The present study focuses on assessing the effects of different numbers of skidders on soil compaction. To assess the skidding effects, four-wheeled small-scale logging equipment attached to an ATV (Automated Transfer Vehicle) was used. Skidding operations were carried out on undistributed forest corridor (20 × 3 m).To measure soil compaction, 11 measurement lines were used, spaced at 2-m intervals. A total of 33 measurement points were used to measure soil compaction. Soil compaction values were measured for different soil depths with 5 cm intervals in the 0-40 cm range as MpA. The results were evaluated for the skidding zone and the wheel zone. Prior to skidding, soil compactions at 0 cm (top soil) is almost 2.5-3 times lower than those at the other depths. The p values indicate that the numbers of passes and the compaction values belong to nonidentical groups. The Bonferroni method was used to determine whether the compaction values are similar. The Dunn test results demonstrate that there were statistically significant differences between the mean values of the number of passes up to the 60th pass. However, there are no statistically significant differences between the means of the compaction values occurring between 60 and 80 and 100 and 120 passes. Generally, soil compaction is expressed as an increase in the soil bulk density. Soil bulk density and soil porosity are negatively correlated. It was found that for each of the zones, soil compaction values between the depths of 15 cm and 40 cm are approximately 4 times higher than at the 0 cm soil depth. Soil compaction values increased 3 times at the depths of between 0 cm and 5 cm. The average soil compactions values in the skidding zone are approximately 1.5 times higher than those at the wheel zones at the depth greater than 5 cm. The use of different skid trails will decrease soil compaction of the forest stand, provide uniformity of soil compactions in forest stand.
We present a broad survey of recent polynomial algorithms for the linear assignment problem. They all use essentially alternating trees and/or strongly feasible trees. Most ofthem employ Dijkstra's shortest path algorithm directly or indirectly. When properly implemented, each has the same complexity: O(n 3) for dense graphs with simple data structures and O(n 2 log n + nm) for sparse graphs using Fibonacci Heaps.
Introduction Forest structural diversity is considered to be one of the most important components of biological diversity because it affects the ecological factor beneath the canopy and creates suitable niches for fauna. Therefore, more complex structures indicate greater biodiversity (Szmyt, 2014). Forest structure is also accepted as one of the main naturalness traits, which may include tree density, vertical heterogeneity, canopy cover, and forest layering (Winter, 2012). Hence, quantitative tree and stand structural parameters must be accurately measured for ecosystem service assessment and state of stand development (Moskal and Zheng, 2012). Traditionally, some structural attributes have been measured in the context of forest inventories using field measurements. With the development of technology, the combination of forest inventory methods and new remote sensing technologies have become powerful integrated tools (Brack, 1997). The airborne laser scanning (ALS) technology, which offers high resolution data to derive structural forest parameters, has become notably popular (Means et al., 2000; Drake et al., 2002; Naesset and Økland, 2002; Morsdorf et al., 2004). However, the measurement of ground-truth data still remains in demand in addition to measurements for forest inventory. In the last decade, terrestrial laser scanner (TLS) based data have also become an alternative tool in forest management studies (Bienert et al., 2006). TLS systems can produce 3D point clouds that allow quantitative analysis of tree parameters (Raumonen et al., 2013). TLS has been used in many forestry studies, such as biomass estimation (
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