Volume XLII-4/W18
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 147–152, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-147-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W18, 147–152, 2019
https://doi.org/10.5194/isprs-archives-XLII-4-W18-147-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  18 Oct 2019

18 Oct 2019

EXPLORING THE POTENTIAL OF FULL WAVEFORM AIRBORNE LIDAR FEATURES AND ITS FUSION WITH RGB IMAGE IN CLASSIFICATION OF A SPARSELY FORESTED AREA

M. Babadi1, M. Sattari1, and S. Iran Pour1,2 M. Babadi et al.
  • 1Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran
  • 2Institute of Geodesy, University of Stuttgart, Stuttgart, Germany

Keywords: Forest Management, Full Waveform LiDAR, Aerial Image, Classification, Tree species, Data Fusion

Abstract. Precise measurements of forest trees is very important in environmental protection. Measuring trees parameters by use of ground- based inventories is time and cost consuming. Employing advanced remote sensing techniques to obtain forest parameters has recently made a great progress step in this research area. Among the information resources of the study field, full waveform LiDAR data have attracted the attention of researchers in the recent years. However, decomposing LiDAR waveforms is one of the challenges in the data processing. In fact, the procedure of waveform decomposition causes some of the useful information in waveforms to be lost. In this study, we aim to investigate the potential use of non-decomposed full waveform LiDAR features and its fusion with optical images in classification of a sparsely forested area. We consider three classes including i) ground, ii) Quercus wislizeni and iii) Quercus douglusii for the classification procedure. In order to compare the results, five different strategies, namely i) RGB image, ii) common LiDAR features, iii) fusion of RGB image and common LiDAR features, iv) LiDAR waveform structural features and v) fusion of RGB image and LiDAR waveform structural features have been utilized for classifying the study area. The results of our study show that classification via using fusion of LiDAR waveform features and RGB image leads to the highest classification accuracy.