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Special Issue "Remote Sensing in Coastal Zone Monitoring and Management—How Can Remote Sensing Challenge the Broad Spectrum of Temporal and Spatial Scales in Coastal Zone Dynamic?"

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

Deadline for manuscript submissions: 15 July 2018

Special Issue Editors

Guest Editor
Dr. David Doxaran

Laboratoire d'Océanographie de Villefranche UMR 7093 - CNRS / UPMC, France
Website | E-Mail
Phone: 0033493763724
Interests: ocean colour remote sensing, optical properties of turbid estuarine and coastal waters; bio-optical modelling; atmospheric corrections; river plumes; sediment transport modelling
Guest Editor
Dr. Javier Bustamante

Estación Biológica de Doñana, CSIC - Dept. Wetland Ecology - Américo Vespucio 26, Spain
Website | E-Mail
Interests: optical remote sensing of wetlands; time series; phenology; wetland ecology; SAV; species distribution models; ornithology
Guest Editor
Dr. Ana Ines Dogliotti

Instituto de Astronomía y Física del Espacio (IAFE), CONICET/UBA, Argentina
Website | E-Mail
Interests: Ocean color remote sensing in coastal areas and estuaries; validation of satellite-derived products; bio-optical algorithm development and evaluation; atmospheric correction in turbid waters
Guest Editor
Dr. Tim J Malthus

Coastal Sensing and Modelling Group - Coastal Development and Management Program - CSIRO Oceans and Atmosphere Business Unit, Australia
Website1 | Website2 | E-Mail
Interests: coastal management; field spectroscopy; airborne and satellite Earth observations data; management of land and water resources
Guest Editor
Dr. Nadia Senechal

University of Bordeaux OASU/ UMR 5805 CNRS, France
Website | E-Mail
Interests: open sandy beach; shoreline; video; storm impact; morphodynamic; recovery; erosion

Special Issue Information

Dear Colleagues,

Coastal zones are sensitive areas responding at various scales (events to long-term trends) where the monitoring and management of physico-chemical, biological, morphological processes, and fluxes are highly challenging. They are directly affected by anthropization (urbanization, industrialization, agri- and aquaculture) and climate change (e.g., river discharges, waves, sea-level rise). Coastal waters only represent 15% of the global ocean, but concentrate 90% of commercial fisheries, contribute to 25% of global biological productivity, and represent 80% of the marine biodiversity, while being associated with an intensive tourism-related economy.

The monitoring and management of coastal zones requires past, present, and future observations adapted to quite diverse and dynamic environments. To complement field measurements, the use of remote sensing data provides useful information to map the hydromorphological (freshwater discharge, currents, shoreline evolution), physico-chemical (water transparency, temperature, salinity, oxygen, nutrients, and pollutants), and biological (habitats, phytoplankton blooms) properties of the coastal zones.

This Special Issue will highlight how remote sensing can tackle the monitoring of nearshore dynamics thanks to recent progress made in terms of sensors’ radiometric, spatial, and temporal resolutions, together with new data processing methods, products, and applications.

We are inviting submissions including, but not limited to:

  • high spatial and high temporal resolution remote sensing observations,
  • atmospheric correction in optically complex waters,
  • synergetic use of multi-mission remote sensing datasets,
  • techniques for assessing change in the coastal zone,
  • dredging activities,
  • mangrove systems,
  • coastal geomorphology and change,
  • turbidity evolution in coastal waters,
  • monitoring changes in river discharge,
  • beach morphology evolution,
  • mapping submerged aquatic vegetation,
  • change dynamic in coastal marshes,
  • coastal urbanization trends.
Dr. David Doxaran
Dr. Javier Bustamante
Dr. Ana Ines Dogliotti
Dr. Tim J Malthus
Dr. Nadia Senechal
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 papers will be 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 monthly 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 1800 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 zones
  • management
  • monitoring
  • remote sensing
  • river plumes
  • estuaries
  • applications
  • optically complex waters
  • shoreline
  • morphology

Published Papers (3 papers)

