A comparison between derivative and numerical optimization methods used for diameter distribution estimation

ABSTRACT

Modeling diameter distribution of forest stands requires suitable function(s) with the appropriate parameter estimation methods. To date, the parameters of most familiar functions in forestry have been estimated with derivative methods such as moment, percentiles, conditional maximum likelihood, etc. with little emphasis on numerical optimization. Therefore, this study compares the suitability of derivative and numerical methods to estimate diameter distribution of forest stands. The derivative and optimization methods were used to fit six commonly used functions in forestry including Weibull (2 and 3-parameter), Johnson’s SB, beta, generalized beta and gamma. The data comprised 303 and 96 permanent sample plots temperate forest (Eucalyptus globulus and Pinus radiata, respectively) and 65 temporary sample plots from tropical forest (Gmelina arborea). Different indices such as Kolmogorov–Smirnov, Cramer-von Mises and mean squared error were used to assess the methods. The results show that the derivative method by moments provided the best fit for beta and Johnson’s S_B distributions. The optimization methods (“lifereg” and “optim”) were more suitable for the Weibull and gamma distributions. Both methods were appropriate for the generalized beta distribution.

Abbreviation: PW2P: Percentile Weibull two-parameter; MW2P: Moments Weibull two-parameter; MLLW2P: Maximum likelihood “lifereg” Weibull two-parameter; MLOW2P: Maximum likelihood “optim” Weibull two-parameter; PW3P: Percentile Weibull three-parameter; MW3P: Moments Weibull three-parameter; MLLW3P: Maximum likelihood “lifereg” Weibull three-parameter; MLOW3P: Maximum likelihood “optim” Weibull three-parameter; CMLJ: Conditional maximum likelihood Johnson’s SB; MJ Moments Johnson’s SB; MLOJ: Maximum likelihood “optim” Johnson’s SB; MB: Moments beta; MLOB Maximum likelihood “optim” beta; MGB: Moments generalized beta; MLOGB Maximum likelihood “optim” generalized beta; MG2P: Moments gamma two-parameter; MLOG2P: Maximum likelihood “optim” gamma two-parameter

KEYWORDS:

• Diameter distribution
• parameter estimation
• numerical optimization

Disclosure statement

No potential conflict of interest was reported by the author(s).

ORCID

José Javier Gorgoso-Varela http://orcid.org/0000-0002-8278-7876

Friday Nwabueze Ogana http://orcid.org/0000-0002-8388-204X

Peter Oluwagbemiga Ige http://orcid.org/0000-0003-3695-3076

Additional information

Funding

This work was supported by the Government of Spain, Ministry of Economy, Industry and Competitiveness under a Torres Quevedo Contract PTQ-16-08445. This study also was supported by the Ministerio de Ciencia e Innovación through the project entitled “Influencia de los tratamientos selvícolas de claras en la producción, estabilidad mecánica y riesgo de incendios forestales en masas de P. radiata D. Don y Pinus pinaster Ait. en el Noroeste de España” (AGL2008-02259), and an ongoing research project entitled “Growth and yield modelling of clonal and seedling plantations of E. globulus Labill. of NW Spain” (code AGL2010-22308-C02-01), funded by the Ministry of Science and Innovation of Spain and the European Comission through the ERDF program for the period 2011–2013.