## 4 Results
### Flood Mapping Results  
The Random Forest classifier with 200 decision trees was incorporated to classify the post-flood image containing 13 input bands into five land cover types: water, woodland, cropland, impervious surface, and bare soil. The final flood map was generated through the merging of non-water land cover types into the non-flooded class and the subtracting of persistent water from the water class (Figure 7). As depicted in Figure 7, the red areas represent the flooded extent while the blue areas stand for the persistent water. In order to manifest the inundated areas more obviously, the original true color remote sensing image is set to be the background on which the flood mapping result is overlaid. Figure 7 also illustrates that the inundated areas show distinct spatial patterns. Large flat areas along the branches of the Yuyao River were submerged due to the overflow of the rivers. The downstream area of the reservoir which lies on the left side of the image was also inundated due to the flood discharge through the dam. Besides, vast areas of low-lying cropland, adjacent to the northwestern part of the city core, were submerged. Moreover, land cover maps just before a flood event should be useful to help extract submerged land cover types and areas to assist disaster loss evaluation and post-disaster reconstruction. HJ-CCD and HJ-IRS images acquired on 10 September 2013 were selected to generate the land cover map before the flood, also using the proposed method in this study. Specifically, MESMA together with Random Forest classifier were utilized to derive the land cover types of the pre-flood image. First, 32 optimal spectral curves (nine for water, ten for vegetation, eight for impervious surface and five for bare soil) were selected for the MESMA model to derive the fractional information. Second, NDWI of the pre-flood image, together with DEM, slope, aspect, original spectral bands, and fractional bands were layer stacked for the Random Forest classifier with 200 decision trees. A number of 200 testing points for each land cover type were selected to calculate the confusion matrix and the result showed high performance with an overall accuracy of 91.20% and a Kappa index of 0.89, which can provide  
Figure 7: Flooded areas generated through RF classifier. accurate and reliable data source to derive the inundated land cover types and areas. The pre-disaster land cover map is shown in Figure 8.  
Figure 8 illustrates that the city core is centered in the study area while the suburban areas possess large areas of cropland and scattered villages. The woodland is mainly located on the mountains adjacent to the floodplain while the bare soil is mainly located where the forest had been cut down. The area (km2) and proportion (%) of pre-flood land cover types are listed in Table 3.  
It can be observed that cropland was the dominant land cover type with a percentage of 56.56%, followed by built up area (26.12%) and woodland (14.59%). Water and bare soil accounted for a small proportion of 1.94% and 0.79%, respectively. The pre-disaster land cover map and the flood map were sent for a GIS analysis to compute the land cover types and areas intersected by flooding (Table 4). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Inundated Land Cover Type** & **Area (km2)** & **Proportion** \\ \hline Woodland & 0.075 & 0.07\% \\ Cropland & 76.133 & 75.66\% \\ Built up area & 24.373 & 24.23\% \\ Bare soil & 0.039 & 0.04\% \\ Total & 100.620 & 100.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Inundated land cover types and area statistics. Figure 8: Land cover types before the flood event. Table 4 indicates that a total area of about 100 km\({}^{2}\) was inundated after the flood event. Among this, about 76 km\({}^{2}\) was cropland and 24 km\({}^{2}\) was built-up areas which altogether account for about 99% of all the inundated areas. There existed very little woodland and bare soil that had been submerged.