A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

Abstract

Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA) and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model). Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS) of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 5-year-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence technology has substantial potential in forest modelling.

Implications of future bioclimatic shifts on Portuguese forests

“…Forest is an important natural resource that sustains numerous socioeconomic sectors and provides a wide range of goods and services (Ashraf et al 2015). As an endogenous and renewable natural resource, forest also promotes wealth distribution (Vedeld et al 2007).…”

Artificial Neural Networks Applied in Forest Biometrics and Modeling: State of the Art (January/2007 to July/2018)

“…Of these, we can mention Castro et al (2013), whose objectives addressed the issue of Mortality, DBH (Diameter at Breast Height) and Total Height. Others include Ashraf et al (2015) and , who worked with the Volume Increase and Basal Area, and Miguel et al (2015), who worked with Volume and Final Height. Nunes and Gorgens (2016) conducted a study on Volume and Diameter, and Vieira et al (2018) dealt with Growth and Future Height.…”

“…Outputs: Each neuron represents a type of native class, as follows: Agricultural land (1), water (2), cerrado (3), deciduous forest (4), eucalyptus (5), others (6), pastures (7) and track (8)." Ashraf et al (2015) This study turned to the construction of a simple growth and yield model that has the capacity to predict the growth of individual trees under different climate change scenarios.…”

Use of artificial neural networks to assess yield projection and average production of eucalyptus stands

“…Besides, RNA may also be employed to predict production improvement and to project forthcoming climate conditions (Ashraf et al, 2015). Thereby, it is possible to estimate upcoming productions in different climate scenarios.…”