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Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling
Tree structure parameters of mangrove forests are hard to measure in the field and therefore inventories of this type of forests are impossible to keep up to date. In this article, we tested a structured pointcloud segmentation method for extracting individual mangrove trees. Structure parameters of...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297589/ http://dx.doi.org/10.1007/978-3-030-49076-8_17 |
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author | Silván-Cárdenas, José L. Gallardo-Cruz, José A. Hernández-Huerta, Laura M. |
author_facet | Silván-Cárdenas, José L. Gallardo-Cruz, José A. Hernández-Huerta, Laura M. |
author_sort | Silván-Cárdenas, José L. |
collection | PubMed |
description | Tree structure parameters of mangrove forests are hard to measure in the field and therefore inventories of this type of forests are impossible to keep up to date. In this article, we tested a structured pointcloud segmentation method for extracting individual mangrove trees. Structure parameters of individual trees were estimated from the segmented pointcloud and its 3d geometry was generated using revolution surfaces. Estimated parameters were then assessed at both plot and tree levels using field data. It was observed that the number of segments in each test plot agreed well with the number of trees observed in the field. Nonetheless, the estimated parameters exhibited mixed accuracy with top height being the most accurate. |
format | Online Article Text |
id | pubmed-7297589 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72975892020-06-17 Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling Silván-Cárdenas, José L. Gallardo-Cruz, José A. Hernández-Huerta, Laura M. Pattern Recognition Article Tree structure parameters of mangrove forests are hard to measure in the field and therefore inventories of this type of forests are impossible to keep up to date. In this article, we tested a structured pointcloud segmentation method for extracting individual mangrove trees. Structure parameters of individual trees were estimated from the segmented pointcloud and its 3d geometry was generated using revolution surfaces. Estimated parameters were then assessed at both plot and tree levels using field data. It was observed that the number of segments in each test plot agreed well with the number of trees observed in the field. Nonetheless, the estimated parameters exhibited mixed accuracy with top height being the most accurate. 2020-04-29 /pmc/articles/PMC7297589/ http://dx.doi.org/10.1007/978-3-030-49076-8_17 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Silván-Cárdenas, José L. Gallardo-Cruz, José A. Hernández-Huerta, Laura M. Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling |
title | Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling |
title_full | Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling |
title_fullStr | Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling |
title_full_unstemmed | Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling |
title_short | Structured Pointcloud Segmentation for Individual Mangrove Tree Modeling |
title_sort | structured pointcloud segmentation for individual mangrove tree modeling |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7297589/ http://dx.doi.org/10.1007/978-3-030-49076-8_17 |
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