This thesis reviews algorithms that have been developed for classifying lidar data and identifies a progressive morphological filter for evaluation and improvement. Two potential weaknesses evaluated include the row-column order bias and grid orientation bias. Four different row-column orderings were developed to test for bias associated with the order choice. Moreover, a method rotating the filter grid to a series of angles was developed for testing bias associated with grid orientation. Measures of success of the improvements include Type I and II errors, where results are compared with a hand-produced "truth" dataset. Two datasets, one urban, the other rural, were selected for testing the modified filters. The results are presented and discussed for each algorithm. It was found that the four row-column orders all classified the dataset exactly the same. After the erosion and dilation functions were completed, the same surface profiles and elevations were produced regardless of row-column ordering. The filter windows used by the algorithm were found to create a rectangular area where the minimum and maximum values within that area were always selected. Therefore, it was found that the row-column orders did not create a bias in the classification. However, grid orientation was found to greatly affect results. Misclassification problems occurred at ridgelines, mounds, and along roads with ditches and steep slopes running along them. Grid angles running parallel to these objects were found to avoid these errors. Buildings also created errors, but were minimized with grid angles crossing them at 45 degrees. The selected angle directions significantly affect the classification results in all cases. Therefore, the grid orientation bias was verified. Two new methods of combining the results from the various angles have been developed. The first method used the best two classifying angles to combine the results. Best results were found in datasets with terrain objects positioned in similar directions for this method. The Multiple Angle method used all of the angle classifications to combine the results. This method performed best on datasets with terrain objects oriented in numerous directions. More accurate terrain models and better overall classification results have been generated using these methods.

Indiana University-Purdue University Indianapolis (IUPUI) / Urban forestry is a very important element of urban structures that can improve the environment and life quality within the urban areas. Having an accurate classification of urban forests and grass areas would help improve focused urban tree planting and urban heat wave mitigation efforts. This research project will compare the use of high – resolution aerial imagery and LiDAR data when used to classify canopy and grass areas. The high – resolution image, with 1 – meter resolution, was captured by The National Agriculture Imagery Program (NAIP) on 6/6/2012. Its coordinate system is the North American Datum of 1983 (NAD83). The LiDAR data, with 1.0 – meter average post spacing, was captured by Indiana Statewide Imagery and LiDAR Program from 03/13/2011 to 04/30/2012.The study area is called the Near East Side Community Organization (NESCO) neighborhood. It is located on the east side of downtown Indianapolis, Indiana. Its boundaries are: 65 interstate, East Massachusetts Avenue, East 21st Street, North Emerson Avenue, and the rail road tracks on the south of the East Washington Street. This research will also perform the accuracy assessment based on the results of classifications using high – resolution aerial imagery and LiDAR data in order to determine and explain which method is more accurate to classify urban canopy and grass areas.