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

Résumé fourni par la source

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 derive plot-level indicators of vertical growth dynamics from mixed-source canopy height model (CHM) time series, harmonized against forest inventory plots at ~1000 m² resolution. We estimated plot-level vertical growth to model reference trajectories as a function of initial canopy height. Plot-level 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 top-of-canopy height bias 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. Plot-level growth estimates decreased as initial heights increased, with steeper declines in broadleaf than coniferous stands. Spatially clustered growth deviations (Moran's I = 0.30–0.44, p < 0.001) suggested systematic spatial variations in performances 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.

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