An alternative simulation framework to evaluate the sustainability of annual harvest on large forest estates

Fortin, Mathieu; Sattler, Derek F.; Schneider, Robert

doi:10.1139/cjfr-2021-0255

Cited by 3 publications

(1 citation statement)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are other individual tree models that could also be applied to CCF using data from remote sensing sources, such as CAPSIS which is already used to assess the sustainability of harvests by predicting the impacts of harvests on the future growth of trees in the stands [110]. Such insights within CCF could allow forest managers to predict the impacts of management and harvests on a CCF stand.…”

Section: Remote Sensing For Ccf Yield Modelling and Forecastingmentioning

confidence: 99%

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

Stoddart,

Suarez,

Mason

et al. 2023

Curr. For. Rep.

View full text Add to dashboard Cite

Purpose of Review Continuous cover forestry (CCF) is a sustainable management approach for forestry in which forest stands are manipulated to create irregular stand structures with varied species composition. This approach differs greatly from the traditional approaches of plantation-based forestry, in which uniform monocultures are maintained, and thus, traditional methods of assessment, such as productivity (yield class) calculations, are less applicable. This creates a need to identify new methods to succeed the old and be of use in operational forestry and research. By applying remote sensing techniques to CCF, it may be possible to identify novel solutions to the challenges introduced through the adoption of CCF. Recent Findings There has been a limited amount of work published on the applications of remote sensing to CCF in the last decade. Research can primarily be characterised as explorations of different methods to quantify the target state of CCF and monitor indices of stand structural complexity during transformation to CCF, using terrestrial and aerial data collection techniques. Summary We identify a range of challenges associated with CCF and outline the outstanding gaps within the current body of research in need of further investigation, including a need for the development of new inventory methods using remote sensing techniques. We identify methods, such as individual tree models, that could be applied to CCF from other complex, heterogenous forest systems and propose the wider adoption of remote sensing including information for interested parties to get started.

show abstract

Section: Remote Sensing For Ccf Yield Modelling and Forecastingmentioning

confidence: 99%

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

Stoddart,

Suarez,

Mason

et al. 2023

Curr. For. Rep.

View full text Add to dashboard Cite

show abstract

A bootstrap-based approach to combine individual-based forest growth models and remotely sensed data

Fortin,

van Lier,

Côté

et al. 2024

Forestry: An International Journal of Forest Research

View full text Add to dashboard Cite

Combining forest growth models with remotely sensed data is possible under a generalized hierarchical model-based (GHMB) inferential framework. This implies the existence of two submodels: the growth model itself ($\mathcal{M}_{1}$) and a second submodel that links the growth predictions to some remotely sensed variables ($\mathcal{M}_{2}$). Analytical GHMB estimators are available to fit submodel $\mathcal{M}_{2}$ and account for the uncertainty stemming from submodel $\mathcal{M}_{1}$, i.e. the growth model. However, when the growth model is individual based, it is usually too complex to be differentiated with respect to its parameters. As a result, the analytical GHMB estimators cannot be used. In this study, we developed a bootstrap approach for the GHMB inferential framework in order to combine individual-based forest growth models with remotely sensed data. Through simulation studies, we showed that the bootstrap estimators were nearly unbiased when both submodels were linear. The estimator of the parameter estimates remained nearly unbiased when submodel $\mathcal{M}_{1}$ became complex, i.e. non-differentiable, and submodel $\mathcal{M}_{2}$ was nonlinear with heterogeneous variances and correlated error terms. The variance estimator showed some biases but these were relatively small. We further demonstrated through a real-world case study that the predictions of a complex individual-based model could be linked to a Landsat-8 near-infrared spectral band in the boreal forest zone of Quebec, Canada.

show abstract

Reconciling individual-based forest growth models with landscape-level studies through a meta-modelling approach

Fortin

Lavoie

2022

Can. J. For. Res.

View full text Add to dashboard Cite

Landscape-level studies such as those on forest management planning and carbon accounting rely on large-area growth projections provided by forest growth models. Nowadays, most of these models are individual tree-based models. The detailed input they require and their complexity are a challenge for the integration into a landscape-level study. A possible alternative consists of approximating the complex model through a meta-model. A meta-model mimics the behaviour of the original model, while being simpler in terms of input and computation. In this study, we developed a Bayesian meta-modelling approach that can be used to obtain a simplified growth model from an individual tree-based model. The approach was exemplified through a real-world case study, namely a forest management unit in the province of Quebec, Canada. Using a Markov chain Monte Carlo method, we managed to fit meta-models based on the Chapman-Richards equation or its derivative for the main potential vegetation types. This meta-modelling approach has the advantages of (i) being an effective method of upscaling, (ii) providing simple meta-models suitable for landscape-level studies and (iii) ensuring a proper error propagation from the original individual tree-based model into the meta-model.

show abstract

An alternative simulation framework to evaluate the sustainability of annual harvest on large forest estates

Cited by 3 publications

References 48 publications

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

Continuous Cover Forestry and Remote Sensing: A Review of Knowledge Gaps, Challenges, and Potential Directions

A bootstrap-based approach to combine individual-based forest growth models and remotely sensed data

Reconciling individual-based forest growth models with landscape-level studies through a meta-modelling approach

Contact Info

Product

Resources

About