ePIC

Multicriteria evaluation of simulated logging scenarios in a tropical rain forest

Edit Item Edit Item

General Information:

Citation:
Huth, A. , Drechsler, M. and Köhler, P. (2004): Multicriteria evaluation of simulated logging scenarios in a tropical rain forest , Journal of Environmental Management, 71 , pp. 321-333 . doi: 10.1016/j.envman.2004.03.008
Cite this page as:
DOI:
Official URL:
Contact Email:
Download:

Supplementary Information:

Abstract:

Forest growth models are often useful tools for investigating the long-term impacts of logging. In this paper the results of the rain forest growth model FORMIND were assessed by a multicriteria decision analysis. The main processes covered by FORMIND include tree growth, mortality, regeneration and competition. Tree growth is calculated based on a carbon balance approach. Trees compete for light and space; dying large trees fall down and create gaps in the forest. Sixty-four different logging scenarios for an initially undisturbed forest stand at Deramakot (Malaysia) were simulated. The scenarios differ regarding the logging cycle, logging method, cutting limit and logging intensities. We characterise the impacts with four criteria describing the yield, canopy opening and changes in species composition. Multicriteria decision analysis was used for the first time to evaluate the scenarios and identify the efficient ones. Our results plainly show that reduced-impact logging scenarios are more efficient than the others, since in these scenarios forest damage is minimised without significantly reducing yield. Nevertheless there is a trade-off between yield and achieving a desired ecological state of logged forest; the ecological state of the logged forests can only be improved by reducing yields and enlarging the logging cycles. Our study also demonstrates that high cutting limits or low logging intensities cannot compensate for the high damage caused by conventional logging techniques.

Further Details:

Imprint
AWI
Policies:
read more
OAI 2.0:
http://epic.awi.de/cgi/oai2
ePIC is powered by:
EPrints 3