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Wetland Mapping and Monitoring Using Advanced Synthetic Aperture RADAR (SAR) Data and Techniques

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (1 June 2020) | Viewed by 20663

Special Issue Editors


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Guest Editor
College of Environmental Science and Forestry (SUNY-ESF), State University of New York, Syracuse, NY 13210, USA
Interests: remote sensing of environment (wetland, permafrost, forest, oil spill, land cover, harmful algal bloom, etc.); SAR (PolSAR and InSAR) remote sensing; photogrammetry and image processing of UAVs; machine learning and image processing; nanosatellite data processing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, Memorial University of Newfoundland, St. John’s, NL A1C 5S7, Canada
Interests: remote sensing; geospatial data; machine learning; geo big data; wetland; GHG monitoring
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Research Scientist, C-CORE and Memorial University of Newfoundland, St. John’s, NL, Canada
Interests: remote sensing; PolSAR data analysis; InSAR for geo-hazard monitoring; deep learning; geo big data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wetlands are complex ecosystems that represent a wide range of biophysical conditions. They are one of the most productive ecosystems and provide several important environmental functionalities. Studies shows that wetlands cover at least 7 million sq. km of the earth. However, wetlands are prone to an accelerated degradation due to many factors, including climate change, agriculturalization, and urbanization. As such, wetland mapping and monitoring using cost- and time-efficient approaches are of great interest for sustainable management and resource assessment. In this regard, satellite remotely sensed images are greatly beneficial, as they capture a synoptic and multitemporal view of landscapes.

The capability of optical remote sensing imagery is often hampered by atmospheric conditions (e.g., cloud cover and sun illumination), making them a less reliable source of data for wetlands studies. Accordingly, synthetic aperture radar (SAR) sensors, which are independent of atmospheric conditions, have gained increasing attention for mapping, monitoring, and characterizing wetland ecosystems in recent years. Additionally, the capability of SAR wavelengths to penetrate through soil, water, and vegetation canopies makes them further advantageous compared to optical data for wetland monitoring.

Particularly, with the increasing availability of space borne SAR sensors, such as RADARSAT Constellation Mission (C-band), Sentinel-1(C-band), ALOS PALSAR (L-band), SAOCOM (L-band), TerraSAR-X (X-band), and the upcoming dual frequency NISAR (L+S-band), the use of SAR data and developing its processing techniques have drawn attention in recent years. As such, SAR data have been used either as the sole earth observation (EO) data or in combination with other EO data (e.g., optical and LiDAR) for understanding wetlands.

This Special Issue on “Wetland Mapping and Monitoring Using Advanced Synthetic Aperture RADAR (SAR) Techniques” is focused on wetland classification, wetland vegetation characterization, wetland change detection, and wetland water level monitoring. We would like to invite articles on wetland-related studies using state-of-the-art SAR data and in combination with other data and techniques. Submissions are encouraged to cover a broad range of topics, which may include, without being limited to, the following subjects:

  • Algorithm and application development for wetlands using SAR, PolSAR, and InSAR data and techniques;
  • Multifrequency SAR fusion for wetland and its vegetation structure characterization;
  • Investigation on spatial and temporal variability of wetland extent;
  • Analysis of time series of SAR data for wetland cover extent monitoring;
  • Developments of new machine learning and deep learning methods and tools for wetland classification and change detection;
  • Large areas (regional and nationwide) wetland mapping and monitoring using SAR data on cloud computing platforms;
  • Detection of long-term changes in wetland ecosystem as a response to climate change;
  • State-of-the-art remote sensing technologies for monitoring water level changes.

Dr. Bahram Salehi
Dr. Masoud Mahdianpari
Dr. Fariba Mohammadimanesh
Dr Brian Brisco
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

  • synthetic aperture RADAR (SAR)
  • remote sensing
  • wetland mapping and monitoring
  • wetland classification
  • polarimetric and interferometric SAR
  • water level monitoring
  • machine learning
  • deep learning

Published Papers (2 papers)

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20 pages, 8692 KiB  
Article
Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine
by András Gulácsi and Ferenc Kovács
Remote Sens. 2020, 12(10), 1614; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101614 - 18 May 2020
Cited by 52 | Viewed by 10746
Abstract
Saline wetlands experience large temporal fluctuations in water supply during the year and are recharged only or mainly through precipitation, meaning they are vulnerable to climate-change-induced aridification. Most passive satellite sensors are unsuitable for continuous wetland monitoring due to cloud cover and their [...] Read more.
Saline wetlands experience large temporal fluctuations in water supply during the year and are recharged only or mainly through precipitation, meaning they are vulnerable to climate-change-induced aridification. Most passive satellite sensors are unsuitable for continuous wetland monitoring due to cloud cover and their relatively low temporal resolution. However, active satellite sensors such as the C-band synthetic aperture radar of Sentinel-1 satellites offer free, cloud-independent data. We examined surface water cover changes from October 2014 to November 2018 in the strictly protected area (13,000 ha) of the Upper-Kiskunság Alkaline Lakes region in the Danube–Tisza Interfluve in Hungary, with the aim of helping with nature protection planning. Changes and sensitivity can be defined based on the knowledge of variability. We developed a method for water cover detection based on automatic classification, applying the so-called WEKA K-Means clustering algorithm. For satellite data processing and analysis, we used the Google Earth Engine cloud processing platform. In terms of validation, we compared our results with the multispectral Modified Normalized Difference Water Index (MNDWI) derived from Landsat 8 and Sentinel-2 top-of-atmosphere reflectance images using a threshold-based binary classifier (receiver operator characteristics) for the MNDWI data. Using two completely distinct methods operating in distinct wavelength ranges, we obtained adequately matching results, with Spearman’s correlation coefficients (ρ) ranging from 0.54 to 0.80. Full article
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Review

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28 pages, 5520 KiB  
Review
Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review
by Sarina Adeli, Bahram Salehi, Masoud Mahdianpari, Lindi J. Quackenbush, Brian Brisco, Haifa Tamiminia and Stephen Shaw
Remote Sens. 2020, 12(14), 2190; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12142190 - 09 Jul 2020
Cited by 64 | Viewed by 8557
Abstract
Despite providing vital ecosystem services, wetlands are increasingly threatened across the globe by both anthropogenic activities and natural processes. Synthetic aperture radar (SAR) has emerged as a promising tool for rapid and accurate monitoring of wetland extent and type. By acquiring information on [...] Read more.
Despite providing vital ecosystem services, wetlands are increasingly threatened across the globe by both anthropogenic activities and natural processes. Synthetic aperture radar (SAR) has emerged as a promising tool for rapid and accurate monitoring of wetland extent and type. By acquiring information on the roughness and moisture content of the surface, SAR offers unique potential for wetland monitoring. However, there are still challenges in applying SAR for mapping complex wetland environments. The backscattering similarity of different wetland classes is one of the challenges. Choosing the appropriate SAR specifications (incidence angle, frequency and polarization), based on the wetland type, is also a subject of debate and should be investigated more thoroughly. The geometric distortion of SAR imagery and loss of coherency are other remaining challenges in applying SAR and its processing techniques for wetland studies. Hence, this study provides a systematic meta-analysis based on compilation and analysis of indexed research studies that used SAR for wetland monitoring. This meta-analysis reviewed 172 papers and documented an upward trend in usage of SAR data, increasing usage of multi-sensor data, increasing integration of C- and L- bands over other configurations and higher classification accuracy with multi-frequency and multi-polarized SAR data. The highest number of wetland research studies using SAR data came from the USA, Canada and China. This meta-analysis highlighted the current challenges and solutions for wetland monitoring using SAR sensors. Full article
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