欢迎访问林草资源研究

FOREST RESOURCES WANAGEMENT ›› 2019›› Issue (5): 33-36.doi: 10.13466/j.cnki.lyzygl.2019.05.007

• Scientific Research • Previous Articles     Next Articles

Study on Stand Volume Models for Sub-compartment Forest Management Inventory in Beijing

CHEN Xinyun1(), WANG Wenwen2, ZENG Weisheng1, DU Pengzhi3, DANG Yongfeng1, WANG Wei1, MENG Jinghui2()   

  1. 1. Academy of Forest Inventory and Planning,National Forestry and Grassland Administration,Beijing 100714,China
    2. Research Center of Forest Management Engineering of National Forestry and Grassland Administration,Beijing Forestry University,Beijing 100083,China
    3. Beijing Municipal Institute of Forest Surveying and Designing,Beijing 100029,China
  • Received:2019-07-12 Revised:2019-10-11 Online:2019-10-28 Published:2020-09-18
  • Contact: MENG Jinghui E-mail:375201918@qq.com;jmeng@bjfu.edu.cn

RichHTML

6

PDF (PC)

309

Abstract:

In order to solve the main problems of using angle gauge to estimate the stand volume in forest management inventory (FMI) of Beijing,the forests were divided into 10 different types according to dominant tree species (groups) based on the FMI data.Then,using the data of the 9th (2016) Chinese National Forest Inventory in Beijing,the non-linear models of stand volume for various tree species groups were developed with stand volume per hectare as the dependent variable and stand parameters and site condition parameters as the independent variables.The goodness-of-fit statistics of the models,i.e.,determination coefficient (R2),total relative error (TRE),standard error of estimate (SEE),mean systematic error (MSE),mean prediction error (MPE),and mean percent standard error (MPSE) were calculated.The results show that the models performed well and the values of R2 were all greater than 0.94,MPE’s were less than 5%,and MPSE were almost less than 10%.The models can be applied to estimate the stand volume in the FMI of Beijing.

Key words: sub-comparment, non-linear model, stand volume, stand variable, forest management inventory

CLC Number: