Abstract

In forest management planning, the dynamic treatment unit (DTU) approach has become an increasingly relevant alternative to the traditional planning approach using fixed stands, due to improved remote sensing techniques and optimization procedures, with the potential for the higher goal fulfillment of forest activities. For the DTU approach, the traditional concept of fixed stands is disregarded, and forest data are kept in units with a high spatial resolution. Forest operations are planned by clustering cells to form treatment units for harvest operations. This paper presents a new model with an exact optimization technique for forming DTUs in forest planning. In comparison with most previous models, this model aims for increased flexibility by modelling the spatial dimension according to cell proximity rather than immediate adjacency. The model is evaluated using a case study with harvest flow constraints for a forest estate in southern Sweden, represented by 3587 cells. The parameter settings differed between cases, resulting in varying degrees of clustered DTUs, which caused relative net present value losses of up to 4.3%. The case without clustering had the lowest net present value when considering entry costs. The solution times varied between 2.2 s and 42 min 6 s and grew rapidly with increasing problem size.

Keywords

dynamic treatment units; mixed integer programming; optimization; remote sensing; spatial forest planning

Published in

Canadian Journal of Forest Research
2021, volume: 51, number: 7, pages: 1065-1071
Publisher: CANADIAN SCIENCE PUBLISHING

Authors' information