生态学报2026，Vol.46Issue(10)：5495-5509,15.DOI:10.20103/j.stxb.202507241929
融合神经网络与集成学习的复杂地形竹林生物量估测
Estimation of bamboo forest biomass in complex terrain via ensemble learning fusing neural networks
摘要
Abstract
Accurate estimation of Aboveground Biomass(AGB)in bamboo forests is of paramount importance for assessing regional carbon budgets and supporting sustainable forest management,particularly given the high carbon sequestration potential of bamboo ecosystems.However,in mountainous regions characterized by complex terrain,such estimations face severe methodological challenges.These include the rapid saturation of optical remote sensing signals caused by the unique high-density,single-layer canopy structure of bamboo,as well as the spatial discontinuity of vertical structure data acquired by spaceborne LiDAR systems.Focusing on the Dendrocalamus giganteus forests in Xinping County,Yunnan,this study proposes a novel collaborative inversion framework that synergistically integrates Empirical Bayesian Kriging Regression Prediction(EBKRP)with a Heterogeneous Stacking Ensemble Learning strategy.To address the spatial limitations of LiDAR data,the framework first employs the EBKRP model,utilizing Sentinel-2 spectral features and topographic factors as auxiliary covariates,to spatially extrapolate discrete structural parameters from ICESat-2 and GEDI footprints into spatially continuous feature layers.Building upon this data foundation,the study constructs a robust two-layer Stacking ensemble model designed to maximize model heterogeneity.A significant methodological innovation of this framework is the strategic incorporation of a Multi-Layer Perceptron(MLP)into the base learner pool,alongside traditional machine learning algorithms including k-Nearest Neighbors(kNN),Random Forest(RF),Gradient Boosting Regression Tree(GBRT),and XGBoost.The introduction of the MLP is motivated by its distinct inductive bias compared to tree-based models;while traditional decision trees rely on recursive partitioning and orthogonal thresholds,the MLP utilizes a feedforward neural network architecture with non-linear activation functions to perform global approximation.This capability allows the model to effectively capture the highly non-linear mapping relationships between biomass and rugged terrain features that traditional shallow models often fail to resolve.A Ridge Regression meta-learner is then applied to integrate the predictions from these heterogeneous base learners,thereby achieving complementary advantages and reducing estimation bias.The experimental results demonstrate that the Stacking model achieved exceptional performance,with a Coefficient of Determination(R2)of 0.78 and a Root Mean Square Error(RMSE)of 12.89 Mg/hm2,significantly outperforming all individual base learners.Notably,the MLP emerged as the most effective single model,surpassing RF and kNN,which empirically validates the superiority of neural networks in handling complex topographic effects.The study estimated the average AGB density of the Dendrocalamus giganteus forest in the study area to be 77.0 Mg/hm2,with a total carbon stock of 1.126 × 106 Mg.Conclusively,this research confirms that a heterogeneous ensemble strategy incorporating neural networks provides a robust,high-precision paradigm for forest biomass mapping in complex mountainous environments,offering valuable methodological insights for future remote sensing applications.关键词
地上生物量/龙竹/数据融合/经验贝叶斯克里金回归预测/堆叠集成学习Key words
above-ground biomass/Dendrocalamus giganteus/data fusion/Empirical Bayesian Kriging regression prediction/stacking ensemble learning引用本文复制引用
付连进,舒清态,夏翠芬..融合神经网络与集成学习的复杂地形竹林生物量估测[J].生态学报,2026,46(10):5495-5509,15.基金项目
"十四五"国家重点研发计划项目(2023YFD2201205) （2023YFD2201205）
云南省农业联合专项-重点项目(202301BD070001-002) （202301BD070001-002）
