Tracking forest carbon and growth in large-scale passive restoration using multi-temporal airborne lidar

Abstract

Tropical forest restoration is a key natural climate solution, yet monitoring structural and carbon changes at regional scales remains challenging. Multi-temporal Airborne Laser Scanning (ALS) provides a powerful tool to capture these dynamics, though sensor inconsistencies can limit comparability, particularly in regenerating landscapes with subtle structural changes. Here, we present the first large-scale, high-resolution (30 m) assessment of tropical forest height and carbon change in passive restoration areas over six years. We developed a framework to correct inter-survey ALS biases arising from terrain model offsets and pulse density differences. A model calibrated with biome-specific field plots (RSE = 43 ± 11 %) converted ALS height changes into aboveground carbon density. Using lidar-derived growth rates, topography, and soil variables, we projected pasture restoration outcomes over 30 years along a known secondary succession gradient. We found that the lidar-measured net carbon accumulation rate of young forests (2.03 Mg C/ha/yr) was among the highest reported in multi-temporal lidar studies and approximately twice that reported for other temperate and tropical biomes. Restoration activities generated a net carbon gain of 45,000 Mg CO2/yr across 69 km2, equivalent to the annual footprint of 20,000 people. Forests younger than 20 years were projected to accumulate on average 1.61 ± 0.83 Mg C/ha/yr, with peak growth at 19 years (2.01 ± 0.92 Mg C/ha/yr). Simulated carbon uptake rates were below Neotropical and IPCC estimates but 45 % above the regional mean from a global chronosequence-based dataset, highlighting the importance of locally calibrated models for accurate carbon accounting. Restoring legally obligated pastures in the study region could sequester 191 Mg CO2/ha over 30 years, totalling ∼14,700,000 Mg CO2 across 770 km2. This study demonstrates that multi-temporal ALS delivers rapid, large-scale, and reliable data on forest structure change, enabling accurate carbon accounting and restoration forecasts. These outputs are central for compliance with carbon market standards and can directly support the planning, prioritisation, and scaling of tropical forest restoration projects.