Data-intensive modeling of forest dynamics

Abstract

a b s t r a c t 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 dataintensive 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.

Autoregressive Modeling of Forest Dynamics

“…As the result, in order to capture patch dynamics of the US and Canadian forests, we need to consider more complicated models. In particular, if we discretize forest dynamics with respect to both time and state variable (biomass) we can achieve an adequate representation of forest patch dynamics within the framework of Markov chains [27,28,39]. Markov chains provide an analytically tractable representation of forest stand dynamics, while they have a discretization error that is challenging to quantify.…”

“…Here, we model dynamics of Quebec forests using a traditional AR(1) process borrowed from quantitative finance without modifications. We select the Quebec forest inventory for this proof of concept work as it is a long term dataset collected over more than 3 decades using the same field survey protocol [27]. We operate with the same biomass and basal area data derived from Quebec forest inventories in our previous publication on Markov chain modeling (data-mining protocol is available in [25,27]).…”

“…We select the Quebec forest inventory for this proof of concept work as it is a long term dataset collected over more than 3 decades using the same field survey protocol [27]. We operate with the same biomass and basal area data derived from Quebec forest inventories in our previous publication on Markov chain modeling (data-mining protocol is available in [25,27]). For both individual patches and the Quebec region, we model logarithms of biomass or basal area as autoregressive process.…”

“…Another challenge to our understanding of forest dynamics is the multidimensional nature of this complex adaptive system [21,27]. Forest disturbances are traditionally associated with a loss of biomass.…”

“…Forest disturbances are traditionally associated with a loss of biomass. However, Markov chain models based only on biomass do not capture forest succession comprehensively [27,39]. This limitation motivates the need for alternative formulations that are able to consider several forest dimensions instead of only one.…”