Estimating soil organic carbon stocks in Pinus halepensis mill. stands using lidar data and field inventory

Abstract. Accurate estimation of soil organic carbon (SOC) in forest ecosystems is essential for quantifying their contribution as carbon sinks and improving management strategies in the face of climate change. The objective of this study was to model SOC in Pinus halepensis Mill. stands using structural metrics derived from LiDAR data from the National Aerial Orthophotography Plan (PNOA). The study area covered 46.8 hectares located in the municipality of Ampudia, Palencia (Spain). To carry out the work, systematic soil sampling and a forest inventory were conducted. LiDAR technology was also applied and 87 structural metrics were obtained. These metrics were integrated with edaphic variables and above-ground biomass data to build predictive models of carbon stock using multivariate regression techniques.

Among the models evaluated, the Random Forest algorithm showed the best performance in cross-validation (R² = 0.81; RMSE = 7.73 Mg/ha), demonstrating adequate predictive capacity compared to other models. The proposed approach made it possible to evaluate the potential of LiDAR data from airborne laser scanning (ALS), acquired within the framework of general mapping programmes, as an effective tool for the spatial estimation of SOC. This procedure, validated on an empirical basis, provides a useful methodological basis for advancing in the estimation of SOC through remote sensing, contributing to improve the quantification of soil-related ecosystem services.