# How Environmental Factors Affect Forest Fire Occurrence in Yunnan Forest Region
## 3 Results
### Spatiotemporal Distribution and Causes of Fire Points  
A total of 4649 forest fire events were detected in 129 counties of Yunnan Province during the period 2005-2019, with great spatial variability. Specifically, fire points were concentrated in Northwest Yunnan, Southwest Yunnan, and Southeast Yunnan (Figure 2a) On average, 310 fire events occurred per year, although 842 fires occurred in 2010, accounting for more than 18% of all forest fires (Figure 2c). Forest fires mainly occurred between December and June, with more than 99% of forest fires occurring in the first half of the year (Figure 2d). The fire season in Yunnan was from 1 December to 15 June, and the number of forest fires in March accounted for more than 26% of the annual number of forest fires.  
Figure 2: _Cont_.  
Multicollinearity and significance tests (\(p<0.05\)) were performed on the full samples, producing a total of 10 independently correlated variables (see Table 2). According to the results of the LR model and random forest ranking, it is shown that the AUC of the LR model constructed from the full sample was 90.82%, and the most important environmental factor was RHmin (Figure 3), which correlates negatively with forest fire occurrence and has a key threshold of 28.07% \(\pm\) 11.85% (Table 2) at 50% forest fire probability. The correlation partial derivative indicates that a 1% decrease in RHmin increases the marginal forest fire probability by 3.04%. The second major factor is Tmax (Figure 3), followed by GPP, with the relative importance between the two being less than 0.1%. For these variables, the key threshold for 50% forest fire probability is 21.23 \(\pm\) 11.15 \({}^{\circ}\)C and 2.58 \(\times\) 10\({}^{3}\) \(\pm\) 1.42 \(\times\) 10\({}^{3}\) kg K, respectively. The associated rates of change of the partial derivative are 1.74% \({}^{\circ}\)C\({}^{-1}\) and 0.03% (kg K)\({}^{-1}\), which both correlate positively with forest fire probability. This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Regression Coefficients** & \
\begin{tabular}{c} **Threshold (\(\pm\)Standard** \\ **Deviation)** \\ **of 50\% IgnitionProbability** \\ \end{tabular} &
\begin{tabular}{c} **Partial** \\ **Derivative** \\ \end{tabular} \\ \hline Precipitation & \(-1.52\times 10^{-1}\) & 8.16 (\(\pm\)6.46) & \(-3.79\) \\ Minimum relative humidity & \(-1.22\times 10^{-1}\) & 28.07 (\(\pm\)11.85) & \(-3.05\) \\ Sunshine duration & \(4.3\times 10^{-2}\) & 7.32 (\(\pm\)3.43) & 1.07 \\ Maximum temperature & \(6.8\times 10^{-2}\) & 21.23 (\(\pm\)11.15) & 1.70 \\ Maximum wind speed & \(1.34\times 10^{-1}\) & 5.32 (\(\pm\)2.19) & 3.35 \\ Distance from residence & \(2.26\times 10^{-4}\) & \(1.33\times 10^{3}\) (\(\pm\)1.07 \(\times\) 10\({}^{3}\)) & \(5.00\times 10^{-3}\) \\ Distance from road & \(9.57\times 10^{-5}\) & \(2.39\times 10^{3}\) (\(\pm\)1.79 \(\times\) 10\({}^{3}\)) & \(2.50\times 10^{-3}\) \\ Slope & \(9.63\times 10^{-3}\) & 46.32 (\(\pm\)24.52) & \(2.50\times 10^{-1}\) \\ Altitude & \(-6.49\times 10^{-5}\) & \(2.45\times 10^{3}\) (\(1.12\times 10^{3}\)) & \(1.50\times 10^{-3}\) \\ GPP & \(1.28\times 10^{-3}\) & \(2.59\times 10^{3}\) (\(1.22\times 10^{3}\)) & \(3.25\times 10^{-2}\) \\ Constant & \(-2.23\) & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fitting eigenvalues for full data logistic regression model.  
Figure 2: (a) Distribution of fire points in forest as detected by satellite in Yunnan, China, 2005-2019. (b) Random non-fire locations generated by ArcGIS. (c) Number of fire points for each year, 2005-2019. (d) Number of fire points for each month, January–June, averaged over the years 2005–2019.