Modeling and spatialization of biomass and carbon stock using unmanned Aerial Vehicle Lidar (Lidar-UAV) metrics and forest inventory in cork oak forest of Maamora

Abstract

Recently, the concerns about climate change have heightened the need for effective methods for estimating and mapping Biomass and Carbon stock at local, national, continental, and global scales. Reliable Biomass and Carbon stock quantification and spatialization is a challenge, especially in degraded Mediterranean Cork oak forest. To estimate and map Biomass (Btree−Total) and Carbon stock (Cst−total), we explored an improved approach using extracted metrics collected by Lidar-UAV (unmanned aerial vehicles Lidar), combined with forest inventory data. We approach three types of models for data analysis: Simple linear regression, multiple linear regressions, and stepwise multiple linear regression. The best Biomass and Carbon stock model fit is the Stepwise multiple linear regressions, involving the following metrics: maximum elevation, canopy cover and point cloud density and intensity. Our finding provides a quantification and spatialization Biomass and Carbon stock model based on Lidar-UAV metrics in Cork Oak Mediterranen forest and the results confirm the degraded state of Maamora Forest with a Biomass and Carbon stock relatively medium to low.