Volume 8, Issue 16 (10-2020)                   ifej 2020, 8(16): 39-47 | Back to browse issues page

XML Persian Abstract Print


Abstract:   (225 Views)
    Sampling methods have a theoretical basis and should be operational in different forests; therefore selecting an appropriate sampling method is effective for accurate estimation of forest characteristics. The purpose of this study was to estimate the stand density (number per hectare) in Arasbaran forest using a variety of the plotless density estimators of the nearest neighbors sampling method includes the Closest Individual, the Nearest Neighbor, the Second Nearest Neighbor, the Compound, the Shared Point and the Continues Nearest Neighbor due to introducing the most suitable estimator for forests. For this purpose, all number of trees was counted per hectare (as control). Then, distances between random sampling points and five closest nearest neighboring trees were measured in a systematic randomized network. The density estimators were calculated in each method. The calculated value of the actual density was compared to estimatorschr('39') values by the one sample t-test (p< 0.05) method in the R software and based on the value of accuracy criterion. Finally, the tree spatial distribution pattern was calculated by Johnson-Zimmer and Hopkins indices. The results showed that the difference between all estimators value was significant (p≤ 0.05) compared to the actual density (339.5 N.ha1), except the Morisita and Cottam estimators in Closest Individual method, Byth and Ripley and Cottam & Curtis 1 estimators in Nearest Neighbor method and 4th and 5th neighbors estimators in Continuous Nearest Neighbor method. The results of the spatial distribution pattern showed the random distribution of trees in the study area. The performance evaluation of these estimators for other quantitative characteristics is recommended in the Arasbaran forest stands.
Full-Text [PDF 1023 kb]   (57 Downloads)    
Type of Study: Research | Subject: Special
Received: 2019/12/17 | Accepted: 2020/05/27 | Published: 2020/12/15