Full-waveform (FWF) lidar receives one dimensional continuous signal. It offers useful information about the structure of the target. Therefore, the analysis of received signal of FWF lidar and obtaining the implicit information is helpful for land cover classification. In the processing of full waveform Lidar data, the waveform parameter extraction and analysis are the important steps. The major objective of this study is to analyze the received waveform and extract its parameters. We select Gaussian distribution as a symmetric function and Weibull distribution as an asymmetric function in waveform decomposition. Then, we calculate several accuracy assessment indicators between raw waveform data and fitting function for quality assessment. We use echo width, amplitude, backscatter cross-section coefficient, elevation, elevation difference, echo number, and echo ratio as waveform parameter of classification. After waveform parameter extraction, we employ Support Vector Machine (SVM) and Random Forests (RF) as classifier for land cover classification. This study employs echo width, amplitude, backscatter cross-section coefficients and other features for classification. Error matrix is used to compare the performance of the classifiers. The experimental results indicate that the accuracy of asymmetric function is slightly better than symmetric function. However, the extracted peak positions from the Gaussian and Weibull are very close. Moreover, Gaussian distribution is relatively simple and easy to implement in the waveform analysis. The result of land cover classification shows that waveform parameters are helpful for classification and Random Forests classifier is slightly better than SVM in our study cases.

The Gaussian decomposition technique has to date played an important role in processing both large- and small-footprint lidar waveforms. When fitting Gaussian functions to received waveforms or the surface response estimated by a deconvolution process, Gaussian coefficients for each detected return can be estimated. These are the temporal position of the Gaussian peak, pulse width and amplitude, which indicate feature characteristics. However, in some circumstance, the estimates may not be fully certain. Little attention has been paid to assessing the reliability or uncertainty of Gaussian estimates. It is necessary to take such indicators into account when application of multiple waveform features is attempted. This study aims to fill this research gap. Whether the reliability of the estimates is affected by the complexity of the waveform shape was investigated. Waveform data collected from simulation experiment and a Riegl LMS-Q560 field campaign was analyzed. Several targets were designed and set in the field to observe how the shape of waveforms varies with the separation distance between two returns and their amplitude magnitude. It was found that the RMSE values for the pulse width estimates based on data simulation were increasingly large when the separation distance was decreased (< 6 ns). The RMSE values for the range estimates based on data simulation remained consistently small (~ 0.04 ns) when decreasing the separation distance.

Efficiently obtaining the information in forest region such as forest structure, forest ecosystems is important for forest management. The purpose of this study is to estimate the Canopy Height Model (CHM) and the Leave Area Index (LAI) of a dense forest area by using airborne LiDAR data. CHM is estimated by taking the difference of DSM and DEM derived from LiDAR data. Estimation of LAI is achieved based on the calculation of Laser Penetration Index (LPI). Five calculations of LPI were applied in this paper: (1.) The ratio between the number of ground points and that of all the points; (2.) the ratio between the intensities of ground points and that of all the points; (3) the ratio between the number of ground points and the number of laser beams; (4) a weighting method modified from index (1); and (5) the ratio between the area of ground points and that of all the points. The study area is in a natural broadleaf forest of south Taiwan. In this study, we use three sets of airborne LiDAR data acquired with different full waveform LiDAR systems including Leica ALS60, Riegl LMS-Q680i and Optech Pegasus HD400. All of these LiDAR systems are capable of recording full waveform data, then we can get the waveform point clouds by the echo detector to do the comparison. Our experiments results show that the accuracy of CHM by different LiDAR data is about 1.5 meter. And the fourth LPI index has the highest coefficient of determination (about 0.8) and the estimation of LAI can be improved by using the waveform points.

Thanks to the development of LiDAR technology, recording full waveform information of return laser signal has become available. Compared with the conventional LiDAR system, waveform LiDAR further encodes the intensity of return signal along the time domain, which enables the users to utilize the continuous return signal for the interpretation of ground objects. Potential of more applications than the use of traditional LiDAR can be expected with the use of full waveform LiDAR. A LiDAR waveform is a recorded energy of the backscattered laser pulse along the time domain. The shape of a waveform is formed according as the characteristics surface reflectance, geometric structure and roughness of the laser footprint. It would be possible to extract the information of surface characteristics from waveform data, and this information can be used for the classification of ground surface. This study focuses on the analysis of LiDAR full-waveform data. The effects of various ground objects and surfaces on the waveform data will be analyzed, and the reparability of waveform features among categories of ground objects will be identified. Based on this analysis, a classification approach is developed for LiDAR full-waveform data. The estimation of classification accuracy will be reported as well. The experiment data were collected with three airborne LiDAR systems of different brands, namely Leica、Riegl and Optech. The land cover objects of the experimental area are mainly categorized into road, canopy, grass & crop, bare ground and buildings. Waveform features were analyzed with respect to the single and multiple return laser paths samples, and waveform classification features were selected according to the analysis. Then, the supervised classification by using Support Vector Machine (SVM) was performed in three defined methods which include echo-based, waveform-based and waveform-based with images. The experiment results show that the overall accuracy of waveform-based method increases about 20% comparing to echo-based method and it can achieve 86% with the images. This study reveals the potential of 3D object classification using airborne LiDAR waveform data.

Bathymetric Lidar utilizes green laser and scanning mechanism to derive the water depth and substrate information. It has been proven to be effective for mapping shallow water and coral reef area due to the airborne manner. In this study, terrain analysis is performed with data collected in an airborne bathymetric Lidar mission for mapping coral patches. Classification with features such as bathymetric position index (BPI) and slope are explored for Dongsha atoll. The classification is validated with manually digitized coral patches based on human interpretation. The applied scale factors of BPI with 600m and 50m reach best result, which is 92.61% for overall accuracy and 66.01% for the KAPPA coefficient.