# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 3 Results
### Temporal Analysis of the Post-Fire Vegetation Trajectory  
The following sections summarize the results of the intra-annual and inter-annual post-fire changes in the EVI, LST and LSWI (Figures 4 and 5). The mean values of different burn severities in the five fire zones were used for analysis. In addition, we selected a non-fire area adjacent to the burned areas (Figure 1) as a control area to eliminate influences from climate change on the forest dynamics. Both the control area and burned areas had similar topographies and climate characteristics and had never experienced any other type of disturbance (e.g., artificial or natural disturbances). The variations in the EVI, LST and LSWI are illustrated in a series of statistical graphs (Figure 3). Figure 3a-c shows the annual changes via the burned- and control-pixel values. The mean values of the EVI and LST in the control pixels were unimodal (Figure 3a,b). The maximum LST was observed in June, and the EVI peaked in July. The LSWI value was the highest in winter, and the maximum value during the growing season occurred in July. Comparing the fire areas indicated immediate post-fire changes in the three indices: the EVI and LSWI decreased by 0.21 and 0.31, respectively, and the LST increased to 6.89 \({}^{\circ}\)C. The variations in the EVI and LSWI were more explicitly caused by the fire than those in the LST. Figure 3d-f displays the index variations under different fire intensities. The results clearly showed that the amplitude changes in the indices were controlled by the fire intensity, and the indices in the fire areas continued to reflect the original disturbance trends. The absolute values of the burned area and control area revealed that the greatest EVI difference occurred in August (a high difference of 0.29 in the severe-fire area). The largest LST and LSWI differences in the severe-fire area appeared in August and September, with differences of 6.9 and 0.44, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fire Damage Class** & **DNBR** & **Area (km2)** & \(\uppi\) & **SD** & **Percentage (\%)** \\ \hline Unburned & \textless{}0.1 & 101.33 & 0.07 & 0.02 & / \\ Low & 0.1–0.3 & 68.42 & 0.25 & 0.04 & 37.70\% \\ Moderate & 0.3–0.6 & 59.59 & 0.42 & 0.03 & 32.83\% \\ High & \textgreater{}0.6 & 53.48 & 0.68 & 0.04 & 29.47\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Fire damage classification when using the DNBR method. Figure 3: Fire damage map of the study area, with fire damage values obtained using Equation (4). Sample points were used to identify fire pixels for subsequent analysis. These sample points were selected to be as distant as possible within the fire intensity area to avoid interference from different burn severities. One study site before (**a**) and after a fire (**b**) and a severity map of the 2000 burned area were derived from the bi-temporal image-differencing approach via the normalized burn ratio (DNBR), with the 1999 Landsat TM as the pre-fire image and the 2000 severity map as the post-fire image (**c**). The temporal profiles of the EVI, LST and LSWI at different burn severity levels during the seventeen-year study period were extracted. An unburned region was selected as the control area for comparison to the burned sites (Figure 5). Changes in the EVI, LST and LSWI were not evident between the fire-occurrence year and the subsequent year, showing no obvious vegetation recovery. The index profiles dramatically increased in the second year following the fire, suggesting that the burned areas were not immediately covered by green vegetation after the fire. The EVI measures the proportion of pure green vegetation, but we could not confirm that the green vegetation was the same as the pre-fire vegetation; in other words, the EVI that was recovered after the fire could have consisted of grasses or shrubs, followed by broadleaf plants, which were the dominant species in the post-fire succession. Generally, grass quickly grew and reached high EVI values after several months.