Early Detection of Pine Wilt Disease by Combining Pigment and Moisture Content Indices Using UAV-Based Hyperspectral Imagery Article Swipe
Rui Li
,
Biyao Zhang
,
Guofei Fang
,
Sihan Yang
,
Lei Guo
,
Wenjiang Huang
,
Jing Yao
,
Quanjun Jiao
,
Hong Sun
,
Jiayu Yan
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17111833
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/rs17111833
Pine wilt disease (PWD) is characterized by rapid transmission, high mortality rates, and difficulty in control, resulting in severe and destructive impacts on both the ecological environment and socioeconomic development in China. Due to the lack of significant symptoms in infected trees during the early stages of the disease, improving the accuracy of early detection has become a major challenge in PWD monitoring. In recent years, the rapid advancement of UAV-based hyperspectral remote sensing technology has provided a promising approach for the early detection of PWD. In this study, we selected classic canopy pigment and moisture content indices to construct a set of recognition indicators. The optimal canopy pigment index (CI) and canopy moisture content index (WASCOSBNDI) were then chosen through significance testing and derivative analysis. Based on the asynchronous variations in canopy moisture and pigment content during the development of PWD, the CI, WASCOSBNDI, and CI-WASCOSBNDI models were developed using a multi-threshold segmentation method to identify trees at different stages of infection. The results demonstrate that the CI-WASCOSBNDI model achieved the highest accuracy in detecting infection stages, with an overall classification accuracy of 92.78%. In comparison, the CI and WASCOSBNDI models achieved classification accuracies of 81.34% and 89.84%, respectively. For the early stage infected trees, which are the primary focus of this study, the CI-WASCOSBNDI model exhibited the best performance with an accuracy rate exceeding 70%, significantly outperforming the other models. Furthermore, the timing of infection in early stage trees significantly influenced the model’s detection accuracy, with trees closer to the disease outbreak period being more easily identified. These findings provide a reference for the accurate early monitoring of PWD using UAV hyperspectral imagery.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/rs17111833
- OA Status
- gold
- References
- 44
- Related Works
- 10
- OpenAlex ID
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- OpenAlex ID
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https://openalex.org/W4410630501Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/rs17111833Digital Object Identifier
- Title
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Early Detection of Pine Wilt Disease by Combining Pigment and Moisture Content Indices Using UAV-Based Hyperspectral ImageryWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-23Full publication date if available
- Authors
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Rui Li, Biyao Zhang, Guofei Fang, Sihan Yang, Lei Guo, Wenjiang Huang, Jing Yao, Quanjun Jiao, Hong Sun, Jiayu YanList of authors in order
- Landing page
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https://doi.org/10.3390/rs17111833Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.3390/rs17111833Direct OA link when available
- Concepts
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Hyperspectral imaging, Environmental science, Remote sensing, Water content, Wilt disease, Horticulture, Geology, Biology, Geotechnical engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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