The 2019 fire crisis in Amazonia dominated global news and triggered fundamental questions about the possible causes behind it. Here we performed an in-depth investigation of the drivers of active fire anomalies in the Brazilian Amazon biome. We assessed a 2003–2019 time-series of active fires, deforestation, and water deficit and evaluated potential drivers of active fire occurrence in 2019, at the biome-scale, state level, and local level. Our results revealed abnormally high monthly fire counts in 2019 for the states of Acre, Amazonas, and Roraima. These states also differed from others by exhibiting in this year extreme levels of deforestation. Areas in 2019 with active fire occurrence significantly greater than the average across the biome had, on average, three times more active fires in the three previous years, six times more deforestation in 2019, and five times more deforestation in the five previous years. Approximately one-third of yearly active fires from 2003 to 2019 occurred up to 1 km from deforested areas in the same year, and one-third of deforested areas in a given year were located up to 500 m from deforested areas in the previous year. These findings provide critical information to support strategic decisions for fire prevention policies and fire combat actions.

We used Moderate Resolution Imaging Spectroradiometer (MODIS) data, processed by the multi–angle implementation of atmospheric correction (MAIAC) algorithm, to investigate the sensitivity of seven vegetation indices (VIs) to bidirectional reflectance distribution function (BRDF) effects in the dry season (June–September) of the Brazilian Amazon. The analysis was first performed over three sites, located from north to south of the Amazon, and then extended into the entire region. We inspected for differences in viewing–illumination parameters and pixel quality retrievals during MODIS data acquisition over the region. By comparing and correlating corrected and non–corrected data for bidirectional effects, we evaluated monthly changes in reflectance and VIs (2000–2014). Finally, we computed the effect size of the BRDF correction using non–parametric Mann–Whitney tests and Cohen’s r metrics. The results showed that the most anisotropic VIs were the enhanced vegetation index (EVI), photochemical reflectance index (PRI), and shortwave infrared normalized difference (SWND). These VIs presented the largest relative changes and the lowest correlation coefficients, between corrected and non–corrected data, because of the large effect size of the BRDF. The least anisotropic VI was the normalized difference water index (NDWI). The anisotropy of these VIs was stronger in the northern Amazon. It increased from the beginning to the end of the dry season, following changes in the relative azimuth angle (RAA) toward the BRDF hotspot in September. The modifications in the relative proportions of backscattering observations used in composite products caused a reflectance increase in all MODIS bands at the end of the dry season, especially in the near infrared (NIR). The reflectance decreased after BRDF correction. Because of the atmospheric effects, the view zenith angle (VZA) of the pixels selected in composite products decreased toward the south of the Amazon. In the southern Amazon, the seasonal amplitude in the solar zenith angle (SZA) reached values close to 18°. For the most anisotropic index, the BRDF correction removed, on average, 30% of the EVI signal in June, and 60% of the EVI signal in September, reducing dry season variations over time. The results reinforce the need for bidirectional correction of MODIS data before the seasonal and inter–annual analyses of the most anisotropic VIs.

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As inundações são os desastres naturais mais frequentes e que causam maiores danos econômicos, sociais e ambientais no mundo. Para este trabalho, entende-se inundação como a elevação do nível de água de um corpo hídrico para além do seu nível normal, alagando a planície aluvial deste corpo. As inundações são causadas devido às características ambientais tais como chuvas, formato da bacia hidrográfica, cobertura vegetal, escoamento superficial; e antrópicas; como a impermeabilização dos solos e o descarte de lixo em locais inadequados. O município de Iguape, localizado no estado de São Paulo, faz parte da bacia hidrográfica do Rio Ribeira do Iguape. Devido às características desta bacia, o município registra inundações frequentemente. Assim, torna-se pertinente a produção de um mapa de suscetibilidade a inundações para o município, a fim de apontar as localidades com maior risco de sofrer inundações. Para isso, foram comparadas duas fontes de dados topográficos: curvas de nível e pontos cotados (criando um MDE) adquiridos no site do Sistema de Informações Geográficas da Bacia do Ribeira do Iguape e Litoral Sul (SIGRB), e dados SRTM (Shuttle Radar Topography Mission) obtidos através do TopoData. O uso e ocupação da terra foram integrados ao MDE para criar o mapa de suscetibilidade, adotando pesos para cada variável. Os mapas gerados estão em escala 1:400.000, considerando a área do município de 1.977,957 km². Os resultados apresentam a variação dos mapas de suscetibilidade de acordo com a fonte e dos dados topográficos utilizados. O mapa com curvas de nível e pontos cotados apontou riscos de inundação maiores e mais generalizados do que o SRTM.