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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 September 2019) | Viewed by 51189

Special Issue Editor

Department of Environmental Science and Technology University of Maryland, College Park, MD 20742, USA
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

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Keywords

marsh loss,

coastal marsh,

wetland mapping,

bottom land wetland,

blue carbon

Published Papers (9 papers)

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Research

36 pages, 11944 KiB  
Article
Evaluation of Approaches for Mapping Tidal Wetlands of the Chesapeake and Delaware Bays
by Brian T. Lamb, Maria A. Tzortziou and Kyle C. McDonald
Remote Sens. 2019, 11(20), 2366; https://doi.org/10.3390/rs11202366 - 12 Oct 2019
Cited by 15 | Viewed by 4567
Abstract
The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and [...] Read more.
The spatial extent and vegetation characteristics of tidal wetlands and their change are among the biggest unknowns and largest sources of uncertainty in modeling ecosystem processes and services at the land-ocean interface. Using a combination of moderate-high spatial resolution (≤30 meters) optical and synthetic aperture radar (SAR) satellite imagery, we evaluated several approaches for mapping and characterization of wetlands of the Chesapeake and Delaware Bays. Sentinel-1A, Phased Array type L-band Synthetic Aperture Radar (PALSAR), PALSAR-2, Sentinel-2A, and Landsat 8 imagery were used to map wetlands, with an emphasis on mapping tidal marshes, inundation extents, and functional vegetation classes (persistent vs. non-persistent). We performed initial characterizations at three target wetlands study sites with distinct geomorphologies, hydrologic characteristics, and vegetation communities. We used findings from these target wetlands study sites to inform the selection of timeseries satellite imagery for a regional scale random forest-based classification of wetlands in the Chesapeake and Delaware Bays. Acquisition of satellite imagery, raster manipulations, and timeseries analyses were performed using Google Earth Engine. Random forest classifications were performed using the R programming language. In our regional scale classification, estuarine emergent wetlands were mapped with a producer’s accuracy greater than 88% and a user’s accuracy greater than 83%. Within target wetland sites, functional classes of vegetation were mapped with over 90% user’s and producer’s accuracy for all classes, and greater than 95% accuracy overall. The use of multitemporal SAR and multitemporal optical imagery discussed here provides a straightforward yet powerful approach for accurately mapping tidal freshwater wetlands through identification of non-persistent vegetation, as well as for mapping estuarine emergent wetlands, with direct applications to the improved management of coastal wetlands. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
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16 pages, 6467 KiB  
Article
Estimating Aboveground Biomass and Its Spatial Distribution in Coastal Wetlands Utilizing Planet Multispectral Imagery
by Gwen J. Miller, James T. Morris and Cuizhen Wang
Remote Sens. 2019, 11(17), 2020; https://doi.org/10.3390/rs11172020 - 28 Aug 2019
Cited by 28 | Viewed by 5251
Abstract
Coastal salt marshes are biologically productive ecosystems that generate and sequester significant quantities of organic matter. Plant biomass varies spatially within a salt marsh and it is tedious and often logistically impractical to quantify biomass from field measurements across an entire landscape. Satellite [...] Read more.
Coastal salt marshes are biologically productive ecosystems that generate and sequester significant quantities of organic matter. Plant biomass varies spatially within a salt marsh and it is tedious and often logistically impractical to quantify biomass from field measurements across an entire landscape. Satellite data are useful for estimating aboveground biomass, however, high-resolution data are needed to resolve the spatial details within a salt marsh. This study used 3-m resolution multispectral data provided by Planet to estimate aboveground biomass within two salt marshes, North Inlet-Winyah Bay (North Inlet) National Estuary Research Reserve, and Plum Island Ecosystems (PIE) Long-Term Ecological Research site. The Akaike information criterion analysis was performed to test the fidelity of several alternative models. A combination of the modified soil vegetation index 2 (MSAVI2) and the visible difference vegetation index (VDVI) gave the best fit to the square root-normalized biomass data collected in the field at North Inlet (Willmott’s index of agreement d = 0.74, RMSE = 223.38 g/m2, AICw = 0.3848). An acceptable model was not found among all models tested for PIE data, possibly because the sample size at PIE was too small, samples were collected over a limited vertical range, in a different season, and from areas with variable canopy architecture. For North Inlet, a model-derived landscape scale biomass map showed differences in biomass density among sites, years, and showed a robust relationship between elevation and biomass. The growth curve established in this study is particularly useful as an input for biogeomorphic models of marsh development. This study showed that, used in an appropriate model with calibration, Planet data are suitable for computing and mapping aboveground biomass at high resolution on a landscape scale, which is needed to better understand spatial and temporal trends in salt marsh primary production. Full article
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
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22 pages, 10459 KiB  
Article
Land-Cover Classification of Coastal Wetlands Using the RF Algorithm for Worldview-2 and Landsat 8 Images
by Xiaoxue Wang, Xiangwei Gao, Yuanzhi Zhang, Xianyun Fei, Zhou Chen, Jian Wang, Yayi Zhang, Xia Lu and Huimin Zhao
Remote Sens. 2019, 11(16), 1927; https://doi.org/10.3390/rs11161927 - 17 Aug 2019
Cited by 71 | Viewed by 5799
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)
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25 pages, 6978 KiB  
Article
Trends in the Seaward Extent of Saltmarshes across Europe from Long-Term Satellite Data
by Marieke Liesa Laengner, Koen Siteur and Daphne van der Wal
Remote Sens. 2019, 11(14), 1653; https://doi.org/10.3390/rs11141653 - 11 Jul 2019
Cited by 30 | Viewed by 5777 | Correction
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)
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18 pages, 11090 KiB  
Article
Retrieval of Salt Marsh Above-Ground Biomass from High-Spatial Resolution Hyperspectral Imagery Using PROSAIL
by Rehman S. Eon, Sarah Goldsmith, Charles M. Bachmann, Anna Christina Tyler, Christopher S. Lapszynski, Gregory P. Badura, David T. Osgood and Ryan Brett
Remote Sens. 2019, 11(11), 1385; https://doi.org/10.3390/rs11111385 - 11 Jun 2019
Cited by 16 | Viewed by 4736
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)
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16 pages, 4492 KiB  
Article
Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery
by Cheryl L. Doughty and Kyle C. Cavanaugh
Remote Sens. 2019, 11(5), 540; https://doi.org/10.3390/rs11050540 - 06 Mar 2019
Cited by 115 | Viewed by 8346
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)
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20 pages, 3198 KiB  
Article
Using Remote Sensing to Identify Drivers behind Spatial Patterns in the Bio-physical Properties of a Saltmarsh Pioneer
by Bas Oteman, Edward Peter Morris, Gloria Peralta, Tjeerd Joris Bouma and Daphne van der Wal
Remote Sens. 2019, 11(5), 511; https://doi.org/10.3390/rs11050511 - 02 Mar 2019
Cited by 4 | Viewed by 6836
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)
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30 pages, 11207 KiB  
Article
Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar
by Ian Paynter, Crystal Schaaf, Jennifer L. Bowen, Linda Deegan, Francesco Peri and Bruce Cook
Remote Sens. 2019, 11(5), 509; https://doi.org/10.3390/rs11050509 - 02 Mar 2019
Cited by 4 | Viewed by 4254
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)
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16 pages, 2825 KiB  
Article
A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland
by Jianbin Tao, Deepak R Mishra, David L. Cotten, Jessica O’Connell, Monique Leclerc, Hafsah Binti Nahrawi, Gengsheng Zhang and Roshani Pahari
Remote Sens. 2018, 10(11), 1831; https://doi.org/10.3390/rs10111831 - 19 Nov 2018
Cited by 16 | Viewed by 4704
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)
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