URBAN TREE BIOMASS ESTIMATION THROUGH TERRESTRIAL LIDAR ANALYSIS

Above-ground biomass (AGB) serves as a critical indicator of carbon storage within terrestrial ecosystems, particularly in urban environments where trees play a vital role. Despite their importance, accurately measuring AGB in urban areas is complicated by diverse species, variable tree forms, and intricate land cover. This study utilizes Light Detection and Ranging (LiDAR) technology to enhance the assessment of AGB in urban trees, offering a cost-effective and efficient solution. By extracting individual tree point clouds from LiDAR data collected at Midwestern State University (MSU Texas), we employ advanced digital tree segmentation and geometric modeling techniques to derive essential structural parameters, including height, diameter at breast height (DBH), volume, and surface area. We then validate our modeling approach by optimizing parameters and comparing model outputs with extensive field measurements of harvested total biomass and DBH from campus trees. This tailored model, focusing on key North Texas species such as Red Oak and Cedar Elm, aims to deliver precise estimates of urban tree AGB. The findings underscore the potential of non-destructive methods for AGB assessment, contributing valuable insights for urban carbon management and sustainable forestry practices.