Felipe N. Begliominia, Fabian Jörg Fischerb, c, Srinivasan Keshavd, Pedro H.S. Brancalione, f, g, Paulo G. Molinh, f, Angelica M. Almeyda Zambranoi, Angélica Faria de Resendee, Cassio Toledoj, Catherine Torres de Almeidak, Danilo Roberti Alves de Almeidal, m, Davi Ernesto Alvarenga Césarn, Eben N. Broadbento, Juliano van Melise, p, Laura Barbosa Vedovatoe, Paulo Ricardo da Silva Rodriguesn, Ricardo Ribeiro Rodriguesj, Vinicius Castro Souzaj, David Coomesa
a Conservation Research Institute and Department of Plant Sciences, David Attenborough Building, University of Cambridge, Pembroke St, Cambridge CB2 3QZ, United Kingdom
b School of Life Sciences, Ecosystem Dynamics and Forest Management in Mountain Landscapes, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, Freising 85354 Germany
c School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, United Kingdom
d Computer Science Department, University of Cambridge, William Gates Building, 15 JJ Thomson Ave, Cambridge CB3 0FD, United Kingdom
e Department of Forest Sciences, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, São Paulo 13418-900, PO Box 9, Brazil
f Center for Carbon Research in Tropical Agriculture, University of São Paulo, Piracicaba, SP 13418-900, Brazil
g Re.green, Rio de Janeiro, RJ 22470-060, Brazil
h Federal University of São Carlos, Center of Natural Sciences, Rua Serafim Libaneo, 04, Campina do Monte Alegre, São Paulo 18245-970, PO Box 64, Brazil
i Spatial Ecology and Conservation (SPEC) Lab, Center for Latin American Studies, University of Florida, Gainesville, FL 32611 USA
j Department of Biological Sciences, Universidade de São Paulo, Luiz de Queiroz College of Agriculture, Piracicaba, SP 13418-900, Brazil
k Department of Geography, Federal University of Paraná, Curitiba, Brazil
l Bioflore, Piracicaba, Brazil
m Universidade do Carbono – brCarbon, Piracicaba, Brazil
n Suzano S.A., Av. Lírio Corrêa, 1465, Cariobinha, Limeira, SP 13473-762, Brazil
o Spatial Ecology and Conservation (SPEC) Lab, School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611 USA
p Centro de Ciências Agrárias, Universidade Federal de São Carlos, Araras, Brazil
b School of Life Sciences, Ecosystem Dynamics and Forest Management in Mountain Landscapes, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, Freising 85354 Germany
c School of Biological Sciences, University of Bristol, Bristol BS8 1TQ, United Kingdom
d Computer Science Department, University of Cambridge, William Gates Building, 15 JJ Thomson Ave, Cambridge CB3 0FD, United Kingdom
e Department of Forest Sciences, Luiz de Queiroz College of Agriculture, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, São Paulo 13418-900, PO Box 9, Brazil
f Center for Carbon Research in Tropical Agriculture, University of São Paulo, Piracicaba, SP 13418-900, Brazil
g Re.green, Rio de Janeiro, RJ 22470-060, Brazil
h Federal University of São Carlos, Center of Natural Sciences, Rua Serafim Libaneo, 04, Campina do Monte Alegre, São Paulo 18245-970, PO Box 64, Brazil
i Spatial Ecology and Conservation (SPEC) Lab, Center for Latin American Studies, University of Florida, Gainesville, FL 32611 USA
j Department of Biological Sciences, Universidade de São Paulo, Luiz de Queiroz College of Agriculture, Piracicaba, SP 13418-900, Brazil
k Department of Geography, Federal University of Paraná, Curitiba, Brazil
l Bioflore, Piracicaba, Brazil
m Universidade do Carbono – brCarbon, Piracicaba, Brazil
n Suzano S.A., Av. Lírio Corrêa, 1465, Cariobinha, Limeira, SP 13473-762, Brazil
o Spatial Ecology and Conservation (SPEC) Lab, School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL 32611 USA
p Centro de Ciências Agrárias, Universidade Federal de São Carlos, Araras, Brazil
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.
Keywords
Repeated lidar; Carbon credits; Canopy height model; Digital terrain model; Digital surface model; Natural regeneration; Atlantic Forest