Forest Growth Modeling and Site Productivity

Forest growth modelling integrates empirical, statistical and process-based approaches to predict stand development, biomass accumulation and carbon dynamics across diverse environments. Central to these efforts is the quantification of site productivity, a measure of the capacity of a location to support tree growth under prevailing climate, soil and management regimes. Traditional methods employ site index curves to relate dominant tree height to stand age, while advanced techniques harness machine learning and remote sensing to capture spatial heterogeneity and temporal trends. By combining forest inventory data, airborne and satellite measurements, and environmental variables, modern models provide high‐resolution assessments of site quality and growth potential. These insights inform sustainable management, yield optimisation and climate adaptation strategies, ensuring the resilience and multifunctionality of forest ecosystems worldwide.

Research from Nature Portfolio

Recent studies have demonstrated how soil moisture gradients drive landscape‐scale variation in growth potential. One investigation applied an age‐independent difference model across boreal plots, using repeated airborne laser scanning and field measurements to estimate site quality in terms of maximum achievable tree height. Results revealed a nonlinear response of productivity to soil moisture, with intermediate moisture conditions supporting the highest growth potential and severely waterlogged sites showing reduced site quality. This integration of high‐resolution remote sensing with rigorous field data refines our understanding of site productivity and enhances the mapping of productive forest landscapes.

Forest Growth Modeling and Site Productivity publication trend

The graph below shows the total number of articles in forest growth modeling and site productivity across all publications each year (not limited to Nature Index journals).

Technical terms

Site index: An empirical measure of potential site productivity based on the average height of dominant trees at a reference age, used to classify site classes.

Site quality: The intrinsic capacity of a location to support forest growth, often quantified by maximum potential tree height or biomass under optimal conditions.

Lorey’s mean height: A basal area–weighted average tree height, serving as an indicator of stand development and site productivity.

Random forest: A machine learning ensemble technique that builds multiple decision trees and averages their outputs for robust regression or classification.

Spectral vegetation index: A numerical indicator derived from ratios of satellite sensor spectral bands, used to assess vegetation properties such as biomass, vigour and growth potential.

References

1. Higher site productivity and stand age enhance forest susceptibility to drought-induced mortality. Agricultural and Forest Meteorology (2023).
2. Predicting the growth suitability of Larix principis-rupprechtii Mayr based on site index under different climatic scenarios. Frontiers in Plant Science (2023).
3. Tree growth potential and its relationship with soil moisture conditions across a heterogeneous boreal forest landscape. Scientific Reports (2024).
4. Combining satellite images with national forest inventory measurements for monitoring post-disturbance forest height growth. Frontiers in Remote Sensing (2024).