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Remote Sensing of Wetland Vegetation Patterns and Dynamics

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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 (20 June 2022) | Viewed by 24464

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Special Issue Editors

Dr. Daniel Gann

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Guest Editor

Biological Sciences, Florida International University, Miami, FL 33199, USA
Interests: landscape and spatial ecology; spatial scaling; remote sensing; wetlands ecology; ecosystems ecology

Prof. Dr. Jennifer Richards

E-Mail Website
Guest Editor

Biological Sciences, Florida International University, Miami, FL 33199, USA
Interests: wetland plant biology; remote sensing of wetland vegetation; wetlands ecology

Special Issue Information

Dear Colleagues,

Wetlands are important global climate regulators, storing C equal to that in forests and more than two-thirds that in the oceans. The plant communities that define wetlands are the basis for wetland food webs and they serve as habitat for other species. Their belowground productivity maintains the structural integrity of wetland soils, thus feeding back to landscape-forming processes, while in coastal wetlands, the fringe vegetation stabilizes shorelines and protects inland habitats from wave action. Wetland ecosystems are highly dynamic, being defined by ephemeral, seasonal or permanent flooding. Wetlands are also threatened, with over 50% having been lost world-wide. Monitoring wetland dynamics is complicated, as it requires accounting for seasonal dynamics in water fluctuation, changes in relative abundance of species, and long-lasting transitions between community types. Modeling feedbacks of wetland vegetation patterns and processes that maintain or degrade wetlands requires reliable detection of wetland vegetation and distribution patterns across landscapes. Remote sensing techniques provide the opportunity to monitor these dynamics across large spatial extents. Remote sensing of wetlands has primarily focused on detection and classification of wetland types and only to a minor degree on the plant communities within types. This diversity within wetlands, however, is important for understanding and modeling wetland nutrient cycling, primary productivity, and C storage.

This special issue is dedicated to the detection of wetland vegetation and the seasonal and inter-annual patterns of wetland vegetation dynamics, and changes in wetland communities. We are especially interested in articles on:

(1) Detection of species or communities at multiple scales.

(2) Retrieval of species- or community-specific productivity or biomass estimates.

(3) Detection of seasonal and inter-annual variability of plant community compositions.

(4) Recovery or trajectories of wetland communities after large-scale disturbances.

(5) Integration of wetland vegetation ecology and the development of new methods in remote sensing technology.

Dr. Daniel Gann
Prof. Dr. Jennifer Richards
Guest Editor

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

Wetlands
Wetland plant communities
Wetland plant species
Wetland vegetation
Multi-seasonal
Multi-sensor
Disturbance
Succession
Primary productivity

Published Papers (7 papers)

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Research

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27 pages, 15419 KiB

Open AccessArticle

Interpreting Mangrove Habitat and Coastal Land Cover Change in the Greater Bay Area, Southern China, from 1924 to 2020 Using Historical Aerial Photos and Multiple Sources of Satellite Data

by Mingfeng Liu, Felix Leung and Shing-Yip Lee

Remote Sens. 2022, 14(20), 5163; https://doi.org/10.3390/rs14205163 - 15 Oct 2022

Cited by 4 | Viewed by 2415

Abstract

Coastal habitat dynamics and ecosystem function in response to human-induced disturbance, especially urbanization, are of increasing concern. However, how changes in landscape composition as well as habitat quantity and quality may affect the long-term sustainability of rapidly urbanizing coasts remains unclear. This study aimed to quantify the extent, change rate, patterns, change process and interrelationships of mangrove habitats, impervious surfaces, and other land cover types in Deep Bay in the Guangdong-Hong Kong-Macau Greater Bay Area (GBA), China, the world’s largest megalopolis, from 1924 to 2020. We processed historical aerial photos (1924–2020) and multiple sources of satellite data (1973–2020) for different types of land cover mapping. Post-classification analysis, including correlation analysis and change detection analysis, was conducted based on the long time-series land cover classification results. Mangrove habitats increased in Deep Bay from 1924 to 2020, except for a large area decrease from 1954 to 1964 due to the construction of tidal aquaculture ponds. Mudflat areas contributed most to the expansion of mangrove habitats of about 275 ha from 1987 to 2020. During this period, reclamation and urbanization for the construction of the megacity of Shenzhen turned large areas of water and mudflat (about 4000 ha) on the northern shore into impervious surface and urban vegetation. Overall, the landscape pattern of mangrove habitats in Deep Bay showed increasing connectivity and decreasing degree of fragmentation from 1987 to 2020. These changes have significant implications for the ecosystem services, e.g., supporting migratory waterbirds, supported by these wetlands. Full article

