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Remote Sensing
Volume 17
Issue 19
10.3390/rs17193274
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Detection of Larch Caterpillar Infestation in Typical Forest Areas of Changbai Mountain, China, Based on Integrated Satellite Hyperspectral and Multispectral Data

by
Mingchang Wang
1,
Dong Cai
1,*,
Fengyan Wang
1,
Jingzheng Zhao
1,
Qing Ding
1,
Yanbing Zhou
2,
Jialin Cai
3,
Luming Liu
4 and
Xiaolong Xu
4
1
College of Geoexploration Science and Technology, Jilin University, Changchun 130026, China
2
Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
3
Beijing Municipal Institute of City Planning & Design, Beijing 100045, China
4
Zhuhai Orbita Satellite Big Data Co., Ltd., Zhuhai 519085, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(19), 3274; https://doi.org/10.3390/rs17193274
Submission received: 14 August 2025 / Revised: 20 September 2025 / Accepted: 22 September 2025 / Published: 23 September 2025
(This article belongs to the Topic Disaster and Environment Monitoring Based on Multisource Remote Sensing Images)
Versions Notes

Abstract

Forests, as one of the most vital ecosystems on Earth, play essential roles in climate regulation, water conservation, and resource provision. However, forest health is threatened by pests, among which the larch caterpillar (Dendrolimus superans) is one of the most destructive defoliators of coniferous forests in northern China. Previous studies have mostly relied on single data sources for pest detection, which are limited by insufficient spectral information or inappropriate selection of sensitive bands, making it difficult to achieve high detection accuracy. Therefore, this study integrates hyperspectral imagery from Zhuhai-1 and multispectral imagery from Sentinel-2, leveraging their high spectral resolution and broad spectral range, thus enhancing discrimination capability. Genetic algorithm (GA) was employed to select optimal features from spectral indices, texture features, and fractional-order derivatives (FOD). Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were compared, and model interpretability was further analyzed using Shapley additive explanations (SHAP). The results showed that XGBoost achieved the highest performance, with an overall accuracy and Kappa coefficient of 93.47% and 89.81%, demonstrating superior adaptability. Moreover, the integration of hyperspectral and multispectral data significantly improved detection accuracy compared to using either data source alone. Among the GA-selected features, Band 15 of Zhuhai-1 hyperspectral imagery exhibited strong sensitivity to pest infestation. This study provides a novel and practical approach for forest pest monitoring based on the synergistic use of hyperspectral and multispectral remote sensing data.
Keywords: larch caterpillar infestation; hyperspectral; fractional-order derivative; genetic algorithm; machine learning; SHAP larch caterpillar infestation; hyperspectral; fractional-order derivative; genetic algorithm; machine learning; SHAP

Share and Cite

MDPI and ACS Style

Wang, M.; Cai, D.; Wang, F.; Zhao, J.; Ding, Q.; Zhou, Y.; Cai, J.; Liu, L.; Xu, X. Detection of Larch Caterpillar Infestation in Typical Forest Areas of Changbai Mountain, China, Based on Integrated Satellite Hyperspectral and Multispectral Data. Remote Sens. 2025, 17, 3274. https://doi.org/10.3390/rs17193274

AMA Style

Wang M, Cai D, Wang F, Zhao J, Ding Q, Zhou Y, Cai J, Liu L, Xu X. Detection of Larch Caterpillar Infestation in Typical Forest Areas of Changbai Mountain, China, Based on Integrated Satellite Hyperspectral and Multispectral Data. Remote Sensing. 2025; 17(19):3274. https://doi.org/10.3390/rs17193274

Chicago/Turabian Style

Wang, Mingchang, Dong Cai, Fengyan Wang, Jingzheng Zhao, Qing Ding, Yanbing Zhou, Jialin Cai, Luming Liu, and Xiaolong Xu. 2025. "Detection of Larch Caterpillar Infestation in Typical Forest Areas of Changbai Mountain, China, Based on Integrated Satellite Hyperspectral and Multispectral Data" Remote Sensing 17, no. 19: 3274. https://doi.org/10.3390/rs17193274

APA Style

Wang, M., Cai, D., Wang, F., Zhao, J., Ding, Q., Zhou, Y., Cai, J., Liu, L., & Xu, X. (2025). Detection of Larch Caterpillar Infestation in Typical Forest Areas of Changbai Mountain, China, Based on Integrated Satellite Hyperspectral and Multispectral Data. Remote Sensing, 17(19), 3274. https://doi.org/10.3390/rs17193274

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