Environmental Monitoring and Conservation Of Urban Green Spaces Using Lidar: Applications In Forest Management, And Carbon Assessment Addis Ababa The Case Of Entoto

Urban forests play a vital role in climate change mitigation by serving as significant carbon sinks. This study focuses on estimating Above Ground Biomass (AGB) and carbon stock in Entoto Natural Park, located in Addis Ababa, using Light Detection and Ranging (LiDAR) technology. Field observations were collected from 471 systematically distributed sample plots, and LiDAR-derived Canopy Height Model (CHM) was utilized to build empirical models for biomass estimation. The study found that the total AGB obtained from field observations was 609.46 Mg/ha, while the AGB estimated from LiDAR point cloud analysis was 605.99 Mg/ha, resulting in a minimal deviation of 3.47 Mg/ha (0.57% difference). The Root Mean Square Error (RMSE) for AGB estimation was calculated at 1.80 Mg/ha, indicating a high level of accuracy in LiDAR derived estimates. Additionally, the carbon stock calculated from field measurements was 286.45 Mg C/ha, compared to 284.82 Mg C/ha from LiDAR data, with an RMSE of 0.85 Mg C/ha, further confirming the reliability of LiDAR technology for carbon stock assessments. Results demonstrated a close agreement between LiDAR estimates and field data, indicating the potential of remote sensing in forest carbon monitoring. The findings underscore the effectiveness of LiDAR as a non-destructive, scalable, and accurate method for estimating biomass and carbon stock in urban forest settings. This approach can aid policymakers, environmental planners, and urban managers in implementing data-driven strategies for sustainable land use and climate resilience. Furthermore, the study contributes valuable insights into integrating geospatial technologies in environmental conservation, especially in developing countries like Ethiopia, where access to ground data and technological infrastructure can be limited.