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Research

Open AccessArticle Using High-Resolution Airborne Data to Evaluate MERIS Atmospheric Correction and Intra-Pixel Variability in Nearshore Turbid Waters
Remote Sens. 2018, 10(2), 274; doi:10.3390/rs10020274
Received: 11 January 2018 / Revised: 6 February 2018 / Accepted: 8 February 2018 / Published: 10 February 2018
PDF Full-text (7242 KB) | HTML Full-text | XML Full-text
Abstract
The implementation of accurate atmospheric correction is a prerequisite for satellite observation and water quality monitoring in coastal areas. The potential of the fast-line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) was investigated here for the medium resolution imaging spectrometer (MERIS). As the comparison
[...] Read more.
The implementation of accurate atmospheric correction is a prerequisite for satellite observation and water quality monitoring in coastal areas. The potential of the fast-line-of-sight atmospheric analysis of spectral hypercubes (FLAASH) was investigated here for the medium resolution imaging spectrometer (MERIS). As the comparison between discrete field sampling points and macro-scale satellite pixels is subject to spatial biases associated with small-scale spatial patchiness in the turbid and highly dynamic nearshore zone, an alternative approach was proposed here using high spatial resolution (1 m) airborne hyperspectral images as radiometric truthing references. While FLAASH was not optimal for moderately turbid offshore waters (suspended particulate matter (SPM) concentration < 50 g∙m−3), it yields satisfactory results in the 50–1500 g∙m−3 range, where MERIS standard atmospheric correction was subject to significant biases and failures. Due to the significant intra-pixel variability of SPM distribution in highly turbid areas, the acquisition of high resolution airborne images should be considered as a consistent strategy for the validation of medium resolution satellite remote sensing in the spatially heterogeneous and optically diverse nearshore waters. Full article
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Open AccessArticle Fusion of Landsat-8/OLI and GOCI Data for Hourly Mapping of Suspended Particulate Matter at High Spatial Resolution: A Case Study in the Yangtze (Changjiang) Estuary
Remote Sens. 2018, 10(2), 158; doi:10.3390/rs10020158
Received: 21 November 2017 / Revised: 17 January 2018 / Accepted: 18 January 2018 / Published: 23 January 2018
Cited by 1 | PDF Full-text (10487 KB) | HTML Full-text | XML Full-text
Abstract
Suspended particulate matter (SPM) concentrations ([SPM]) in the Yangtze estuary, which has third-order bifurcations and four outlets, exhibit large spatial and temporal variations. Studying the characteristics of these variations in [SPM] is important for understanding sediment transport and pollutant diffusion in the estuary
[...] Read more.
Suspended particulate matter (SPM) concentrations ([SPM]) in the Yangtze estuary, which has third-order bifurcations and four outlets, exhibit large spatial and temporal variations. Studying the characteristics of these variations in [SPM] is important for understanding sediment transport and pollutant diffusion in the estuary as well as for the construction of port and estuarine engineering structures. The 1-h revisit frequency of the Geostationary Ocean Color Imager (GOCI) sensor and the 30-m spatial resolution of the Landsat 8 Operational Land Imager (L8/OLI) provide a new opportunity to study the large spatial and temporal variations in the [SPM] in the Yangtze estuary. In this study, [SPM] images with a temporal resolution of 1 h and a spatial resolution of 30 m are generated through the product-level fusion of [SPM] data derived from L8/OLI and GOCI images using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The results show that the details and accuracy of the spatial and temporal variations are maintained well in the [SPM] images that are predicted based on the fused images. Compared to the [SPM] observations at fixed field stations, the mean relative error (MRE) of the predicted SPM is 17.7%, which is lower than that of the GOCI-derived [SPM] (27.5%). In addition, thanks to the derived high-resolution [SPM] with high spatiotemporal dynamic changes, both natural phenomena (dynamic variation of the maximum turbid zone) and human engineering changes leading to the dynamic variability of SPM in the channel are observed. Full article
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Open AccessArticle Examining Land Cover and Greenness Dynamics in Hangzhou Bay in 1985–2016 Using Landsat Time-Series Data
Remote Sens. 2018, 10(1), 32; doi:10.3390/rs10010032
Received: 9 November 2017 / Revised: 7 December 2017 / Accepted: 23 December 2017 / Published: 25 December 2017
PDF Full-text (7237 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land
[...] Read more.
Land cover changes significantly influence vegetation greenness in different regions. Dense Landsat time series stacks provide unique opportunity to analyze land cover change and vegetation greenness trends at finer spatial scale. In the past three decades, large reclamation activities have greatly changed land cover and vegetation growth of coastal areas. However, rarely has research investigated these frequently changed coastal areas. In this study, Landsat Normalized Difference Vegetation Index time series (1984–2016) data and the Breaks For Additive Seasonal and Trend algorithm were used to detect the intensity and dates of abrupt changes in a typical coastal area—Hangzhou Bay, China. The prior and posterior land cover categories of each change were classified using phenology information through a Random Forest model. The impacts of land cover change on vegetation greenness trends of the inland and reclaimed areas were analyzed through distinguishing gradual and abrupt changes. The results showed that the intensity and date of land cover change were detected successfully with overall accuracies of 88.7% and 86.1%, respectively. The continuous land cover dynamics were retrieved accurately with an overall accuracy of 91.0% for ten land cover classifications. Coastal reclamation did not alleviate local cropland occupation, but prompted the vegetation greenness of the reclaimed area. Most of the inland area showed a browning trend. The main contributors to the greenness and browning trends were also quantified. These findings will help the natural resource management community generate better understanding of coastal reclamation and make better management decisions. Full article
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