Mapping the distribution of pine wilt disease based on selected machine learning algorithms and high-resolution Gaofen-2/7 remote sensing
Under the influence of human activities and climate change, pine wilt disease (PWD) has caused significant damage to Masson’s pine (Pinus massoniana Lamb.) forests in subtropical China. Existing research has struggled to accurately capture the large-scale spatial distribution of the PWD, particularl...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2509841 |
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| Summary: | Under the influence of human activities and climate change, pine wilt disease (PWD) has caused significant damage to Masson’s pine (Pinus massoniana Lamb.) forests in subtropical China. Existing research has struggled to accurately capture the large-scale spatial distribution of the PWD, particularly for precise extraction at provincial level. This study focuses on Fujian province and proposes a novel method for extracting PWD information at the sub-stand level. This approach uses forest age, canopy height, and temporal vegetation indices (VIs) data for deadwood distribution sub-stands to identify the suspected outbreak areas. In key counties and cities, High-resolution satellite imagery (GF-2 and GF-7) was used to construct a bi-level scale-set model (BSM) for efficient image segmentation, followed by selection of the best classification algorithm for data extraction. For non-key counties, sentinel imagery with 10-meter resolution was used on the GEE cloud platform with RF classification. The results showed an overall annual extraction accuracy exceeding 90%, and statistical analysis revealed a significant reduction in the number of dead trees from 2021 to 2022, indicating effective control measures. This study demonstrated that multi-source remote sensing data can efficiently extract PWD distribution information, fill data gaps for provincial-level monitoring, and support forest pest management. |
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| ISSN: | 1753-8947 1753-8955 |