Abstract. In the Brazilian savannas (Cerrado biome) fires are natural and a tool for shifting land use; therefore, temporal and spatial patterns result from the interaction of climate, vegetation condition and human activities. Moreover, orbital sensors are the most effective approach to establish patterns in the biome. We aimed to characterize fire, precipitation and vegetation condition regimes and to establish spatial patterns of fire occurrence and their correlation with precipitation and vegetation condition in the Cerrado. The Cerrado was first and second biome for the occurrence of burned areas (BA) and hotspots, respectively. Occurrences are higher during the dry season and in the savanna land use. Hotspots and BA tend to decrease, and concentrate in the north, but more intense hotspots are not necessarily located where concentration is higher. Spatial analysis showed that averaged and summed values can hide patterns, such as for precipitation, which has the lowest average in August, but minimum precipitation in August was found in 7 % of the Cerrado. Usually, there is a 2–3-month lag between minimum precipitation and maximum hotspots and BA, while minimum VCI and maximum hotspots and BA occur in the same month. Hotspots and BA are better correlated with VCI than precipitation, qualifying VCI as an indicator of the susceptibility of vegetation to ignition.
Deforestation in the Brazilian Amazon is related to the use of fire to remove natural vegetation and install crop cultures or pastures. In this study, we evaluated the relation between deforestation, land-use and land-cover (LULC) drivers and fire emissions in the Apyterewa Indigenous Land, Eastern Brazilian Amazon. In addition to the official Brazilian deforestation data, we used a geographic object-based image analysis (GEOBIA) approach to perform the LULC mapping in the Apyterewa Indigenous Land, and the Brazilian biomass burning emission model with fire radiative power (3BEM_FRP) to estimate emitted particulate matter with a diameter less than 2.5 µm (PM2.5), a primary human health risk. The GEOBIA approach showed a remarkable advancement of deforestation, agreeing with the official deforestation data, and, consequently, the conversion of primary forests to agriculture within the Apyterewa Indigenous Land in the past three years (200 km2), which is clearly associated with an increase in the PM2.5 emissions from fire. Between 2004 and 2016 the annual average emission of PM2.5 was estimated to be 3594 ton year−1, while the most recent interval of 2017–2019 had an average of 6258 ton year−1. This represented an increase of 58% in the annual average of PM2.5 associated with fires for the study period, contributing to respiratory health risks and the air quality crisis in Brazil in late 2019. These results expose an ongoing critical situation of intensifying forest degradation and potential forest collapse, including those due to a savannization forest-climate feedback, within “protected areas” in the Brazilian Amazon. To reverse this scenario, the implementation of sustainable agricultural practices and development of conservation policies to promote forest regrowth in degraded preserves are essential.
Abstract:In this study we assessed METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) model performance to estimate energy balance fluxes and evapotranspiration (ET) in two heterogeneous landscapes in the Brazilian Cerrado, including fluxes and ET in both agricultural and natural vegetation. The estimates were evaluated by comparing them to flux tower data collected over sugarcane (USR site), woody savanna (PDG site) and stricto-sensu savanna (RECOR site) areas. The selection of the study years (2005)(2006)(2007) for USR/PDG sites and 2011-2015 for RECOR site) was based on the availability of meteorological data (to be used as inputs in METRIC) and of flux tower data for energy balance fluxes and ET comparisons. The broadband albedo submodel was adjusted in order to improve Net Radiation estimates. For this adjustment, we applied at-surface solar radiation simulations obtained from the SMARTS2 model under different conditions of land elevation, precipitable water content and solar angles. We also tested the equivalence between the measured crop coefficient (Kc _ec ) and the reference evapotranspiration fraction (ETrF or F), seeking to extrapolate from instantaneous to daily values of actual evapotranspiration (ETa). Surface albedo was underestimated by 10% at the USR site (showing a better performance for full crop coverage), by 15% at the PDG site (following the woody savanna dynamics pattern through dry and wet seasons) and was overestimated by 21% at the RECOR site. METRIC was effective in simulating the spatial and temporal variability of energy balance fluxes and ET over agricultural and natural vegetation in the Brazilian Cerrado, with errors within those reported in the literature. Net radiation (Rn) presented consistent results (coefficient of determination (R 2 ) > 0.94) but it was overestimated by 8% and 9% in sugarcane and woody savanna, respectively. METRIC-derived ET estimates showed an agreement with ground data at USR and PDG sites (R 2 > 0.88, root mean square error (RMSE) up to 0.87 mm day −1 ), but at the RECOR site, ET was overestimated by 14% (R 2 = 0.96, mean absolute error (MAE) = 0.62 mm.day −1 and RMSE = 0.75 mm day −1 ). Surface energy balance fluxes and ET were marked by seasonality, with direct dependence on available energy, rainfall distribution, soil moisture and other parameters like albedo and NDVI.
