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Remote Sensing for the Study of the Changes in Wetlands

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 3800

Special Issue Editors


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Guest Editor
Institut Universitaire Européen de la Mer (IUEM), Université de Brest (UBO), 29238 Brest, France
Interests: remote sensing of environment; wetlands; land cover/land use dynamics; image classification and mapping; sensor fusion; natural risk of coastal areas
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Council for Scientific and Industrial Research (CSIR), University of Pretoria, Pretoria 0001, South Africa
Interests: freshwater ecosystem typing; freshwater essential biodiversity variables; change detection and monitoring of estuarine and freshwater ecosystems; using hyperspectral; multispectral; radar sensors and time-series analysis

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Guest Editor
Geography Department, University of California, Santa Barbara, CA 93106, USA
Interests: imaging spectroscopy; thermal remote sensing; LiDAR; sensor fusion; spectral mixture analysis; remote sensing of wildfire; trace gas mapping; urban remote sensing; change identification; plant species mapping; vegetation drought response
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wetlands are important and valuable ecosystems, providing a range of ecosystem services that are considered critical buffers against climate change, yet they remain threatened worldwide (IPBES, 2019). Among estuarine and freshwater inland wetlands, coastal wetlands along the transition zone between the freshwater and estuarine realms represent very interesting areas for this Remote Sensing Special Issue (SI). The coastline is a high-stakes, vulnerable area, and contains coastal wetlands under pressure from both anthropogenic and climate change stresses. The Ramsar Convention (Ramsar Convention Secretariat 2022) considers mangroves, salt marshes, seagrass beds, coral reefs, beaches, estuaries, and coastal water bodies less than 6 m deep to be coastal wetlands. In addition, other forested wetlands such as floodplain and riverine and swamp forests are freshwater habitats interspersed or fringing estuarine habitats. These coastal wetlands represent a wealth of valuable, but highly fragile ecosystems, yet despite the essential ecosystem services they provide they remain threatened with increasing degradation, risking their persistence (Millennium Ecosystem Assessment, 2005). On the current time scale, tidal wetlands are biologically productive ecosystems with high biodiversity, providing multiple benefits to the ecosystem; however, the advantages of these wetlands are not fully recognized or even precisely known. We know that these wetlands are an important contributing factor in mitigating the impact of floods, delaying the effects of drought, but they also facilitate biological production for fishing and shellfish farming, create reservoirs of biodiversity, improve water quality, regulate the water cycle, store carbon in the mangrove soil, and maintain green areas at the periphery of urban areas.

Earth observation plays a critical role in informing changes to the extent, integrity and connectivity of these wetlands, of which targets for measuring these changes are currently in discussion for Goal A of the post-2020 Global Biodiversity Framework of the Convention of Biological Diversity (CBD, 2021). In addition, Earth observation is key to the monitoring of essential biodiversity variables, such as changes in composition, integrity and structure (Turak et al., 2017). Since no global monitoring system is in place for reporting on changes in coastal wetlands to the CBD or the Sustainable Development Goals (SDGs), this SI is focused on providing evidence from Earth observation technologies to quantify changes in coastal ecosystems for global reporting to targets. One of the major challenges is to distinguish natural dynamics in these systems from artificial and climate change impacts.

For this SI we invite you to submit your research on the use of Earth observation technologies to respond to the challenge of quantifying changes in wetlands for global reporting, including estuarine, coastal and freshwater wetlands.

References

Dr. Simona Niculescu
Dr. Heidi Van Deventer
Prof. Dr. Dar Roberts
Dr. Junshi Xia
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. 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

  • earth observation monitoring of wetlands
  • essential biodiversity variables
  • time-series analysis to distinguish natural dynamics from artificial and climate change impacts
  • wetlands, including lacustrine and palustrine biome wetlands, in the estuarine, coastal and freshwater realms

Published Papers (3 papers)

