Special Issue "Advanced Technologies in Wetland and Vegetation Ecological Monitoring"

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

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. Jeremy L. May
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Guest Editor
Biology Department, Florida International University, USA
Interests: remote sensing; climate change; vegetation; community ecology
Dr. Sergio Vargas Zesati
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Guest Editor
Biology Department, University of Texas El Paso, El Paso, Texas 79968, USA
Interests: vegetation; geospatial mapping; LiDAR; digital elevation modeling; digital photogrammetry; unmanned aerial systems (UAS); multi/hyperspectral remote sensing

Special Issue Information

Dear Colleague,

Wetlands are an important habitat worldwide and play a crucial role in ecosystem services including maintaining species habitat, promoting ecosystem function, mitigating storm surge impacts, improving water quality, and influencing land use changes. Wetland vegetation monitoring presents a variety of unique features that make large-scale assessments of cover and function across heterogeneous wetland landscapes challenging. Advances in remote sensing technologies (e.g., spectral, airborne, LIDAR, eddy covariance towers, satellite) and techniques are useful applications to monitor the impact of a range of variables on wetland landscapes, including habitat delineation, climate change, and natural and anthropogenic disturbances. 

This Special Issue seeks original and innovative applications of remote sensing techniques and technologies in wetland vegetation monitoring. We welcome a broad variety of applications and scales that show the potential for use in the field of wetland monitoring

Dr. Jeremy L. May
Dr. Sergio Vargas Zesati
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 2400 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

  • wetland
  • vegetation monitoring
  • remote sensing
  • wetland management
  • ecosystem function

Published Papers (1 paper)

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Research

Article
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery
Remote Sens. 2021, 13(11), 2046; https://doi.org/10.3390/rs13112046 - 22 May 2021
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Abstract
Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use [...] Read more.
Wetlands are important ecosystems that are linked to climate change mitigation. As 25% of global wetlands are located in Canada, accurate and up-to-date wetland classification is of high importance, nationally and internationally. The advent of deep learning techniques has revolutionized the current use of machine learning algorithms to classify complex environments, specifically in remote sensing. In this paper, we explore the potential and possible limitations to be overcome regarding the use of ensemble deep learning techniques for complex wetland classification and discusses the potential and limitation of various solo convolutional neural networks (CNNs), including DenseNet, GoogLeNet, ShuffleNet, MobileNet, Xception, Inception-ResNet, ResNet18, and ResNet101 in three different study areas located in Newfoundland and Labrador, Canada (i.e., Avalon, Gros Morne, and Grand Falls). Moreover, to improve the classification accuracies of wetland classes of bog, fen, marsh, swamp, and shallow water, the results of the three best CNNs in each study area is fused using three supervised classifiers of random forest (RF), bagged tree (BTree), Bayesian optimized tree (BOT), and one unsupervised majority voting classifier. The results suggest that the ensemble models, in particular BTree, have a valuable role to play in the classification of wetland classes of bog, fen, marsh, swamp, and shallow water. The ensemble CNNs show an improvement of 9.63–19.04% in terms of mean producer’s accuracy compared to the solo CNNs, to recognize wetland classes in three different study areas. This research indicates a promising potential for integrating ensemble-based learning and deep learning for operational large area land cover, particularly complex wetland type classification. Full article
(This article belongs to the Special Issue Advanced Technologies in Wetland and Vegetation Ecological Monitoring)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-spectral Satellite Imagery

Authors:Masoud Mahdianpari, Ali Jamali, Brian Brisco; Jean Granger; Fariba Mohammadimanesh; Bahram Salehi
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