| Urban and metropolitan areas have grown significantly during the 21st century. With more than 50% of the global population living in cities, they are uniquely susceptible to high temperatures, poor air quality, and increases in peak storm water runoff during inclement weather; however, urban and metropolitan areas often have significant forest resources that can greatly ameliorate these factors. To maintain urban forests and maximize their benefits, tree surveys are often performed requiring extensive fieldwork. However, automatable techniques using LiDAR data and aerial orthoimagery have the potential to provide similar metrics over larger areas, more rapidly, and at lower cost. This study sought to develop a method to accurately and efficiently estimate tree height and stem diameters of roadside trees using tools readily available to geographic information system (GIS) operators. Incorporating two prior parkway tree surveys for the City of DeKalb as a starting point, I repaired and updated an urban tree database using orthoimagery, utilized LiDAR to estimate heights of new and existing trees, and estimated diameters using allometric equations. Results suggest that LiDAR can reasonably estimate tree height in an urban environment (R2 = 0.80; RMSE = 3.36 m) and further utilize those estimates to predict diameter at breast height (dbh) using a simple regression (R2 = 0.85; RMSE = 0.13 m) derived from a sample of approximately 1,000 trees. |
