Framework for commercial thinning assessment with multitemporal lidar and spatially explicit growth simulation

Commercial thinning (CT) aims to mitigate inter-tree competition, allow immediate timber recovery, and improve the growth, value, and resilience of residual trees. However, the implementation and assessment of CT is often complicated by costly field plots that may not capture the heterogeneity of pre-treatment and post-treatment CT stands. Field plots may also struggle to quantify CT responses, particularly when sample sizes are small, repeated measurements are not possible, and unthinned controls are not available. To address these issues, we propose a spatial CT assessment framework that employs multitemporal airborne laser scanning, individual tree detection, and a spatially-explicit growth model to quantify CT removals, project long-term CT responses, and compare yield outcomes between thinned and unthinned scenarios. Then, we apply this framework to eight CT stands in British Columbia, Canada (295 ha total; 366,863 lidar-detected trees), model these stands from initial stand ages of 31–40 to age 100, and explore CT impacts on growth, competition, merchantable volume, and piece size. CT treatment intensity was not uniform and varied spatially between and within stands, with mean basal area removals ranging from 23.9% to 42.6%. Under long-term projections, thinned stands yielded 49% more large-log volume (>40 cm diameter) at age 80 (195 vs 131 m 3 ha −1 ), and unthinned stands maintained higher overall stem volumes with fewer large trees. After accounting for CT removals, cumulative volume growth was similar between thinned and unthinned scenarios. This framework shows promise to support the establishment, monitoring, and enhancement of CT treatments in operational stands.