E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Satellite-Based Wetland Observation"

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

Deadline for manuscript submissions: closed (30 June 2019).

Special Issue Editor

Guest Editor
Prof. Michael S. Kearney

Department of Environmental Science and Technology University of Maryland College Park, MD 20742, USA
Website | E-Mail
Phone: 301 405-4057
Interests: applications of remote sensing technologies to coastal wetlands (marsh loss, effects of eutrophication, adaptations to sea level rise), and their use in coastal engineering

Special Issue Information

Dear Colleagues,

Modern use of remote sensing to wetlands go back to the late 1970s and the application of the Cowardin system (Cowardin et al. 1979) with color aerial photography (Dahl 2004). Since the advent of Landsat Thematic Mapper, with it 30 m pixel resolution and usable multispectral capabilities, satellite remote sensing of wetlands has really come of age. I propose to look at two aspects of the application of present remote sensing methods to wetlands. The first concerns how various aerial and satellite platforms have been used to map wetlands, and what advantages they offer and what wetlands they have been applied to. The wetlands to be covered will include:

  • Tidal and non-tidal marshes, each presenting issues related to daily or seasonal hydroperiod variations;
  • Bottom land seasonal wetlands
  • Cypress swamps
  • Prairie potholes[1]

The range of papers should also discuss the problems of applying such methods to certain types of wetlands, despite the fact that a particular sensor may be the default approach. An example here is the use of Lidar in forest wetlands or increasingly in coastal marshes, given the problem of water absorption on the signal return intensity.

The other major thrust of the special issue will focus on how remote sensing has furthered wetland science. The topics here could range from:

  • Insights into wetland loss, particularly coastal wetlands and sea level rise
  • Changes in vegetation as a response to climate change
  • Wetland resilience: response to perturbations, trajectory of recovery it if occurs
  • Evaluation of ecosystem services

One or two papers should be considered as issue bookends, looking into future directions in wetland research where remote sensing could play a pivotal role in achieving new insights in such directions. These papers should be forward looking with respect to what new sensors may be deployed in the next decade or so. Moreover, the papers could address emerging spatial data models using remotely-sensed data, as well as what new archival methods especially tailored to such data are either coming online now, or will in the immediate future.

[1] Some of these wetlands have not received that much attention, and the scientists I have mind may be want to contribute or have moved on.

Dr. Michael Kearney
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 papers will be 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 1800 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

marsh loss,

coastal marsh,

wetland mapping,

bottom land wetland,

blue carbon

Published Papers (7 papers)

View options order results:
result details:
Displaying articles 1-7
Export citation of selected articles as:

Research

Open AccessArticle
Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images
Remote Sens. 2019, 11(16), 1927; https://doi.org/10.3390/rs11161927
Received: 30 June 2019 / Revised: 9 August 2019 / Accepted: 13 August 2019 / Published: 17 August 2019
PDF Full-text (10459 KB) | HTML Full-text | XML Full-text
Abstract
Wetlands are one of the world’s most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, [...] Read more.
Wetlands are one of the world’s most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (RF) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang—Worldview-2 and Landsat-8 images—were used for land cover classification using the RF method. To demonstrate the preferable classification accuracy of the RF algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of RF classification. The study results showed that (1) the overall accuracy of the RF method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images; (2) at the same time, the classification accuracies of RF, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images; (3) for some land cover types having only a small number of samples, the RF algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the RF method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the RF algorithm can provide better classification accuracy and is more suitable for coastal wetland classification than the SVM and k-NN algorithms are. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Figures

Graphical abstract

Open AccessArticle
Trends in the Seaward Extent of Saltmarshes across Europe from Long-Term Satellite Data
Remote Sens. 2019, 11(14), 1653; https://doi.org/10.3390/rs11141653
Received: 20 May 2019 / Revised: 4 July 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
PDF Full-text (6734 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Saltmarshes provide crucial functions for flora, fauna, and humankind. Thus far, studies of their dynamics and response to environmental drivers are limited in space and time. Satellite data allow for looking at saltmarshes on a large scale and over a long time period. [...] Read more.
Saltmarshes provide crucial functions for flora, fauna, and humankind. Thus far, studies of their dynamics and response to environmental drivers are limited in space and time. Satellite data allow for looking at saltmarshes on a large scale and over a long time period. We developed an unsupervised decision tree classification method to classify satellite images into saltmarsh vegetation, mudflat and open water, integrating additional land cover information. By using consecutive stacks of three years, we considered trends while taking into account water level variations. We used Landsat 5 TM data but found that other satellite data can be used as well. Classification performance for different periods of the Western Scheldt was almost perfect for this site, with overall accuracies above 90% and Kappa coefficients of over 0.85. Sensitivity analysis characterizes the method as being robust. Generated time series for 125 sites across Europe show saltmarsh area changes between 1986 and 2010. The method also worked using a global approach for these sites. We reveal transitions between saltmarsh, mudflat and open water, both at the saltmarsh lower edge and interior, but our method cannot detect changes at the saltmarsh-upland boundary. Resulting trends in saltmarsh dynamics can be coupled to environmental drivers, such as sea level, tidal currents, waves, and sediment availability. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Figures