(This article belongs to the Special Issue Remote Sensing of Wetland Vegetation Patterns and Dynamics)

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17 pages, 3117 KiB

Open AccessArticle

Detecting Vegetation to Open Water Transitions in a Subtropical Wetland Landscape from Historical Panchromatic Aerial Photography and Multispectral Satellite Imagery

by Lukas M. Lamb, Daniel Gann, Jesse T. Velazquez and Tiffany G. Troxler

Remote Sens. 2022, 14(16), 3976; https://doi.org/10.3390/rs14163976 - 16 Aug 2022

Cited by 1 | Viewed by 1945

Abstract

Over the last century, direct human modification has been a major driver of coastal wetland degradation, resulting in widespread losses of wetland vegetation and a transition to open water. High-resolution satellite imagery is widely available for monitoring changes in present-day wetlands; however, understanding the rates of wetland vegetation loss over the last century depends on the use of historical panchromatic aerial photographs. In this study, we compared manual image thresholding and an automated machine learning (ML) method in detecting wetland vegetation and open water from historical panchromatic photographs in the Florida Everglades, a subtropical wetland landscape. We compared the same classes delineated in the historical photographs to 2012 multispectral satellite imagery and assessed the accuracy of detecting vegetation loss over a 72 year timescale (1940 to 2012) for a range of minimum mapping units (MMUs). Overall, classification accuracies were >95% across the historical photographs and satellite imagery, regardless of the classification method and MMUs. We detected a 2.3–2.7 ha increase in open water pixels across all change maps (overall accuracies > 95%). Our analysis demonstrated that ML classification methods can be used to delineate wetland vegetation from open water in low-quality, panchromatic aerial photographs and that a combination of images with different resolutions is compatible with change detection. The study also highlights how evaluating a range of MMUs can identify the effect of scale on detection accuracy and change class estimates as well as in determining the most relevant scale of analysis for the process of interest. Full article

(This article belongs to the Special Issue Remote Sensing of Wetland Vegetation Patterns and Dynamics)

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35 pages, 195399 KiB

Open AccessArticle

A Fused Radar–Optical Approach for Mapping Wetlands and Deepwaters of the Mid–Atlantic and Gulf Coast Regions of the United States

by Brian T. Lamb, Maria A. Tzortziou and Kyle C. McDonald

Remote Sens. 2021, 13(13), 2495; https://doi.org/10.3390/rs13132495 - 26 Jun 2021

Cited by 7 | Viewed by 3432

Abstract

Tidal wetlands are critically important ecosystems that provide ecosystem services including carbon sequestration, storm surge mitigation, water filtration, and wildlife habitat provision while supporting high levels of biodiversity. Despite their importance, monitoring these systems over large scales remains challenging due to difficulties in obtaining extensive up-to-date ground surveys and the need for high spatial and temporal resolution satellite imagery for effective space-borne monitoring. In this study, we developed methodologies to advance the monitoring of tidal marshes and adjacent deepwaters in the Mid-Atlantic and Gulf Coast United States. We combined Sentinel-1 SAR and Landsat 8 optical imagery to classify marshes and open water in both regions, with user’s and producer’s accuracies exceeding 89%. This methodology enables the assessment of marsh loss through conversion to open water at an annual resolution. We used time-series Sentinel-1 imagery to classify persistent and non-persistent marsh vegetation with greater than 93% accuracy. Non-persistent marsh vegetation serves as an indicator of salinity regimes in tidal wetlands. Additionally, we mapped two invasive species: wetlands invasive Phragmites australis (common reed) with greater than 80% accuracy and deepwater invasive Trapa natans (water chestnut) with greater than 96% accuracy. These results have important implications for improved monitoring and management of coastal wetlands ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing of Wetland Vegetation Patterns and Dynamics)

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22 pages, 1018 KiB

Open AccessArticle

Mangrove and Saltmarsh Distribution Mapping and Land Cover Change Assessment for South-Eastern Australia from 1991 to 2015

by Alejandro Navarro, Mary Young, Peter I. Macreadie, Emily Nicholson and Daniel Ierodiaconou

