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Remote Sensing for Mapping and Monitoring Wetlands and Their Ecosystems

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 1067

Special Issue Editor

Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth, UK
Interests: remote sensing; GIS; hydrological/environmental modeling; spatial statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wetlands provide countless services that underpin the livelihoods of those living amongst these ecosystems, as well as the flora and fauna that rely upon them, yet they have become one of the most threatened ecosystems on Earth.

Entire agricultural systems rely on the annual pulse of flood water in wetland environments, with devastating impacts where annual rains fail, or extreme flooding events which destroy vital infrastructure. Areas of inundation give rise to a vast extent of habitat for mosquito-borne disease, like malaria and dengue fever. Natural wetlands play a globally important role in both the storage of carbon and the emission of greenhouse gases. Wetlands have a vital role in governing water resources at vast scales, with direct links to conflict and the economic welfare of entire regions.

In the face of increasing pressures on land use and water resources, confounded by the impact of a changing climate, it has never been more important for us to derive information about the world’s wetlands. Given their inherent dynamics over time and space, remote sensing has proven to be a key tool to provide information about the extent and condition of wetlands. This is particularly poignant with the increasing use of sophisticated analytical approaches and cloud-based processes, enabling an observation of wetlands to be made over large scales.

This Special Issue provides an opportunity to collate research related to the “remote sensing of wetlands” and highlight ongoing investigations and new applications of remote sensing in this field. Articles may include, but are not limited to, the following topics:

  • Wetland mapping and monitoring using multi-source remote sensing data, including optical, LiDAR, synthetic aperture radar (SAR), and UAV.
  • Wetland conservation using remote sensing tools and data;
  • Wetland change detection;
  • Wetland characterization based on satellite remote sensing;
  • Research approaches in wetland environments integrating field data with remote sensing and/or aerial imaging.

Dr. Andy Hardy
Guest Editor

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

  • remote sensing
  • wetland mapping
  • wetland ecology
  • wetland condition assessment
  • wetland hydrological monitoring
  • watershed modeling
  • hydrological dynamics

Published Papers (1 paper)

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Research

28 pages, 20313 KiB  
Article
Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data
by Rana Waqar Aslam, Hong Shu, Iram Naz, Abdul Quddoos, Andaleeb Yaseen, Khansa Gulshad and Saad S. Alarifi
Remote Sens. 2024, 16(5), 928; https://doi.org/10.3390/rs16050928 - 06 Mar 2024
Cited by 1 | Viewed by 708
Abstract
Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote [...] Read more.
Wetlands provide vital ecological and socioeconomic services but face escalating pressures worldwide. This study undertakes an integrated spatiotemporal assessment of the multifaceted vulnerabilities shaping Khinjhir Lake, an ecologically significant wetland ecosystem in Pakistan, using advanced geospatial and machine learning techniques. Multi-temporal optical remote sensing data from 2000 to 2020 was analyzed through spectral water indices, land cover classification, change detection and risk mapping to examine moisture variability, land cover modifications, area changes and proximity-based threats over two decades. The random forest algorithm attained the highest accuracy (89.5%) for land cover classification based on rigorous k-fold cross-validation, with a training accuracy of 91.2% and a testing accuracy of 87.3%. This demonstrates the model’s effectiveness and robustness for wetland vulnerability modeling in the study area, showing 11% shrinkage in open water bodies since 2000. Inventory risk zoning revealed 30% of present-day wetland areas under moderate to high vulnerability. The cellular automata–Markov (CA–Markov) model predicted continued long-term declines driven by swelling anthropogenic pressures like the 29 million population growth surrounding Khinjhir Lake. The research demonstrates the effectiveness of integrating satellite data analytics, machine learning algorithms and spatial modeling to generate actionable insights into wetland vulnerability to guide conservation planning. The findings provide a robust baseline to inform policies aimed at ensuring the health and sustainable management and conservation of Khinjhir Lake wetlands in the face of escalating human and climatic pressures that threaten the ecological health and functioning of these vital ecosystems. Full article
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