Optimal Continuous Cover Forest Management with a Lower Bound Constraint on Dead Wood
16/07/2020
We analyze economically optimal continuous cover forestry with dead wood as a biodiversity indicator. We study mixed-species stands consisting of Norway spruce (Picea abies L. Karst.), birch (Betula pendula Roth.), and other broadleaves (e.g., oak Quercus sp., maple Acer sp.). The analysis is based on an economic description of continuous cover forest management using an empirically estimated size-structured transition matrix model. We use size-specific decomposition rates for dead wood, with the lower limit on total dead wood volume varying between 0 and 40 m3 ha–1. The optimization problem is solved in its general dynamic form using gradient-based interior point methods. Increasing the dead wood volume requirement affects total stand density only slightly, but increases stand heterogeneity as other broadleaves are grown in higher numbers. In addition, increasing the dead wood requirement has only a minor effect on the total felled volume, but harvests shift from timber harvests to biodiversity fellings to maintain the required dead wood volume. In the optimal steady state with a high dead wood requirement, two harvesting cohorts emerge: one for timber harvests and the other for biodiversity fellings. Increasing the dead wood requirement decreases steady-state net timber income by up to 30 percent compared to the unconstrained solution.
Janne Rämö, Aino Assmuth, Olli Tahvonen, Optimal Continuous Cover Forest Management with a Lower Bound Constraint on Dead Wood, Forest Science, Volume 66, Issue 2, April 2020, Pages 202–209,
Monitoring Mixed Harvesting EconomicsA Transferable and Effective Method for Monitoring Continuous Cover Forestry at the Individual Tree Level Using UAVs
09/06/2020
Transformation to Continuous Cover Forestry (CCF) is a long and difficult process in which frequent management interventions rapidly alter forest structure and dynamics with long lasting impacts. Therefore, a critical component of transformation is the acquisition of up-to-date forest inventory data to direct future management decisions. Recently, the use of single tree detection methods derived from unmanned aerial vehicle (UAV) has been identified as being a cost effective method for inventorying forests. However, the rapidly changing structure of forest stands in transformation amplifies the difficultly in transferability of current individual tree detection (ITD) methods. This study presents a novel ITD Bayesian parameter optimisation approach that uses quantile regression and external biophysical tree data sets to provide a transferable and low cost ITD approach to monitoring stands in transformation. We applied this novel method to 5 stands in a variety of transformation stages in the UK and to a independent test study site in California, USA, to assess the accuracy and transferability of this method. Requiring small amounts of training data (15 reference trees) this approach had a mean test accuracy (F-score = 0.88) and provided mean tree diameter estimates (RMSE = 5.6 cm) with differences that were not significance to the ground data (p < 0.05). We conclude that this method can be used to monitor forests stands in transformation and thus can also be applied to a wide range of forest structures with limited manual parameterisation between sites
Monitoring Transformation Temperate