关键词: 林分蓄积量, 杉木, 气候因子, 混合效应模型

Abstract:

The nonlinear mixed-effect model of Cunninghamia lanceolata forest volume was developed using easily accessible stand characteristic factors,site,and climate factors obtained from field investigation.This model provides technical support for forest volume monitoring by UAV LiDAR technology.Based on mensuration data from 580 sample plots of Cunninghamia lanceolata forest and comprehensive ecological monitoring of grassland in Hunan Province and Guangxi Zhuang Autonomous Region between 2021 and 2023,a basic model for stand volume,including stand characteristic factors,was constructed.Subsequently,a nonlinear mixed-effects model was developed by incorporating site and climate factors,and determining both fixed and random effect variables.The models were evaluated using 10-fold cross-validation.The results revealed that stand characteristic factors(such as average dominant height and canopy density),site factors(elevation),and climate factors(annual mean temperature)were strongly correlated with the hectare volume of stand.The basic stand volume model,based on average dominant height,canopy density and age group,was constructed.Compared to the basic model,the nonlinear mixed-effect model,which included altitude as a fixed effect and the annual mean temperature difference among plots as a random effect,showed improved fitting accuracy.The determination coefficient(R²)increased from 0.616 to 0.654,while the standard error of the estimate(E_SE),mean prediction error(E_MP),and mean percent standard errors(E_MPS)were reduced.The relationship between stand volume and factors such as average dominant height,canopy density,site and climate factors are effectively explored.The resulting stand volume model demonstrates practical applicability,and all the variables used in modeling could be derived from UAV LiDAR point cloud data or coordinate information,providing a valuable reference for regional forest volume monitoring using LiDAR technology.

Key words: forest volume, Cunninghamia lanceolata, climate factor, mixed effect model