====== N V Thinner ======
Cutting in the different diameter classes to reach the target values for N (total number of trees harvested) and V (total harvested volume), defined from historical management records.
Lafond V, Cordonnier T, De Coligny F, Courbaud B (2012) Reconstructing harvesting diameter distribution from aggregate data. Annals of Forest Sciences 69:235-243.
Algorithm description :
We developed a simple algorithm to reconstruct diameter distribution of selection harvesting from an initial (before cut) diameter distribution and aggregate information on the harvest to be reproduced, namely the number of trees (N, t/ha) and the total volume (V, m3/ha) to be removed.
A list of trees is created from the initial diameter distribution and contains only trees with DBH larger than a given (commercial) threshold.
The algorithm consists in randomly selecting the target number of trees (N) from the initial tree list, then iteratively exchanging random pairs of selected vs. unselected trees to converge towards the target volume (V) to be removed.
At each step, the exchange is done only if the new volume gets closer to the target one; otherwise the algorithm selects another pair. The algorithm stops when the predicted volume equals the target volume, more or less one percent.
Algorithm validation :
The ability of the algorithm to reconstruct consistent harvesting diameter distributions have been tested for different theoretical cases and against real inventory data (see Lafond et al., 2012) and proved efficient.
Algorithm application in Capsis:
In Capsis, this algorithm is applied by running several replications of the process (defined as an input parameter, by default = 1000) and then selecting the distribution closest to the mean distribution (estimated by the different replications) thanks to a Chi2 test.
Further modifications (February 2014):
Two minor modifications have been made, to allow (freashly) dead trees and/or the largest trees to be harvested in priority :