A comparison of GPS and lidar salt marsh DEMs

Chassereau, Jessica E.; Bell, Joseph Martin; Torres, Raymond

doi:10.1002/esp.2199

Cited by 44 publications

(34 citation statements)

References 25 publications

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“…Common filter algorithms are capable of removing points reflected from higher vegetation and thus can improve accuracies of bare earth models, but fail to detect short vegetation, which is not significantly higher than surrounding bare ground (Chassereau et al 2011). Thus in short stature environments, specifically in herbaceous cover, vegetation and ground could not be differentially mapped by simple filtering.…”

Section: Methods and Algorithmsmentioning

confidence: 99%

“…First, dense vegetation, characterized by relatively less openings in vegetation cover limits laser penetration into the ground (Toyra et al 2003), making terrain and thus vegetation height estimates increasingly challenging and less accurate (Chassereau et al 2011, Hopkinson et al 2005. Second, relatively shorter vegetation and low variation in vegetation height and canopy characteristics along vertical profile of the canopy erode predictability of lidar derived variables in vegetation estimates (Li et al 2015, Rosso et al 2006, Wang et al 2007).…”

Section: Effects On Vegetation Estimatesmentioning

confidence: 99%

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Airborne lidar remote sensing applications in non-forested short stature environments: a review

2017

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Abstract. Lidar (light detection and ranging) remote sensing technology provides promising tools for 3D characterization of the earth's surface. In ecosystem studies, lidar derived structural parameters relating to vegetation and terrain have been extensively used in many applications and are rapidly expanding. Yet, most of the lidar applications have focused on tall, woody vegetation in forested environments and less research attention is given to non-forest, short stature vegetation dominated ecosystems. Similar to the lidar developments in forestry, novel methodological approaches and algorithm developments will be necessary to improve estimates of structural and biophysical properties (i.e. biomass and carbon storage) in non-forested short stature environments. Under changing climate scenarios, the latter is particularly useful to improve our understanding of their future role as terrestrial carbon sinks. In an attempt to identify research gaps in airborne lidar remote sensing application in short stature vegetation studies, in this review article we provide a comprehensive overview on the current state of airborne lidar applications. Our focus is mainly on the levels of accuracies and errors reported, as well as the potentials and limitations of the methods applied in these studies. We also provide insights into future research needs and applications in these environments.

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Section: Methods and Algorithmsmentioning

confidence: 99%

Section: Effects On Vegetation Estimatesmentioning

confidence: 99%

Airborne lidar remote sensing applications in non-forested short stature environments: a review

2017

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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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A comparison of GPS and lidar salt marsh DEMs

Cited by 44 publications

References 25 publications

Airborne lidar remote sensing applications in non-forested short stature environments: a review

Airborne lidar remote sensing applications in non-forested short stature environments: a review

Unsupervised detection of salt marsh platforms: a topographic method

Unsupervised detection of salt marsh platforms: a topographic method

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