Wetland Evolution Based on Remote Sensing Data

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Marine Environmental Science".

Deadline for manuscript submissions: closed (5 February 2024) | Viewed by 3861

Special Issue Editors


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Guest Editor
Department of Biology and Geology, Physics and Inorganic Chemistry of the Rey Juan Carlos University, Mostoles, Spain
Interests: coastal wetlands; coastal geomorphology; aeolic transport of coastal dunes; coastal dynamics; remote sensing and GIS applied to coastal processes
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E-Mail Website
Guest Editor
Department of Biology and Geology, Physics and Inorganic Chemistry of the Rey Juan Carlos University, Mostoles, Spain
Interests: coastal wetlands; coastal ecology; plankton ecology; aquatic biogeochemical cycles; biological oceanography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Coastal wetlands represent highly productive habitats and provide protection from waves and storms. One of the most important roles of wetlands is the regulation of global climate change through sequestering and releasing a major proportion of the fixed carbon in the biosphere. Nevertheless, the wetland areas are losing ground due to human activities and the sea level rise induced by climate change. High-resolution satellite images and images acquired by drones, as well as LiDAR data, contribute to the rapid and effective analysis of the current conditions of wetlands and their temporal evolution. We invite submissions on “Remote Sensing Monitoring of Wetland Habitat Evolution” from a range of disciplines (e.g., biogeomorphology, biology, ecology, geomorphology, oceanography, hydrology, and coastal zone management). Studies can be based on field observations, modeling, and geospatial techniques.

Prof. Dr. Inmaculada Rodriguez
Prof. Dr. Nuria Navarro
Guest Editors

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Published Papers (1 paper)

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Research

18 pages, 2812 KiB  
Article
Analysing Spatiotemporal Variability of Chlorophyll-a Concentration and Water Surface Temperature in Coastal Lagoons of the Ebro Delta (NW Mediterranean Sea, Spain)
by Lara Talavera, José Antonio Domínguez-Gómez, Nuria Navarro and Inmaculada Rodríguez-Santalla
J. Mar. Sci. Eng. 2024, 12(6), 941; https://doi.org/10.3390/jmse12060941 - 3 Jun 2024
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Abstract
Coastal lagoons are highly productive transitional water bodies threatened by human factors and vulnerable to global climate change effects. Monitoring biophysical parameters in these ecosystems is crucial for their preservation. In this work, we used Sentinel-2 and Landsat imagery combined with in situ [...] Read more.
Coastal lagoons are highly productive transitional water bodies threatened by human factors and vulnerable to global climate change effects. Monitoring biophysical parameters in these ecosystems is crucial for their preservation. In this work, we used Sentinel-2 and Landsat imagery combined with in situ data to (1) develop preliminary algorithms for retrieving the Chl-a concentration and water surface temperature of six lagoons located in the Ebro Delta (NE Mediterranean Sea, Spain), and to (2) compute maps and trend lines for analysing their spatiotemporal evolution from 2015 to 2022. Our findings showed that the algorithms’ accuracy ranged from 72% to 78% and had limited potential under high Chl-a concentration regimes. Even so, they revealed the lagoons’ trophic status, usual fluctuations, and deviations of both parameters attributed to seasonal (i.e., light and temperature) and short-term physical (i.e., winds) forcing, as well as valuable spatial patterns potentially useful for conservation efforts and land use planning. Future work will focus on the acquisition of a larger in situ data sample under a range of environmental conditions to improve the algorithms’ robustness, which in turn will allow the investigation of natural and human factors controlling the dynamics of the two investigated parameters. Full article
(This article belongs to the Special Issue Wetland Evolution Based on Remote Sensing Data)
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