Assessment of the stand structure of protective forest monitoring based on statistical models in Irano-Turanian phytogeographical regions of Iran

Environ Monit Assess. 2023 Dec 6;196(1):18. doi: 10.1007/s10661-023-12140-6.

Abstract

One of the most important data that forest management planners need for effective decisions is the data related to the forest structure. The aim of the study is to investigate and analyse the structure of protective forests in Irano-Turanian phytogeographical regions, Iran. Since there are many shrubs in this phytogeographical region, it is very difficult to measure the stand diameter at any height (breast or root height). For this reason, it is necessary to analyse the parameters of height and crown cover to investigate and analyse forest structure. For that purpose, two study plots were selected, and basic data were analysed by using statistical distributions, scatter plots and R2 coefficients. With EasyFit software and Anderson‒Darling test, it was found that the Weibull (3P) and Pearson 6 (4P) distributions for the crown cover factor and the Gen-Pareto and Pert distributions for the height factor have the best goodness-of-fit for the distribution of the different crown cover classes and heights in the studied forest. Moreover, the results confirm that there is a very weak R2 coefficient between crown cover and root collar diameter, with R2 = 0.513 and 0.369 in plots 1 and 2, respectively. Therefore, the combination of crown cover and height parameters is more suitable for use when analysing stand structure in such forests, although the values of R2 are still low (0.673 and 0.524 in plots 1 and 2, respectively). The results of this study show that in protective forests with many shrubs, it is better to focus on the height and crown cover of ​trees and of shrubs rather than on parameters related to stand/tree diameter when analysing stand structure.

Keywords: Distribution of crown cover classes; Protective forests; Scatterplot of crown cover and height; Weibull (3P).

MeSH terms

  • Environmental Monitoring* / methods
  • Forests*
  • Iran
  • Models, Statistical
  • Trees