Assessment of Elevation Uncertainty in Salt Marsh Environments using Discrete-Return and Full-Waveform Lidar

Rogers, Jeffrey N.; Parrish, Christopher; Ward, Larry G.; Burdick, David M.

doi:10.2112/si76-010

Cited by 23 publications

(25 citation statements)

References 33 publications

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“…DEFRA provides the last return for every point (the density of which does not exceed 6 pts·m −2 ). This does not necessarily imply that the last return is the ground or bare earth, as dense vegetation on the marsh platform may prevent the laser from hitting the ground [49,51,52]. However, thanks to pastoral activities in Moricambe Bay, vegetation rarely exceeds 5 cm and does not cause significant errors in measured elevations such as those reported reported by Hladik and Alber [51] on marshes with tall vegetation.…”

Section: Collection and Pre-processing Of Topographic Datamentioning

confidence: 95%

Detecting the Morphology of Prograding and Retreating Marsh Margins—Example of a Mega-Tidal Bay

Goodwin

Mudd

2019

Remote Sensing

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Retreat and progradation make the edges of salt marsh platforms their most active features. If we have a single topographic snapshot of a marsh, is it possible to tell if some areas have retreated or prograded recently or if they are likely to do so in the future? We explore these questions by characterising marsh edge topography in mega-tidal Moricambe Bay (UK) in 2009, 2013 and 2017. We first map outlines of marsh platform edges based on lidar data and from these we generate transverse topographic profiles of the marsh edge 10 m long and 20 m apart. By associating profiles with individual retreat or progradation events, we find that they produce distinct profiles when grouped by change event, regardless of event magnitude. Progradation profiles have a shallow scarp and low relief that decreases with event magnitude, facilitating more progradation. Conversely, steep-scarped, high-relief retreat profiles dip landward as retreat reveals older platforms. Furthermore, vertical accretion of the marsh edge is controlled by elevation rather than its lateral motion, suggesting an even distribution of deposition that would allow bay infilling were it not limited by the migration of creeks. While we demonstrate that marsh edges can be quantified with currently available DTMs, oblique observations are crucial to fully describe scarps and better inform their sensitivity to wave and current erosion.Remote Sens. 2020, 12, 13 2 of 26 constriction of salt marsh habitat [29][30][31], as well as the mutual interaction between wave impact, retreat processes and the morphology of retreating marsh margins [32][33][34]. While marsh retreat is demonstrably linked to nearby channel deepening in a macro-tidal setting [35,36], the action of tidal currents on marsh margins remains poorly understood relative to wave action.Likewise, remote observation of salt marsh margins are scarce in the literature, in contrast with the wealth of documentation on the use of light detection and ranging (lidar) and hyperspectral data to characterise marsh platform elevation and vegetation [37][38][39][40]. This knowledge gap hampers our understanding of present coastal mobility in general but also our predictions of the future retreat or advance (which we refer to as progradation) of salt marshes. The mobility of marsh edges is often studied through the determination of wave-or current-generated stresses rather than direct observation of marsh edges. This lack of observation data prevents us from contextualising results on the influence of scarp topography on wave action [32].The paucity of data on marsh edge topography may be due to technical difficulties: in many micro-tidal systems and some meso-tidal systems the foot of the marsh scarp is rarely exposed [41] and few sites have as good topo-bathymetric data as the repeatedly studied Venice Lagoon in Italy [42] and Plum Island in Massachussets, USA [43], both of which are the object of long-term monitoring campaigns. Moreover, the spatial resolution of airborne lidar images is usually in the range of 1-...

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Section: Collection and Pre-processing Of Topographic Datamentioning

confidence: 95%

Detecting the Morphology of Prograding and Retreating Marsh Margins—Example of a Mega-Tidal Bay

Goodwin

Mudd

2019

Remote Sensing

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“…These DEMs were more dense, approximately 5 centimeters. Previous studies have discussed the difficulty of getting a true bare Earth DEM in a salt marsh because the laser inconsistently penetrates the marsh grass [21,66,67]. This is less of a concern with the MEED method because the edge of the marsh platform is identified with maximum change in slope.…”

Section: Meed Methodologymentioning

confidence: 99%

Identifying Salt Marsh Shorelines from Remotely Sensed Elevation Data and Imagery

