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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: closed (15 April 2023) | Viewed by 14323

Special Issue Editors

Biology Department, Florida International University, Miami, FL, USA
Interests: remote sensing; climate change; vegetation; community ecology
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 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

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

Published Papers (5 papers)

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Research

17 pages, 7079 KiB  
Article
An Effective Deep Learning Model for Monitoring Mangroves: A Case Study of the Indus Delta
by Chen Xu, Juanle Wang, Yu Sang, Kai Li, Jingxuan Liu and Gang Yang
Remote Sens. 2023, 15(9), 2220; https://doi.org/10.3390/rs15092220 - 22 Apr 2023
Cited by 6 | Viewed by 2147
Abstract
Rapid and accurate identification of mangroves using remote sensing images is of great significance for assisting ecological conservation efforts in coastal zones. With the rapid development of artificial intelligence, deep learning methods have been successfully applied to a variety of fields. However, few [...] Read more.
Rapid and accurate identification of mangroves using remote sensing images is of great significance for assisting ecological conservation efforts in coastal zones. With the rapid development of artificial intelligence, deep learning methods have been successfully applied to a variety of fields. However, few studies have applied deep learning methods to the automatic detection of mangroves and few scholars have used medium-resolution Landsat images for large-scale mangrove identification. In this study, cloud-free Landsat 8 OLI imagery of the Indus Delta was acquired using the GEE platform, and NDVI and land use data were used to produce integrated labels to reduce the complexity and subjectivity of manually labeled samples. We proposed the use of MSNet, a semantic segmentation model fusing multiple-scale features, for mangrove extraction in the Indus Delta, and compared the performance of the MSNet model with three other semantic segmentation models, FCN-8s, SegNet, and U-Net. The overall performance ranking of the deep learning methods was MSNet > U-Net > SegNet > FCN-8s. The parallel-structured MSNet model was easy to train, had the fewest parameters and the highest validation accuracy, and provided the best results for the extraction of mangrove pixels with weak features. The MSNet model not only maintains the high-resolution features of the image and fully learns the pixels with weak features during the training process but also fuses the multiple-scale underlying features at different scales to enhance the semantic information and improve the accuracy of feature recognition and segmentation localization. Finally, the areas covered by mangroves in the Indus Delta in 2014 and 2022 were extracted using the best-performing MSNet. The statistics show an increase in mangrove-covered areas in the Indus Delta between 2014 and 2022, with a reduction of 44.37 km2, an increase of 170.48 km2, and a net increase of 126.11 km2. Full article
(This article belongs to the Special Issue Advanced Technologies in Wetland and Vegetation Ecological Monitoring)
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24 pages, 2950 KiB  
Article
Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?
by Hana L. Sellers, Sergio A. Vargas Zesati, Sarah C. Elmendorf, Alexandra Locher, Steven F. Oberbauer, Craig E. Tweedie, Chandi Witharana and Robert D. Hollister
Remote Sens. 2023, 15(8), 1972; https://doi.org/10.3390/rs15081972 - 08 Apr 2023
Cited by 1 | Viewed by 1743
Abstract
Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field [...] Read more.
Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m2 plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover. Full article
(This article belongs to the Special Issue Advanced Technologies in Wetland and Vegetation Ecological Monitoring)
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27 pages, 57978 KiB  
Article
Machine Learning Classification and Accuracy Assessment from High-Resolution Images of Coastal Wetlands
by Ricardo Martínez Prentice, Miguel Villoslada Peciña, Raymond D. Ward, Thaisa F. Bergamo, Chris B. Joyce and Kalev Sepp
Remote Sens. 2021, 13(18), 3669; https://doi.org/10.3390/rs13183669 - 14 Sep 2021
Cited by 24 | Viewed by 3997
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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22 pages, 12367 KiB  
Article
Remote Sensing of Ecosystem Structure—Part 2: Initial Findings of Ecosystem Functioning through Intra- and Inter-Annual Comparisons with Earth Observation Data
by Daniel L. Peters, K. Olaf Niemann and Robert Skelly
Remote Sens. 2021, 13(16), 3219; https://doi.org/10.3390/rs13163219 - 13 Aug 2021
Cited by 3 | Viewed by 1639
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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25 pages, 17299 KiB  
Article
Comparing Solo Versus Ensemble Convolutional Neural Networks for Wetland Classification Using Multi-Spectral Satellite Imagery
by Ali Jamali, Masoud Mahdianpari, Brian Brisco, Jean Granger, Fariba Mohammadimanesh and Bahram Salehi
Remote Sens. 2021, 13(11), 2046; https://doi.org/10.3390/rs13112046 - 22 May 2021
Cited by 20 | Viewed by 3033
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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