Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images
Multispectral imagery from unmanned aerial vehicles (UAVs) can provide high-resolution data to map tree mortality caused by pests or diseases. Although many studies have investigated UAV-imagery-based methods to detect trees under acute stress followed by tree mortality, few have tested the feasibil...
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/271 |
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| author | Langning Huo Iryna Matsiakh Jonas Bohlin Michelle Cleary |
| author_facet | Langning Huo Iryna Matsiakh Jonas Bohlin Michelle Cleary |
| author_sort | Langning Huo |
| collection | DOAJ |
| description | Multispectral imagery from unmanned aerial vehicles (UAVs) can provide high-resolution data to map tree mortality caused by pests or diseases. Although many studies have investigated UAV-imagery-based methods to detect trees under acute stress followed by tree mortality, few have tested the feasibility and accuracy of detecting trees under chronic stress. This study aims to develop methods and test how well UAV-based multispectral imagery can detect pine needle disease long before tree mortality. Multispectral images were acquired four times through the growing season in an area with pine trees infected by needle pathogens. Vegetation indices (VIs) were used to quantify the decline in vitality, which was verified by tree needle retention (%) estimated from the ground. Results showed that several VIs had strong correlations with the needle retention level and were used to identify severely defoliated trees (<75% needle retention) with 0.71 overall classification accuracy, while the accuracy of detecting slightly defoliated trees (>75% needle retention) was very low. The results from one study area also implied more defoliation observed from the UAV (top view) than from the ground (bottom view). We conclude that using UAV-based multispectral imagery can efficiently identify severely defoliated trees caused by needle-cast pathogens, thus assisting forest health monitoring. |
| format | Article |
| id | doaj-art-ccc4fdc38a3b4017b759eec0936b82f8 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-ccc4fdc38a3b4017b759eec0936b82f82025-01-24T13:47:57ZengMDPI AGRemote Sensing2072-42922025-01-0117227110.3390/rs17020271Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone ImagesLangning Huo0Iryna Matsiakh1Jonas Bohlin2Michelle Cleary3Department of Forest Resource Management, Swedish University of Agriculture Sciences, 901 83 Umea, SwedenSouthern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, 230 53 Alnarp, SwedenDepartment of Forest Resource Management, Swedish University of Agriculture Sciences, 901 83 Umea, SwedenSouthern Swedish Forest Research Centre, Swedish University of Agricultural Sciences, 230 53 Alnarp, SwedenMultispectral imagery from unmanned aerial vehicles (UAVs) can provide high-resolution data to map tree mortality caused by pests or diseases. Although many studies have investigated UAV-imagery-based methods to detect trees under acute stress followed by tree mortality, few have tested the feasibility and accuracy of detecting trees under chronic stress. This study aims to develop methods and test how well UAV-based multispectral imagery can detect pine needle disease long before tree mortality. Multispectral images were acquired four times through the growing season in an area with pine trees infected by needle pathogens. Vegetation indices (VIs) were used to quantify the decline in vitality, which was verified by tree needle retention (%) estimated from the ground. Results showed that several VIs had strong correlations with the needle retention level and were used to identify severely defoliated trees (<75% needle retention) with 0.71 overall classification accuracy, while the accuracy of detecting slightly defoliated trees (>75% needle retention) was very low. The results from one study area also implied more defoliation observed from the UAV (top view) than from the ground (bottom view). We conclude that using UAV-based multispectral imagery can efficiently identify severely defoliated trees caused by needle-cast pathogens, thus assisting forest health monitoring.https://www.mdpi.com/2072-4292/17/2/271unmanned aerial vehicle (UAV)multispectral imagerypine needle disease<i>Lophodermium</i>forest monitoringsurveillance |
| spellingShingle | Langning Huo Iryna Matsiakh Jonas Bohlin Michelle Cleary Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images Remote Sensing unmanned aerial vehicle (UAV) multispectral imagery pine needle disease <i>Lophodermium</i> forest monitoring surveillance |
| title | Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images |
| title_full | Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images |
| title_fullStr | Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images |
| title_full_unstemmed | Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images |
| title_short | Estimation of Tree Vitality Reduced by Pine Needle Disease Using Multispectral Drone Images |
| title_sort | estimation of tree vitality reduced by pine needle disease using multispectral drone images |
| topic | unmanned aerial vehicle (UAV) multispectral imagery pine needle disease <i>Lophodermium</i> forest monitoring surveillance |
| url | https://www.mdpi.com/2072-4292/17/2/271 |
| work_keys_str_mv | AT langninghuo estimationoftreevitalityreducedbypineneedlediseaseusingmultispectraldroneimages AT irynamatsiakh estimationoftreevitalityreducedbypineneedlediseaseusingmultispectraldroneimages AT jonasbohlin estimationoftreevitalityreducedbypineneedlediseaseusingmultispectraldroneimages AT michellecleary estimationoftreevitalityreducedbypineneedlediseaseusingmultispectraldroneimages |