Rapid Large-Scale Monitoring of Pine Wilt Disease Using Sentinel-1/2 Images in GEE

Pine wilt disease (PWD) is a severe forest disease caused by the infestation of pine wood nematodes. Due to its short disease cycle and strong transmission ability, it has caused significant damage to China’s forestry resources. To achieve large-scale monitoring of PWD, this study utilized machine l...

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Veröffentlicht in:	Forests Jg. 16; H. 6; S. 981
Hauptverfasser:	Zhi, Junjun, Li, Lin, Fang, Yifan, Zhi, Dandan, Guang, Yi, Liu, Wangbin, Qu, Lean, Fu, Xinwu, Zhao, Haoshan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.06.2025
Schlagworte:	Accuracy Algorithms China Chlorophyll Clustering Data mining Datasets Deep learning Disease Disease transmission Efficiency Epidemics Forestry Forests Geospatial data Infections Machine learning Monitoring Neural networks Pest outbreaks Pine Remote sensing Spatial distribution Statistical analysis Trees Wilt China Dabie Mountains Anhui China
ISSN:	1999-4907, 1999-4907
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Abstract	Pine wilt disease (PWD) is a severe forest disease caused by the infestation of pine wood nematodes. Due to its short disease cycle and strong transmission ability, it has caused significant damage to China’s forestry resources. To achieve large-scale monitoring of PWD, this study utilized machine learning/deep learning algorithms with Sentinel-1/2 images in the Google Earth Engine cloud platform to implement province-wide PWD monitoring in Anhui Province, China. The study also analyzed the spatial distribution of PWD in Anhui Province from two perspectives—spatiotemporal patterns and influencing factors—aiming to investigate the spatiotemporal evolution patterns and the impact of influencing factors on the occurrence of PWD. The results show that (1) the random forest model exhibited the strongest performance, followed by the CNN model, while the DNN model performed the worst. Using the RF model to monitor PWD and calculate the affected area in Anhui Province from 2019 to 2024 yielded errors within 30% compared to official statistics. (2) PWD in Anhui Province showed a clear clustering trend, with global Moran’s indices all exceeding 0.79 from 2019 to 2024. The LISA map revealed a spread pattern from south to north and from west to east. (3) Topographic and temperature factors had the greatest influence on PWD distribution. SHAP analysis indicated that topographic and climatic factors were the primary drivers of PWD-affected areas, with slope and temperature being the two most significant contributing factors. This study helps to rapidly and accurately identify outbreak areas during epidemics and enables precise quarantine measures and targeted control efforts.
AbstractList	Pine wilt disease (PWD) is a severe forest disease caused by the infestation of pine wood nematodes. Due to its short disease cycle and strong transmission ability, it has caused significant damage to China’s forestry resources. To achieve large-scale monitoring of PWD, this study utilized machine learning/deep learning algorithms with Sentinel-1/2 images in the Google Earth Engine cloud platform to implement province-wide PWD monitoring in Anhui Province, China. The study also analyzed the spatial distribution of PWD in Anhui Province from two perspectives—spatiotemporal patterns and influencing factors—aiming to investigate the spatiotemporal evolution patterns and the impact of influencing factors on the occurrence of PWD. The results show that (1) the random forest model exhibited the strongest performance, followed by the CNN model, while the DNN model performed the worst. Using the RF model to monitor PWD and calculate the affected area in Anhui Province from 2019 to 2024 yielded errors within 30% compared to official statistics. (2) PWD in Anhui Province showed a clear clustering trend, with global Moran’s indices all exceeding 0.79 from 2019 to 2024. The LISA map revealed a spread pattern from south to north and from west to east. (3) Topographic and temperature factors had the greatest influence on PWD distribution. SHAP analysis indicated that topographic and climatic factors were the primary drivers of PWD-affected areas, with slope and temperature being the two most significant contributing factors. This study helps to rapidly and accurately identify outbreak areas during epidemics and enables precise quarantine measures and targeted control efforts.
Audience	Academic
Author	Li, Lin Zhi, Dandan Zhao, Haoshan Guang, Yi Fang, Yifan Qu, Lean Zhi, Junjun Liu, Wangbin Fu, Xinwu
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Snippet	Pine wilt disease (PWD) is a severe forest disease caused by the infestation of pine wood nematodes. Due to its short disease cycle and strong transmission...
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SubjectTerms	Accuracy Algorithms China Chlorophyll Clustering Data mining Datasets Deep learning Disease Disease transmission Efficiency Epidemics Forestry Forests Geospatial data Infections Machine learning Monitoring Neural networks Pest outbreaks Pine Remote sensing Spatial distribution Statistical analysis Trees Wilt
Title	Rapid Large-Scale Monitoring of Pine Wilt Disease Using Sentinel-1/2 Images in GEE
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