The Pantanal faced an unprecedented drought event in 2020. The hydrological year ended in July, 2020 had an annual average rainfall 26 % lower than the average from 1982 to 2020. Consequently, catastrophic wildfires burned out of control. Active fires during this year have also increased, and were 123 % higher than the 2002-2020 Pantanal's average. Approximately 95 % of these active fires occurred in natural land covers with 28 % of them occurring in areas classified as wetlands that likely dried out due to the drought. Therefore, the development of a special policy is needed to minimize the impact of this crisis on the biodiversity, conservation, and traditional people of the Pantanal.
Over the decades, hydropower complexes have been built in several hydrographic basins of Brazil including the Amazon region. Therefore, it is important to understand the effects of these constructions on the environment and local communities. This work presents a land use and land cover change temporal analysis considering a 33-year period (1985–2018) in the direct influence zone of the Braço Norte Hydropower Complex, Brazilian Amazonia, using the Collection 4.1 level 3 of the freely available MapBiomas dataset. Additionally, we have assessed the Brazilian Amazon large-scale deforestation process acting as a land use and land cover change driver in the study area. Our findings show that the most impacted land cover was forest formation (from 414 km2 to 287 km2, a reduction of 69%), which primarily shifted into pasturelands (increase of 664%, from 40 km2 to 299 km2). The construction of the hydropower complex also triggered indirect impacts such as the presence of urban areas in 2018 and the consequent increased local demand for crops. Together with the ongoing large-scale Amazonian deforestation process, the construction of the complex has intensified changes in the study area as 56.42% of the pixels were changed between 1985 and 2018. This indicates the importance of accurate economic and environmental impact studies for assessing social and environmental consequences of future construction in this unique region. Our results reveal the need for adopting special policies to minimize the impact of these constructions, for example, the creation of Protected Areas and the definition of locally-adjusted parameters for the ecological-economic zoning considering environmental and social circumstances derived from the local actors that depend on the natural environment to subsist such as indigenous peoples, riverine population, and artisanal fishermen.
Todos os anos as doenças transmitidas pelo Aedes aegypti faz inúmeras vítimas no Brasil tornando-se um problema de saúde pública. Estudos destacam a dificuldade no controle desse vetor e a necessidade de pesquisas focadas no desenvolvimento de novos métodos para conter esse mosquito, principalmente em escalas pontuais. Entre os fatores que podem favorecer a proliferação do Aedes aegypti a precipitação e a temperatura se destacam. Assim, o objetivo deste trabalho foi buscar uma metodologia para a obtenção dessas variáveis associada a proliferação desse mosquito em escalas sutis e por meio de dados de satélites (produtos MOD11A1 e 3B42). A área de estudo foram os distritos do município de São Paulo e foi feita uma análise temporal de 2010 a 2016. Foram retroagidos a partir do primeiro dia de cada uma das Semanas Epidemiológicas, de cada ano, 36 dias e considerado para o estudo o intervalo entre o vigésimo segundo e trigésimo sexto dia (período máximo e mínimo para o desenvolvimento do mosquito, do vírus e da doença). Para este período, obteve-se dos dados de satélites a moda da temperatura de superfície e da precipitação de cada Distrito e, a partir dessas modas, foi calculado a mediana da temperatura da superfície e o acumulado de precipitação, que foram correlacionados com o número de casos de dengue. Identificou-se amplitudes de temperatura de superfície e precipitação correlacionadas com a incidência do Aedes aegypti em cada distrito. Espera-se com este estudo, oferecer parâmetros que subsidiem modelos e alertas para o controle deste vetor.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
334 Leonard St
Brooklyn, NY 11211
Copyright © 2023 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.