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Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems

Volumetric models with known biases are shown to provide bounds for the uncertainty in estimations of volume for ecologically interesting objects, observed with a terrestrial laser scanner (TLS) instrument. Bounding cuboids, three-dimensional convex hull polygons, voxels, the Outer Hull Model and Sq...

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Detalles Bibliográficos
Autores principales: Paynter, Ian, Genest, Daniel, Peri, Francesco, Schaaf, Crystal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829184/
https://www.ncbi.nlm.nih.gov/pubmed/29503722
http://dx.doi.org/10.1098/rsfs.2017.0043
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author Paynter, Ian
Genest, Daniel
Peri, Francesco
Schaaf, Crystal
author_facet Paynter, Ian
Genest, Daniel
Peri, Francesco
Schaaf, Crystal
author_sort Paynter, Ian
collection PubMed
description Volumetric models with known biases are shown to provide bounds for the uncertainty in estimations of volume for ecologically interesting objects, observed with a terrestrial laser scanner (TLS) instrument. Bounding cuboids, three-dimensional convex hull polygons, voxels, the Outer Hull Model and Square Based Columns (SBCs) are considered for their ability to estimate the volume of temperate and tropical trees, as well as geomorphological features such as bluffs and saltmarsh creeks. For temperate trees, supplementary geometric models are evaluated for their ability to bound the uncertainty in cylinder-based reconstructions, finding that coarser volumetric methods do not currently constrain volume meaningfully, but may be helpful with further refinement, or in hybridized models. Three-dimensional convex hull polygons consistently overestimate object volume, and SBCs consistently underestimate volume. Voxel estimations vary in their bias, due to the point density of the TLS data, and occlusion, particularly in trees. The response of the models to parametrization is analysed, observing unexpected trends in the SBC estimates for the drumlin dataset. Establishing that this result is due to the resolution of the TLS observations being insufficient to support the resolution of the geometric model, it is suggested that geometric models with predictable outcomes can also highlight data quality issues when they produce illogical results.
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spelling pubmed-58291842018-03-02 Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems Paynter, Ian Genest, Daniel Peri, Francesco Schaaf, Crystal Interface Focus Articles Volumetric models with known biases are shown to provide bounds for the uncertainty in estimations of volume for ecologically interesting objects, observed with a terrestrial laser scanner (TLS) instrument. Bounding cuboids, three-dimensional convex hull polygons, voxels, the Outer Hull Model and Square Based Columns (SBCs) are considered for their ability to estimate the volume of temperate and tropical trees, as well as geomorphological features such as bluffs and saltmarsh creeks. For temperate trees, supplementary geometric models are evaluated for their ability to bound the uncertainty in cylinder-based reconstructions, finding that coarser volumetric methods do not currently constrain volume meaningfully, but may be helpful with further refinement, or in hybridized models. Three-dimensional convex hull polygons consistently overestimate object volume, and SBCs consistently underestimate volume. Voxel estimations vary in their bias, due to the point density of the TLS data, and occlusion, particularly in trees. The response of the models to parametrization is analysed, observing unexpected trends in the SBC estimates for the drumlin dataset. Establishing that this result is due to the resolution of the TLS observations being insufficient to support the resolution of the geometric model, it is suggested that geometric models with predictable outcomes can also highlight data quality issues when they produce illogical results. The Royal Society 2018-04-06 2018-02-16 /pmc/articles/PMC5829184/ /pubmed/29503722 http://dx.doi.org/10.1098/rsfs.2017.0043 Text en © 2018 The Author(s) http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
spellingShingle Articles
Paynter, Ian
Genest, Daniel
Peri, Francesco
Schaaf, Crystal
Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
title Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
title_full Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
title_fullStr Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
title_full_unstemmed Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
title_short Bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
title_sort bounding uncertainty in volumetric geometric models for terrestrial lidar observations of ecosystems
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5829184/
https://www.ncbi.nlm.nih.gov/pubmed/29503722
http://dx.doi.org/10.1098/rsfs.2017.0043
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