EVALUATION OF LIGHT DETECTION AND RANGING (LIDAR) FOR MEASURING RIVER CORRIDOR TOPOGRAPHY<sup>1</sup>

Bowen, Zachary H.; Waltermire, Robert G.

doi:10.1111/j.1752-1688.2002.tb01532.x

Cited by 101 publications

(57 citation statements)

References 8 publications

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“…More recently, its potential in fluvial geomorphology [21,22], floodplain mapping [16], soil science [23], and wetland mapping [24][25][26][27] have begun to be explored. However, within the coastal marsh environment, the ability of TLS systems to resolve centimeter-level elevation differences between the vegetation and the bare earth surface is limited [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

et al. 2018

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Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the "k" clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point-and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes.

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Section: Introductionmentioning

confidence: 99%

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

et al. 2018

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“…Bowen and Waltermire (2002) documented lidar overestimation of bare-ground elevation in wetlands was most pronounced in areas of dense riparian vegetation with steep bank slopes. Toyra and others (2003) evaluated lidar technology for hydrological applications in wetlands in northeastern Alberta and observed that lidar data did not reflect accurate ground elevations in areas with thick willow cover and dense layers of graminoids (grasses and sedges), even after the bare-ground lidar elevations were adjusted for bias with a block adjustment.…”

Section: Light Detection and Ranging Data Quality Control And Adjustmentmentioning

confidence: 98%

Hydrologic and hydraulic analyses of Great Meadow wetland, Acadia National Park, Maine

Lombard¹

2017

Scientific Investigations Report

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For more information on the USGS-the Federal source for science about the Earth, its natural and living resources, natural hazards, and the environment-visit http://www.usgs.gov or call 1-888-ASK-USGS.For an overview of USGS information products, including maps, imagery, and publications, visit http://store.usgs.gov.Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.Suggested citation: Lombard, P.J., 2017, Hydrologic and hydraulic analyses of Great Meadow wetland, Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2016-5159, 39 p., https://doi.org/10.3133/sir20165159. ISSN 2328-0328 (online) iii Acknowledgments I wish to thank Bill Gawley and Bruce Connery of Acadia National Park for sharing their extensive knowledge of the Cromwell Brook watershed; Brian Henkel, the Wild Acadia Project Coordinator for sharing his photos and observations from rainstorms in the watershed; and David Perconti, Course Superintendent at Kebo Valley Golf Club, for granting access and providing invaluable information on beaver activity and historic floods within the study watershed.Finally, I would like to thank Terrence Talbot, Jeffrey Kinsey, and Sean Andrews of the U.S. Geological Survey for their work collecting field data, and Luke Sturtevant of the U.S. Geological Survey for assistance in geographic information system work and map preparation. AbstractThe U.S. Geological Survey completed hydrologic and hydraulic analyses of Cromwell Brook and the Sieur de Monts tributary in Acadia National Park, Maine, to better understand causes of flooding in complex hydrologic and hydraulic environments, like those in the Great Meadow wetland and Sieur de Monts Spring area. Regional regression equations were used to compute peak flows with from 2 to 100-year recurrence intervals at seven locations. Light detection and ranging data were adjusted for bias caused by dense vegetation in the Great Meadow wetland; and then combined with local ground surveys used to define the underwater topography and hydraulic structures in the study area. Hydraulic modeling was used to evaluate flood response in the study area to a variety of hydrologic and hydraulic scenarios.Hydraulic modeling indicates that enlarging the culvert at Park Loop Road could help mitigate flooding near the Sieur de Monts Nature Center that is caused by streamflows with large recurrence intervals; however, hydraulic modeling also indicates that the Park Loop Road culvert does not aggravate flooding near the Nature Center caused by the more frequent high intensity rainstorms. That flooding is likely associated with overland flow resulting from (1) quick runoff from the steep Dorr Mountain hitting the lower gradient Great Meadow wetland area and (...

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“…thin shrub/ scrub lands and mixed vegetation types), and very short-statured (i.e. grasslands), lidar systems may not be able to detect centimeter-level height differences between vegetation and ground surface (Bowen & Waltermire 2002) and may decrease accuracies of lidar derived DEMs (Hodgson & Bresnahan 2004, Reutebuch et al 2003. However, studies in short stature environments have reported variable effects of vegetation while some studies indicated minimum or no significant vegetation effects (Montane & Torres 2006) on the accuracies of terrain estimates using lidar.…”

Section: Effects Of Vegetation On Laser Scanning and Lidar Data Effecmentioning

confidence: 99%

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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EVALUATION OF LIGHT DETECTION AND RANGING (LIDAR) FOR MEASURING RIVER CORRIDOR TOPOGRAPHY¹

Cited by 101 publications

References 8 publications

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Hydrologic and hydraulic analyses of Great Meadow wetland, Acadia National Park, Maine

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

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EVALUATION OF LIGHT DETECTION AND RANGING (LIDAR) FOR MEASURING RIVER CORRIDOR TOPOGRAPHY1

Cited by 101 publications

References 8 publications

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

Hydrologic and hydraulic analyses of Great Meadow wetland, Acadia National Park, Maine

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

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EVALUATION OF LIGHT DETECTION AND RANGING (LIDAR) FOR MEASURING RIVER CORRIDOR TOPOGRAPHY¹