基于无人机LiDAR特征变量的南方集体林区蓄积量估测
收稿日期: 2021-06-16
修回日期: 2021-07-14
网络出版日期: 2021-09-26
基金资助
国家林草局自主研发计划项目(LC-1-04);湖南省研究生科研创新项目(CX20200705);中南林业科技大学研究生科技创新基金(CX20201006)
Estimating Standing Volume in Southern Collective Forest Region Based on the Unmanned Aerial Vehicle LiDAR Characteristic Variables
Received date: 2021-06-16
Revised date: 2021-07-14
Online published: 2021-09-26
基于广西壮族自治区森林资源年度监测评价成果数据,采用逐步回归选择机载激光雷达特征变量,建立多元线性回归、Logistic回归和随机森林模型,预测南方集体林区桉树、杉木和天然阔叶林样地的蓄积量。结果表明:1)桉树和杉木样地的逐步回归特征变量多为高度和强度变量,而天然阔叶林样地则是间隙率、覆盖度、叶面积指数等综合变量;2)桉树和天然阔叶林样地,随机森林模型的蓄积量估测精度(桉树R2=0.97,RMSE=12.60m3/hm2;天然阔叶林:R2=0.90,RMSE=18.45m3/hm2)高于多元线性回归和Logistic回归模型,而杉木样地在多元线性回归模型中得到了最优的蓄积量估测结果(R2=0.91,RMSE=24.30m3/hm2);3)在3种模型估测精度中,人工桉树和杉木样地均优于天然阔叶林样地。可见,高密度的激光雷达点云可以获取更优的特征变量,针对复杂的样地条件需要灵活选择估测模型实现蓄积量调查,以便为林草部门进行森林资源调查、监测和经营管理提供科学依据。
杜志 , 陈振雄 , 马开森 , 刘紫薇 , 顾兴贵 . 基于无人机LiDAR特征变量的南方集体林区蓄积量估测[J]. 林草资源研究, 2021 , 0(4) : 157 -165 . DOI: 10.13466/j.cnki.lyzygl.2021.04.020
Based on the annual monitoring and evaluation data of forest resources in Guangxi Zhuang Autonomous Region,the characteristic variables of airborne LiDAR were selected with the stepwise regression,and multiple linear regression,logistic regression and random forest models were established to predict the volume of sample plots of Eucalyptus robusta,Cunninghamia lanceolate and natural broad-leaved forest in southern collective forest region.The results showed that:1) For the plots of Eucalyptus robusta and Cunninghamia lanceolate,the stepwise regression characteristic variables were mainly height and intensity variables,while for the natural broad-leaved forest plots,they were comprehensive variables,such as clearance ratio,coverage,and leaf area index;2) For Eucalyptus robusta and natural broad-leaved forest plots,the estimation accuracy( Eucalyptus robusta:R 2=0.97,RMSE=12.60 m3/hm2;natural broad-leaved:R2=0.90,RMSE=18.45 m3/hm2) of the volume of the random forest model was higher than that of the multiple linear regression and logistic regression models,for the Cunninghamia lanceolate plot,the multiple linear regression model obtained the best estimation result(R 2=0.91,RMSE=24.30 m3/hm2);3) Among the estimation accuracy of the three models,the artificial Eucalyptus robusta and Cunninghamia lanceolate plots were higher than of the natural broad-leaved forest plots.It can be seen that the high-density LiDAR point cloud can obtain better characteristic variables.In view of the complex sample plot conditions,we need to flexibly select the estimation model to realize the volume survey,which can provide a scientific basis for the forestry and grassland authorities to carry out the forest resources survey,monitoring and management.
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