世界林业研究2026，Vol.39Issue(1)：14-21,8.DOI:10.13348/j.cnki.sjlyyj.2026.0012.y
机器学习在森林地上生物量估测中的应用
Application of Machine Learning in Forest Aboveground Biomass Estimation
摘要
Abstract
Accurate estimation of forest aboveground biomass(AGB)is essential for assessing forest carbon sequestration and underpinning sustainable forest management.The choice of machine learning models depends heavily on data quality and scenario features.This paper reviews the application of machine learning in forest AGB estimation:1)For small-to-medium samples and the scenarios with explicit engineered features,it evaluates the strengths and limitations of traditional models such as random forest,support vector machine,and gradient boosting trees.2)For the needs of high-dimensional features and hyperparameter optimization,it assesses the effectiveness of global optimization methods,including Bayesian optimization,particle swarm optimization,and genetic algorithms,in improving model accuracy,along with their trade-offs in computational cost and convergence stability.3)For massive raw remote sensing data,it examines the strong potential of deep learning models,exemplified by convolutional neural networks,in end-to-end feature learning.Based on these analyses,the paper proposes the future research directions in intelligent feature engineering,data fusion with synergistic inversion,and improved interpretability of deep learning models,with the aim of providing a reference for science-based selection and optimization of machine learning models under varying data regimes,and advancing remote sensing-based forest AGB estimation towards higher accuracy and intelligence.关键词
机器学习/全局优化算法/深度学习/森林地上生物量Key words
machine learning/global optimization algorithms/deep learning/forest aboveground biomass分类
农业科技引用本文复制引用
李赜羽,舒清态,张依然,付连进,张霄..机器学习在森林地上生物量估测中的应用[J].世界林业研究,2026,39(1):14-21,8.基金项目
国家自然科学基金项目"生态脆弱区典型森林生态系统生化参数高光谱遥感反演关键技术研究"(31860205) （31860205）
云南省农业联合专项重点项目"基于深度学习无人机高光谱协同LiDAR数据的云南松松材线虫早期预警研究"(202301BD070001-002). （202301BD070001-002）
