Mapping Forest Carbon by Fusing Terrestrial and Airborne LiDAR Datasets

Abstract:

The storage and flux of terrestrial carbon (C) is one of the largest and most uncertain components of the global C budget, the vast majority of which is held within the biomass of the world’s forests. However, the spatial distribution and quantification of forest C remains difficult to measure on a large scale. Remote sensing of forests with airborne LiDAR has proven to be an extremely effective method of bridging the gap between data from plot-level forestry mensuration and landscape-scale C storage estimates, but the standard method of assessing forest C is typically based on national or regional-scale allometric equations that are often not representative on the local-scale. Improvement of these measurements is necessary in order for collaborative multi-national carbon monitoring programs such as REDD implemented by the UNFCCC to be successful in areas, such as tropical forests, with tree species that have insufficiently documented allometric relationships. The primary goal of this study is to set forth a pipeline for precise non-destructive monitoring of C storage by: 1) determining C storage on 15 1/10^th ha plots in a 25.6 ha Virginia temperate forest using the recently updated national allometric equations from Chojnacky et. al 2014, 2) comparing these estimates to non-destructively determined individual tree biomass using several semi-automated approaches of three-dimensionally analyzing the point cloud from a high-precision Terrestrial Laser Scanner (TLS), and 3) creating a predictive model of forest C storage by fusing airborne LiDAR data to the plot-level TLS measurements. Our findings align with several other studies, indicating a strong relationship between allometrically-derived C estimates and TLS-derived C measurements (R²=0.93, n=30) using relatively few individuals, suggesting the potential application of these methods to species that are understudied or are without allometric relationships. Voxel based C storage was estimated on the plot level and used to develop a model of C storage on the whole-forest scale. The proposed pipeline has the potential to significantly reduce labor costs associated with destructive sampling and simplify the process of detailed C monitoring, while improving the quality and quantity of data retrieved.