Quantifying carbon storage from atmospheric and LiDAR measurements

By Lena Sakalak

Abstract
Forests cover 31% of the world’s total land area, absorbing twice as much carbon as they emit annually (FAO, 2020). Monitoring the forest carbon cycle is crucial, prompting the development of methodologies that combine in situ atmospheric and LiDAR (Light Detection and Ranging) measurements. The purpose of this study is to investigate the applicability of this combination technique in a Scots pine (Pinus sylvestris) forest located in the Loobos, Netherlands. By using the AHN (Actueel Hoogtebestand Nederland) data, collected during the last decade (2010-2022), and the Eddy Covariance (EC) from the local flux tower, the yearly CO2 emissions were calculated and compared. Additionally, Nederlandse Bosinventarisatie (NBI; Dutch Forest Inventory) plot validation data, which includes biomass per tree component (branches, roots, needles, and stem), was used. The findings show that EC measurements indicated higher carbon levels in comparison to Airborne Laser Scanning (ALS) measurements. Different variables may impact the analysis, as the meteorological conditions; however, in this study, only the AHN resolution was examined. While more research is needed to evaluate the combination of the two datasets, this study demonstrates the potential of using LiDAR and atmospheric data for forest carbon monitoring.