Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations

Sadro, Steven; Gastil-Buhl, M.; Mélack, John M.

doi:10.1016/j.rse.2007.02.024

Cited by 74 publications

(60 citation statements)

References 38 publications

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“…Passive and active remote sensing has been widely applied to classify and map coastal wetland vegetation, and been widely reviewed [50][51][52][53]. The broadest extent of literature has emphasized defining individual plant reflectance spectra and optimizing spectral discrimination of vegetation classes [54][55][56][57][58][59][60][61][62][63][64]. Other studies have tested innovative approaches to classifying vegetation, including comparing use of different remote sensing data sources [65][66][67][68][69], testing object-based image analysis techniques [69][70][71][72][73][74][75], and other methods [76][77][78].…”

Section: Invoking Multiple Stable State Theorymentioning

confidence: 99%

“…In addition to automated analysis methods, vegetation classes have long been digitized by hand [79][80][81]. Remote sensing imagery has been used to map coastal topography and flooding [56,63,[82][83][84][85][86] and to map physiological responses of vegetation to salinity or pollution [87][88][89][90]. Some studies have used remote sensing to map coastal or vegetation change over time at two, or a few, time points [91][92][93][94][95][96][97], including analyses of effects of invasive species [59,98].…”

Section: Invoking Multiple Stable State Theorymentioning

confidence: 99%

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Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

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Multiple stable states are established in coastal tidal wetlands (marshes, mangroves, deltas, seagrasses) by ecological, hydrological, and geomorphological feedbacks. Catastrophic shifts between states can be induced by gradual environmental change or by disturbance events. These feedbacks and outcomes are key to the sustainability and resilience of vegetated coastlines, especially as modulated by human activity, sea level rise, and climate change. Whereas multiple stable state theory has been invoked to model salt marsh responses to sediment supply and sea level change, there has been comparatively little empirical verification of the theory for salt marshes or other coastal wetlands. Especially lacking is long-term evidence documenting if or how stable states are established and maintained at ecosystem scales. Laboratory and field-plot studies are informative, but of necessarily limited spatial and temporal scope. For the purposes of long-term, coastal-scale monitoring, remote sensing is the best viable option. This review summarizes the above topics and highlights the emerging promise and challenges of using remote sensing-based analyses to validate coastal OPEN ACCESS Remote Sens. 2015, 7 10185 wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field.

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Section: Invoking Multiple Stable State Theorymentioning

confidence: 99%

Section: Invoking Multiple Stable State Theorymentioning

confidence: 99%

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

et al. 2015

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“…The effect of enabling these optional treatments is further discussed in the results section. Although methods exist to account for vegetation cover in the DEM (Hladik and Alber, 2012;Wang et al, 2009;Sadro et al, 2007;Chassereau et al, 2011;Montané and Torres, 2006), we chose not to apply these corrections as we wanted to ensure that the TIP method can be applied without information on the vegetation assemblages at a given site.…”

Section: Preprocessing Topographic Datamentioning

confidence: 99%

“…The topographic data necessary to identify marsh platforms already exist: the proliferation of freely available highresolution topographic datasets from lidar or structure from motion (SfM) techniques means that DEMs with a grid cell size below 1 m are increasingly common on salt marshes and offer vertical accuracies below 20 cm even without correcting for vegetation (Sadro et al, 2007;Wang et al, 2009;Chassereau et al, 2011). At these resolutions, most scarps and channels are detectable on a DEM, and several automated topographic methods already allow the identification of tidal channel networks (Fagherazzi et al, 1999;Liu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Unsupervised detection of salt marsh platforms: a topographic method

