May 10, 2014

Data-intensive multidimensional modeling of forest dynamics

BioRxiv : the Preprint Server for Biology
Jean F LienardNikolay S. Strigul

Abstract

1. Forest dynamics are highly dimensional phenomena that are poorly understood theoretically. Modeling these dynamics is data-intensive and requires repeated measurements taken with a consistent methodology. 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 methodology for predicting forest stand dynamics using such datasets. 2. 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 independent dimension using Gibbs sampling, and 4) deriving predictions of forest developments at different timescales. 3. Applying our methodology to the Quebec forest inventory database, we discovered that four independent dimensions were required to describe the stand structure. 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, biodiversit...Continue Reading

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Mentioned in this Paper

2-Dimensional
Trees (plant)
Standing Position
Description
Disease Management
Species
Analysis
Sampling - Surgical Action

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