Remote sensing-assisted mapping of quantitative attributes in Zagros open forests of Iran

The Zagros forests come as one of the most valuable ecosystems in western Iran. Therefore, accurate and up-todate information on basal area, canopy cover, and stem number per hectare of these forests are the important factors in the context of forest management and conservation. The main objective of this study was to estimate quantitative forest attributes using Landsat 8-OLI image data and Random Forest, a well-known machine learning technique. The results were shown the lowest out of bag error with the combination of 800 trees and 8 variables in each node as the optimal model parameters to classify forest canopy cover with overall accuracy and Kappa coefficient of 83% and 0.73 respectively, while those of classified mapping of basal area were 78% and 0.72, and also those of stem number per hectare were 75% and 0.69 respectively. All in all, the Random Forest classifier algorithm provided comparatively successful mapping results of quantitative attributes in Zagros open forests of Iran from Landsat 8-OLI image data

Economically optimal cutting cycle in a beech forest, Iranian Caspian Forests

The aim of this study was to determine the optimal cutting cycle in an uneven-aged beech forest in the North of Iran. First of all, a logistic growth model was determined for an uneven aged forest. Then, the stumpage price was predicted via an autoregressive model. The average stumpage price of beech was derived from actual timber, round wood, fire and pulpwood prices at road side minus the variable harvesting costs. Price and growth models were used in order to determine the optimal cutting cycle under different rates of interest and setup costs. The Faustmann’s model was used for optimal cutting cycle. The results indicated that the optimal cutting cycle will decrease if the rate of interest increased. The results also indicated that if the setup costs increase, the optimal cutting cycle will also increase

Wound healing rate in oriental beech trees following logging damage

Beech is the most important commercial species in the Caspian forests of Iran. Selective cutting and harvesting methods may adversely impact the quality of the residual trees, as the injuries make the trees prone to future disease, insect infestations or timber defects. Although attempts to better understand how wounds affect the residual trees have been made in many different contexts, there are still few investigations on uneven-aged forests. In this study the key objectives were to determine and model the healing rate for different wound parameters (width, length, and area of wound); to analyse the relationship between wound healing rate (WHR), tree diameter growth and tree height growth; to analyse the WHR in relation to wound position on the stem; and to analyse the relationship between WHR, width and area of wound in DBH classes and social classes, with the aim of enabling the prognosis of logging wounds. Wounded beech trees were examined immediately after selective logging and after a 5-year period. The WHR was 31.2 ±7.7 cm2 year-1. The wound width healing rate (18.4 ±3.4 mm·year-1) was significantly higher than the wound length healing rate (4.5 ±1.6 mm·year-1). Only 12% of wounds were completely closed after a 5-year period, and 15 years are necessary for the complete closure of 80% of total wounds. The ratio of wound area to stem area at wound height (RWS) showed a more pronounced effect on diameter than on height. Regression analysis showed that WHR was correlated negatively with wound area and width and positively with tree diameter growth, but no significant relationship was found between height growth and WHR parameters. The WHR was significantly higher at an upper position than at a lower one, and statistical tests showed that the tree vertical layering classes had a significant effect on WHR. Finally, it was shown that WHRs in upper-storey trees are significantly higher than in the middle and lower storeys