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Remote Sensing Applications in Coastal Areas

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 20148

Special Issue Editors

Senior Researcher, Institute of Methodologies for Environmental Analysis, National Research Council of Italy, 85050 Tito Scalo, PZ, Italy
Interests: multi-sensor optical and microwave remote sensing; natural hazards; climate changes; hydrogeological risk; water quality assessment
Special Issues, Collections and Topics in MDPI journals
National Research Council, Institute of Methodologies for Environmental Analysis, 85050 Tito Scalo, PZ, Italy
Interests: remote sensing of ocean colour; water quality; earth observation (EO) data processing and image analysis; assessment of satellite-derived products; bio-optical algorithm development and evaluation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue on “Remote Sensing applications in coastal areas” aims at collecting high quality papers that focus on satellite-based applications for regional/local scale monitoring of coastal environment. Hence, it will include, but will not be limited to, the following topics:

  • Original methods and algorithms for water constituent (chlorophyll, suspended matter, etc.) retrieval;
  • Continental shelf and estuarine ecosystems studies based on remote sensing observations;
  • Integration of multi-source (in-situ, UAV, airborne and satellite) data for damage assessment in coastal areas;
  • Techniques for assessing water quality variability in the coastal zone;
  • Multi-mission remote sensing data integration.

Dr. Teodosio Lacava
Dr. Emanuele Ciancia
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. Sensors 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 2600 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

  • Water quality
  • Ocean colour
  • Bio-optical water constituents
  • Coastal ecosystem
  • Data integration
  • Marine Litter
  • Oil spill
  • Monitoring
  • Damage assessment

Published Papers (6 papers)

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Editorial

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2 pages, 145 KiB  
Editorial
Remote Sensing Applications in Coastal Areas
by Teodosio Lacava and Emanuele Ciancia
Sensors 2020, 20(9), 2673; https://doi.org/10.3390/s20092673 - 08 May 2020
Cited by 4 | Viewed by 1866
Abstract
Coastal areas are regions of remarkable relevance for humans, providing essential components for social and economic development from the local to the national scale [...] Full article
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Areas)

