Summary
Published in Canadian Journal of Forest Research 47(8): 1066-1074. https://doi.org/10.1139/cjfr-2016-0498
Survival analysis methods make better use of temporal information, accommodate multiple levels of explanatory variables, and are meant to deal with interval-censored data. In a context of harvest modeling, this approach could improve some known limitations. In this study, we used data from a network of permanent plots in the province of Quebec, Canada, as a real-world case study. We tested the potential of survival analysis to predict plot-level harvest probabilities from plot- and regional-level variables. The approach also included random effects to account for spatial correlations. The results showed the potential of survival analysis to provide annual predictions of harvest occurrence. Both regional and time-varying variables, as well as spatial patterns, had important effects on the probability of a plot to be harvested. Respectively, reductions in the annual allowable cut volumes led to a decrease in the harvest probabilities. Greater harvest probabilities were associated with the broadleaved dynamics class and higher values of basal area. In contrast, they were decreased by stem density and slope classes. The spatial random effect resulted in an improvement of the model fit. Our plot-level model improved some limitations reported in previous studies by taking the effect of a time-varying regional variable into account.
Sector(s):
Forests
Categorie(s):
Scientific Article
Theme(s):
Forest Growth and Yield Modelling, Forestry Research, Forests
Departmental author(s):
Author(s)
MELO, L.C., R. SCHNEIDER, R. MANSO, Jean-Pierre SAUCIER and M. FORTIN
Year of publication :
2017
Format :
PDF available upon request
How to get the publication :
Keywords :
interval-censored data, regional-level variable, random effect, likelihood, harvest model, long-term projection, strategic planning, forestry research scientific article
