جستجوی مقالات مرتبط با کلیدواژه « وینچینگ » در نشریات گروه « جنگلداری »

Logging is one of the critical operation in forest management that causes damage to remaining trees in forest. Reducing frequency and intensity of damages during logging operation is one of the main goals in section cutting forests. The aim of this study was analysis of effective factors on damage during logging of each tree in Nav forests.
From total of selected trees to cut in the two parcels of the Nav forests, the number of 83 trees (22.7%) were randomly selected and damages to remaining trees were individual-level analyzed during felling, winching and skidding operation.
Results indicated an average 13.3 damaged residual trees per each harvested tree, which 3 trees were damaged at felling stage, 8.9 trees were damaged at winching stage, and 1.4 trees were damaged at skidding stage. Diameter at breast height (DBH) of felled trees were from 25 to 107 with average 68.4 cm, and their heights were from 12.5 to 27.3 m with average 21 m, and damaged trees were increased by increasing of DBH and height of harvested trees. Winching distance were from 3 to 47 m with average 24.5 m, and winching angle were 0 to 47 degree with average 19.1 degree, and damaged trees increased by increasing the distance and angle of winching. Damaged trees were increased by increasing of number of skidd-trail curves and by decreasing of skid-trail radius. Damaged trees due to felling of trees on suitable directions, and winching of the on suitable routes were lower 42.4 and 17.1 percent than unsuitable directions and routes, respectively. The DBH of wounded and destroyed trees at felling stage (63.5 and 34.6 cm, respectively) were more than their averages at winching and skidding stages. Average size of bole wounds was 327.1 cm2, average depth of bole wound was 20.1 mm, and average height of bole wounds from ground level was 0.86 m. The averages of the bole-wound sizes and the bole-wound height from ground level were higher at felling stage, the average bole-wound depths at the winching stage were higher than other stages.
The results reveled that appropriate management of selection cutting forests need to develop of sciences and techniques of logging operation. Implementation of tree directional felling, and limiting of winching operation to areas without standing trees, regeneration and ground slopes less than 50 percent are necessary for reducing logging damages to residual trees in these forests.

Estimating of forest equipment productivity is an important aspect of managing cost in forestry, which leads to reduction of operations expenses. In other words, high capital cost in forest harvesting, is a good reason to argue forest engineering research and time modeling. This paper applied one of the Artificial intelligence subsets, which are called Artificial Neural Networks (ANNs), to predict winching time of wheeled skidder Timber jack 450C in Neka Choob forests. Continuous time study was performed to collect winching data during skidding operations. Winching time and related effective factors including slope, distance, number and volume at every winching cycle was measured simultaneously. Two neural networks type- Radial Basis Function and Multi Layer Perceptron- were used to develop winching time model. Moreover, in order to compare accuracy of ANNs and mathematical model, the regression analysis method was developed. Results showed that RBF network provided more accurate results in winching net time estimation compare to MLP neural network. The most effective variable in both networks was determined distance to the center of the skid trail. The results showed that the model developed by neural networks has more precision than the linear regression method.