Unveiling spatial variations of high forest live biomass carbon stocks of Gabon using advanced remote sensing techniques

Abstract

Gabon is one of 11 high-forest, low-deforestation (HFLD) countries in the world. It has the highest proportion of preserved forests in the Congo Basin and is the first country to create large forest carbon offset credits in the market. However, about 60% of forests in Gabon is allocated to logging concessions, causing concerns for forest degradation and the sustainability of carbon credits. Here, we use a combination of air- and space-borne remote sensing data and the-state-of-the-art gradient boosted regression trees to estimate forest structure and aboveground biomass carbon density (ACD) of trees at 100 m resolution for the year circa 2020. Mapping spatial variations of ACD across floristically diverse landscapes, we estimate average density and total living carbon storage of trees at the national and sub-national levels. The estimated ACD of trees in forestlands within the country was 142.12 ± 7.3 Mg C ha ^−1 with the highest values found in central Gabon (150.08 ± 5.8 Mg C ha ^−1 ) and on highlands (161.18 ± 6.7 Mg C ha ^−1 ). On average, in every region, ACD of forests found within logging concessions (149.89 ± 6.1 Mg C ha ^−1 ) was higher than unmanaged forests of unprotected areas (122.81 ± 4.4 Mg C ha ^−1 ), indicating the combined effects of logging in carbon-rich forests and increased productivity due to management. The country’s total estimated biomass carbon for trees (above and belowground) stored within the forests was 4.14 ± 0.3 Pg C with 68% found within logging concessions and 14% within protected areas. The map provides high precision and comprehensive assessment of carbon stocks of trees in Gabon’s forests, significantly improving the country’s prospects to implement climate mitigation policies and to participate in carbon markets.

Keywords

• carbon stock
• Gabon
• logging
• gradient boosted regression trees
• remote sensing