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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Bibliographic Details
Published in:Forests Vol. 16; no. 6; p. 981
Main Authors: Zhi, Junjun, Li, Lin, Fang, Yifan, Zhi, Dandan, Guang, Yi, Liu, Wangbin, Qu, Lean, Fu, Xinwu, Zhao, Haoshan
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2025
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:1999-4907
1999-4907
DOI:10.3390/f16060981