Yao, C., & Fricker, P. (2024). Building Green Decarbonization for Urban Digital Twin – Estimating Carbon Sequestration of Urban Trees by Allometric Equations using Blend Types of Point Cloud. Blucher Design Proceedings, 94–105. https://doi.org/10.5151/sigradi2023-246
For more detailed information please contact: chaowen.yao@aalto.fi
Research work supervised by Dr. Prof. Pia Fricker at Aalto university
2021 – 2022
Building Green Decarbonization for Urban Digital Twin – Estimating Carbon Sequestration of Urban Trees by Allometric Equations using Blend Types of Point Cloud
Abstract
The achievement of climate neutrality is a fundamental goal for cities in the next 30 years. In order to achieve this goal, this research focuses on a novel tree carbon sequestration utilizing point clouds. Using a multi-algorithm workflow, tree information is extracted to calculate carbon storage from airborne and mobile laser scanning data, using Helsinki as a test case. The study employs local maximum and seeded region growing algorithms to detect tree locations and crown extents from aerial point clouds. A Python script is generated using the DBSCAN algorithm to extract tree point clouds and trunk diameters. The established allometric equations are utilized to calculate the carbon sequestration of trees. The results are integrated into the digital platform, filling the gap in urban digital twins’ carbon storage information. This innovative approach will contribute significantly to urban planning and decision-making for sustainable cities in the face of climate challenges.
This study aims to measure the CS of trees in urban areas and support decision-making in carbon neutrality and UDTs. CS is calculated based on tree trunk diameter and absolute height, using ALS data for tree height extraction and MLS data for trunk diameter extraction. In the Nordic urban environment, trees are often intertwined with other urban facilities, making it challenging to accurately cluster tree point clouds using conventional methods. To address this issue, a Python script is developed to extract tree point clouds and trunk diameters from MLS data based on point cloud density. Due to the lack of local data, the established allometric equations from existing studies are imported to calculate tree carbon sequestration. Compared to methods employed by other researchers, our approach offers precise tree extraction, and the extracted point cloud information can be integrated into a digital twin platform for more intuitive public display.
Figure 1. Point cloud model of single tree.
Figure 2. Clustered trees in the urban context.
YAO Chaowen 