Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects
Abstract Pine Wilt Disease (PWD), caused by the pine wood nematode, is a major global forest pathology characterized by the rapid death of pine trees within a span of three months. Forestry management policy requires the eradication of all infected trees during the initial outbreak phase of a diseas...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-10696-6 |
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| author | Xiao Zhang Yingqun Gao Lianjin Fu Yiran Zhang Zeyu Li Qingtai Shu |
| author_facet | Xiao Zhang Yingqun Gao Lianjin Fu Yiran Zhang Zeyu Li Qingtai Shu |
| author_sort | Xiao Zhang |
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| description | Abstract Pine Wilt Disease (PWD), caused by the pine wood nematode, is a major global forest pathology characterized by the rapid death of pine trees within a span of three months. Forestry management policy requires the eradication of all infected trees during the initial outbreak phase of a disease to contain its spread. This measure substantially relies on the timely identification of diseased trees. Accurate early diagnosis is a critical core component for effective disease control, preventing the spread of the epidemic, and maintaining the integrity of forest ecosystems. Therefore, this study proposes a new approach for early detection of PWD using hyperspectral data combined with measured physiological parameters to obtain diagnostic spectra and optimal biochemical parameters for early detection. This study investigated early-stage PWD by integrating 350–2500 nm hyperspectral data acquired with an ASD FieldSpec 4 and biochemical analysis. Results revealed significant declines in total sugar, reducing sugar, and moisture content during early infection. Study identified the spectral ranges 455–677 nm and 1974–2340 nm as optimal diagnostic windows. Using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA), we identified diagnostic spectral bands: CARS selected 20 moisture-sensitive bands in red-edge (680–760 nm) and SWIR regions, while SPA pinpointed 4 critical bands (758, 1074, 1124, 1663 nm) across red-edge, NIR, and SWIR. This leaf-scale methodology establishes a technical foundation for regional-scale airborne and satellite hyperspectral monitoring of PWD. The XGBoost classifier achieved 91% accuracy (CARS) and 83% accuracy (SPA) in distinguishing healthy from early-stage infected trees, with AUC > 0.8 for both feature sets, demonstrating reliable spectral discrimination of infection status. This study proposes a novel method for the early detection of PWD based on spectral characteristics, offering valuable insights for the application of hyperspectral remote sensing at a regional scale. |
| format | Article |
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| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
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| spelling | doaj-art-aadf618d1cd2453a8d4c42e3bc66a4f52025-08-20T04:03:07ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-10696-6Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objectsXiao Zhang0Yingqun Gao1Lianjin Fu2Yiran Zhang3Zeyu Li4Qingtai Shu5College of Soil and Water Conservation, Southwest Forestry UniversityCollege of Forestry, Southwest Forestry UniversityCollege of Soil and Water Conservation, Southwest Forestry UniversityCollege of Soil and Water Conservation, Southwest Forestry UniversityCollege of Soil and Water Conservation, Southwest Forestry UniversityCollege of Forestry, Southwest Forestry UniversityAbstract Pine Wilt Disease (PWD), caused by the pine wood nematode, is a major global forest pathology characterized by the rapid death of pine trees within a span of three months. Forestry management policy requires the eradication of all infected trees during the initial outbreak phase of a disease to contain its spread. This measure substantially relies on the timely identification of diseased trees. Accurate early diagnosis is a critical core component for effective disease control, preventing the spread of the epidemic, and maintaining the integrity of forest ecosystems. Therefore, this study proposes a new approach for early detection of PWD using hyperspectral data combined with measured physiological parameters to obtain diagnostic spectra and optimal biochemical parameters for early detection. This study investigated early-stage PWD by integrating 350–2500 nm hyperspectral data acquired with an ASD FieldSpec 4 and biochemical analysis. Results revealed significant declines in total sugar, reducing sugar, and moisture content during early infection. Study identified the spectral ranges 455–677 nm and 1974–2340 nm as optimal diagnostic windows. Using Competitive Adaptive Reweighted Sampling (CARS) and Successive Projections Algorithm (SPA), we identified diagnostic spectral bands: CARS selected 20 moisture-sensitive bands in red-edge (680–760 nm) and SWIR regions, while SPA pinpointed 4 critical bands (758, 1074, 1124, 1663 nm) across red-edge, NIR, and SWIR. This leaf-scale methodology establishes a technical foundation for regional-scale airborne and satellite hyperspectral monitoring of PWD. The XGBoost classifier achieved 91% accuracy (CARS) and 83% accuracy (SPA) in distinguishing healthy from early-stage infected trees, with AUC > 0.8 for both feature sets, demonstrating reliable spectral discrimination of infection status. This study proposes a novel method for the early detection of PWD based on spectral characteristics, offering valuable insights for the application of hyperspectral remote sensing at a regional scale.https://doi.org/10.1038/s41598-025-10696-6Pine wilt diseasePinus yunnanensisHyperspectral dataForest health monitoring |
| spellingShingle | Xiao Zhang Yingqun Gao Lianjin Fu Yiran Zhang Zeyu Li Qingtai Shu Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects Scientific Reports Pine wilt disease Pinus yunnanensis Hyperspectral data Forest health monitoring |
| title | Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects |
| title_full | Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects |
| title_fullStr | Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects |
| title_full_unstemmed | Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects |
| title_short | Early feature study of Yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects |
| title_sort | early feature study of yunnan pine pinewood nematode disease based on hyperspectral remote sensing of ground objects |
| topic | Pine wilt disease Pinus yunnanensis Hyperspectral data Forest health monitoring |
| url | https://doi.org/10.1038/s41598-025-10696-6 |
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