Remote Sens. 2021, 13(8), 1450; https://doi.org/10.3390/rs13081450 - 08 Apr 2021

Cited by 14 | Viewed by 4038

Abstract

Coastal wetland ecosystems, such as saltmarsh and mangroves, provide a wide range of important ecological and socio-economic services. A good understanding of the spatial and temporal distribution of these ecosystems is critical to maximising the benefits from restoration and conservation projects. We mapped mangrove and saltmarsh ecosystem transitions from 1991 to 2015 in south-eastern Australia, using remotely sensed Landsat data and a Random Forest classification. Our classification results were improved by the addition of two physical variables (Shuttle Radar Topographic Mission (SRTM), and Distance to Water). We also provide evidence that the addition of post-classification, spatial and temporal, filters improve overall accuracy of coastal wetlands detection by up to 16%. Mangrove and saltmarsh maps produced in this study had an overall User Accuracy of 0.82–0.95 and 0.81–0.87 and an overall Producer Accuracy of 0.71–0.88 and 0.24–0.87 for mangrove and saltmarsh, respectively. We found that mangrove ecosystems in south-eastern Australia have lost an area of 1148 ha (7.6%), whilst saltmarsh experienced an overall increase in coverage of 4157 ha (20.3%) over this 24-year period. The maps developed in this study allow local managers to quantify persistence, gains, and losses of coastal wetlands in south-eastern Australia. Full article

(This article belongs to the Special Issue Remote Sensing of Wetland Vegetation Patterns and Dynamics)

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30 pages, 11363 KiB

Open AccessArticle

Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

by Simona Niculescu, Jean-Baptiste Boissonnat, Cédric Lardeux, Dar Roberts, Jenica Hanganu, Antoine Billey, Adrian Constantinescu and Mihai Doroftei

Remote Sens. 2020, 12(14), 2188; https://doi.org/10.3390/rs12142188 - 08 Jul 2020

Cited by 26 | Viewed by 3632 | Correction

Abstract

In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L. Full article

(This article belongs to the Special Issue Remote Sensing of Wetland Vegetation Patterns and Dynamics)

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Review

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38 pages, 10558 KiB

Open AccessReview

Remote Sensing of Wetlands in the Prairie Pothole Region of North America

by Joshua Montgomery, Craig Mahoney, Brian Brisco, Lyle Boychuk, Danielle Cobbaert and Chris Hopkinson

Remote Sens. 2021, 13(19), 3878; https://doi.org/10.3390/rs13193878 - 28 Sep 2021

Cited by 15 | Viewed by 5195

Abstract

The Prairie Pothole Region (PPR) of North America is an extremely important habitat for a diverse range of wetland ecosystems that provide a wealth of socio-economic value. This paper describes the ecological characteristics and importance of PPR wetlands and the use of remote sensing for mapping and monitoring applications. While there are comprehensive reviews for wetland remote sensing in recent publications, there is no comprehensive review about the use of remote sensing in the PPR. First, the PPR is described, including the wetland classification systems that have been used, the water regimes that control the surface water and water levels, and the soil and vegetation characteristics of the region. The tools and techniques that have been used in the PPR for analyses of geospatial data for wetland applications are described. Field observations for ground truth data are critical for good validation and accuracy assessment of the many products that are produced. Wetland classification approaches are reviewed, including Decision Trees, Machine Learning, and object versus pixel-based approaches. A comprehensive description of the remote sensing systems and data that have been employed by various studies in the PPR is provided. A wide range of data can be used for various applications, including passive optical data like aerial photographs or satellite-based, Earth-observation data. Both airborne and spaceborne lidar studies are described. A detailed description of Synthetic Aperture RADAR (SAR) data and research are provided. The state of the art is the use of multi-source data to achieve higher accuracies and hybrid approaches. Digital Surface Models are also being incorporated in geospatial analyses to separate forest and shrub and emergent systems based on vegetation height. Remote sensing provides a cost-effective mechanism for mapping and monitoring PPR wetlands, especially with the logistical difficulties and cost of field-based methods. The wetland characteristics of the PPR dictate the need for high resolution in both time and space, which is increasingly possible with the numerous and increasing remote sensing systems available and the trend to open-source data and tools. The fusion of multi-source remote sensing data via state-of-the-art machine learning is recommended for wetland applications in the PPR. The use of such data promotes flexibility for sensor addition, subtraction, or substitution as a function of application needs and potential cost restrictions. This is important in the PPR because of the challenges related to the highly dynamic nature of this unique region. Full article

(This article belongs to the Special Issue Remote Sensing of Wetland Vegetation Patterns and Dynamics)

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2 pages, 166 KiB

Open AccessCorrection

Correction: Niculescu, S., et al. Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta. Remote Sensing 2020, 12(14), 2188

by Simona Niculescu, Jean-Baptiste Boissonnat, Cédric Lardeux, Dar Roberts, Jenica Hanganu, Antoine Billey, Adrian Constantinescu and Mihai Doroftei

Remote Sens. 2020, 12(16), 2529; https://doi.org/10.3390/rs12162529 - 06 Aug 2020

Viewed by 2199

Abstract

The authors wish to make the following corrections to this paper [...] Full article

(This article belongs to the Special Issue Remote Sensing of Wetland Vegetation Patterns and Dynamics)

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