# Post-Fire Vegetation Succession and Surface Energy Fluxes Derived from Remote Sensing
## 4 Discussion
### Other Factors that Affected Post-Fire Recovery  
The higher spatial resolution of Landsat TM/ETM+ sensors enables the temporal and spatial characteristics of fire recovery processes to be more precisely delineated, and these factors can be used simultaneously to assess the accuracy of the MODIS data. The selected Landsat images were acquired during the growing season, when the vegetation restoration characteristics were most obvious. The MODIS 8-day images that were closest to the Landsat samples' acquisition dates were selected to ensure that the data had comparable temporal resolution. Figure 9 shows similar EVI trends in the post-fire vegetation regeneration via MODIS and Landsat. Positive bias appeared in the MODIS dataset compared to the Landsat EVI values. This result confirms that the MODIS dataset overestimated the EVI for post-fire vegetation regeneration in this region. This result likely occurred because the MODIS EVI primarily consisted of maximum acquisitionsThe EVI is a universal indicator of vegetation's fractional coverage and growth conditions [38]. The Landsat TM data responded to spatial-recovery characteristics in detail (Figure 9), and the results showed the topographic effect on the vegetation recovery dynamics, particularly the investigated aspects. Table 3 and Figure 10 present the results from the examination of the response of vegetation regrowth to topography after the fire. The EVI appeared to decrease immediately after the fire in both the northern and southern regions, and subsequent regeneration also appeared in both areas but exhibited a faster rate along the northern region compared to the southern region. This same conclusion has been reported by other studies within the Northern Hemisphere [43; 56; 57]. The dates of the results indicate that the topography plays a key role in post-fire vegetation succession. The differences reflect the importance of local hydrological variability and soil hydrophobicity to post-fire vegetation regeneration.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Landsat TM Image Data**} & \multirow{2}{*}{\begin{tabular}{c} **Minimum** \\ **EVI** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Maximum** \\ **EVI** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Mean** \\ **EVI** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } \\ \hline \hline \multicolumn{5}{c}{**(a) North-facing slopes only**} \\ \hline
**August 1999 (Pre-fire)** & \(-0.066\) & \(0.887\) & \(0.675\) & \(0.120\) \\
**September 2000** & \(-0.080\) & \(0.885\) & \(0.182\) & \(0.150\) \\
**July 2001** & \(-0.133\) & \(0.88\) & \(0.256\) & \(0.138\) \\
**July 2004** & \(-0.095\) & \(0.877\) & \(0.422\) & \(0.095\) \\
**August 2006** & \(-0.109\) & \(0.872\) & \(0.44\) & \(0.126\) \\
**August 2014** & \(-0.114\) & \(0.886\) & \(0.692\) & \(0.122\) \\
**August 2016** & \(-0.05\) & \(0.882\) & \(0.673\) & \(0.127\) \\ \hline \hline \multicolumn{5}{c}{**(b) South-facing slopes only**} \\ \hline
**August 1999 (Pre-fire)** & \(-0.032\) & \(0.887\) & \(0.614\) & \(0.146\) \\
**September 2000** & \(-0.012\) & \(0.865\) & \(0.165\) & \(0.140\) \\
**July 2001** & \(-0.126\) & \(0.881\) & \(0.272\) & \(0.125\) \\
**July 2004** & \(-0.013\) & \(0.845\) & \(0.377\) & \(0.097\) \\
**August 2006** & \(-0.09\) & \(0.865\) & \(0.412\) & \(0.128\) \\
**August 2014** & \(-0.117\) & \(0.876\) & \(0.622\) & \(0.122\) \\
**August 2016** & \(-0.103\) & \(0.885\) & \(0.627\) & \(0.132\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Descriptive statistics for the EVI throughout the study area in the burn scar along the north- and south-facing slopes.  
Figure 9: Comparison between the MODIS EVI and Landsat EVI. The curves represent the EVI profiles along the north-facing and south-facing slopes for MODIS and Landsat, respectively.