基于归一化植被点云的林分平均高及蓄积量反演
收稿日期: 2021-10-09
修回日期: 2021-11-10
网络出版日期: 2022-01-12
基金资助
中国煤炭地质总局自然资源智能感知科技创新团队(ZMKJ-2020-T04);国家林业和草原局自主研发项目(LC-1-05);中煤总局科技项目(ZMKJ-2020-J09-02)
The Inversion of Average Stand Height and Stock Volume based on Normalized Vegetation Point Cloud
Received date: 2021-10-09
Revised date: 2021-11-10
Online published: 2022-01-12
王照利 , 王浩伟 , 杨佳乐 , 段梦琦 , 马胜利 . 基于归一化植被点云的林分平均高及蓄积量反演[J]. 林草资源研究, 2021 , 0(6) : 37 -42 . DOI: 10.13466/j.cnki.lyzygl.2021.06.007
This paper proposed a normalized vegetation point cloud computing method,which used vertical elevation difference characterization between vegetation point cloud and ground point cloud to remove the absolute height value of forest influenced by topography,on this basis,it extracted forest characteristics variables,and used the random forest algorithm to invert and estimate the average tree height and the forest stock volume within the study area. The result showed that this method can effectively improve the estimation accuracy of forest factors,and the fitting accuracy of average tree height and forest stock volume were 0.946 and 0.936,respectively.
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