Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform

Climate-smart forestry is a sustainable forest management approach for increasing positive climate impacts on society. As climate-smart forestry is focusing on more sustainable solutions that are resource-efficient and circular, digitalization plays an important role in its implementation. The artic...

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Autor principal:	Mihai Daniel Niță
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	digital twinning climate smart LiDAR artificial intelligence digitalization Plant ecology QK900-989
Acceso en línea:	https://doaj.org/article/b43a420dc6e244918086303159d65227
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spelling	oai:doaj.org-article:b43a420dc6e244918086303159d652272021-11-25T17:39:00ZTesting Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform10.3390/f121115761999-4907https://doaj.org/article/b43a420dc6e244918086303159d652272021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4907/12/11/1576https://doaj.org/toc/1999-4907Climate-smart forestry is a sustainable forest management approach for increasing positive climate impacts on society. As climate-smart forestry is focusing on more sustainable solutions that are resource-efficient and circular, digitalization plays an important role in its implementation. The article aimed to validate an automatic workflow of processing 3D pointclouds to produce digital twins for every tree on large 1-ha sample plots using a GeoSLAM mobile LiDAR scanner and VirtSilv AI platform. Specific objectives were to test the efficiency of segmentation technique developed in the platform for individual trees from an initial cloud of 3D points observed in the field and to quantify the efficiency of digital twinning by comparing the automatically generated results of (DBH, H, and Volume) with traditional measurements. A number of 1399 trees were scanned with LiDAR to create digital twins and, for validation, were measured with traditional tools such as forest tape and vertex. The segmentation algorithm developed in the platform to extract individual 3D trees recorded an accuracy varying between 95 and 98%. This result was higher in accuracy than reported by other solutions. When compared to traditional measurements the bias for diameter at breast height (DBH) and height was not significant. Digital twinning offers a blockchain solution for digitalization, and AI platforms are able to provide technological advantage in preserving and restoring biodiversity with sustainable forest management.Mihai Daniel NițăMDPI AGarticledigital twinningclimate smartLiDARartificial intelligencedigitalizationPlant ecologyQK900-989ENForests, Vol 12, Iss 1576, p 1576 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	digital twinning climate smart LiDAR artificial intelligence digitalization Plant ecology QK900-989
spellingShingle	digital twinning climate smart LiDAR artificial intelligence digitalization Plant ecology QK900-989 Mihai Daniel Niță Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform
description	Climate-smart forestry is a sustainable forest management approach for increasing positive climate impacts on society. As climate-smart forestry is focusing on more sustainable solutions that are resource-efficient and circular, digitalization plays an important role in its implementation. The article aimed to validate an automatic workflow of processing 3D pointclouds to produce digital twins for every tree on large 1-ha sample plots using a GeoSLAM mobile LiDAR scanner and VirtSilv AI platform. Specific objectives were to test the efficiency of segmentation technique developed in the platform for individual trees from an initial cloud of 3D points observed in the field and to quantify the efficiency of digital twinning by comparing the automatically generated results of (DBH, H, and Volume) with traditional measurements. A number of 1399 trees were scanned with LiDAR to create digital twins and, for validation, were measured with traditional tools such as forest tape and vertex. The segmentation algorithm developed in the platform to extract individual 3D trees recorded an accuracy varying between 95 and 98%. This result was higher in accuracy than reported by other solutions. When compared to traditional measurements the bias for diameter at breast height (DBH) and height was not significant. Digital twinning offers a blockchain solution for digitalization, and AI platforms are able to provide technological advantage in preserving and restoring biodiversity with sustainable forest management.
format	article
author	Mihai Daniel Niță
author_facet	Mihai Daniel Niță
author_sort	Mihai Daniel Niță
title	Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform
title_short	Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform
title_full	Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform
title_fullStr	Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform
title_full_unstemmed	Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform
title_sort	testing forestry digital twinning workflow based on mobile lidar scanner and ai platform
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b43a420dc6e244918086303159d65227
work_keys_str_mv	AT mihaidanielnita testingforestrydigitaltwinningworkflowbasedonmobilelidarscannerandaiplatform
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Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform

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