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: 15 September 2022 | Viewed by 4597

Special Issue Editors

Dr. Jeremy L. May
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Guest Editor
Biology Department, Florida International University, Miami, FL, 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, TX 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 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 2500 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 (3 papers)

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Research

Article
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands
Remote Sens. 2021, 13(18), 3669; https://doi.org/10.3390/rs13183669 - 14 Sep 2021
Cited by 6 | Viewed by 1554
Abstract
High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning [...] Read more.
High-resolution images obtained by multispectral cameras mounted on Unmanned Aerial Vehicles (UAVs) are helping to capture the heterogeneity of the environment in images that can be discretized in categories during a classification process. Currently, there is an increasing use of supervised machine learning (ML) classifiers to retrieve accurate results using scarce datasets with samples with non-linear relationships. We compared the accuracies of two ML classifiers using a pixel and object analysis approach in six coastal wetland sites. The results show that the Random Forest (RF) performs better than K-Nearest Neighbors (KNN) algorithm in the classification of pixels and objects and the classification based on pixel analysis is slightly better than the object-based analysis. The agreement between the classifications of objects and pixels is higher in Random Forest. This is likely due to the heterogeneity of the study areas, where pixel-based classifications are most appropriate. In addition, from an ecological perspective, as these wetlands are heterogeneous, the pixel-based classification reflects a more realistic interpretation of plant community distribution. Full article
(This article belongs to the Special Issue Advanced Technologies in Wetland and Vegetation Ecological Monitoring)
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Article
Remote Sensing of Ecosystem Structure—Part 2: Initial Findings of Ecosystem Functioning through Intra- and Inter-Annual Comparisons with Earth Observation Data
Remote Sens. 2021, 13(16), 3219; https://doi.org/10.3390/rs13163219 - 13 Aug 2021
Cited by 1 | Viewed by 795
Abstract
This study examines the response of a cold-regions deltaic wetland ecosystem in northwestern Canada to two separate and differing seasonal wetting cycles. The goal of this paper was to examine the nature of reflected electromagnetic energy measured by earth observation (EO) satellites, and [...] Read more.
This study examines the response of a cold-regions deltaic wetland ecosystem in northwestern Canada to two separate and differing seasonal wetting cycles. The goal of this paper was to examine the nature of reflected electromagnetic energy measured by earth observation (EO) satellites, and to assess whether seasonal wetland hydroperiod and episodic flooding events impact the information retrieved by the Sentinel-2 sensors. The year 2018 represents a year characterized by a large spring freshet and ice-jam flooding, while 2019 represents a year characterized more by summer open-water flooding. We applied the Modified Normalized Difference Wetness Index (MNDWI) to address the effects of the wetting cycles. The response of the vegetative cover was tracked using the fraction of the absorbed photosynthetically active radiation (fAPAR) and the Leaf Area Index (LAI). All three indices were viewed through the lens of cover classes as derived through a previously published study by the authors. The study provides a framework for designing longer-term studies where multiple intra- and inter-annual hydrological cycles can be accessed via EO data. Future studies will enable the examination of lag times inherent in the response to the various water sources applied to spectral response and incorporate this EO approach into a monitoring framework. Full article
(This article belongs to the Special Issue Advanced Technologies in Wetland and Vegetation Ecological Monitoring)
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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
Cited by 8 | Viewed by 1314
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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