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Forest and Grassland Resources Research >

2021 , Vol. 0 >Issue 6: 37 - 42

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2021.06.007

The Inversion of Average Stand Height and Stock Volume based on Normalized Vegetation Point Cloud

  • Zhaoli WANG ,
  • Haowei WANG ,
  • Jiale YANG ,
  • Mengqi DUAN ,
  • Shengli MA
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  • 1. Northwest Surveying,Planning and Designing Institute of National Forestry and Grassland Administration,Xi'an,710048,China
    2. Aerial Photography and Remote Sensing Group Co.Ltd.,Xi'an 710199,China

Received date: 2021-10-09

  Revised date: 2021-11-10

  Online published: 2022-01-12

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Abstract

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.

Key words： LiDAR; normalized vegetation point cloud; forest factor inversion; reserve forest

Cite this article

Zhaoli WANG , Haowei WANG , Jiale YANG , Mengqi DUAN , Shengli MA . The Inversion of Average Stand Height and Stock Volume based on Normalized Vegetation Point Cloud[J]. Forest and Grassland Resources Research, 2021 , 0(6) : 37 -42 . DOI: 10.13466/j.cnki.lyzygl.2021.06.007

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