Graphical abstract

Open AccessArticle
Retrieval of Salt Marsh Above-Ground Biomass from High-Spatial Resolution Hyperspectral Imagery Using PROSAIL
Remote Sens. 2019, 11(11), 1385; https://doi.org/10.3390/rs11111385
Received: 25 April 2019 / Revised: 31 May 2019 / Accepted: 8 June 2019 / Published: 11 June 2019
PDF Full-text (11090 KB) | HTML Full-text | XML Full-text
Abstract
Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, [...] Read more.
Salt marsh vegetation density varies considerably on short spatial scales, complicating attempts to evaluate plant characteristics using airborne remote sensing approaches. In this study, we used a mast-mounted hyperspectral imaging system to obtain cm-scale imagery of a salt marsh chronosequence on Hog Island, VA, where the morphology and biomass of the dominant plant species, Spartina alterniflora, varies widely. The high-resolution hyperspectral imagery allowed the detailed delineation of variations in above-ground biomass, which we retrieved from the imagery using the PROSAIL radiative transfer model. The retrieved biomass estimates correlated well with contemporaneously collected in situ biomass ground truth data ( R 2 = 0.73 ). In this study, we also rescaled our hyperspectral imagery and retrieved PROSAIL salt marsh biomass to determine the applicability of the method across spatial scales. Histograms of retrieved biomass changed considerably in characteristic marsh regions as the spatial scale of the imagery was progressively degraded. This rescaling revealed a loss of spatial detail and a shift in the mean retrieved biomass. This shift is indicative of the loss of accuracy that may occur when scaling up through a simple averaging approach that does not account for the detail found in the landscape at the natural scale of variation of the salt marsh system. This illustrated the importance of developing methodologies to appropriately scale results from very fine scale resolution up to the more coarse-scale resolutions commonly obtained in airborne and satellite remote sensing. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Figures

Graphical abstract

Open AccessArticle
Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery
Remote Sens. 2019, 11(5), 540; https://doi.org/10.3390/rs11050540
Received: 31 January 2019 / Revised: 1 March 2019 / Accepted: 2 March 2019 / Published: 6 March 2019
PDF Full-text (4492 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in [...] Read more.
Salt marsh productivity is an important control of resiliency to sea level rise. However, our understanding of how marsh biomass and productivity vary across fine spatial and temporal scales is limited. Remote sensing provides a means for characterizing spatial and temporal variability in marsh aboveground biomass, but most satellite and airborne sensors have limited spatial and/or temporal resolution. Imagery from unmanned aerial vehicles (UAVs) can be used to address this data gap. We combined seasonal field surveys and multispectral UAV imagery collected using a DJI Matrice 100 and Micasense Rededge sensor from the Carpinteria Salt Marsh Reserve in California, USA to develop a method for high-resolution mapping of aboveground saltmarsh biomass. UAV imagery was used to test a suite of vegetation indices in their ability to predict aboveground biomass (AGB). The normalized difference vegetation index (NDVI) provided the strongest correlation to aboveground biomass for each season and when seasonal data were pooled, though seasonal models (e.g., spring, r2 = 0.67; RMSE = 344 g m−2) were more robust than the annual model (r2 = 0.36; RMSE = 496 g m−2). The NDVI aboveground biomass estimation model (AGB = 2428.2 × NDVI + 120.1) was then used to create maps of biomass for each season. Total site-wide aboveground biomass ranged from 147 Mg to 205 Mg and was highest in the spring, with an average of 1222.9 g m−2. Analysis of spatial patterns in AGB demonstrated that AGB was highest in intermediate elevations that ranged from 1.6–1.8 m NAVD88. This UAV-based approach can be used aid the investigation of biomass dynamics in wetlands across a range of spatial scales. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Figures

