A1 Journal article (refereed)
Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests (2022)


Kuronen, M., Särkkä, A., Vihola, M., & Myllymäki, M. (2022). Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests. Environmental and Ecological Statistics, 29(1), 185-205. https://doi.org/10.1007/s10651-021-00514-3


JYU authors or editors


Publication details

All authors or editorsKuronen, Mikko; Särkkä, Aila; Vihola, Matti; Myllymäki, Mari

Journal or seriesEnvironmental and Ecological Statistics

ISSN1352-8505

eISSN1573-3009

Publication year2022

Publication date20/08/2021

Volume29

Issue number1

Pages range185-205

PublisherSpringer Science and Business Media LLC

Publication countryUnited Kingdom

Publication languageEnglish

DOIhttps://doi.org/10.1007/s10651-021-00514-3

Publication open accessOpenly available

Publication channel open accessPartially open access channel

Publication is parallel published (JYX)https://jyx.jyu.fi/handle/123456789/77454

Publication is parallel publishedhttps://arxiv.org/abs/2005.01962


Abstract

We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points xx affects another set of points yy but not vice versa. We use the model to investigate the effect of large trees on the locations of seedlings. In the model, every point in xx has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The parameters of the model are estimated in a Bayesian framework using Markov chain Monte Carlo where a Laplace approximation is used for the Gaussian field of the LGCP model. The proposed model is used to analyze the effect of large trees on the success of regeneration in uneven-aged forest stands in Finland.


Keywordsmathematical modelsBayesian analysisMonte Carlo methodsMarkov chainssilviculturetree standregeneration (biology)

Free keywordsBayesian inference; competition kernel; Laplace approximation; MCMC; spatial random effects; tree regeneration


Contributing organizations


Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-14-06 at 23:47