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FOREST RESOURCES WANAGEMENT ›› 2023›› Issue (3): 128-133.doi: 10.13466/j.cnki.lyzygl.2023.03.017

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Estimation of Pinus massoniana Index Based on Geostatistical Spatial Analysis

LI Cheng1(), TANG Daisheng2(), JIA Jianbo3   

  1. 1. College of Forestry,Central South University of Forestry & Technology,Changsha 410004,China
    2. Key Laboratory of Soil and Water Conservation and Desertification Combating,Ministry of Education,Changsha 410004,China
    3. National Engineering Laboratory for Applied Forest Ecological Technology,Central South University of Forestry & Technology,Changsha 410004,China
  • Received:2023-04-17 Revised:2023-05-19 Online:2023-06-28 Published:2023-08-09

Abstract:

Based on the analytical data of 81 blocks of 100m2 sample circles in the special forest survey of Chongqing,the nonlinear regression method was used to calculate the Pinus massoniana standing index,and based on the geostatistical analysis method,the optimal variogram model was selected for spatial interpolation estimation of the ground index,and the spatial distribution of the Pinus massoniana Standing Index was analyzed and the areas suitable for planting Pinus massoniana were identified.The preferred tree growth model is the Richards model,and the final mathematical model of the guide curve is H=141.898×[1-exp(-0.0006×t)]0.6661.The fitting results show that the Gaussian model is a preferred variogram model,and the ratio of gold value to the base value of Pinus massoniana variogram in the study area is 20.9%,which is less than 25%,indicating that the Pinus massoniana standing index in the study area has a strong degree of spatial correlation Spatial autocorrelation and spatial interpolation can be used to predict the ground index.The central region of the study area and the northeastern region had high site indexes.The area with a site index greater than 10.43 accounted for a large part,which was closely related to the average altitude,soil type and average precipitation in the study area.

Key words: Pinus massoniana, variogram, nonlinear regression, ordinary kriging interpolation, site index, nugget value, abutment value

CLC Number: