Theses and Dissertations


Wes Jones

Issuing Body

Mississippi State University


Grado, C. Stephen

Committee Member

Grebner, Donald

Committee Member

Parker, C. Robert

Committee Member

Schultz, Emily B.

Committee Member

Fan, Zhaofei

Date of Degree


Document Type

Dissertation - Open Access



Degree Name

Doctor of Philosophy


College of Forest Resources


Department of Forest Products


Market and nonmarket urban forest resource values can be achieved through many cost reductions (e.g., improved air quality, fossil fuels for heating and cooling, stormwater runoff) and increases in tax bases for communities from improved property values. These benefits need to be measured quantitatively so decision makers can understand economic gains or losses provided by street trees. Resource inventories are often undertaken as part of the planning phase in a tree management program. It is a comprehensive assessment that requires an inventory of a community's tree resources and it acts as a fundamental starting point for most urban and community forestry programs. Whether an inventory is an estimate or a complete count, quantitative benefits and costs for urban forestry programs cannot accurately be represented without one. This study provides a new approach to understanding a city’s street tree structure using data from a Light Detection And Ranging (LiDAR) sensor and other publicly available data (e.g., roads, city boundaries, aerial imagery). This was accomplished through feature (e.g., trees, buildings) extraction from LiDAR data to identify individual trees. Feature extraction procedures were used with basic geographic information system (GIS) techniques and LiDAR Analyst to create street tree inventory maps to be used in determining a community’s benefit/cost ratio (BCR) for its urban forest. Only by explaining an urban forest’s structure can dollar values be assigned to street trees. Research was performed with LiDAR data and a sample of ground control trees in Pass Christian, and Hattiesburg, Mississippi, located in the lower U.S. South where many communities have publicly available geospatial data warehouses (e.g., MARIS in Mississippi, ATLAS in Louisiana). Results from each city’s estimated street trees revealed a BCR 3.23:1 and 6.91:1 for Pass Christian and Hattiesburg, respectively. This study validated a regression model for predicting street tree occurrence in cities using LiDAR Analyst and a street sample. Results demonstrated that using LiDAR Analyst as a street tree inventory tool with publicly available LiDAR data and a sample adequately described 88% of a community’s street trees which was used to calculate both market and nonmarket resource values.