## 4 Discussion
[54] did not observe significant GPP trends in the Atlantic forest of Brazil (Itataia National Park) from the non-parametric Mann-Kendall test. The surface temperature was the variable with the largest number of pixels with significant changes in the study areas. Despite this, there is no observable pattern in phytophysiognomies, both those in which the increase in temperature was highly related to the fire recurrence as in those with medium-low proportion. The rise in surface temperature occurs immediately after the fire event, but these effects are attenuated over time [12]. However, the results demonstrate that surface temperature changes were consistent throughout the time series, even in areas not affected by the fire, suggesting other factors as determinants in the alteration of the surface temperature in the medium-long term. Moreover, most of the Brazilian surface temperature has a positive trend, especially in the Amazon-Cerrado transition with intense land-use change [55]. The annual temperature rise reached 0.25 K in the ANP-CSP area and 0.19 K in the JPRB area, higher than the values found in urban areas that may increase 0.14 K per year [56]. The research results indicate that even protected areas, such as the ANP-CSP, showed significant medium-term changes in biophysical parameters. In this case, the discussions for changes in the Brazilian fire policy must be based on the historical and current fire scenarios in each protected unit, and how they have impacted the vegetation pattern [57; 58]. Remote sensing identification of critical locations optimizes the management of these areas with continuous vegetation monitoring. Thus, it is possible to carry out on-site activities, with prescribed burning practices to avoid the drastic changes observed in this research in the most sensitive areas. The results demonstrate the AAT method's effectiveness in evaluating vegetation changes from temporal series with moderate spatial resolution images such as MODIS. The MODIS time series allows identifying long-term changes in vegetation, besides having high reliability given its high relation with data observed in the field. Despite the usefulness of MODIS data in this research, there are some limitations in its performance and possibilities to expand the analysis in future works. We mention the effect of low spatial resolution [21] and the spectral mixture among the limitations. Future works may improve the analyzes carried out in this research. Despite the loss of spatial resolution to 500 m, studies may use the product Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo-MCD43), attenuating the changes in radiation caused by scattering (anisotropy) [59], that result in changes unrelated to vegetation and incorrect data readings [60; 61]. In this research, the inclusion of pre-processing steps with the Savitzky-Golay filter helped to attenuate these effects. In addition to the product MCD43, other biophysical parameters (not included in this analysis) can be highly affected by fire, such as the near-infrared reflectance of vegetation (NIRv) [62]. The NIRv index can bring advantages in the estimation of GPP data by satellite, and together with other methodologies such as Solar-induced chlorophyll fluorescence (SIF) [63], it can better detail the pre- and post-fire vegetation conditions.