Raster-Based Analysis of Coastal Terrain Dynamics from Multitemporal Lidar Data

Mitášová, Helena; Overton, Margery F.; Recalde, J. J.; Bernstein, David J.; Freeman, C. W.

doi:10.2112/07-0976.1

Cited by 66 publications

(57 citation statements)

References 7 publications

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“…Previous studies of barrier island geomorphology have delineated shorelines and dunes by digitizing aerial photographs [8,31,32] or subtracting DEMs [2,9,17,19,23,33]. Others have developed automated processes to map shoreline positions from DEMs [34] and used Lidar to compute beach profiles [35], but these studies did not map all barrier island features.…”

Section: Discussionmentioning

confidence: 99%

“…Coastal morphology has been studied using Lidar and other remotely sensed data [1,2,8,[15][16][17][18][19][20]; however, these studies have mapped individual features such as shorelines, dunes, and marshes. To our knowledge, there has been little research addressing a comprehensive mapping method for classifying all geomorphic features on a barrier island, from the ocean to the sound.…”

Section: Introductionmentioning

confidence: 99%

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An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

2018

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Abstract:Previous research has documented the usefulness of Lidar data to derive a variety of topographic products (e.g., DEM, DTM, canopy and forest structure, and urban infrastructure). Lidar has been used to map coastal environments and geomorphology; however, there is no comprehensive model to derive coastal geomorphology. Therefore, the purpose of this project was to build on existing research and develop an automated modeling approach to classify coastal geomorphology across barrier islands. The model was developed and tested at four sites in North Carolina including two undeveloped and two developed islands. Barrier island geomorphology is shaped by natural coastal processes, such as storms and longshore sediment transport, as well as human influences, such as beach nourishment and urban development. The model was developed to classify ten geomorphic features over four time-steps from 1998 to 2014. Model results were compared to compute change through time and derived the rate and direction of feature movement. Tropical storms and hurricanes had the most influence in geomorphic change and movement. On the developed islands, there was less influence of storms due to the inability of features to move because of coastal infrastructure. From 2005 to 2010, beach nourishment was the dominant influence on developed beaches because this activity ameliorated the natural tendency for an island to erode. Understanding how natural and anthropogenic processes influence barrier island geomorphology is critical to predicting an island's future response to changing environmental factors such as sea-level rise. The development of an automated model enables it to be replicated in other locations where policy makers and coastal managers may use this information to make development and conservation decisions.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

2018

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“…Furthermore, UAS could enhance models of coastal change through temporally frequent surveys to capture short-term changes related to storm impact and recovery, similar to previous work using stereophotogrammetry and lidar for change assessments [8]. Although the datasets are highly accurate and precise, using them for change assessment will require rigorous calibration between the datasets, such as those performed for lidar data [8,52].…”

Section: Advantages Trade-offs and Limitationsmentioning

confidence: 96%

“…Previous works have demonstrated the high quality of elevation data produced by UAS-SfM (e.g., [18,[49][50][51][52]). We confirm these findings and build on the applicability of these datasets to coastal assessment.…”

Section: Uas-sfm Elevation For Coastal Zonesmentioning

confidence: 99%

UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

et al. 2017

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Abstract:The vulnerability of coastal systems to hazards such as storms and sea-level rise is typically characterized using a combination of ground and manned airborne systems that have limited spatial or temporal scales. Structure-from-motion (SfM) photogrammetry applied to imagery acquired by unmanned aerial systems (UAS) offers a rapid and inexpensive means to produce high-resolution topographic and visual reflectance datasets that rival existing lidar and imagery standards. Here, we use SfM to produce an elevation point cloud, an orthomosaic, and a digital elevation model (DEM) from data collected by UAS at a beach and wetland site in Massachusetts, USA. We apply existing methods to (a) determine the position of shorelines and foredunes using a feature extraction routine developed for lidar point clouds and (b) map land cover from the rasterized surfaces using a supervised classification routine. In both analyses, we experimentally vary the input datasets to understand the benefits and limitations of UAS-SfM for coastal vulnerability assessment. We find that (a) geomorphic features are extracted from the SfM point cloud with near-continuous coverage and sub-meter precision, better than was possible from a recent lidar dataset covering the same area; and (b) land cover classification is greatly improved by including topographic data with visual reflectance, but changes to resolution (when <50 cm) have little influence on the classification accuracy.

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“…For example, 3D mapping of the shoreline could help to determine the volume of organic material (leaves) falling in a lake, accurately identify inflows of water, quantify coastal erosion, etc. The observation of coastal erosion using rangefinder laser is already an active field in geology [Mitasova et al, 2009], relying mainly on airborne surveys. However, this survey method can not provide a good viewpoint of a cliff, and the deployment costs are quite high.…”

Section: Shoreline Monitoringmentioning

confidence: 99%

A Review of Point Cloud Registration Algorithms for Mobile Robotics

Pomerleau

Colas²,

Siegwart³

2015

FNT in Robotics

579

241

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Raster-Based Analysis of Coastal Terrain Dynamics from Multitemporal Lidar Data

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 66 publications

References 7 publications

An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

An Automated Model to Classify Barrier Island Geomorphology Using Lidar Data and Change Analysis (1998–2014)

UAS-SfM for Coastal Research: Geomorphic Feature Extraction and Land Cover Classification from High-Resolution Elevation and Optical Imagery

A Review of Point Cloud Registration Algorithms for Mobile Robotics

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