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Research

30 pages, 9009 KiB  
Article
Comparing Pixel- and Object-Based Approaches for Classifying Multispectral Drone Imagery of a Salt Marsh Restoration and Reference Site
by Gregory S. Norris, Armand LaRocque, Brigitte Leblon, Myriam A. Barbeau and Alan R. Hanson
Remote Sens. 2024, 16(6), 1049; https://doi.org/10.3390/rs16061049 - 15 Mar 2024
Viewed by 517
Abstract
Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north [...] Read more.
Monitoring salt marshes with remote sensing is necessary to evaluate their state and restoration. Determining appropriate techniques for this can be overwhelming. Our study provides insight into whether a pixel- or object-based Random Forest classification approach is best for mapping vegetation in north temperate salt marshes. We used input variables from drone images (raw reflectances, vegetation indices, and textural features) acquired in June, July, and August 2021 of a salt marsh restoration and reference site in Aulac, New Brunswick, Canada. We also investigated the importance of input variables and whether using landcover classes representing areas of change was a practical way to evaluate variation in the monthly images. Our results indicated that (1) the classifiers achieved overall validation accuracies of 91.1–95.2%; (2) pixel-based classifiers outperformed object-based classifiers by 1.3–2.0%; (3) input variables extracted from the August images were more important than those extracted from the June and July images; (4) certain raw reflectances, vegetation indices, and textural features were among the most important variables; and (5) classes that changed temporally were mapped with user’s and producer’s validation accuracies of 86.7–100.0%. Knowledge gained during this study will inform assessments of salt marsh restoration trajectories spanning multiple years. Full article
(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)
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25 pages, 36769 KiB  
Article
Spatiotemporal Dynamics and Driving Factors of Small and Micro Wetlands in the Yellow River Basin from 1990 to 2020
by Guangqing Zhai, Jiaqiang Du, Lijuan Li, Xiaoqian Zhu, Zebang Song, Luyao Wu, Fangfang Chong and Xiya Chen
Remote Sens. 2024, 16(3), 567; https://doi.org/10.3390/rs16030567 - 01 Feb 2024
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Abstract
Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, [...] Read more.
Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 105 hm2, with the predominant decline occurring in the 0–1 hm2 and 1–3 hm2 size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors. Full article
(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)
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28 pages, 7457 KiB  
Article
Monitoring and Mapping Floods and Floodable Areas in the Mekong Delta (Vietnam) Using Time-Series Sentinel-1 Images, Convolutional Neural Network, Multi-Layer Perceptron, and Random Forest
by Chi-Nguyen Lam, Simona Niculescu and Soumia Bengoufa
Remote Sens. 2023, 15(8), 2001; https://doi.org/10.3390/rs15082001 - 10 Apr 2023
Cited by 5 | Viewed by 1681
Abstract
The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and [...] Read more.
The annual flood cycle of the Mekong Basin in Vietnam plays an important role in the hydrological balance of its delta. In this study, we explore the potential of the C-band of Sentinel-1 SAR time series dual-polarization (VV/VH) data for mapping, detecting and monitoring the flooded and flood-prone areas in the An Giang province in the Mekong Delta, especially its rice fields. Time series floodable area maps were generated from five images per month taken during the wet season (6–7 months) over two years (2019 and 2020). The methodology was based on automatic image classification through the application of Machine Learning (ML) algorithms, including convolutional neural networks (CNNs), multi-layer perceptrons (MLPs) and random forests (RFs). Based on the segmentation technique, a three-level classification algorithm was developed to generate maps of the development of floods and floodable areas during the wet season. A modification of the backscatter intensity was noted for both polarizations, in accordance with the evolution of the phenology of the rice fields. The results show that the CNN-based methods can produce more reliable maps (99%) compared to the MLP and RF (97%). Indeed, in the classification process, feature extraction based on segmentation and CNNs has demonstrated an effective improvement in prediction performance of land use land cover (LULC) classes, deriving complex decision boundaries between flooded and non-flooded areas. The results show that between 53% and 58% of rice paddies areas and 9% and 14% of built-up areas are affected by the flooding in 2019 and 2020 respectively. Our methodology and results could support the development of the flood monitoring database and hazard management in the Mekong Delta. Full article
(This article belongs to the Special Issue Remote Sensing for the Study of the Changes in Wetlands)
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