## 2 Materials and Methods
### Temporal Trend  
The change detection in 2001-2016 trends for the different parameters used the aggregated annual time series (AAT) method [36]. The AAT is available in the Land Surface Phenology and Trend Analysis package in the R software ([[http://greenbrown.r-forge.r-project.org/](http://greenbrown.r-forge.r-project.org/)]([http://greenbrown.r-forge.r-project.org/](http://greenbrown.r-forge.r-project.org/))). The AAT method estimates each pixel's trend and the predictable significance of each temporal segment using the Mann-Kendall test [37; 38]. The annual average is the basis for detecting structural changes or significant breakpoints in a regression analysis, requiring data with at least four years [39]. The result indicates the annual magnitude of increase or decrease per pixel, and if that value is significant. Simple linear regression analysis demonstrated the relationship between AAT trends with fire recurrence, from an average of 100 random points within each fire recurrence class. In addition, we estimated the relationship  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site Name** & **Study Area** & **Lat** & **Long** & **Variable** & **Period** \\ \hline Tocantins-Javares Flux Tower & ANP-CSP & \(-\)9.824 & \(-\)50.159 & LST/GPP & 10/2003 to 12/2004 \\ Reserva Jaru Flux Tower & JPRB & \(-\)10.07800 & \(-\)61.93310 & LST/GPP & 04/2000 to 06/2002 \\ Capsusu 8-year Pasture & ANP-CSP & \(-\)10.04658 & \(-\)49.8998 & Reflectance & 06/2002 \\ Capsusu Cerrado woodland & ANP-CSP & \(-\)10.06633 & \(-\)49.90696 & Reflectance & 06/2002 \\ Capsusu 3-year pasture & ANP-CSP & \(-\)10.06339 & \(-\)49.91326 & Reflectance & 06/2002 \\ Capsusu 1-year pasture & ANP-CSP & \(-\)10.06155 & \(-\)49.90547 & Reflectance & 06/2002 \\ Santana do Araguiaia 20-year pasture & ANP-CSP & \(-\)9.71817 & \(-\)50.40072 & Reflectance & 06/2002 \\ Santana do Araguiaia Capoeira & ANP-CSP & \(-\)9.7454 & \(-\)50.30674 & Reflectance & 06/2002 \\ Ji-Parama LC 1 & JPRB & \(-\)10.896 & \(-\)62.081 & Reflectance & 07/2003 \\ Ji-Parama LC 2 & JPRB & \(-\)10.714 & \(-\)61.988 & Reflectance & 07/2003 \\ Presidentite Medici PR & JPRB & \(-\)11.242 & \(-\)61.976 & Reflectance & 07/2003 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Large-Scale Biosphere-Atmosphere (LBA) data for the study areas: Araguaia National Park and Cantão State Park (ANP-CSP) and Ji-Paramá River Basin (JPRB). The variables used were Land-Surface Temperature (LST), Gross Primary Productivity (GPP), and reflectance.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Amazon Forest**} & \multicolumn{3}{c}{**Cerrado**} \\ \hline
\begin{tabular}{c} **Scenes** (Path-Row)** \\ \end{tabular} & **Image Date** & **Sensor** \\ \hline  & 01/10/2002 & ETM+ & & 20/09/2001 & ETM+ \\
223–67 & 01/10/2005 & TM & 231–67 & 18/09/2003 & TM \\
223–68 & 21/09/2007 & TM & 231–68 & 13/09/2007 & TM \\
10/10/2014 & OLI & & 21/09/2010 & TM \\
27/09/2015 & OLI & & 16/09/2014 & OLI \\ \hline \hline \end{tabular}
\end{table}
Table 2: Landsat database used to define the best threshold for mapping of burned areas, considering the sensors: Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI).  
between biophysical variables trends, land use/land cover, and recurrence fire, using an average of 50 random points in each possible combination of fire recurrence and type of land use/land cover.  
To illustrate the changes caused by the fire in the temporal profile of biophysical variables, we have also selected some of the variables with greater representativeness. In this case, we selected random pixels from the same 50 pixels used for the regression analysis for time profile analysis.