What does this research mean for the field?

High-resolution photogrammetric Canopy Height Models provide significantly greater structural fidelity and precision for forest disturbance monitoring compared to global 25m satellite-derived models, which systematically underestimate mature forest canopies due to signal saturation. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

June 6, 2026Open Access

Evaluation of Global and High-Resolution Canopy Height Models for Forest Monitoring and Disturbance Detection: From GEDI Footprint to Deep Learning High-Resolution Mapping

Puntos clave

The central aim is to evaluate the usability of global and high-resolution canopy height models for forest monitoring and disturbance detection.
Compared four global canopy height models (GFCH, GMTCH, HRCH, EUCH) at 25 m resolution to ALS reference layers from 2018 and 2024.
Assessed high-resolution model PALS with photogrammetry for vertical stability and precision on slopes >25°.
Evaluated deep learning approaches like ArcGIS Living Atlas for canopy modeling and disturbance detection.
PALS achieved an RMSE of 4.89 m and ME of 1.86 m, outperforming global models on structural fidelity.
High-resolution models like PALS detected clear-cuts with an F1 score of 0.353 but only captured 51% of the reference area.
The Living Atlas model detected 118.6% of the area with an F1 score of 0.326, showing balanced sensitivity despite geometric inaccuracies.

Resumen

Accurate mapping of forest canopy height is fundamental to modern forestry, providing essential structural data for biomass estimation and monitoring forest health. This study evaluates the broad usability of global (25 m) and high-resolution (1 m) Canopy Height Models (CHMs) by comparing them against temporally aligned Airborne Laser Scanning (ALS) reference layers from 2018 and 2024. At the 25 m scale, we evaluated four global products: Global Forest Canopy Height (GFCH), Global Map of Tree Canopy Height (GMTCH), High-Resolution Canopy Height model of Earth (HRCH), and Europe Temporal Canopy Height (EUCH). These satellite-derived models exhibit significant height-dependent limitations, systematically underestimating mature forest canopies (>30 m) by more than 15 m due to signal saturation, though EUCH and GMTCH performed moderately better. Transitioning to 1 m high-resolution data revealed a dramatic recovery in structural fidelity. A photogrammetrically derived model (PALS) achieved an RMSE of 4.89 m and a Mean Error (ME) of 1.86 m, demonstrating remarkable vertical stability across complex topography, even on slopes >25°. While coniferous stands produced higher absolute errors (RMSE = 6.75 m) than deciduous stands (RMSE = 6.19 m) due to spire-like architectures, PALS effectively captured fine-scale canopy textures. Experimental deep learning architectures, specifically the ArcGIS Living Atlas model, showed promise with an RMSE of 8.90 m, though out-of-the-box implementations struggle without local calibration. For forest disturbance monitoring, a distinct performance trade-off emerged. High-resolution photogrammetry (PALS) provided the highest overall precision for identifying clear-cuts (F1 = 0.353) but was conservative, capturing only 51% of the reference area. In contrast, the global HRCH model captured the total spatial footprint (103.9% of area) despite its geometric inaccuracies. The Living Atlas deep learning model offered the most balanced sensitivity, detecting 118.6% of the area with a competitive F1 score of 0.326. Ultimately, digital aerial photogrammetry provides a cost-effective solution for frequent operational updates, such as the two-year national mapping cycle in the Czech Republic.

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