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New Insights into the Application of Remote Sensing and GIS Technology for Monitoring Coastal Ecosystems

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Biogeosciences Remote Sensing".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 6210

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Special Issue Editor

Dr. Kurt McLaren

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Guest Editor

Department of Geography and Environmental Sciences, Northumbria University, Ellison Place, Newcastle-upon-Tyne NE1 8ST, UK
Interests: tropical forest ecology; remote sensing; disturbance ecology; biodiversity conservation; climate change ecology

Special Issue Information

Dear Colleagues,

Coastal ecosystems provide important habitats for many floral and faunal species. They are unique and dynamic, and include wetlands, salt marshes, estuaries, bays, mangroves, and coastal waters. Coastal ecosystems provide several important ecosystem services and functions but are impacted by natural disturbance events and the effects of anthropogenic activities, not least of which is global climate change. As such, there is a need to identify, quantify and monitor spatiotemporal changes in coastal ecosystems and to use this information to inform and improve their protection, conservation, and management.

Remote Sensing and Geographic Information Systems (GIS) can be integrated and used for mapping, monitoring, quantifying, and analyzing dynamic changes and the processes that occur in these ecosystems at different spatial and temporal scales. The special issue is therefore aimed at exploring their application in novel or innovative (and where possible, cost-effective) ways, particularly for monitoring or mapping short or long-term changes in coastal ecosystems at various spatial scales.

The aim of this special issue is to explore the application of remote sensing and GIS for monitoring or mapping short or long-term changes in coastal ecosystems at various spatial scales. The stated aim relates to the following journal scope: biogeoscience remote sensing, multi-spectral and hyperspectral remote sensing, active and passive microwave remote sensing, lidar and laser scanning, change detection, image processing and pattern recognition, data fusion and data assimilation, spaceborne, airborne and terrestrial platforms and remote sensing applications.

Authors are encouraged to submit articles that are focused on monitoring or mapping short or long-term changes in:

Coastal wetlands, salt marshes, estuaries, and coastal forests
Benthic ecosystems/habitats (e.g., submerged aquatic vegetation and coral reefs)
Benthic sediment composition and physical properties
Shoreline topography/coastal erosion or accretion and bathymetry
Physiochemical properties of coastal waters
Damage and/or recovery of coastal ecosystems from anthropogenic and/or natural disturbance events
Impacts of climate change (e.g., sea level rise)
River discharge, runoff events, terrigenous sediment input or turbidity in coastal waters

Dr. Kurt McLaren
Guest Editor

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

remote sensing
GIS
coastal environments
coastal wetlands
benthic ecosystems
spatiotemporal mapping

Published Papers (5 papers)

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Research

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36 pages, 13382 KiB

Open AccessArticle

Long-Term Spatial Pattern Predictors (Historically Low Rainfall, Benthic Topography, and Hurricanes) of Seagrass Cover Change (1984 to 2021) in a Jamaican Marine Protected Area

by Kurt McLaren, Jasmine Sedman, Karen McIntyre and Kurt Prospere

Remote Sens. 2024, 16(7), 1247; https://doi.org/10.3390/rs16071247 - 31 Mar 2024

Viewed by 533

Abstract

Climate change and other anthropogenic factors have caused a significant decline in seagrass cover globally. Identifying the specific causes of this decline is paramount if they are to be addressed. Consequently, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in a marine protected area on Jamaica’s southern coast. Two random forest regression (RFr) models were built using 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary and 2013 Landsat 7 and 8 reflectance data as the predictors. These were used to generate 24 SAV percentage cover and benthic feature maps (SAV present, absent, and coral reef) for the period 1984–2021 (37 years) from Landsat satellite series reflectance data. These maps and rainfall data were used to determine if SAV extent/area (km²) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Additionally, rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed. Finally, the most important spatial pattern predictors of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified. Predictors included rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance). SAV area/extent was largely stable, with >70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km², or 13%) during 2002–2006, and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by >1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change and had a positive overall effect on the spatial pattern of SAV cover percentage change (across the entire bay) and gain (close to the mouth of a river). The most important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Specifically, a hurricane that made landfall in 1988 (Gilbert), resulted in higher SAV percentage cover loss in 1987–1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002–2006. Additionally, exposure to Ivan explained percentage cover loss during 2006–2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013–2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years, SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future. Full article

