## 3 Materials and Methods
This ensemble machine learning method combines different \"weak\" classification sub-models to obtain a \"strong\" one. The processing of the model was carried out using the MATLAB R2020a (see: [[https://www.mathworks.com/products/matlab.html?s_tid=hp_products_matlab](https://www.mathworks.com/products/matlab.html?s_tid=hp_products_matlab)]([https://www.mathworks.com/products/matlab.html?s_tid=hp_products_matlab](https://www.mathworks.com/products/matlab.html?s_tid=hp_products_matlab)) (accessed on 10 April 2022)). Bagging (which is short for bootstrap aggregating) consists of building different submodels using random samples, with replacement, and then assembling the results. Various subsets of the training set data are created. The model has that name, since it trains the submodels using bagging. A model is trained with each subset, and the final  
Figure 2: Binary dependent variable, ignition of wildland fires in the Maule region between the years 2013 to 2015.  
predictions are averaged, making it more robust. An example of a bagging model is shown in Figure 3. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables Classes** & **Name** & **Label** & **Source** \\ \hline  & DensPop & Population density of the point (inhabitants/census district) & Instituto Nacional de Estadisticas: [[https://www.ine.cl/](https://www.ine.cl/)]([https://www.ine.cl/](https://www.ine.cl/)) \\ Human activity & DistCit & Distance from point to nearest city (m) & herramientas/portal-de-mapas/geodatos-abierto \\  & DistRoad\_buff & Average distance from the 500 m radius & Ministerio de Obras Publicas: \\  & DistRoad\_buff & buffer to the nearest road (m) & [[https://ide.mpe.gob.cl/geomop/](https://ide.mpe.gob.cl/geomop/)]([https://ide.mpe.gob.cl/geomop/](https://ide.mpe.gob.cl/geomop/)) (accessed on 2 April 2022) [72] \\ \hline  & & & Earth Resources Observation and Science Center (EROS), \\ Geography and topography & Exposition & Point exposition (indices) & [[https://www.usgs.gov/centers/eros/data-tools](https://www.usgs.gov/centers/eros/data-tools)]([https://www.usgs.gov/centers/eros/data-tools](https://www.usgs.gov/centers/eros/data-tools)) (accessed on April 2022) [73] \\  & Slope & Point slope (grades) & \\  & Elev & Point elevation (m.a.s.l.) & \\ \hline  & Crop\_buff & Proportion of crops in a 500 m radius & \\  & & buffer (proportion) & \\  & Nat\_buff & Proportion of native forest in a 500 m & \\  & & radius buffer (proportion) & \\  & ForPlan\_buff & Proportion of forests plantation in a 500 m & \\  & & radius buffer (proportion) & \\ Land cover & Grass\_buff & Proportion of grassland in a 500 m radius & \\  & & buffer (proportion) & [PERSON] et al. 2016 [74] \\  & Scrub\_buff & Proportion of scruls in a 500 m radius & \\  & & buffer (proportion) & \\  & Imper\_buff & Proportion of impermeable land in a & \\  & & 500 m radius buffer (proportion) & \\  & & & \\  & BareSoil\_buff & Proportion of bare soil in a 500 m radius & \\  & & buffer (proportion) & \\  & LC & Type of land cover predominant in the & \\  & & point (categorical) & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Independent variables, names, and sources. Figure 3: Diagram of the bagged model. For this work, we use the bagged decision tree, where each model is a decision tree. The training set was built with 80% of the database, and the remaining 20% was used to test and verify the accuracy of the results. Once the model was trained, it generated the fire ignition susceptibility classification for each point on the 30 m \(\times\) 30 m quadrant map. Finally, a comparative temporal matrix of four WUI was built to analyze changes in the distribution of wildland fire ignition. The matrix was elaborated through the QGIS software. The satellite images come from the Sentinel-2 mission. These images present an atmospheric correction at ground level, providing spectral radiance levels similar to reality. The vector layer of the fire scar was extracted from the Landscape Fire Scars database for Chile [76].