Special Issue "Remote Sensing of Coastal Waters, Land Use/Cover, Lakes, Rivers and Watersheds"

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

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Dr. Jiayi Pan
E-Mail Website
Guest Editor
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, Jiangxi, China & Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
Interests: Coastal and Lake Remote Sensing; Coastal Ocean Dynamics; Marine Remote Sensing Physics
Prof. Dr. Bo Huang
E-Mail Website
Guest Editor
Chinese University of Hong Kong, Department of Geography and Resource Management, Hong Kong, China
Tel. +852-39436536
Interests: Spatio-temporal data analytics GIS for sustainable urban/transportation/land use planning Unified image fusion for sustainable urban environment Spatial statistics for land use change modeling Multi-objective optimization for spatial planning with consideration of vulnerability and resilience
Special Issues and Collections in MDPI journals
Dr. Hongsheng Zhang
E-Mail Website
Guest Editor
Department of Geography, The University of Hong Kong, Hong Kong, China
Interests: Coastal Ecosystems and Sustainability; Coastal Remote Sensing; Land Use/Land Cover Change, Multi-sensor Data Fusion
Prof. Dr. Adam T. Devlin
E-Mail Website
Guest Editor
School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, Jiangxi, China & Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong, China
Interests: Sea Level Change; Lake Study; Climate and Earth System; Hydrodynamic Simulation; Coastal Oceanography; Remote Sensing Image Processing

Special Issue Information

Dear Colleagues,

coastal regions, lands, lakes, rivers, and watersheds are important elements of the Earth system environment. Remote sensing of these elements is a vital component of environmental monitoring. Although these elements individually play important roles in environmental changes, the study of the interactions between components is crucial for better understanding of environmental change mechanisms. As remote sensing observation technology develops, more accurate observational data of the coastal regions, lands, lakes, rivers, and watersheds are available, which provide an effective approach to monitoring the Earth system environment in real-time with high accuracy. Advances in data fusion technology help to efficiently integrate remote sensing data from multiple sensors and platforms. Remote sensing is an increasingly important methodology for advancing in-depth understanding of environmental change processes and the associated mechanisms in phenomena. Thus, this Special Issue endeavors to assemble novel studies that utilize advanced remote sensing technology and apply these techniques to coastal regions, lands, lakes, rivers, and watersheds as well as their interactions and help to improve the knowledge base of environmental change processes and mechanisms.

Prof. Jiayi Pan
Prof. Dr. Bo Huang
Dr. Hongsheng Zhang
Prof. Dr. Adam T. Devlin
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 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 2000 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 of coastal waters and zones
  • Remote sensing data fusion technology
  • Land cover/use
  • Remote sensing of lakes, rivers, and watersheds
  • Remote sensing and GIS technology
  • Marine remote sensing

Published Papers (2 papers)

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Research

Open AccessArticle
Monitoring of Fine-Scale Warm Drain-Off Water from Nuclear Power Stations in the Daya Bay Based on Landsat 8 Data
Remote Sens. 2020, 12(4), 627; https://doi.org/10.3390/rs12040627 - 13 Feb 2020
Abstract
Monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor [...] Read more.
Monitoring the drain-off water from nuclear power stations by high-resolution remote sensing satellites is of great significance for ensuring the safe operation of nuclear power stations and monitoring environmental changes. In order to select the optimal algorithm for Landsat 8 Thermal Infrared Sensor (TIRS) data to monitor warm drain-off water from the Daya Bay Nuclear Power Station (DNPS) and the Ling Ao Nuclear Power Station (LNPS) located on the southern coast of China, this study applies the edge detection method to remove stripes and produces estimates of four Sea Surface Temperature (SST) inversion methods, the Radiation Transfer Equation Method (RTM), the Single Channel algorithm (SC), the Mono Window algorithm (MW) and the Split Window algorithm (SW), using the buoy and Minimum Orbit Intersection Distances (MOIDS) SST data. Among the four algorithms, the SST from the SW algorithm is the most consistent with the buoy, the MODIS SST, the ERA-Interim and the Optimum Interpolation Sea Surface Temperature (OISST). Based on the SST retrieved from the SW algorithm, the tidal currents calculated by the Finite-Volume Coastal Ocean Model (FVCOM) and winds from ERA-Interim, the distribution of the warm drain-off from the two nuclear power stations is analyzed. First, warm drain-off water is mainly distributed in a fan-shaped area from the two nuclear power stations to the center of the Daya Bay. The SST of the warm drain-off is about 1–4 °C higher than the surrounding water and exceeds 6 °C at the drain-off outfall. Second, the tide determines the shape and distribution characteristics of the warm drain-off area. The warm drain-off water flows to the northeast during the flood tide. During the ebb tide, the warm drain-off water flows toward the southwest direction as the tide flows toward the bay mouth, forming a fan-shaped area. Moreover, the temperature increase intensity in the combined discharge channel during the flood tide is lower than that during the ebb tide, and the low temperature rising area during the flood tide is smaller than that during the ebb tide. Full article
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Open AccessArticle
Spatiotemporal Dynamics and Driving Forces of Urban Land-Use Expansion: A Case Study of the Yangtze River Economic Belt, China
Remote Sens. 2020, 12(2), 287; https://doi.org/10.3390/rs12020287 - 15 Jan 2020
Abstract
It is important to analyze the expansion of an urban area and the factors that drive its expansion. Therefore, this study is based on Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night lighting data, using the landscape index, spatial expansion strength index, [...] Read more.
It is important to analyze the expansion of an urban area and the factors that drive its expansion. Therefore, this study is based on Defense Meteorological Satellite Program Operational Linescan System (DMSP/OLS) night lighting data, using the landscape index, spatial expansion strength index, compactness index, urban land fractal index, elasticity coefficient, the standard deviation ellipse, spatial correlation analysis, and partial least squares regression to analyze the spatial and temporal evolution of urban land expansion and its driving factors in the Yangtze River Economic Belt (YREB) over a long period of time. The results show the following: Through the calculation of the eight landscape pattern indicators, we found that during the study period, the number of cities and towns and the area of urban built-up areas in the YREB are generally increasing. Furthermore, the variations in these landscape pattern indicators not only show more frequent exchanges and interactions between the cities and towns of the YREB, but also reflect significant instability and irregularity of the urbanization development in the YREB. The spatial expansion intensity indices of 1992–1999, 1999–2006, and 2006–2013 were 0.03, 0.16, and 0.34, respectively. On the whole, the urban compactness of the YREB decreased with time, and the fractal dimension increased slowly with time. Moreover, the long axis and the short axis of the standard deviation ellipse of the YREB underwent a small change during the inspection period. The spatial distribution generally showed the pattern of “southwest-north”. In terms of gravity shift, during the study period, the center of gravity moved from northeast to southwest. In addition, the Moran's I values for the four years of 1992, 1999, 2006, and 2013 were 0.451, 0.495, 0.506, and 0.424, respectively. Furthermore, by using correlation analysis, we find that the correlation coefficients between these four driving indicators and the urban expansion of the YREB were: 0.963, 0.998, 0.990 and 0.994, respectively. Through the use of partial least squares regression, we found that in 1992-2013, the four drivers of urban land expansion in the YREB were ranked as follows: gross domestic product (GDP), total fixed asset investment, urban population, total retail sales of consumer goods. Full article
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