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Remote Sensing
Volume 17
Issue 23
10.3390/rs17233830
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Individual Tree-Level Biomass Mapping in Chinese Coniferous Plantation Forests Using Multimodal UAV Remote Sensing Approach Integrating Deep Learning and Machine Learning

by
Yiru Wang
1,2,3,
Zhaohua Liu
4,
Jiping Li
1,2,3,
Hui Lin
1,2,3,5,
Jiangping Long
1,2,3,5,
Guangyi Mu
6,
Sijia Li
4 and
Yong Lv
1,2,3,*
1
Faculty of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
2
Key Laboratory of National Forestry and Grassland Administration on Forest Resources Management and Monitoring in Southern China, Changsha 410004, China
3
Research Center of Forestry Remote Sensing & Information Engineering, Central South University of Forestry & Technology, Changsha 410004, China
4
Northeast Institute of geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
5
Hunan Provincial Key Laboratory of Forestry Remote Sensing Based Big Data & Ecological Security, Changsha 410004, China
6
Jilin Provincial Key Laboratory of Municipal Wastewater Treatment, Changchun Institute of Technology, Changchun 130012, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(23), 3830; https://doi.org/10.3390/rs17233830
Submission received: 19 October 2025 / Revised: 21 November 2025 / Accepted: 26 November 2025 / Published: 26 November 2025
(This article belongs to the Special Issue Satellite Remote Sensing for Monitoring Forest Carbon and Supporting Nature-Based Carbon Crediting Mechanisms)
Review Reports Versions Notes

Abstract

Accurate estimation of individual tree aboveground biomass (AGB) is essential for understanding forest carbon dynamics, optimizing resource management, and addressing climate change. Conventional methods rely on destructive sampling, whereas unmanned aerial vehicle (UAV) remote sensing provides a non-destructive alternative. In this study, spectral indices, textural features, and canopy height attributes were extracted from high-resolution UAV optical imagery and Light Detection And Ranging (LiDAR) point clouds. We developed an improved YOLOv8 model (NB-YOLOv8), incorporating Neural Architecture Manipulation (NAM) attention and a Bidirectional Feature Pyramid Network (BiFPN), for individual tree detection. Combined with a random forest algorithm, this hybrid framework enabled accurate biomass estimation of Chinese fir, Chinese pine, and larch plantations. NB-YOLOv8 achieved superior detection performance, with 92.3% precision and 90.6% recall, outperforming the original YOLOv8 by 4.8% and 4.2%, and the watershed algorithm by 12.4% and 11.7%, respectively. The integrated model produced reliable tree-level AGB predictions (R2 = 0.65–0.76). SHapley Additive exPlanation (SHAP) analysis further revealed that local feature contributions often diverged from global rankings, underscoring the importance of interpretable modeling. These results demonstrate the effectiveness of combining deep learning and machine learning for tree-level AGB estimation, and highlight the potential of multi-source UAV remote sensing to support large-scale, fine-resolution forest carbon monitoring and management.
Keywords: individual tree detection; aboveground biomass; deep learning; machine learning; SHAP values individual tree detection; aboveground biomass; deep learning; machine learning; SHAP values

Share and Cite

MDPI and ACS Style

Wang, Y.; Liu, Z.; Li, J.; Lin, H.; Long, J.; Mu, G.; Li, S.; Lv, Y. Individual Tree-Level Biomass Mapping in Chinese Coniferous Plantation Forests Using Multimodal UAV Remote Sensing Approach Integrating Deep Learning and Machine Learning. Remote Sens. 2025, 17, 3830. https://doi.org/10.3390/rs17233830

AMA Style

Wang Y, Liu Z, Li J, Lin H, Long J, Mu G, Li S, Lv Y. Individual Tree-Level Biomass Mapping in Chinese Coniferous Plantation Forests Using Multimodal UAV Remote Sensing Approach Integrating Deep Learning and Machine Learning. Remote Sensing. 2025; 17(23):3830. https://doi.org/10.3390/rs17233830

Chicago/Turabian Style

Wang, Yiru, Zhaohua Liu, Jiping Li, Hui Lin, Jiangping Long, Guangyi Mu, Sijia Li, and Yong Lv. 2025. "Individual Tree-Level Biomass Mapping in Chinese Coniferous Plantation Forests Using Multimodal UAV Remote Sensing Approach Integrating Deep Learning and Machine Learning" Remote Sensing 17, no. 23: 3830. https://doi.org/10.3390/rs17233830

APA Style

Wang, Y., Liu, Z., Li, J., Lin, H., Long, J., Mu, G., Li, S., & Lv, Y. (2025). Individual Tree-Level Biomass Mapping in Chinese Coniferous Plantation Forests Using Multimodal UAV Remote Sensing Approach Integrating Deep Learning and Machine Learning. Remote Sensing, 17(23), 3830. https://doi.org/10.3390/rs17233830

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