(This article belongs to the Special Issue New Insights into the Application of Remote Sensing and GIS Technology for Monitoring Coastal Ecosystems)

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22 pages, 21668 KiB

Open AccessArticle

Honduran Reef Island Shoreline Change and Planform Evolution over the Last 15 Years: Implications for Reef Island Monitoring and Futures

by Emi Husband, Holly K. East, Emma P. Hocking and James Guest

Remote Sens. 2023, 15(19), 4787; https://doi.org/10.3390/rs15194787 - 30 Sep 2023

Viewed by 1064

Abstract

Assessing the vulnerability of low-lying coral reef islands is a global concern due to predictions that climate and environmental change will increase reef island instability and cause reef island populations to be among the first environmental refugees. Reef islands in the Pacific and Indian Oceans are highly dynamic environments that morphologically adjust to changing environmental conditions over annual-decadal timescales. However, there is a paucity of reef island shoreline change data from the Caribbean where sea-level rise, ecological and environmental disturbance and hydrodynamic regimes are considerably different than in other oceans globally. Here we present shoreline change analysis of 16 reef islands in northern Honduras, at the southern end of the Mesoamerican Barrier Reef. Satellite imagery from a maximum period of 12.4 years from Utila (2006–2019), and 2.4 years from Cayos Cochinos (2018–2021) was analysed to quantify island shoreline change and planform morphological adjustments. We identified accretion as the dominant island behaviour in Utila, where 5 of 7 islands increased in area and 61.7% of shorelines accreted, contributing to an overall net area increase of 9.4%. Island behaviour was more variable in Cayos Cochinos, where 55.7% of shorelines eroded, 5 of 9 islands remained stable, and net island area change was insignificant (2%). Conversely, the 4 smallest Cayos Cochinos islands (all <1500 m²) experienced significant shoreline change, potentially highlighting a new size threshold for considering reef island evolution. Across both sites, reef islands demonstrated a range of modes of planform change, including lateral accretion and erosion, and migration. Consequently, we provide the first empirical evidence of the dynamic nature of Caribbean reef islands during a period coincident with sea-level rise and highlight the heterogeneous nature of reef island evolution between and within two neighbouring sites at timescales relevant for island adaptation efforts. Full article

(This article belongs to the Special Issue New Insights into the Application of Remote Sensing and GIS Technology for Monitoring Coastal Ecosystems)

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17 pages, 6220 KiB

Open AccessArticle

Variations of Remote-Sensed Forel-Ule Index in the Bohai and Yellow Seas during 1997–2019

by Baohua Zhang, Junting Guo, Zengrui Rong and Xianqing Lv

Remote Sens. 2023, 15(14), 3487; https://doi.org/10.3390/rs15143487 - 11 Jul 2023

Viewed by 970

Abstract

Water color, often quantified using the Forel-Ule Index (FUI), is a crucial parameter for assessing the water quality and ecological health of coastal waters. However, there is limited research on the spatiotemporal variations of FUI and the associated influencing factors in the Bohai and Yellow Seas. In this study, we utilized multi-sensor satellite datasets to retrieve monthly FUI products for the Bohai and Yellow Seas spanning the period from September 1997 to December 2019. Subsequently, we examined significant spatial disparities and variations across multiple timescales in the remotely sensed FUI time series. The climatological annual mean FUI map reveals a decreasing trend from nearshore to offshore regions, with similar spatial patterns observed in terms of overall and interannual FUI variability. The annual variations in wind field, sea surface temperature (SST), and ocean stratification play a key role in the seasonal dynamics of FUI by modulating the sediment resuspension process, resulting in low FUI values in summer and high FUI values in winter. Linear regression analysis of FUI anomaly indicates a long-term decreasing trend in FUI for the three bays of the Bohai Sea, while upward trends in FUI predominantly prevail in the central Yellow Sea. Factors related to interannual FUI variations, such as surface winds, SST, river outflow, rainfall, and anthropogenic activities, are qualitatively discussed. The findings of this study provide the first comprehensive evaluation of water color variations and their underlying mechanisms in the Bohai and Yellow Seas. Full article

