Application of Lidar Technology for Green Inventory in GIS Environments and Statistical Analysis Using Programming Languages

Objective: This study aims to integrate LiDAR technology with GIS environments to improve the accuracy and efficiency of urban forest inventory analysis, specifically focusing on Klaipėda, Lithuania. The goal is to create detailed models of urban tree canopies and structures, contributing to better forest resource management. Theoretical Framework: The research is grounded in the theories of remote sensing, LiDAR technology, and geographic information systems (GIS). Key models and frameworks that inform this study include spatial data analysis techniques, digital terrain modeling (DTM), and forest resource management principles. Method: The methodology used in this study involves collecting LiDAR data from the Klaipėda City Municipality, followed by the integration of this data into GIS platforms such as ArcGIS Pro and QGIS. Statistical analysis using the R programming language, with the Forest Tools R package, was applied to analyze key forest metrics, such as tree height and canopy coverage. Results and Discussion: The results of this study revealed detailed digital canopy models (DCMs), showing tree top points and tree canopy boundaries that enhance forest management practices. The discussion highlights the potential of combining LiDAR data with GIS and statistical methods to facilitate decision-making regarding forest management, focusing on urban forestry in Klaipėda. Research Implications: The practical implications of this research provide valuable insights into urban forest management, helping to optimize forest planning and resource allocation. The study also contributes to broader applications in environmental monitoring and urban planning. Originality/Value: This research contributes to the body of knowledge by demonstrating the utility of LiDAR technology combined with GIS and statistical tools in urban forestry. The findings underscore the importance of advanced technologies in improving the precision and sustainability of urban forest management practices.