# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 4 Results
### Comparison of Vegetation Index from Active and Passive Sensor  
Passive sensors (e.g., optical multispectral images) can only be used to detect naturally available energy (e.g., solar energy). By contrast, active sensors (e.g., LiDAR) provide their own energy source for illumination and obtain data regardless of time of day or season; therefore, active LiDAR sensors are more flexible than optical sensors in different weather conditions. Vegetation detection is an important task in ground-point selection for the digital terrain model (DTM) generation from LiDAR. Traditionally, \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**(a) Completeness**} & \multicolumn{4}{c}{**(b) Correctness**} \\ \hline
**Combinations** & **Grass** & **Build.** & **Road** & **Tree** & **Soil** & **Combinations** & **Grass** & **Build.** & **Road** & **Tree** & **Soil** \\ \hline Case 2-1 & 96.6\% & 81.5\% & 98.2\% & 96.9\% & 88.3\% & Case 2-1 & 98.7\% & 98.0\% & 84.7\% & 93.1\% & 86.7\% \\ Case 2-2 & 18.3\% & 95.0\% & 62.3\% & 100\% & 50.7\% & Case 2-2 & 34.0\% & 100\% & 34.3\% & 99.9\% & 69.3\% \\ Case 2-3 & 93.8\% & 99.5\% & 99.9\% & 100\% & 94.0\% & Case 2-3 & 98.1\% & 100\% & 99.9\% & 96.9\% & 87.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Completeness and correctness for different classes in Case 2-1 to Case 2-3. Figure 9: Completeness and correctness for different classes in Case 2-1 to Case 2-3.  
vegetated areas are detected using the surface roughness from irregular points; intensity from LiDAR data is seldom used in the separation of vegetation and non-vegetation. With the development of multi-wavelength LiDAR, the intensities from different wavelengths are possibly similar to passive sensors in vegetation detection. Our goal is to discover the capability of the vegetation index from LiDAR, and the aim of this section is to compare the performance of vegetation index from active and passive sensors. The NDVI has been widely used in vegetation detection, but it is calculated from passive multispectral images. Because the passive sensor has inherent limitations, such as day operation and haze, an active sensor like LiDAR can be operated at night and penetrate haze and might be an option to overcome these restrictions. We designed two cases to analyze the similarity and consistency (Table 8) between vegetation indices from active and passive sensors. Because the reflectance of the multispectral WV-3 image represents the radiance of the object surface (e.g., tree top), we selected the intensity of the highest point in a 1.2 \(\times\) 1.2 m cell (i.e., pixel size of WV-3 multispectral image) as the LiDAR intensity for LiDAR-derived vegetation index so that both reflectance of the passive sensor and return energy of the active sensor represent the energy of object surface in different wavelengths. For the training and test data, the grass and tree classes were merged into a vegetation class, and the building, road, and soil classes were combined into a non-vegetation class; only the vegetation indices were used in segmentation and classification. A visual comparison of the two results of vegetation detection (Figure 11) show high consistency between these two vegetation indices. Completeness and correctness were compared between vegetation and non-vegetation in different combinations (Table 9 and Figure 12). Here we considered only two classes: vegetation and non-vegetation; therefore, the results were better than the results in Sections 4.1 and 4.2 (i.e., five classes). The completeness and correctness analysis for active LiDAR and passive imagery were higher than 96%. The correctness of vegetation detection from the passive sensor was 98% in test samples and slightly lower, 96%, for the active sensor. A small amount of vegetation and non-vegetation was misclassified, but the overall accuracy reached 98% in both cases.