(This article belongs to the Special Issue New Insights into the Application of Remote Sensing and GIS Technology for Monitoring Coastal Ecosystems)

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24 pages, 2095 KiB

Open AccessArticle

Determination of Bayesian Cramér–Rao Bounds for Estimating Uncertainties in the Bio-Optical Properties of the Water Column, the Seabed Depth and Composition in a Coastal Environment

by Mireille Guillaume, Audrey Minghelli, Malik Chami and Manchun Lei

Remote Sens. 2023, 15(9), 2242; https://doi.org/10.3390/rs15092242 - 23 Apr 2023

Viewed by 1689

Abstract

The monitoring of coastal areas using remote sensing techniques is an important issue to determine the bio-optical properties of the water column and the seabed composition. New hyperspectral satellite sensors (e.g., PRISMA, DESIS or EnMap) are developed to periodically observe ecosystems. The uncertainties in the retrieved geophysical products remain a key issue to release reliable data useful for the end-users. In this study, an analytical approach based on Information theory is proposed to investigate the Cramér–Rao lower Bounds (CRB) for the uncertainties in the ocean color parameters. Practically, during the inversion process, an a priori knowledge on the estimated parameters is used since their range of variation is supposed to be known. Here, a Bayesian approach is attempted to handle such a priori knowledge. A Bayesian CRB (BCRB) is derived using the Lee et al. semianalytical radiative transfer model dedicated to shallow waters. Both environmental noise and bio-optical parameters are supposed to be random vectors that follow a Gaussian distibution. The calculation of CRB and BCRB is carried out for two hyperspectral images acquired above the French mediterranean coast. The images were obtained from the recently launched hyperspectral sensors, namely the DESIS sensor (DLR Earth Sensing Imaging Spectrometer, German Aerospace Center), and PRISMA (Precursore IpperSpettrale della Mission Applicativa—ASI, Italian Space Adjency) sensor. The comparison between the usual CRB approach, the proposed BCRB approach and experimental errors obtained for the retrieved bathymetry shows the better ability of the BCRB to determine minimum error bounds. Full article

(This article belongs to the Special Issue New Insights into the Application of Remote Sensing and GIS Technology for Monitoring Coastal Ecosystems)

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Other

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17 pages, 6322 KiB

Open AccessTechnical Note

Development of GNSS Buoy for Sea Surface Elevation Observation of Offshore Wind Farm

by Guanhui Liang, Shujiang Li, Ke Bao, Guanlin Wang, Fei Teng, Fengye Zhang, Yanfeng Wang, Sheng Guan and Zexun Wei

Remote Sens. 2023, 15(22), 5323; https://doi.org/10.3390/rs15225323 - 11 Nov 2023

Viewed by 1104

Abstract

This study presents the development and testing of a global navigation satellite system (GNSS) buoy designed for measuring the sea surface elevation and tide level. The precision-point-positioning (PPP) technology is adopted for precise observation. The design of the buoy body is optimized by stability and hydrodynamic calculations. A high-performance embedded data acquisition system with big storage and high-frequency sampling is developed for long-term observation. The GNSS buoy is deployed in a wind farm approximately 70 km offshore of China, and undergoes a 60-day ocean test. A comparison of the sea level elevations obtained from the GNSS buoy and the pressure sensor shows that there is a strong correlation between them, with a correlation coefficient of 0.99. A harmonic analysis is applied to derive the harmonic constants for four key tidal components (M2, S2, O1, and K1). The amplitude differences are −1.2 cm, 1.4 cm, −0.6 cm, and −1.2 cm, respectively, and the phase differences are 1.8°, 2.2°, −1.3°, and −2.9°, respectively. The strong correlation between the measurements of the GNSS buoy and the pressure sensor and the relatively small differences of the amplitude and phase of the main tidal components indicate that the compact GNSS buoy demonstrates a capability to continuously measure the sea surface elevation and tide level with an elevation reference in the open sea. Full article

(This article belongs to the Special Issue New Insights into the Application of Remote Sensing and GIS Technology for Monitoring Coastal Ecosystems)

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