2019

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Salt marshes are valuable ecosystems that are vulnerable to lateral erosion, submergence, and internal disintegration due to sea level rise, storms, and sediment deficits. Because many salt marshes are losing area in response to these factors, it is important to monitor their lateral extent at high resolution over multiple timescales. In this study we describe two methods to calculate the location of the salt marsh shoreline. The marsh edge from elevation data (MEED) method uses remotely sensed elevation data to calculate an objective proxy for the shoreline of a salt marsh. This proxy is the abrupt change in elevation that usually characterizes the seaward edge of a salt marsh, designated the "marsh scarp." It is detected as the maximum slope along a cross-shore transect between mean high water and mean tide level. The method was tested using lidar topobathymetric and photogrammetric elevation data from Massachusetts, USA. The other method to calculate the salt marsh shoreline is the marsh edge by image processing (MEIP) method which finds the unvegetated/vegetated line. This method applies image classification techniques to multispectral imagery and elevation datasets for edge detection. The method was tested using aerial imagery and coastal elevation data from the Plum Island Estuary in Massachusetts, USA. Both methods calculate a line that closely follows the edge of vegetation seen in imagery. The two methods were compared to each other using high resolution unmanned aircraft systems (UAS) data, and to a heads-up digitized shoreline. The root-mean-square deviation was 0.6 meters between the two methods, and less than 0.43 meters from the digitized shoreline. The MEIP method was also applied to a lower resolution dataset to investigate the effect of horizontal resolution on the results. Both methods provide an accurate, efficient, and objective way to track salt marsh shorelines with spatially intensive data over large spatial scales, which is necessary to evaluate geomorphic change and wetland vulnerability.

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“…LiDAR is commonly used to derive Digital Elevation Models (DEMs) in the coastal zone and the accuracy of DEMs is dependent on the density of points collected especially in areas with lots of vegetative cover [20][21][22][23][24][25]. NOAA has distributed LiDAR data for many years and the Digital Coast is a tremendous asset to managers and researchers who are investigating coastal patterns [26].…”

Section: Island Topography and Fox Den Entrance Locationsmentioning

confidence: 99%

Distribution Pattern of Red Fox (Vulpes vulpes) Dens and Spatial Relationships with Sea Turtle Nests, Recreation, and Environmental Characteristics

Halls

Hill

Urbanek

et al. 2018

IJGI

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Abstract:Although sea turtles are formidable prey as adults, their nests are highly vulnerable to terrestrial predation. Along the Southeastern coast of the United States, a primary predator of sea turtle nests is the red fox (Vulpes vulpes). Examining the relationship between fox populations and nest predation is often difficult due to coastal development. Masonboro Island, North Carolina is an undeveloped, natural, 13-km-long barrier island complex that is a component of the North Carolina National Estuarine Research Reserve (NERR). Masonboro Island consists of beaches, a dune ridge, back barrier flats, an expansive salt marsh, a lagoon, and spoil islands seaward of the Intracoastal Waterway. A field survey, which was conducted each spring from 2009 through 2012, recorded den entrance coordinates based upon recent use by foxes. Sea turtle nests were located using a similar survey methodology, which identifies viable and predated nests as well as false crawls. A series of spatial-temporal pattern analysis techniques were used to identify trends through time. The results indicated that: (1) fox den entrances and predated sea turtle nests were clustered throughout the island (p = 0.01); (2) den entrances in the northern part of the island were closer to the sea turtle nests than other locations on the island; (3) fox den entrances were positively correlated (p = 0.01) with dune height, (4) fox den entrances were located closer to the island boat access sites than expected (p = 0.01). A variety of spatial sensitivity tests were used to test the validity of the statistically significant cluster analyses. A Geographically Weighted Regression model was created to predict the location of fox dens using dune elevation, the distance to predated sea turtle nests, and the distance to boat access sites. The model accounted for 40% of the variance and had a small residual error, which indicates that the independent variables were statistically valid. Results from this project will be used by the NC NERR staff to develop management plans and to further study fox-related impacts on the island. For example, given the higher density of fox den entrances on the northern part of the island, managers may consider targeted wildlife control measures during the sea turtle nesting season to diminish predation.

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Assessment of Elevation Uncertainty in Salt Marsh Environments using Discrete-Return and Full-Waveform Lidar

Abstract: BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Cited by 23 publications

References 33 publications

Detecting the Morphology of Prograding and Retreating Marsh Margins—Example of a Mega-Tidal Bay

Detecting the Morphology of Prograding and Retreating Marsh Margins—Example of a Mega-Tidal Bay

Identifying Salt Marsh Shorelines from Remotely Sensed Elevation Data and Imagery

Distribution Pattern of Red Fox (Vulpes vulpes) Dens and Spatial Relationships with Sea Turtle Nests, Recreation, and Environmental Characteristics

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