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Monitoring Salt Marsh Condition with Remote Sensing
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Monitoring Salt Marsh Condition with Remote Sensing

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 11290

Special Issue Editors

Dr. Charles M. Bachmann

E-Mail Website1 Website2
Guest Editor
Chester F. Carlson Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623-5604, USA
Interests: hyperspectral and multi-sensor remote sensing; radiative transfer; BRDF; goniometers; coastal science; wetlands; manifold and graph algorithms
Dr. Christy Tyler

E-Mail Website
Guest Editor
Thomas H. Gosnell School of Life Sciences, Rochester Institute of Technology, Rochester, NY 14623-5604, USA
Interests: aquatic ecology and biogeochemistry; wetlands; invasive species; ecosystem restoration

Special Issue Information

Dear Colleagues,

Salt marshes are a critical transition zone in the coastal region, providing habitats for numerous ecologically and economically important species, the removal of land-derived nutrients, protection from storms, and a potentially important sink for atmospheric carbon dioxide, so called “blue carbon”. In recent decades, salt marshes have been subject to increasing stressors, including sea-level rise, temperature extremes, storms, excess nutrients, and cascading shifts in the biological community structure induced by the overharvest of commercial species. As a result, there are many reports of die-off events that cover significant areas of coastal salt marsh systems. To monitor and understand the nature of these and other changes in salt marsh ecosystems, remote sensing can play a critical role, allowing for a synoptic scale perspective and the ability to monitor short- and long-term change. Models developed locally for remote sensing data can be extended broadly across salt marsh systems to assess marsh condition, observe change, and predict the future trajectory of coastal salt marsh environments. The use of spectral imaging, LiDAR, thermal-, radar-, and multi-sensor imaging can play an important role in understanding the condition of marsh ecosystems, and with the now widespread use of unmanned aerial systems (UAS), a broader range of spatial scales in marsh systems can be understood.

This Special Issue invites contributed articles that emphasize the monitoring and assessment of salt marsh condition from remote sensing. Potential topics may include, but are not limited to, the following:

(1) Methods for retrieving biophysical parameters of salt marshes, using, for example, vegetation indices, radiative transfer models, or statistical pattern recognition methodologies.

(2) Improvements to hyperspectral; multi-spectral; LiDAR; thermal, RADAR, or multi-sensor approaches to assess marsh condition.

(3) Remote sensing approaches that emphasize one or more scales within marsh ecosystems from various platforms, namely: satellite, airborne, UAS, or other novel imaging platforms.

(4) Remote sensing imagery time series and change detection in marsh ecosystems.

(5) The analysis of marsh spatial structure and species distribution from remotely sensed data.

(6) The remote assessment of the carbon sequestration potential in coastal salt marsh systems. Both original research articles and review articles addressing one or more of these or similar topics are welcome.

Dr. Charles M. Bachmann
Dr. Christy Tyler
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

  • blue carbon
  • marsh biomass
  • marsh die-off
  • marsh stressors
  • marsh biophysical properties
  • radiative transfer
  • vegetation indices
  • statistical pattern recognition
  • marsh heterogeneity and scaling
  • spectral imaging
  • LiDAR
  • thermal imaging
  • RADAR
  • multi-sensor systems

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Published Papers (3 papers)

