Analysis of chain saw selective felling operations on damage rate of residual trees during winter time in a mixed conifer-broad-leaved forest in China | Land Portal

Informações sobre recurso

Date of publication: 
Dezembro 2011
Resource Language: 
ISBN / Resource ID: 
AGRIS:US201600032639
Pages: 
283-289

Decreasing the damage rate of residual trees during selective cutting operations is quite important for forest landowners to reduce wood production waste and to use forest resources sustainably. In this article we analyze the impacts of chain saw selective felling operations on the damage rate of residual trees during winter in a mixed conifer-road-leaved forest. A case study was conducted in Dongfanghong Forest Farm located in northeast China. After theoretical analysis, the influencing factors were identified, and a mathematical model that considers the relationships among damage rate of residual trees, harvesting intensity, initial stand density, and single stem volume to be harvested was established. The theoretical model was verified using the data collected from harvesting sites. Results show that the residual trees' damage rate increases linearly with an increase in stand density and in the volume per stem of felled trees. The damage rate of residual trees increases initially then decreases as the selective cutting intensity increases. In theory, the damage rate is at its highest value when the selective cutting intensity reaches 50 percent. The damage rate is significantly reduced by controlling the falling direction of felled trees, which shifts the maximum damage rate such that the highest rate occurs when the selective cutting intensity is 39 percent. The following recommendations for loggers are proposed to reduce damage rate: (1) effectively control the falling direction of trees being felled, (2) conduct cutting operation in nonfrozen seasons, and (3) design the cutting intensity (E) to be either E 60 percent.

Autores e editores

Author(s), editor(s), contributor(s): 

Wang, Lihai
Wang, Na
You, Xiangfei
Meng, Chun

Provedor de dados

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