# Analysis of Land Cover Classification Using Multi-Wavelength LiDAR System
## 4 Results
### Comparison of Vegetation Index from Active and Passive Sensor
The overall accuracies and kappa coefficients were also similar in this test area (Figure 13), with vegetation indices generated from multi-wavelength LiDAR were similar to those from passive multispectral imagery. The kappa of the passive sensor was slightly better (1%) than that for the active sensor. This experiment demonstrated that the vegetation index from active LiDAR sensor could be used to detect vegetation area effectively. Figure 11: Results of vegetation detection. (**a**) Reference image; (**b**) result of Case 3-1; (**c**) result of Case 3-2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Combinations** & **Features** \\ \hline Case 3-1 & NDFI\({}_{\text{MIR-G}}\) and NDFI\({}_{\text{NIR-G}}\) (from multi-wavelength LiDAR) \\ Case 3-2 & NDVI\({}_{\text{1}}\) and NDVI\({}_{\text{2}}\) (from WV-3 multispectral image) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The combinations for active and passive sensors classification. We also used the training data to calculate the coefficient of determination (R\({}^{2}\)) among these vegetation indices (Figure 14). For each training dataset, four vegetation indices were extracted from both active and passive sensors. Any two vegetation indices were selected to determine the similarity between them, and six combinations were selected for similarity analysis. The R\({}^{2}\) between two passive vegetation indices (i.e., NDVI\({}_{1}\) and NDVI\({}_{2}\)) reached 0.99 while the R\({}^{2}\) between two active vegetation indices (i.e., NDFIMIR-G and NDFINIR-G) reached 0.88. The  
In the analysis across sensors, the R\({}^{2}\) for combinations of (NDFI\({}_{\text{MIR-G}}\) and NDVI\({}_{1}\)) and (NDFI\({}_{\text{MIR-G}}\) and NDVI\({}_{2}\)) were less than 0.7 because the NDFI\({}_{\text{MIR-G}}\) used the MIR while the NDVI\({}_{1}\) and NDVI\({}_{2}\) used NIR. The root-mean-square errors (RMSEs) of (NDFI\({}_{\text{MIR-G}}\) and NDVI\({}_{1}\)) and (NDFI\({}_{\text{MIR-G}}\) and NDVI\({}_{2}\)) (i.e., 0.2405 and 0.2386) also had a large fitting error compared to other cases. For NDFI\({}_{\text{NIR-G}}\), NDVI\({}_{1}\), and NDVI\({}_{2}\), all calculated from NIR, the R\({}^{2}\) of combinations (NDFI\({}_{\text{NIR-G}}\) and NDVI\({}_{1}\)) and (NDFI\({}_{\text{NIR-G}}\) and NDVI\({}_{2}\)) were higher than 0.87 for both combinations. [PERSON] et al. (2016) [38] also found that the vegetation indices from active sensors had high correlations. They reported that the vegetation indices measured from active and passive sensors were correlated (R\({}^{2}\) > 0.70) for the same plant species. 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 vegetation indices from active LiDAR were also lower than those from passive imagery and were highly correlated (R\({}^{2}\) > 0.87) between active and passive sensors. In addition, the overall accuracy in classification of vegetation from non-vegetation was higher than 98%. Our analysis clearly showed that vegetation indices from multi-wavelength could be a helpful feature for vegetation detection.