Fire probability mapping and prediction from environmental data: What a comprehensive savanna-forest transition can tell us

• Published in: Forest ecology and management 2022-09, Vol.520, p.120354, Article 120354
• Main Authors: Barros-Rosa, Lucas, de Arruda, Paulo Henrique Zanella, Machado, Nadja Gomes, Pires-Oliveira, João Carlos, Eisenlohr, Pedro V.
• Format: Article
• Language: English
• Subjects: Fire predictions; Forest-savanna transition; Land cover; Protected areas; Tropical ecosystems
• ISSN: 0378-1127; 1872-7042
• DOI: 10.1016/j.foreco.2022.120354

•Fires are strongly associated with agriculture areas, silt and temperature.•Fire occurrences are higher during the dry season.•Protected areas act as fire barrier.•Random Forest is a high performance fire prediction algorithm.•Species Distribution Models can be adequately used in fire prediction. The Cerrado-Amazon transition (CAT) is located between the two largest biogeographic domains of the Neotropical region, coinciding with the “Arc of Deforestation”, an agricultural frontier that expands towards the Amazon. Fire plays an important role in the landscape changes that have been occurring in this transition. Thus, the objectives of this study were (i) evaluate the accuracy of different algorithms/techniques in predicting fires in the CAT; (ii) investigate the season of highest fire probabilities in the CAT; and (iii) identify, among anthropic, climatic, topographic and soil variables, the main drivers of fires spatial distribution in the CAT. Fire occurrence data (active fire pixels) from the MODIS sensor were used as input data in species distribution models. The SDM allowed the construction of monthly fire probability models for the study area. The model's quality was assessed by R-squared, RMSE, AUC and TSS metrics. In addition, we assessed the weight of variables in building the models. To obtain the fire seasonality, we used the bootstrap technique to calculate the 95% confidence interval (CI) about the mean. As result, we found that all algorithms obtained satisfactory performance, as well as the ensemble model technique used, which obtained an average AUC of 0.812 ± 0.001. The algorithm with the best individual performance was Random Forest, with a mean AUC 0.851 ± 0.002. Similar patterns were found when considering TSS metrics. The predicted and observed fire occurrences showed strong association (R2 = 0.81). The high fire probability area was higher during the dry season (15304.37 ± 41.65), showing a significant difference (p-value ≤ 0.05) from the rainy season. The variables that most explained the fire occurrences along the CAT were 'Silt Fraction', 'Distance from Agriculture' and 'Maximum Temperature'. In addition, ‘Distance from Roads’, ‘Distance from Protected Areas’ and Elevation’ were relevant for the monthly models. Anthropogenic variables were more important during the dry season. The results demonstrate that SDMs can be adequately used in fire prediction. Protected areas have shown an important role in acting as a fire barrier. Howev