Figure 1

Open AccessArticle
Using Remote Sensing to Identify Drivers behind Spatial Patterns in the Bio-physical Properties of a Saltmarsh Pioneer
Remote Sens. 2019, 11(5), 511; https://doi.org/10.3390/rs11050511
Received: 29 January 2019 / Revised: 22 February 2019 / Accepted: 26 February 2019 / Published: 2 March 2019
PDF Full-text (3198 KB) | HTML Full-text | XML Full-text
Abstract
Recently, spatial organization in salt marshes was shown to contain vital information on system resilience. However, in salt marshes, it remains poorly understood what shaping processes regulate spatial patterns in soil or vegetation properties that can be detected in the surface reflectance signal. [...] Read more.
Recently, spatial organization in salt marshes was shown to contain vital information on system resilience. However, in salt marshes, it remains poorly understood what shaping processes regulate spatial patterns in soil or vegetation properties that can be detected in the surface reflectance signal. In this case study we compared the effect on surface reflectance of four major shaping processes: Flooding duration, wave forcing, competition, and creek formation. We applied the ProSail model to a pioneering salt marsh species (Spartina anglica) to identify through which vegetation and soil properties these processes affected reflectance, and used in situ reflectance data at the leaf and canopy scale and satellite data on the canopy scale to identify the spatial patterns in the biophysical characteristics of this salt marsh pioneer in spring. Our results suggest that the spatial patterns in the pioneer zone of the studied salt marsh are mainly caused by the effect of flood duration. Flood duration explained over three times as much of the variation in canopy properties as wave forcing, competition, or creek influence. It particularly affects spatial patterns through canopy properties, especially the leaf area index, while leaf characteristics appear to have a relatively minor effect on reflectance. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Figures

Graphical abstract

Open AccessArticle
Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar
Remote Sens. 2019, 11(5), 509; https://doi.org/10.3390/rs11050509
Received: 1 February 2019 / Revised: 25 February 2019 / Accepted: 28 February 2019 / Published: 2 March 2019
PDF Full-text (11207 KB) | HTML Full-text | XML Full-text
Abstract
Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied [...] Read more.
Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied region of saltmarsh (Plum Island, Massachusetts, United States) were acquired in multiple years (2014, 2015 and 2016). These airborne lidar data provide characterizations of important saltmarsh components, as well as specifications for effective surveys. The invasive Phragmites australis was observed to increase in extent from 8374 m2 in 2014, to 8882 m2 in 2015 (+6.1%), and again to 13,819 m2 in 2016 (+55.6%). Validation with terrestrial lidar supported this increase, but suggested the total extent was still underestimated. Estimates of Spartina alterniflora extent from airborne lidar were within 7% of those from terrestrial lidar, but overestimation of height of Spartina alterniflora was found to occur at the edges of creeks (+83.9%). Capturing algae was found to require observations within ±15° of nadir, and capturing creek structure required observations within ±10° of nadir. In addition, 90.33% of creeks and ditches were successfully captured in the airborne lidar data (8206.3 m out of 9084.3 m found in aerial imagery). Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Figures

Graphical abstract

Open AccessArticle
A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland
Remote Sens. 2018, 10(11), 1831; https://doi.org/10.3390/rs10111831
Received: 28 August 2018 / Revised: 30 October 2018 / Accepted: 14 November 2018 / Published: 19 November 2018
PDF Full-text (2825 KB) | HTML Full-text | XML Full-text
Abstract
Despite the importance of tidal ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these wetlands remain poorly understood. This limited understanding results from the challenges associated with in situ flux studies and their correlation with satellite [...] Read more.
Despite the importance of tidal ecosystems in the global carbon budget, the relationships between environmental drivers and carbon dynamics in these wetlands remain poorly understood. This limited understanding results from the challenges associated with in situ flux studies and their correlation with satellite imagery which can be affected by periodic tidal flooding. Carbon dioxide eddy covariance (EC) towers are installed in only a few wetlands worldwide, and the longest eddy-covariance record from Georgia (GA) wetlands contains only two continuous years of observations. The goals of the present study were to evaluate the performance of existing MODIS Gross Primary Production (GPP) products (MOD17A2) against EC derived GPP and develop a tide-robust Normalized Difference Moisture Index (NDMI) based model to predict GPP within a Spartina alterniflora salt marsh on Sapelo Island, GA. These EC tower-based observations represent a basis to associate CO2 fluxes with canopy reflectance and thus provide the means to use satellite-based reflectance data for broader scale investigations. We demonstrate that Light Use Efficiency (LUE)-based MOD17A2 does not accurately reflect tidal wetland GPP compared to a simple empirical vegetation index-based model where tidal influence was accounted for. The NDMI-based GPP model was capable of predicting changes in wetland CO2 fluxes and explained 46% of the variation in flux-estimated GPP within the training data, and a root mean square error of 6.96 g C m−2 in the validation data. Our investigation is the first to create a MODIS-based wetland GPP estimation procedure that demonstrates the importance of filtering tidal observations from satellite surface reflectance data. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
Figures

Graphical abstract

Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top