Date of Award


Document Type


Degree Name

Master of Science (MS)

Legacy Department

Forest Resources

Committee Chair/Advisor

Post, Christopher J

Committee Member

Mikhailova , Elena A

Committee Member

Gerard , Patrick D


Light Detection and Ranging (LIDAR) is becoming a widely used tool in forestry and natural resource fields. The availability of free and low cost datasets gives LIDAR the ability to save time and money over traditional forest inventory practices. In this study, the effectiveness of low density, small footprint LIDAR compared to forest field inventory measurements from the Clemson Experimental Forest was determined. LIDAR based estimates were analyzed to determine if LIDAR is a viable tool for estimating particular forest inventory features in the Southeastern U.S. and whether a transition could be made to a more GIS based analysis. Standard field inventory methods were used to assess forest stand measurements throughout the Clemson Experimental Forest. Processed LIDAR data was used in conjunction with Treevaw, a LIDAR software application, to extract forest inventory features at the individual tree level. Statistical correlation and regression comparisons were made between the data at the plot level. Comparisons were also made between stand types to determine the type of effects that leaf-off conditions could have on the LIDAR data analysis. Overall, results of the entire sample comparing tree heights, diameter at breast height, and above ground biomass were varied. Correlations between inventory and LIDAR measurements were high, with a minimum value of 0.70. Dividing the plots by stand cover type showed variations in the dataset. Pine plantation plots achieved the best overall results, followed by pine-hardwood plots. Natural pine, upland hardwood, and cove hardwood plots each produced similar results, but were not as accurate as the stands mentioned previously. Results show that low density, small footprint LIDAR can be used to accurately estimate certain features of individual trees in particular forest stand types. The use of higher density LIDAR would most likely provide a more accurate analysis across a broader range of forest types.

Included in

Agriculture Commons



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