Utilizing DEMs derived from LIDAR data to analyze morphologic change in the North Carolina coastline

White, Stephen; Wang, Yong

doi:10.1016/s0034-4257(02)00185-2

Cited by 173 publications

(112 citation statements)

References 5 publications

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“…They prefer to use the isohypse at the highest tidal position (datum-based shoreline) as it would be more reliable for beach profile changes. This 3D information is normally obtained by GNSS mapping (Global Navigation Satellite System) [9,10], LiDAR [11][12][13][14] or TLS [15] (Terrestrial Laser Scanner). These 3D resources may offer high accuracy data: up to 5 cm (horizontal and vertical) on differential GNSS surveys; and up to 10 cm (horizontal) and 20 cm (vertical) depending on each of the LiDAR flight demands.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

et al. 2018

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This paper evaluates the accuracy of shoreline positions obtained from the infrared (IR) bands of Landsat 7, Landsat 8, and Sentinel-2 imagery on natural beaches. A workflow for sub-pixel shoreline extraction, already tested on seawalls, is used. The present work analyzes the behavior of that workflow and resultant shorelines on a micro-tidal (<20 cm) sandy beach and makes a comparison with other more accurate sets of shorelines. These other sets were obtained using differential GNSS surveys and terrestrial photogrammetry techniques through the C-Pro monitoring system. 21 sub-pixel shorelines and their respective high-precision lines served for the evaluation. The results prove that NIR bands can easily confuse the shoreline with whitewater, whereas SWIR bands are more reliable in this respect. Moreover, it verifies that shorelines obtained from bands 11 and 12 of Sentinel-2 are very similar to those obtained with bands 6 and 7 of Landsat 8 (−0.75 ± 2.5 m; negative sign indicates landward bias). The variability of the brightness in the terrestrial zone influences shoreline detection: brighter zones cause a small landward bias. A relation between the swell and shoreline accuracy is found, mainly identified in images obtained from Landsat 8 and Sentinel-2. On natural beaches, the mean shoreline error varies with the type of image used. After analyzing the whole set of shorelines detected from Landsat 7, we conclude that the mean horizontal error is 4.63 m (±6.55 m) and 5.50 m (±4.86 m), respectively, for high and low gain images. For the Landsat 8 and Sentinel-2 shorelines, the mean error reaches 3.06 m (±5.79 m).

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Section: Introductionmentioning

confidence: 99%

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

et al. 2018

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“…In contrast to the methods used in these studies, airborne Light Detection And Ranging (LiDAR) scanning enables short term mapping of small objects and surfaces with very little texture and contrast and offers new applications for coastal erosion studies. White and Wang (2003) and Young and Ashford (2006) used repeat airborne LiDAR data to estimate volumetric erosion and sediment pathways of non-permafrost coasts. Jones et al (2013) demonstrated suitability of airborne LIDAR data for landscape changes of arctic coastal lowlands, including volumetric changes due to coastal erosion.…”

Section: Introductionmentioning

confidence: 99%

Coastal erosion and mass wasting along the Canadian Beaufort Sea based on annual airborne LiDAR elevation data

et al. 2017

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Erosion of permafrost coasts has received increasing scientific attention since 1990s because of rapid land loss and the mobilisation potential of old organic carbon. The majority of permafrost coastal erosion studies are limited to time periods from a few years to decades. Most of these studies emphasize the spatial variability of coastal erosion, but the intensity of inter-annual variations, including intermediate coastal aggradation, remains poorly documented. We used repeat airborne Light Detection And Ranging (LiDAR) elevation data from 2012 and 2013 with 1 m horizontal resolution to study coastal erosion and accompanying mass-wasting processes in the hinterland. Study sites were selected to include different morphologies along the coast of the Yukon Coastal Plain and on Herschel Island. We studied elevation and volume changes and coastline movement and compared the results between geomorphic units. Results showed simple uniform coastal erosion from low coasts (up to 10 m height) and a highly diverse erosion pattern along coasts with higher backshore elevation. This variability was particularly pronounced in the case of active retrogressive thaw slumps, which can decrease coastal erosion or even cause temporary progradation by sediment release. Most of the extremes were recorded in study sites with active slumping (e.g. 22 m of coastline retreat and 42 m of coastline progradation). Coastline progradation also resulted from the accumulation of slope collapse material. These occasional events can significantly affect the coastline position on a specific date and can affect coastal retreat rates as estimated in long term by coastline digitalisation from air photos and satellite imagery. These deficiencies can be overcome by short-term airborne LiDAR measurements, which provide detailed and high-resolution information about quickly changing elevations in coastal areas.

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“…Applications of remote sensing have proved particularly effective in the delineation of coastal configuration, coastal landforms, and landform changes (Ryu et al, 2002;Maiti and Bhattacharya, 2009) but temporal frequency of images acquisition and/or spatial resolution of images can be limiting. Other techniques like Light Detection And Ranging (LiDAR) or Terrestrial Laser Scanning (TLS) can provide very high resolution and accurate data but are typically expensive (White and Wang, 2003;Nagihara et al, 2004;Letortu et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Accuracy Assessment of Coastal Topography Derived From Uav Images

Long

Millescamps

Pouget

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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To monitor coastal environments, Unmanned Aerial Vehicle (UAV) is a low-cost and easy to use solution to enable data acquisition with high temporal frequency and spatial resolution. Compared to Light Detection And Ranging (LiDAR) or Terrestrial Laser Scanning (TLS), this solution produces Digital Surface Model (DSM) with a similar accuracy. To evaluate the DSM accuracy on a coastal environment, a campaign was carried out with a flying wing (eBee) combined with a digital camera. Using the Photoscan software and the photogrammetry process (Structure From Motion algorithm), a DSM and an orthomosaic were produced. Compared to GNSS surveys, the DSM accuracy is estimated. Two parameters are tested: the influence of the methodology (number and distribution of Ground Control Points, GCPs) and the influence of spatial image resolution (4.6 cm vs 2 cm). The results show that this solution is able to reproduce the topography of a coastal area with a high vertical accuracy (< 10 cm). The georeferencing of the DSM require a homogeneous distribution and a large number of GCPs. The accuracy is correlated with the number of GCPs (use 19 GCPs instead of 10 allows to reduce the difference of 4 cm); the required accuracy should be dependant of the research problematic. Last, in this particular environment, the presence of very small water surfaces on the sand bank does not allow to improve the accuracy when the spatial resolution of images is decreased.

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Utilizing DEMs derived from LIDAR data to analyze morphologic change in the North Carolina coastline

Cited by 173 publications

References 5 publications

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery

Coastal erosion and mass wasting along the Canadian Beaufort Sea based on annual airborne LiDAR elevation data

Accuracy Assessment of Coastal Topography Derived From Uav Images

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