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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/12/11/1576 |
| _version_ | 1797510244089200640 |
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| author | Mihai Daniel Niță |
| author_facet | Mihai Daniel Niță |
| author_sort | Mihai Daniel Niță |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T05:28:50Z |
| format | Article |
| id | doaj.art-b43a420dc6e244918086303159d65227 |
| institution | Directory Open Access Journal |
| issn | 1999-4907 |
| language | English |
| last_indexed | 2024-03-10T05:28:50Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forests |
| spelling | doaj.art-b43a420dc6e244918086303159d652272023-11-22T23:25:45ZengMDPI AGForests1999-49072021-11-011211157610.3390/f12111576Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI PlatformMihai Daniel Niță0Faculty of Silviculture and Forest Engineering, Transilvania University of Brașov, 500036 Brașov, RomaniaClimate-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.https://www.mdpi.com/1999-4907/12/11/1576digital twinningclimate smartLiDARartificial intelligencedigitalization |
| spellingShingle | Mihai Daniel Niță Testing Forestry Digital Twinning Workflow Based on Mobile LiDAR Scanner and AI Platform Forests digital twinning climate smart LiDAR artificial intelligence digitalization |
| title | 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_short | 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 |
| topic | digital twinning climate smart LiDAR artificial intelligence digitalization |
| url | https://www.mdpi.com/1999-4907/12/11/1576 |
| work_keys_str_mv | AT mihaidanielnita testingforestrydigitaltwinningworkflowbasedonmobilelidarscannerandaiplatform |