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25 pages, 4492 KiB  
Article
Assessing the Fractional Abundance of Highly Mixed Salt-Marsh Vegetation Using Random Forest Soft Classification
by Zhicheng Yang, Andrea D’Alpaos, Marco Marani and Sonia Silvestri
Remote Sens. 2020, 12(19), 3224; https://doi.org/10.3390/rs12193224 - 3 Oct 2020
Cited by 8 | Viewed by 3305
Abstract
Coastal salt marshes are valuable and critical components of tidal landscapes, currently threatened by increasing rates of sea level rise, wave-induced lateral erosion, decreasing sediment supply, and human pressure. Halophytic vegetation plays an important role in salt-marsh erosional and depositional patterns and marsh [...] Read more.
Coastal salt marshes are valuable and critical components of tidal landscapes, currently threatened by increasing rates of sea level rise, wave-induced lateral erosion, decreasing sediment supply, and human pressure. Halophytic vegetation plays an important role in salt-marsh erosional and depositional patterns and marsh survival. Mapping salt-marsh halophytic vegetation species and their fractional abundance within plant associations can provide important information on marsh vulnerability and coastal management. Remote sensing has often provided valuable methods for salt-marsh vegetation mapping; however, it has seldom been used to assess the fractional abundance of halophytes. In this study, we developed and tested a novel approach to estimate fractional abundance of halophytic species and bare soil that is based on Random Forest (RF) soft classification. This approach can fully use the information contained in the frequency of decision tree “votes” to estimate fractional abundance of each species. Such a method was applied to WorldView-2 (WV-2) data acquired for the Venice lagoon (Italy), where marshes are characterized by a high diversity of vegetation species. The proposed method was successfully tested against field observations derived from ancillary field surveys. Our results show that the new approach allows one to obtain high accuracy (6.7% < root-mean-square error (RMSE) < 18.7% and 0.65 < R2 < 0.96) in estimating the sub-pixel fractional abundance of marsh-vegetation species. Comparing results obtained with the new RF soft-classification approach with those obtained using the traditional RF regression method for fractional abundance estimation, we find a superior performance of the novel RF soft-classification approach with respect to the existing RF regression methods. The distribution of the dominant species obtained from the RF soft classification was compared to the one obtained from an RF hard classification, showing that numerous mixed areas are wrongly labeled as populated by specific species by the hard classifier. As for the effectiveness of using WV-2 for salt-marsh vegetation mapping, feature importance analyses suggest that Yellow (584–632 nm), NIR 1 (near-infrared 1, 765–901 nm) and NIR 2 (near-infrared 2, 856–1043 nm) bands are critical in RF soft classification. Our results bear important consequences for mapping and monitoring vegetation-species fractional abundance within plant associations and their dynamics, which are key aspects in biogeomorphic analyses of salt-marsh landscapes. Full article
(This article belongs to the Special Issue Monitoring Salt Marsh Condition with Remote Sensing)
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22 pages, 7201 KiB  
Article
Assessing Salt Marsh Vulnerability Using High-Resolution Hyperspectral Imagery
by Sarah B. Goldsmith, Rehman S. Eon, Christopher S. Lapszynski, Gregory P. Badura, David T. Osgood, Charles M. Bachmann and Anna Christina Tyler
Remote Sens. 2020, 12(18), 2938; https://doi.org/10.3390/rs12182938 - 10 Sep 2020
Cited by 6 | Viewed by 3929
Abstract
Change in the coastal zone is accelerating with external forcing by sea-level rise, nutrient loading, drought, and over-harvest, leading to significant stress on the foundation plant species of coastal salt marshes. The rapid evolution of marsh state induced by these drivers makes the [...] Read more.
Change in the coastal zone is accelerating with external forcing by sea-level rise, nutrient loading, drought, and over-harvest, leading to significant stress on the foundation plant species of coastal salt marshes. The rapid evolution of marsh state induced by these drivers makes the ability to detect stressors prior to marsh loss important. However, field work in coastal salt marshes can be challenging due to limited access and their fragile nature. Thus, remote sensing approaches hold promise for rapid and accurate determination of marsh state across multiple spatial scales. In this study, we evaluated the use of remote sensing tools to detect three dominant stressors on Spartina alterniflora. We took advantage of a barrier island salt marsh chronosequence in Virginia, USA, where marshes of different ages and level of stressor exist side by side. We collected hyperspectral imagery of plants along with salinity, sediment redox potential, and foliar nitrogen content in the field. We also conducted a greenhouse study where we manipulated environmental conditions. We found that models developed for stressors based on plant spectral response correlated well with salinity and foliar nitrogen within the greenhouse and field data, but were not transferable from lab to field, likely due to the limited range of conditions explored within the greenhouse experiments and the coincidence of multiple stressors in the field. This study is an important step towards the development of a remote sensing tool for tracking of ecosystem development, marsh health, and future ecosystem services. Full article
(This article belongs to the Special Issue Monitoring Salt Marsh Condition with Remote Sensing)
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Other

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19 pages, 11534 KiB  
Letter
Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery
by Rehman S. Eon, Charles M. Bachmann, Christopher S. Lapszynski, Anna Christina Tyler and Sarah Goldsmith
Remote Sens. 2020, 12(3), 422; https://doi.org/10.3390/rs12030422 - 28 Jan 2020
Cited by 5 | Viewed by 3234
Abstract
This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple [...] Read more.
This work describes a study using multi-view hyperspectral imagery to retrieve sediment filling factor through inversion of a modified version of the Hapke radiative transfer model. We collected multi-view hyperspectral imagery from a hyperspectral imaging system mounted atop a telescopic mast from multiple locations and viewing angles of a salt panne on a barrier island at the Virginia Coast Reserve Long-Term Ecological Research site. We also collected ground truth data, including sediment bulk density and moisture content, within the common field of view of the collected hyperspectral imagery. For samples below a density threshold for coherent effects, originally predicted by Hapke, the retrieved sediment filling factor correlates well with directly measured sediment bulk density ( R 2 = 0.85 ). The majority of collected samples satisfied this condition. The onset of the threshold occurs at significantly higher filling factors than Hapke’s predictions for dry sediments because the salt panne sediment has significant moisture content. We applied our validated inversion model to successfully map sediment filling factor across the common region of overlap of the multi-view hyperspectral imagery of the salt panne. Full article
(This article belongs to the Special Issue Monitoring Salt Marsh Condition with Remote Sensing)
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