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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ftdoajarticles:oai:doaj.org/article:e2009b391ed343d1807d149e2339f172 2023-05-15T18:29:51+02:00 Different Paths for Developing Terrestrial LiDAR Data for Comparative Analyses of Topographic Surface Changes Waldemar Kociuba 2020-10-01T00:00:00Z https://doi.org/10.3390/app10217409 https://doaj.org/article/e2009b391ed343d1807d149e2339f172 EN eng MDPI AG https://www.mdpi.com/2076-3417/10/21/7409 https://doaj.org/toc/2076-3417 doi:10.3390/app10217409 2076-3417 https://doaj.org/article/e2009b391ed343d1807d149e2339f172 Applied Sciences, Vol 10, Iss 7409, p 7409 (2020) terrestrial laser scanning DEM of difference Cloth Simulation Filter ground point classification bulk rock density mineral resource estimation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 article 2020 ftdoajarticles https://doi.org/10.3390/app10217409 2022-12-31T08:28:57Z 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 m 2 , 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. Article in Journal/Newspaper Svalbard Directory of Open Access Journals: DOAJ Articles Scott River ENVELOPE(-103.284,-103.284,56.267,56.267) Svalbard Applied Sciences 10 21 7409 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
terrestrial laser scanning DEM of difference Cloth Simulation Filter ground point classification bulk rock density mineral resource estimation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
spellingShingle |
terrestrial laser scanning DEM of difference Cloth Simulation Filter ground point classification bulk rock density mineral resource estimation Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 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 Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 |
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 m 2 , 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 |
Article in Journal/Newspaper |
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 |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/app10217409 https://doaj.org/article/e2009b391ed343d1807d149e2339f172 |
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, Vol 10, Iss 7409, p 7409 (2020) |
op_relation |
https://www.mdpi.com/2076-3417/10/21/7409 https://doaj.org/toc/2076-3417 doi:10.3390/app10217409 2076-3417 https://doaj.org/article/e2009b391ed343d1807d149e2339f172 |
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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1766213270683779072 |