Monitoring forest growth dynamics from harmonized multi-source time series of canopy height

Abstract

Most remote sensing assessments of forest change focus on canopy cover, whereas the dynamics of vertical growth remain less investigated despite their sensitivity to external forcings. Yet, the increasing availability of canopy height products from multiple platforms facilitates spatially explicit characterization of vertical growth dynamics, provided that systematic measurement biases between sources are properly addressed. Here, we present a transferable workflow to map vertical growth dynamics from mixed-source canopy height model (CHM) time series, harmonized against forest inventory plots at ∼ 1000 m² resolution. We estimated vertical growth to model reference trajectories as a function of initial canopy height. Deviations from these trajectories were quantified, providing contextualized growth information. This approach was illustrated in the Belgian Ardenne using multi-source CHMs derived from recent aerial imagery and lidar data (2006–2021). Across all acquisitions, harmonization reduced systematic bias in dominant height estimates from 2.53 m to 0.01 m (RMSE = 1.79 m, R2 = 0.92), with canopy structure rather than acquisition parameters as the primary driver of bias. Subsequent growth decreased as initial heights increased, with earlier declines in broadleaf than coniferous stands. Spatially clustered growth deviations (Moran’s I = 0.31–0.46, p < 0.001) suggested systematic variation in performance among otherwise similar forests. By leveraging heterogeneous sources of canopy height, this workflow enables spatially exhaustive characterization of forest growth, adding structural understanding into multi-scale forest monitoring and resilience assessments.