2018

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Abstract. Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The sustained existence of the salt marsh ecosystem depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. The determination of platform boundaries currently relies on supervised classification methods requiring near-infrared data to detect vegetation, or demands labourintensive field surveys and digitisation. We propose a novel, unsupervised method to reproducibly isolate salt marsh scarps and platforms from a digital elevation model (DEM), referred to as Topographic Identification of Platforms (TIP). Field observations and numerical models show that salt marshes mature into subhorizontal platforms delineated by subvertical scarps. Based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six salt marshes in England with varying tidal ranges and geometries, for which topographic platforms were manually isolated from tidal flats. Agreement between manual and unsupervised classification exceeds 94 % for DEM resolutions of 1 m, with all but one site maintaining an accuracy superior to 90 % for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitised platforms and have similar elevation distributions. We also find that our method allows for the accurate detection of local block failures as small as 3 times the DEM resolution. Detailed inspection reveals that although tidal creeks were digitised as part of the marsh platform, unsupervised classification categorises them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method may benefit from combination with existing creek detection algorithms. Fallen blocks and high tidal flat portions, associated with potential pioneer zones, can also lead to differences between our method and supervised mapping. Although pioneer zones prove difficult to classify using a topographic method, we suggest that these transition areas should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms. Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possible and sufficient to monitor and analyse topographic evolution.

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“…The topographic data necessary to identify marsh platforms already exist: the multiplication of freely available high resolution topographic datasets from lidar or structure from motion (SfM) techniques means that DEMs of horizontal resolutions below 1 m are increasingly common on salt marshes, and offer vertical accuracies below 20 cm even without correcting for vegetation (Sadro et al, 2007;Wang et al, 2009;Chassereau et al, 2011). At these resolutions, most scarps and channels are detectable on a DEM, and several automated topographic methods already allow the identification of tidal channel networks (Fagherazzi et al, 1999;Liu et al, 2015).…”

mentioning

confidence: 99%

Unsupervised detection of salt marsh platforms: a topographic method

Goodwin¹,

Mudd²,

Clubb³

2017

Preprint

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Abstract.Salt marshes filter pollutants, protect coastlines against storm surges, and sequester carbon, yet are under threat from sea level rise and anthropogenic modification. The productivity and even survival of salt marsh vegetation depends on the topographic evolution of marsh platforms. Quantifying marsh platform topography is vital for improving the management of these valuable landscapes. Determining platform boundaries currently relies on supervised classification methods requiring near-infrared data 5 to detect vegetation, or demands labor-intensive field surveys and digitization. We propose a novel, unsupervised method to reproducibly isolate saltmarsh scarps and platforms from a DEM, referred to as Topographic Identification of Platforms (TIP).Field observations and numerical models show that saltmarshes mature into sub-horizontal platforms delineated by sub-vertical scarps: based on this premise, we identify scarps as lines of local maxima on a slope raster, then fill landmasses from the scarps upward, thus isolating mature marsh platforms. We test the TIP method using lidar-derived DEMs from six saltmarshes in 10England with varying tidal ranges and geometries, for which topographic platforms were manually distinguished from tidal flats. Agreement between manual and unsupervised classification exceeds 94% for DEM resolutions of 1 m, with all but one sites maintaining an accuracy superior to 90% for resolutions up to 3 m. For resolutions of 1 m, platforms detected with the TIP method are comparable in surface area to digitized platforms, and have similar elevation distributions. We also find that our method allows the accurate detection of local bloc failures as small as 3 times the DEM resolution. Detailed inspection 15 reveals that although tidal creeks were digitized as part of the marsh platform, unsupervised classification categorizes them as part of the tidal flat, causing an increase in false negatives and overall platform perimeter. This suggests our method would have increased accuracy if used in combination with existing creek detection algorithms. Fallen blocs and high tidal flat portions, associated with potential pioneer zones, may also be areas of discordance between our method and supervised mapping.Although pioneer zones prove difficult to classify using a topographic method, it also suggests that these transition areas 20 should be considered when analysing erosion and accretion processes, particularly in the case of incipient marsh platforms.Ultimately, we have shown that unsupervised classification of marsh platforms from high-resolution topography is possibleand sufficient to monitor and analyze topographic evolution.

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Characterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations

Cited by 74 publications

References 38 publications

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Unsupervised detection of salt marsh platforms: a topographic method

Unsupervised detection of salt marsh platforms: a topographic method

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