Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method
The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as t...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/24/5712 |
| _version_ | 1797379508633862144 |
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| author | Marek Hrdina Peter Surový |
| author_facet | Marek Hrdina Peter Surový |
| author_sort | Marek Hrdina |
| collection | DOAJ |
| description | The health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models. |
| first_indexed | 2024-03-08T20:24:18Z |
| format | Article |
| id | doaj.art-984145c747964a1b92f129f9f975289b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T20:24:18Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-984145c747964a1b92f129f9f975289b2023-12-22T14:39:07ZengMDPI AGRemote Sensing2072-42922023-12-011524571210.3390/rs15245712Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning MethodMarek Hrdina0Peter Surový1Faculty of Forestry and Wood Science, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague, Czech RepublicFaculty of Forestry and Wood Science, Czech University of Life Sciences Prague, Kamýcká 129, 165 21 Prague, Czech RepublicThe health and stability of trees are essential information for the safety of people and property in urban greenery, parks or along roads. The stability of the trees is linked to root stability but essentially also to trunk decay. Currently used internal tree stem decay assessment methods, such as tomography and penetrometry, are reliable but usually time-consuming and unsuitable for large-scale surveys. Therefore, a new method based on close-range remotely sensed data, specifically close-range photogrammetry and iPhone LiDAR, was tested to detect decayed standing tree trunks automatically. The proposed study used the PointNet deep learning algorithm for 3D data classification. It was verified in three different datasets consisting of pure coniferous trees, pure deciduous trees, and mixed data to eliminate the influence of the detectable symptoms for each group and species itself. The mean achieved validation accuracies of the models were 65.5% for Coniferous trees, 58.4% for Deciduous trees and 57.7% for Mixed data classification. The accuracies indicate promising data, which can be either used by practitioners for preliminary surveys or for other researchers to acquire more input data and create more robust classification models.https://www.mdpi.com/2072-4292/15/24/5712close-range photogrammetrymobile laser scanningdeep learningstanding treesclassificationacoustic tomography |
| spellingShingle | Marek Hrdina Peter Surový Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method Remote Sensing close-range photogrammetry mobile laser scanning deep learning standing trees classification acoustic tomography |
| title | Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method |
| title_full | Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method |
| title_fullStr | Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method |
| title_full_unstemmed | Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method |
| title_short | Internal Tree Trunk Decay Detection Using Close-Range Remote Sensing Data and the PointNet Deep Learning Method |
| title_sort | internal tree trunk decay detection using close range remote sensing data and the pointnet deep learning method |
| topic | close-range photogrammetry mobile laser scanning deep learning standing trees classification acoustic tomography |
| url | https://www.mdpi.com/2072-4292/15/24/5712 |
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