3D Modeling Global Warming Lidar Research Smart Cities Surveying

Urban Tree Carbon Storage Estimated with Lidar

point cloud of urban tree carbon
PA/Royal Botanic Gardens

Human settlements in urban environments account for less than one percent of the Earth’s land surface but support more than half of the world’s population (Huang et al., 2021). Human settlements account for the majority of global energy consumption and carbon dioxide emissions (International Energy Agency, 2021). Because substantial amounts of carbon emitted from human settlements have accelerated climate change and contributed to the occurrence of extreme weather events, carbon capture and storage have become major concerns for humanity. Urban vegetation, including street trees, parks, lawns, and private gardens, is an important sink for the urban carbon cycle. Urban trees directly reduce atmospheric carbon content and help mitigate urban heat stress by reducing energy consumption (Tan et al., 2016, Aminipouri et al., 2019). As urban areas continue to expand, it is essential to accurately estimate and map urban tree carbon storage (CS) at a high resolution for effective management and policy implementation.

From a paper by Yeonsu Lee, et al.

One method for estimating urban tree CS is using allometric equations that relate the diameter at breast height (DBH) of a tree to its carbon storage (referred to as “field-measured CS” in this study). This approach requires field measurements of DBH data for individual trees. The estimated CS for individual trees is summed to calculate the site-level CS and multiplied by the total urban area to calculate the city-level CS (Nowak et al., 2013, Raciti et al., 2014). Accurate allometric equations have been established for individual species through destructive sampling; however, they are site-specific and may vary based on location (e.g., forest or urban), soil characteristics, climate, and tree management practices (Vorster et al., 2020). Therefore, more species-specific allometric equations for specific urban areas are required to improve CS estimates (Monteiro et al., 2016).

Remote sensing can be used to effectively monitor urban tree growth and estimate CS by providing spatially explicit spectral and structural information on urban trees over large areas (Pasher et al., 2014, Parmehr et al., 2016, Li et al., 2019, Zhang and Shao, 2021). Passive sensors can describe vegetation vitality and phenology at the tree canopy level. In contrast, active sensors, such as synthetic aperture radar or light detection and ranging (LiDAR), can measure three-dimensional tree structure. Researchers have combined very high spatial resolution spectral images and active sensors to create urban tree canopy cover (TCC) maps (Parmehr et al., 2016, Hanssen et al., 2021), which improve urban tree CS estimation by reducing tree area omissions (Strohbach and Haase, 2012). The fusion of LiDAR and optical images can also enable the derivation of dendrometric parameters, such as crown area, tree height, and DBH, which can be used to estimate the CS of individual urban trees (Dalponte et al., 2018, Zhang et al., 2019). However, accurately delineating individual trees using a large volume of data generated by LiDAR sensors requires complex deep learning structures and large amounts of computational power (Martins et al., 2021, Lassalle et al., 2022).

For the complete paper CLICK HERE.

Note – If you liked this post click here to stay informed of all of the 3D laser scanning, geomatics, UAS, autonomous vehicle, Lidar News and more. If you have an informative 3D video that you would like us to promote, please forward to editor@lidarnews.com and if you would like to join the Younger Geospatial Professional movement click here

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.