LienardGravelStrigul2015

Référence

Lienard, J.F., Gravel, D., Strigul, N.S. (2015) Data-intensive modeling of forest dynamics. Environmental Modelling and Software, 67:138-148. (Scopus )

Résumé

Forest dynamics are highly dimensional phenomena that are not fully understood theoretically. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a data-intensive methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management. © 2015 Elsevier Ltd.

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@ARTICLE { LienardGravelStrigul2015,
    AUTHOR = { Lienard, J.F. and Gravel, D. and Strigul, N.S. },
    TITLE = { Data-intensive modeling of forest dynamics },
    JOURNAL = { Environmental Modelling and Software },
    YEAR = { 2015 },
    VOLUME = { 67 },
    PAGES = { 138-148 },
    NOTE = { cited By 3 },
    ABSTRACT = { Forest dynamics are highly dimensional phenomena that are not fully understood theoretically. Forest inventory datasets offer unprecedented opportunities to model these dynamics, but they are analytically challenging due to high dimensionality and sampling irregularities across years. We develop a data-intensive methodology for predicting forest stand dynamics using such datasets. Our methodology involves the following steps: 1) computing stand level characteristics from individual tree measurements, 2) reducing the characteristic dimensionality through analyses of their correlations, 3) parameterizing transition matrices for each uncorrelated dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. Applying our methodology to a forest inventory database from Quebec, Canada, we discovered that four uncorrelated dimensions were required to describe the stand structure: the biomass, biodiversity, shade tolerance index and stand age. We were able to successfully estimate transition matrices for each of these dimensions. The model predicted substantial short-term increases in biomass and longer-term increases in the average age of trees, biodiversity, and shade intolerant species. Using highly dimensional and irregularly sampled forest inventory data, our original data-intensive methodology provides both descriptions of the short-term dynamics as well as predictions of forest development on a longer timescale. This method can be applied in other contexts such as conservation and silviculture, and can be delivered as an efficient tool for sustainable forest management. © 2015 Elsevier Ltd. },
    AUTHOR_KEYWORDS = { Data-intensive model; Forest dynamics; Gibbs sampling; Markov chain model; Markov chain Monte Carlo; Patch-mosaic concept; Plant population and community dynamics },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1016/j.envsoft.2015.01.010 },
    KEYWORDS = { Biodiversity; Chains; Dynamics; Forecasting; Markov processes; Matrix algebra; Population statistics, Community dynamics; Data intensive; Forest dynamics; Gibbs sampling; Markov chain models; Markov Chain Monte-Carlo; Patch-mosaic concept, Forestry, community dynamics; forest dynamics; forest inventory; forestry modeling; Gibbs free energy; Markov chain; sampling; silviculture, Biodiversity; Data Bases; Forests; Sampling, Canada; Quebec [Canada] },
    SOURCE = { Scopus },
    URL = { http://www.scopus.com/inward/record.url?eid=2-s2.0-84922665184&partnerID=40&md5=1a31aff92b56f66e68cee145ab8f0d84 },
}

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