Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes
High resolution terrestrial laser scanning data (TLS; terrestrial LiDAR) provide an excellent background for quantitative resource estimation through the comparative analysis of topographic surface changes. However, unlike airborne LiDAR data, which is usually provided as classified and contains a c...
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ftmdpi:oai:mdpi.com:/2076-3417/10/21/7409/ 2023-08-20T04:10:05+02:00 Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes Waldemar Kociuba agris 2020-10-22 application/pdf https://doi.org/10.3390/app10217409 EN eng Multidisciplinary Digital Publishing Institute Earth Sciences and Geography https://dx.doi.org/10.3390/app10217409 https://creativecommons.org/licenses/by/4.0/ Applied Sciences; Volume 10; Issue 21; Pages: 7409 terrestrial laser scanning DEM of difference Cloth Simulation Filter ground point classification bulk rock density mineral resource estimation Text 2020 ftmdpi https://doi.org/10.3390/app10217409 2023-08-01T00:19:50Z High resolution terrestrial laser scanning data (TLS; terrestrial LiDAR) provide an excellent background for quantitative resource estimation through the comparative analysis of topographic surface changes. However, unlike airborne LiDAR data, which is usually provided as classified and contains a class of ground points, raw TLS data include all of the points of the scanned space within the specified scanner range. In effect, utilizing the latter data to estimate the volume of the resource by the differential analysis of digital elevation models (DEMs) requires the data to be specially prepared, i.e., separating from the point cloud only the data that represent the relevant class. In the case of natural resources, e.g., mineral resources, the class is represented by ground points. This paper presents the results that were obtained by differential analysis of high resolution DEMs (DEM of difference (DoD) method) using TLS data that were processed both manually (operator noise removal) and with the use of the automatic Cloth Simulation Filter (CSF) algorithm. Three different time pairs of DoD data were analyzed for a potential gravel-cobble deposit area of 45,444 m2, which was located at the bottom of the mouth section of the Scott River in south-east Svalbard. It was found that the applied method of ground point classification had very little influence on the errors in the range of estimating volumetric parameters of the mineral resources and measurement uncertainty. Moreover, it was shown that the point cloud density had an influence on the CSF filtering efficiency and spatial distribution of errors. Text Svalbard MDPI Open Access Publishing Scott River ENVELOPE(-103.284,-103.284,56.267,56.267) Svalbard Applied Sciences 10 21 7409 |
institution |
Open Polar |
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MDPI Open Access Publishing |
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ftmdpi |
language |
English |
topic |
terrestrial laser scanning DEM of difference Cloth Simulation Filter ground point classification bulk rock density mineral resource estimation |
spellingShingle |
terrestrial laser scanning DEM of difference Cloth Simulation Filter ground point classification bulk rock density mineral resource estimation Waldemar Kociuba Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes |
topic_facet |
terrestrial laser scanning DEM of difference Cloth Simulation Filter ground point classification bulk rock density mineral resource estimation |
description |
High resolution terrestrial laser scanning data (TLS; terrestrial LiDAR) provide an excellent background for quantitative resource estimation through the comparative analysis of topographic surface changes. However, unlike airborne LiDAR data, which is usually provided as classified and contains a class of ground points, raw TLS data include all of the points of the scanned space within the specified scanner range. In effect, utilizing the latter data to estimate the volume of the resource by the differential analysis of digital elevation models (DEMs) requires the data to be specially prepared, i.e., separating from the point cloud only the data that represent the relevant class. In the case of natural resources, e.g., mineral resources, the class is represented by ground points. This paper presents the results that were obtained by differential analysis of high resolution DEMs (DEM of difference (DoD) method) using TLS data that were processed both manually (operator noise removal) and with the use of the automatic Cloth Simulation Filter (CSF) algorithm. Three different time pairs of DoD data were analyzed for a potential gravel-cobble deposit area of 45,444 m2, which was located at the bottom of the mouth section of the Scott River in south-east Svalbard. It was found that the applied method of ground point classification had very little influence on the errors in the range of estimating volumetric parameters of the mineral resources and measurement uncertainty. Moreover, it was shown that the point cloud density had an influence on the CSF filtering efficiency and spatial distribution of errors. |
format |
Text |
author |
Waldemar Kociuba |
author_facet |
Waldemar Kociuba |
author_sort |
Waldemar Kociuba |
title |
Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes |
title_short |
Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes |
title_full |
Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes |
title_fullStr |
Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes |
title_full_unstemmed |
Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes |
title_sort |
different paths for developing terrestrial lidar data for comparative analyses of topographic surface changes |
publisher |
Multidisciplinary Digital Publishing Institute |
publishDate |
2020 |
url |
https://doi.org/10.3390/app10217409 |
op_coverage |
agris |
long_lat |
ENVELOPE(-103.284,-103.284,56.267,56.267) |
geographic |
Scott River Svalbard |
geographic_facet |
Scott River Svalbard |
genre |
Svalbard |
genre_facet |
Svalbard |
op_source |
Applied Sciences; Volume 10; Issue 21; Pages: 7409 |
op_relation |
Earth Sciences and Geography https://dx.doi.org/10.3390/app10217409 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3390/app10217409 |
container_title |
Applied Sciences |
container_volume |
10 |
container_issue |
21 |
container_start_page |
7409 |
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1774723998346641408 |