# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 5 Conclusions and Future Works  
The multi-wavelength LiDAR system is a new technology for obtaining additional spectral information for LiDAR point analysis. This study analyzed the benefits of multi-wavelength LiDAR in land cover classification. Object-based classification is an effective method for land cover classification and vegetation inventory, and this study established an object-based land cover classification scheme using spectral and geometrical features from multi-wavelength LiDAR. The major contributions of this study were to (1) analyze the classification results from single- and multi-wavelength LiDARs; (2) evaluate the improvement rates of different land covers using geometrical and spectral features; and (3) compare the vegetation indices derived from active and passive sensors. The conclusions are summarized as follows:  
1. In the comparison of single- and multi-wavelength LiDARs, the multi-wavelength LiDAR generates more spectral information than the traditional single-wavelength LiDAR. The improvements of OA ranged from 4 to 14 percentage points when multi-wavelength features are available. The spectral features from multi-wavelength LiDAR are useful for the classification of grass, road, and bare soil classes. 2. In the comparison of geometrical and spectral features, the geometrical features are suitable for identifying objects with different heights and roughness, for example, buildings and trees. spectral features are suitable for identifying objects with different intensities, for example, asphalt roads and grass. The results of geometrical feature showed lower accuracy (i.e., OA 64%), whereas the integration of geometrical and spectral features showed higher accuracy (i.e., OA 98%). The experiment showed the benefit of integrating different features in land cover classification. Figure 14: R\({}^{2}\) and RMSE between NDFIs (active) and NDVIs (passive). 3. The concept of the vegetation index is to maximize differences between vegetation and non-vegetation cover using infrared and visible bands and can be utilized for active sensors when intensities from different wavelengths can be obtained. In comparing the vegetation indices from active and passive sensors, both achieved higher than 90% OA using a supervised SVM classification. Although the vegetation index recorded by the active sensor was consistently lower than that of the passive sensor, differences were small and within the range of possibility. In this study, the R\({}^{2}\) from active LiDAR was also lower than that from passive imagery and was highly correlated (R\({}^{2}\) > 0.87). The experiment demonstrated the possibility of using vegetation indices from active sensors in vegetation classification. In this study, the proposed method mainly focused on land cover in topographic mapping. Because the multi-wavelength LiDAR is applicable to bathymetric application, future work will evaluate the ability of multi-wavelength LiDAR to separate water from non-water regions and assess the accuracy of underwater mapping, such as water depth and sediment classification. Because data on system parameters and trajectories are limited or unavailable, this study focused on the intensities from LiDAR's standard product, which is the amplitude of the return reflection without intensity calibration. Future work will focus on the calibrated reflectance using additional system parameters and trajectories. Furthermore, this study used a trial-and-error method to find a set of proper segmentation parameters for different types of input data. The parameter optimization [39] for different data sets is needed to improve the results of classification in future study. The authors would like to thank Teledyne Optech Inc. for providing the multi-wavelength LiDAR data.