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Remote Sensing of Wetlands: Conditions and Dynamics of Water, Vegetation, and Soil

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

Deadline for manuscript submissions: closed (15 July 2023) | Viewed by 5236

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Special Issue Editors

Dr. Boya Zhang

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Guest Editor

Civil Engineering Department, University of California Merced, Merced, CA 95340, USA
Interests: SAR and optical applications on wetlands; forest canopy height mapping; machine/deep learning

Dr. Sebastian Palomino

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Guest Editor

Earth and Environment Department, Florida International University, 11350 SW Village Pkwy, Port St. Lucie, FL 34987, USA
Interests: remote sensing of wetlands; surface hydrology; land cover change detection

Dr. Shimon Wdowinski

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Guest Editor

Department of Earth and Environment, Florida International University, Miami, FL 33199, USA
Interests: space geodesy; natural hazards; wetland hydrology

Dr. Erin L. Hestir

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Guest Editor

Department of Civil and Environmental Engineering, UC Merced, Merced, CA, USA
Interests: wetland; biodiversity; water resources

Special Issue Information

Dear Colleagues,

Wetlands provide essential ecosystem services, including habitats for various species, water storage, flood flow alteration, water quality improvement, and sediment stabilization. Wetland management and conservation rely on investigating and monitoring the three most important components: water, vegetation, and soil. First, wetland hydrology, referring to the timing and extent of flooding, is the most important abiotic factor controlling wetland functions. Water level fluctuations have significant impacts on the diversity and habitat values of plant communities. Second, wetland vegetation provides significant and various ecosystem services. Wetland vegetation composition and structure are often characterized by spatial heterogeneity and temporal changes. For example, coastal wetland vegetation can be impacted by short-term disturbances (e.g., hurricanes) and long-term stresses (e.g., elevated soil salinity). Third, wetland soils are saturated or flooded for an extended period of time. Monitoring soil parameters (e.g., moisture and salinity) on a regional scale is important to wetland management. Considering the inaccessibility of wetlands, investigating and monitoring the water, vegetation, and soil conditions can be challenging tasks, especially for wetlands with large geographical extents.

Earth observations acquired by different platforms (e.g., satellite or airplane) and different sensors (e.g., Synthetic Aperture Radar (SAR), optical, and Light Detection and Ranging (LiDAR)) offer an excellent opportunity to monitor and understand wetland conditions and dynamics. Such observations can be the only source of information in remote and non-instrumented areas. This Special Issue focuses on studies that use single- or multi-sensor remote sensing data to investigate various processes related to wetland hydrology (e.g., flooding extent, water level), vegetation (e.g., distribution mapping), and soil (e.g., moisture and salinity) conditions and their changes over time. We look forward to studies that apply traditional or advanced regression methods, such as machine learning and deep learning models, to estimate/map water, vegetation, and soil-related parameters. The Special Issue aims to provide innovative knowledge for future wetlands conservation and management.

The Special Issue aims at investigating and monitoring conditions and dynamics of water, vegetation, and soil for wetland ecosystems using single or multiple types of remote sensing observations. The subject encompasses various techniques and datasets directly related to the scope of Remote Sensing journal:

Multi-spectral and hyperspectral remote sensing
Active microwave remote sensing
Lidar and laser scanning
Change detection
Image processing and pattern recognition
Data fusion
Remote sensing application

The research topics include, but are not limited to:

Wetland conditions mapping: flooding extent, water level, vegetation types, soil moisture and salinity
Wetland dynamics: changes in hydrological regime, vegetation, and soil conditions caused by natural and anthropogenic disturbances (e.g., fires, hurricanes)

Dr. Boya Zhang
Dr. Sebastian Palomino
Dr. Shimon Wdowinski
Dr. Erin L. Hestir
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

SAR
optical images
wetlands
flood extent
water level
vegetation
soil moisture
soil salinity
machine learning
deep learning
disturbance

Published Papers (3 papers)

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Research

24 pages, 6233 KiB

Open AccessArticle

Rapid Large-Scale Wetland Inventory Update Using Multi-Source Remote Sensing

by Victor Igwe, Bahram Salehi and Masoud Mahdianpari

Remote Sens. 2023, 15(20), 4960; https://doi.org/10.3390/rs15204960 - 14 Oct 2023

