The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level
In this paper, we present an identification of rockfall-injured trees based on multiband images obtained by an unmanned aerial vehicle (UAV). A survey with a multispectral camera was performed on three rockfall sites with versatile tree species (<i>Fagus sylvatica</i> L., <i>Larix...
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MDPI AG
2022-07-01
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| Series: | Forests |
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| Online Access: | https://www.mdpi.com/1999-4907/13/7/1039 |
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| author | Barbara Žabota Milan Kobal |
| author_facet | Barbara Žabota Milan Kobal |
| author_sort | Barbara Žabota |
| collection | DOAJ |
| description | In this paper, we present an identification of rockfall-injured trees based on multiband images obtained by an unmanned aerial vehicle (UAV). A survey with a multispectral camera was performed on three rockfall sites with versatile tree species (<i>Fagus sylvatica</i> L., <i>Larix decidua</i> Mill., <i>Pinus sylvestris</i> L., <i>Picea abies</i> (L.) Karsten, and <i>Abies alba</i> Mill.) and with different characterizations of rockfalls and rockfall-induced injuries. At one site, rockfall injuries were induced in the same year as the survey. At the second site, they were induced one year after the initial injuries, and at the third site, they were induced six years after the first injuries. At one site, surveys were performed three years in a row. Multiband images were used to extract different vegetation indices (VIs) at the tree crown level and were further studied to see which VIs can identify the injured trees and how successfully. A total of 14 VIs were considered, including individual multispectral bands (green, red, red edge, and near-infrared) by using regression models to differentiate between the injured and uninjured groups for a single year and for three consecutive years. The same model was also used for VI differentiations among the recorded injury groups and size of the injuries. The identification of injured trees based on VIs was possible at the sites where rockfall injuries were induced at least one year before the UAV survey, and they could still be identifiable six years after the initial injuries. At the site where injuries were induced only four months before the UAV survey, the identification of injured trees was not possible. VIs that could explain the largest variability (R<sup>2</sup> > 0.3) between injured and uninjured trees were: inverse ratio index (IRVI), green–red vegetation index (GRVI), normalized difference vegetation index (NDVI), normalized ratio index (NRVI), and ratio vegetation index (RVI). RVI was the most successful, explaining 40% of the variance at two sites. R<sup>2</sup> values only increased by a few percentages (up to 10%) when the VIs of injured trees were observed over a period of three years and mostly did not change significantly, thus not indicating if the vitality of the trees increased or decreased. Differentiation among the injured groups did not show promising results, while, on the other hand, there was a strong correlation between the VI values (RVI) and the size of the injury according to the basal area of the trees (so-called injury index). Both in the case of broadleaves and conifers at two sites, the R<sup>2</sup> achieved a value of 0.82. The presented results indicate that the UAV-acquired multiband images at the tree crown level can be used for surveying rockfall protection forests in order to monitor their vitality, which is crucial for maintaining the protective effect through time and space. |
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| language | English |
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| spelling | doaj.art-2a4fae5575354f49a5af2ce7925005102023-12-03T15:03:07ZengMDPI AGForests1999-49072022-07-01137103910.3390/f13071039The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown LevelBarbara Žabota0Milan Kobal1Department of Forestry and Forest Renewable Resources, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, SloveniaDepartment of Forestry and Forest Renewable Resources, Biotechnical Faculty, University of Ljubljana, 1000 Ljubljana, SloveniaIn this paper, we present an identification of rockfall-injured trees based on multiband images obtained by an unmanned aerial vehicle (UAV). A survey with a multispectral camera was performed on three rockfall sites with versatile tree species (<i>Fagus sylvatica</i> L., <i>Larix decidua</i> Mill., <i>Pinus sylvestris</i> L., <i>Picea abies</i> (L.) Karsten, and <i>Abies alba</i> Mill.) and with different characterizations of rockfalls and rockfall-induced injuries. At one site, rockfall injuries were induced in the same year as the survey. At the second site, they were induced one year after the initial injuries, and at the third site, they were induced six years after the first injuries. At one site, surveys were performed three years in a row. Multiband images were used to extract different vegetation indices (VIs) at the tree crown level and were further studied to see which VIs can identify the injured trees and how successfully. A total of 14 VIs were considered, including individual multispectral bands (green, red, red edge, and near-infrared) by using regression models to differentiate between the injured and uninjured groups for a single year and for three consecutive years. The same model was also used for VI differentiations among the recorded injury groups and size of the injuries. The identification of injured trees based on VIs was possible at the sites where rockfall injuries were induced at least one year before the UAV survey, and they could still be identifiable six years after the initial injuries. At the site where injuries were induced only four months before the UAV survey, the identification of injured trees was not possible. VIs that could explain the largest variability (R<sup>2</sup> > 0.3) between injured and uninjured trees were: inverse ratio index (IRVI), green–red vegetation index (GRVI), normalized difference vegetation index (NDVI), normalized ratio index (NRVI), and ratio vegetation index (RVI). RVI was the most successful, explaining 40% of the variance at two sites. R<sup>2</sup> values only increased by a few percentages (up to 10%) when the VIs of injured trees were observed over a period of three years and mostly did not change significantly, thus not indicating if the vitality of the trees increased or decreased. Differentiation among the injured groups did not show promising results, while, on the other hand, there was a strong correlation between the VI values (RVI) and the size of the injury according to the basal area of the trees (so-called injury index). Both in the case of broadleaves and conifers at two sites, the R<sup>2</sup> achieved a value of 0.82. The presented results indicate that the UAV-acquired multiband images at the tree crown level can be used for surveying rockfall protection forests in order to monitor their vitality, which is crucial for maintaining the protective effect through time and space.https://www.mdpi.com/1999-4907/13/7/1039UAVmultispectral imageryrockfallsmonitoringforestsprotection function |
| spellingShingle | Barbara Žabota Milan Kobal The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level Forests UAV multispectral imagery rockfalls monitoring forests protection function |
| title | The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level |
| title_full | The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level |
| title_fullStr | The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level |
| title_full_unstemmed | The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level |
| title_short | The Use of UAV-Acquired Multiband Images for Detecting Rockfall-Induced Injuries at Tree Crown Level |
| title_sort | use of uav acquired multiband images for detecting rockfall induced injuries at tree crown level |
| topic | UAV multispectral imagery rockfalls monitoring forests protection function |
| url | https://www.mdpi.com/1999-4907/13/7/1039 |
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