satellite-derived sea ice extent in the Arctic and Antarctic reveals the details and new trends in the process of polar sea ice coverage changes. The speed of Arctic sea ice extent reduction and the interannual difference significantly increased after 2004. Trend analysis suggests that the Arctic Ocean may experience an ice-free period around 2060. The maximum anomaly of Arctic sea ice extent has gradually transitioned from September to October, indicating a trend of prolonged melting period. The center of gravity of sea ice in the Arctic Ocean is biased towards the Pacific side, and the spatial distribution pattern of sea ice is greatly influenced by the Atlantic warm current. The dynamism of the sea ice extent on the Atlantic side is significantly greater than in other regions. Since 2014, the Antarctic sea ice extent has shifted from slow growth to a rapid decreasing trend; the sea ice extent reached a historical minimum in 2022, decreasing by 2.02 × 106 km² compared to 2014. The Antarctic experiences seven months of ice growth each year and five months of ice melting period, the annual change patterns of sea ice extent in the Arctic and Antarctic are slightly different. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Ocean and Coastal Ecology)

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Review

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33 pages, 6092 KiB

Open AccessEditor’s ChoiceReview

Mapping Compound Flooding Risks for Urban Resilience in Coastal Zones: A Comprehensive Methodological Review

by Hai Sun, Xiaowei Zhang, Xuejing Ruan, Hui Jiang and Wenchi Shou

Remote Sens. 2024, 16(2), 350; https://doi.org/10.3390/rs16020350 - 16 Jan 2024

Viewed by 1627

Abstract

Coastal regions, increasingly threatened by floods due to climate-change-driven extreme weather, lack a comprehensive study that integrates coastal and riverine flood dynamics. In response to this research gap, we conducted a comprehensive bibliometric analysis and thorough visualization and mapping of studies of compound flooding risk in coastal cities over the period 2014–2022, using VOSviewer and CiteSpace to analyze 407 publications in the Web of Science Core Collection database. The analytical results reveal two persistent research topics: the way to explore the return periods or joint probabilities of flood drivers using statistical modeling, and the quantification of flood risk with different return periods through numerical simulation. This article examines critical causes of compound coastal flooding, outlines the principal methodologies, details each method’s features, and compares their strengths, limitations, and uncertainties. This paper advocates for an integrated approach encompassing climate change, ocean–land systems, topography, human activity, land use, and hazard chains to enhance our understanding of flood risk mechanisms. This includes adopting an Earth system modeling framework with holistic coupling of Earth system components, merging process-based and data-driven models, enhancing model grid resolution, refining dynamical frameworks, comparing complex physical models with more straightforward methods, and exploring advanced data assimilation, machine learning, and quasi-real-time forecasting for researchers and emergency responders. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Ocean and Coastal Ecology)

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