Cited by 1 | Viewed by 1080

Abstract

Rapid impacts from both natural and anthropogenic sources on wetland ecosystems underscore the need for updating wetland inventories. Extensive up-to-date field samples are required for calibrating methods (e.g., machine learning) and validating results (e.g., maps). The purpose of this study is to design a dataset generation approach that extracts training data from already existing wetland maps in an unsupervised manner. The proposed method utilizes the LandTrendr algorithm to identify areas least likely to have changed over a seven-year period from 2016 to 2022 in Minnesota, USA. Sentinel-2 and Sentinel-1 data were used through Google Earth Engine (GEE), and sub-pixel water fraction (SWF) and normalized difference vegetation index (NDVI) were considered as wetland indicators. A simple thresholding approach was applied to the magnitude of change maps to identify pixels with the most negligible change. These samples were then employed to train a random forest (RF) classifier in an object-based image analysis framework. The proposed method achieved an overall accuracy of 89% with F1 scores of 91%, 81%, 88%, and 72% for water, emergent, forested, and scrub-shrub wetland classes, respectively. The proposed method offers an accurate and cost-efficient method for updating wetland inventories as well as studying areas impacted by floods on state or even national scales. This will assist practitioners and stakeholders in maintaining an updated wetland map with fewer requirements for extensive field campaigns. Full article

(This article belongs to the Special Issue Remote Sensing of Wetlands: Conditions and Dynamics of Water, Vegetation, and Soil)

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22 pages, 34374 KiB

Open AccessArticle

Spatial Analysis of Intra-Annual Reed Ecosystem Dynamics at Lake Neusiedl Using RGB Drone Imagery and Deep Learning

by Claudia Buchsteiner, Pamela Alessandra Baur and Stephan Glatzel

Remote Sens. 2023, 15(16), 3961; https://doi.org/10.3390/rs15163961 - 10 Aug 2023

Cited by 3 | Viewed by 1469

Abstract

The reed belt of Lake Neusiedl, covering half the size of the lake, is subject to massive changes due to the strong decline of the water level over the last several years, especially in 2021. In this study, we investigated the spatial and temporal variations within a long-term ecosystem research (LTER) site in a reed ecosystem at Lake Neusiedl in Austria under intense drought conditions. Spatio-temporal data sets from May to November 2021 were produced to analyze and detect changes in the wetland ecosystem over a single vegetation period. High-resolution orthomosaics processed from RGB imagery taken with an unmanned aerial vehicle (UAV) served as the basis for land cover classification and phenological analysis. An image annotation workflow was developed, and deep learning techniques using semantic image segmentation were applied to map land cover changes. The trained models delivered highly favorable results in terms of the assessed performance metrics. When considering the region between their minima and maxima, the water surface area decreased by 26.9%, the sediment area increased by 23.1%, and the vegetation area increased successively by 10.1% over the investigation period. Phenocam data for lateral phenological monitoring of the vegetation development of Phragmites australis was directly compared with phenological analysis from aerial imagery. This study reveals the enormous dynamics of the reed ecosystem of Lake Neusiedl, and additionally confirms the importance of remote sensing via drone and the strengths of deep learning for wetland classification. Full article

(This article belongs to the Special Issue Remote Sensing of Wetlands: Conditions and Dynamics of Water, Vegetation, and Soil)

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Graphical abstract

22 pages, 9545 KiB

Open AccessArticle

Integrating UAV-Derived Information and WorldView-3 Imagery for Mapping Wetland Plants in the Old Woman Creek Estuary, USA

by Md Kamrul Islam, Anita Simic Milas, Tharindu Abeysinghe and Qing Tian

Remote Sens. 2023, 15(4), 1090; https://doi.org/10.3390/rs15041090 - 16 Feb 2023

Cited by 4 | Viewed by 2153

Abstract

The classification of wetland plants using unmanned aerial vehicle (UAV) and satellite synergies has received increasing attention in recent years. In this study, UAV-derived training and validation data and WorldView-3 satellite imagery are integrated in the classification of five dominant wetland plants in the Old Woman Creek (OWC) estuary, USA. Several classifiers are explored: (1) pixel-based methods: maximum likelihood (ML), support vector machine (SVM), and neural network (NN), and (2) object-based methods: Naïve Bayes (NB), support vector machine (SVM), and k-nearest neighbors (k-NN). The study evaluates the performance of the classifiers for different image feature combinations such as single bands, vegetation indices, principal components (PCs), and texture information. The results showed that all classifiers reached high overall accuracy (>85%). Pixel-based SVM and object-based NB exhibited the best performance with overall accuracies of 93.76% and 93.30%, respectively. Insignificantly lower overall accuracy was achieved with ML (92.29), followed by NN (90.95) and object-oriented SVM (90.61). The k-NN method showed the lowest (but still high) accuracy of 86.74%. All classifiers except for the pixel-based SVM required additional input features. The pixel-based SVM achieved low errors of commission and omission, and unlike the other classifiers, exhibited low variability and low sensitivity to additional image features. Our study shows the efficacy of combining very high spatial resolution UAV-derived information and the super spectral observation capabilities of WorldView-3 in machine learning for mapping wetland vegetation. Full article

(This article belongs to the Special Issue Remote Sensing of Wetlands: Conditions and Dynamics of Water, Vegetation, and Soil)

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