Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests
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
Published inEnvironmental and Ecological Statistics
© 2021 the Authors
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. ...
PublisherSpringer Science and Business Media LLC
Publication in research information system
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Additional information about fundingOpen access funding provided by Natural Resources Institute Finland (LUKE). MK, MM and MV were financially supported by the Academy of Finland (Project Numbers 306875, 327211, 295100 and 315619) and AS by the Swedish Research Council (VR 2018-03986).
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