Estimation of tree biomass using multisource remote sensing data

Abstract. Forests serve as crucial carbon sinks, sequestering a significant portion of global terrestrial aboveground biomass and playing a crucial role in the carbon cycle. Similarly, urban greenery acts as a carbon sinks within urban areas, emphasizing the importance of accurately estimating biomass in both natural and urban environments for effective climate mitigation strategies. This paper proposes a method for extracting tree attributes, diameter at breast height (DBH) and height, utilizing point cloud data generated from LiDAR technology and Terrestrial Laser Scanning (TLS). Accurate calculations of trunk shape and radius were archived by extracting point cloud data in the trunk area for each tree. The paper compares the efficacy of UAV LiDAR (ULS) technology with TLS in the estimation of the DBH radius of the tree. This research highlights the potential of point cloud datasets in biomass estimation for urban greenery and dense forests, contributing to advancements in remote sensing technology for forest management and climate mitigation efforts.