Modelling height growth of temperate mixedwood forests using an age-independent approach and multi-temporal airborne laser scanning data

Forest inventories provide information regarding the status of a range of attributes as well as enabling predictive applications. Growth and yield models are essential tools for sustainable forest management, importantly enabling projections of future forest conditions (such as height growth). To select the most appropriate growth trajectory, site index models are commonly used to quantify the productivity of a given site. However, applying these methods to more complex, multi-species, and multi-age forests can be challenging due to deviations from the assumptions made for even-aged stands. In this study, we provide a comprehensive indicator of site quality for more complex and irregular stand structures by developing age-independent height growth models for various forest types. We used multi-temporal airborne laser scanning (ALS) data from 2005, 2012, and 2018 in the Great Lakes-St. Lawrence forest region in southern Ontario, Canada. The stochastic differential equations approach was used to develop age-independent height models and a height growth rate index as a proxy of site quality from ALS-derived height metrics. We evaluated the sensitivity of the models using two different modelling approaches and found that the model that incorporated data from both periods (i.e., 2005-2012 and 2012-2018) generally provided the lower root mean square error (RMSE) value for most forest types. Overall, our results showed good agreement between the model predictions of top height and observed top height in 2018 from field plots for all forest types. We demonstrated the use of these models by creating a system of height growth curves for each forest type and producing a map of site quality for a mixedwood forest (similar to 10,000 ha) at a spatial resolution of 25 m. The approach developed herein leverages the accurate, spatially detailed characterization of canopy heights afforded by ALS data and is independent of stand age, which is challenging to measure accurately and is typically not available at a spatial resolution that is commensurate with the ALS data. Additionally, the demonstrated approach can be adapted to other data sources that accurately capture canopy heights (i.e., digital aerial photogrammetric or DAP), thereby increasing the possible geographic extent of height growth estimates.