Research

Jump to: Editorial

28 pages, 7307 KiB  
Article
A Depth-Adaptive Waveform Decomposition Method for Airborne LiDAR Bathymetry
by Shuai Xing, Dandi Wang, Qing Xu, Yuzhun Lin, Pengcheng Li, Lin Jiao, Xinlei Zhang and Chenbo Liu
Sensors 2019, 19(23), 5065; https://doi.org/10.3390/s19235065 - 20 Nov 2019
Cited by 27 | Viewed by 2656
Abstract
Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform [...] Read more.
Airborne LiDAR bathymetry (ALB) has shown great potential in shallow water and coastal mapping. However, due to the variability of the waveforms, it is hard to detect the signals from the received waveforms with a single algorithm. This study proposed a depth-adaptive waveform decomposition method to fit the waveforms of different depths with different models. In the proposed method, waveforms are divided into two categories based on the water depth, labeled as “shallow water (SW)” and “deep water (DW)”. An empirical waveform model (EW) based on the calibration waveform is constructed for SW waveform decomposition which is more suitable than classical models, and an exponential function with second-order polynomial model (EFSP) is proposed for DW waveform decomposition which performs better than the quadrilateral model. In solving the model’s parameters, a trust region algorithm is introduced to improve the probability of convergence. The proposed method is tested on two field datasets and two simulated datasets to assess the accuracy of the water surface detected in the shallow water and water bottom detected in the deep water. The experimental results show that, compared with the traditional methods, the proposed method performs best, with a high signal detection rate (99.11% in shallow water and 74.64% in deep water), low RMSE (0.09 m for water surface and 0.11 m for water bottom) and wide bathymetric range (0.22 m to 40.49 m). Full article
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Areas)
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19 pages, 7346 KiB  
Article
Normalized Method for Land Surface Temperature Monitoring on Coastal Reclaimed Areas
by Bahaa Mohamadi, Shuisen Chen, Timo Balz, Khansa Gulshad and Stephen C. McClure
Sensors 2019, 19(22), 4836; https://doi.org/10.3390/s19224836 - 06 Nov 2019
Cited by 11 | Viewed by 2568
Abstract
The temporal analysis of land surface temperature (LST) has generally been studied using data from the same season, as temperature varies greatly over time. However, the cloud cover in thermal remotely sensed images and the coarse resolution of passive sensor system significantly limits [...] Read more.
The temporal analysis of land surface temperature (LST) has generally been studied using data from the same season, as temperature varies greatly over time. However, the cloud cover in thermal remotely sensed images and the coarse resolution of passive sensor system significantly limits data availability of same season for comparative temporal analysis in many parts of the world. To address this problem, we propose a new method for temporal monitoring of surface temperature based on LST normalization (LSTn); deploying the average open water temperature to normalize LST when monitoring temporal change in the surface temperature of newly coastal reclaimed areas. This method was applied in the Lingding Bay area, Guangdong Province, Southern China. Original LST and LSTn values were calculated for years 1987, 1997, 2007, and 2017. In contrast to the original LST, results show that LSTn can reduce seasonal variability when monitoring temporal change in surface temperatures. Additionally, LSTn revealed pronounced differences between the temperature of impervious surfaces and other land cover types. This method offers more robust detection of surface urban heat islands than original LST in newly developed coastal areas. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Areas)
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23 pages, 16338 KiB  
Article
Observing the Ocean Submesoscale with Enhanced-Color GOES-ABI Visible Band Data
by Jason K. Jolliff, M. David Lewis, Sherwin Ladner and Richard L. Crout
Sensors 2019, 19(18), 3900; https://doi.org/10.3390/s19183900 - 10 Sep 2019
Cited by 9 | Viewed by 3315
Abstract
Ocean color remote sensing has long been utilized as a fundamental research tool in the oceanographic investigations of coupled biological-physical processes. Despite numerous technical advances in the application of space borne ocean-viewing radiometers, host satellite platforms in a polar-orbiting configuration often render the [...] Read more.
Ocean color remote sensing has long been utilized as a fundamental research tool in the oceanographic investigations of coupled biological-physical processes. Despite numerous technical advances in the application of space borne ocean-viewing radiometers, host satellite platforms in a polar-orbiting configuration often render the temporal frequency of sensor data acquisition insufficient for studies of ocean processes that occur within increasingly smaller space-time scales. Whereas geostationary ocean color missions are presently the exception (GOCI) rather than the rule, this paper presents a method to convolve ocean reflectance data obtained from contemporary ocean-viewing multispectral radiometers (VIIRS, OLCI) with spectrally-limited Advanced Baseline Imager (ABI) data obtained from the GOES-R meteorological satellites. The method, Chromatic Domain Mapping (CDM), employs a colorimetry approach to visible range ocean reflectance data. The true color space is used as a frame-of-reference that is mapped by the dedicated yet temporally sparse ocean color sensors; coincident and spectrally coarse information from ABI is then used to estimate the evolution of the true color scene. The procedure results in very high resolution (~5 min) true color image sequences. Herein, example CDM applications of rapid frontal boundary evolution and feature displacement in the Gulf of Mexico are presented and future applications of this technique are discussed. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Areas)
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14 pages, 2161 KiB  
Article
Accurate Evaluation of Vertical Tidal Displacement Determined by GPS Kinematic Precise Point Positioning: A Case Study of Hong Kong
by Guoguang Wei, Qijie Wang and Wei Peng
Sensors 2019, 19(11), 2559; https://doi.org/10.3390/s19112559 - 05 Jun 2019
Cited by 10 | Viewed by 2454
Abstract
Global Positioning System (GPS) kinematic precise point positioning (KPPP) is an effective approach for estimating the Earth’s tidal deformation. The accuracy of KPPP is usually evaluated by comparing results with tidal models. However, because of the uncertainties of the tidal models, the accuracy [...] Read more.
Global Positioning System (GPS) kinematic precise point positioning (KPPP) is an effective approach for estimating the Earth’s tidal deformation. The accuracy of KPPP is usually evaluated by comparing results with tidal models. However, because of the uncertainties of the tidal models, the accuracy of KPPP-estimated tidal displacement is difficult to accurately determine. In this paper, systematic vector differences between GPS estimates and tidal models were estimated by least squares methods in complex domain to analyze the uncertainties of tidal models and determine the accuracy of KPPP-estimated tidal displacements. Through the use of GPS data for 12 GPS reference stations in Hong Kong from 2008 to 2017, vertical ocean tide loading displacements (after removing the body tide effect) for eight semidiurnal and diurnal tidal constituents were obtained by GPS KPPP. By an in-depth analysis of the systematic and residual differences between the GPS estimates and nine tidal models, we demonstrate that the uncertainty of the tidal displacement determined by GPS KPPP for the M2, N2, O1, and Q1 tidal constituents is 0.2 mm, and for the S2 constituent it is 0.5 mm, while the accuracy of the GPS-estimated K1, P1, and K2 tidal constituents is weak because these three tidal constituents are affected by significant common-mode errors. These results suggest that GPS KPPP can be used to precisely constrain the Earth’s vertical tidal displacement in the M2, N2, O1, and Q1 tidal frequencies. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Areas)
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14 pages, 4056 KiB  
Article
Rapid Invasion of Spartina Alterniflora in the Coastal Zone of Mainland China: Spatiotemporal Patterns and Human Prevention
by Dehua Mao, Mingyue Liu, Zongming Wang, Lin Li, Weidong Man, Mingming Jia and Yuanzhi Zhang
Sensors 2019, 19(10), 2308; https://doi.org/10.3390/s19102308 - 19 May 2019
Cited by 87 | Viewed by 6759
Abstract
Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series [...] Read more.
Given the extensive spread and ecological consequences of exotic Spartina alterniflora (S. alterniflora) over the coast of mainland China, monitoring its spatiotemporal invasion patterns is important for the sake of coastal ecosystem management and ecological security. In this study, Landsat series images from 1990 to 2015 were used to establish multi-temporal datasets for documenting the temporal dynamics of S. alterniflora invasion. Our observations revealed that S. alterniflora had a continuous expansion with the area increasing by 50,204 ha during the considered 25 years. The largest expansion was identified in Jiangsu Province during the period of 1990–2000, and in Zhejiang Province during the periods 2000–2010 and 2010–2015. Three noticeable hotspots for S. alterniflora invasion were Yancheng of Jiangsu, Chongming of Shanghai, and Ningbo of Zhejiang, and each had a net area increase larger than 5000 ha. Moreover, an obvious shrinkage of S. alterniflora was identified in three coastal cities including the city of Cangzhou of Hebei, Dongguan, and Jiangmen of Guangdong. S. alterniflora invaded mostly into mudflats (>93%) and shrank primarily due to aquaculture (55.5%). This study sheds light on the historical spatial patterns in S. alterniflora distribution and thus is helpful for understanding its invasion mechanism and invasive species management. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Coastal Areas)
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