| Exploring the potential of full waveform airborne LiDAR features and its fusion with RGB image in classification of a sparsely forested area |
| Paper ID : 1109-SMPR |
| Authors: |
|
Masoud Babadi *, Mehran Sattari, Siavash Iran Pour Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan , Iran |
| Abstract: |
| Precise Measurements of forest trees is very important in environment protection. Measuring trees parameters by the use of ground based inventories is time and cost consuming. With the advent of remote sensing techniques in different fields, using them for obtaining forest parameters has been very much considered. One of the remote sensing data in which has attracted the attention of many experts in this field is full waveform LiDAR data. Decomposing LiDAR waveforms is one of the issues involved in processing these data which cause some of the information in waveforms to be lost. In this study, it has been tried to investigate the potential of non-decomposed full waveform LIDAR features and its fusion with optical images in classification of a sparsely forested area. The classes considered in this study are ground, Quercus wislizeni and Quercus douglusii classes. In order to comparing the results, five different strategies were used for classification and finally, results showed that classification using fusion of LiDAR waveform features and RGB image led to the highest classification accuracy. |
| Keywords: |
| Full Waveform LiDAR, Aerial Image, Classification, Data Fusion, Forest Management |
| Status : Conditional Accept (Poster) |
