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In recent years, Brazil has become a major global contributor to the occurrence of national fires and greenhouse gas emissions. Therefore, this study aimed to evaluate the fire foci data of the past 20 years to determine their relationship with climatic variables in various Brazilian regions. The variables evaluated included fire foci, land surface temperature, rainfall, and standardized precipitation index, which were obtained via remote sensing from 2000 to 2019. The data were subjected to trend analyses (Mann–Kendall and Pettitt tests) and a multivariate analysis of canonical variables for evaluation. The results showed that the Midwest and North regions had the highest occurrence of fire foci throughout the study period, and that the North region had the highest accumulated annual rainfall. Thus, these regions require specific public policies to prevent future fires. Overall, the Midwest, Southeast, and South regions exhibit significant increasing fire foci tendencies. Our results reveal that this trend is related to the El Niño-Southern Oscillation (ENSO) phenomena, which alter climatic variables such as precipitation, land surface temperature, and the standardized precipitation index. Finally, the sugarcane growing area had a significant linear relationship with fire foci in the Southeast region, especially in the state of São Paulo, the major national sugarcane producer. [ABSTRACT FROM AUTHOR]

The guidance on decision-making regarding deforestation in Amazonia has been efficient as a result of monitoring programs using remote sensing techniques. Thus, the objective of this study was to identify the expansion of soybean farming in disagreement with the Soy Moratorium (SoyM) in the Amazonia biome of Mato Grosso from 2008 to 2019. Deforestation data provided by two Amazonia monitoring programs were used: PRODES (Program for Calculating Deforestation in Amazonia) and ImazonGeo (Geoinformation Program on Amazonia). For the identification of soybean areas, the Perpendicular Crop Enhancement Index (PCEI) spectral model was calculated using a cloud platform. To verify areas (polygons) of largest converted forest-soybean occurrences, the Kernel Density (KD) estimator was applied. Mann–Kendall and Pettitt tests were used to identify trends over the time series. Our findings reveal that 1,387,288 ha were deforested from August 2008 to October 2019 according to PRODES data, of which 108,411 ha (7.81%) were converted into soybean. The ImazonGeo data showed 729,204 hectares deforested and 46,182 hectares (6.33%) converted into soybean areas. Based on the deforestation polygons of the two databases, the KD estimator indicated that the municipalities of Feliz Natal, Tabaporã, Nova Ubiratã, and União do Sul presented higher occurrences of soybean fields in disagreement with the SoyM. The results indicate that the PRODES system presents higher data variability and means statistically superior to ImazonGeo. [ABSTRACT FROM AUTHOR]

The collapse of mining tailing dams in Brumadinho, Minas Gerais, Brazil, that occurred in 2019 was one of the worst environmental and social disasters witnessed in the country. In this sense, monitoring any impacted areas both before and after the disaster is crucial to understand the actual scenario and problems of disaster management and environmental impact assessment. In order to find answers to that problem, the aim of this study was to identify and analyze the spatiality of the impacted area by rupture of the tailing dam of the Córrego do Feijão mine in Brumadinho, Minas Gerais, by using orbital remote sensing. Land use and land occupation, phytoplankton chlorophyll-a, water turbidity, total suspended solids on water, and carbon sequestration efficiency by vegetation (CO2Flux) were estimated by orbital imagery from the Landsat-8/OLI and MSI/Sentinel-2 sensors in order to assess the environmental impacts generated by the disaster. Data were extracted from spectral models in which the variables that best demonstrated the land use variation over the years were sought. Mean comparison by t-test was performed to compare the time series analyzed, that is, before and after the disaster. Through the analysis of water quality, it was observed that the environmental impact was calamitous to natural resources, especially water from Córrego do Feijão. [ABSTRACT FROM AUTHOR]

The aims of this study were: i) to compare no-till areas in two municipalities located in different regions of Brazil, along with the influence on CO2Flux and GPP, and ii) to verify the difference between environmental factors followed by the trends of these variables regarding future scenarios (ARIMA time-series model number). The study was carried out in two areas with different latitudes in the municipalities of Sinop-MT and Passo Fundo-RS, both in Brazil. A time series of 19 years was performed with data acquired by remote sensing from the following satellites: i) Landsat-8 (OLI and TIRS), and ii) TERRA/AQUA (MODIS). The results propound that the spectro-temporal variables are directly influenced by soil management and agricultural practices over the observation time, with a satisfactory correlation in future predictions of the variables for the next ten years, in which presented that the variation of GPP and albedo values for the two study sites would gradually increase until 2028 and the temperature remained constant between the range of its seasonality, and CO2Flux tends to decrease in its seasonality, indicating a higher CO2 absorption. [ABSTRACT FROM AUTHOR]

• Remotely sensed data in establishment of new boundary between biomes. • Land Use and Land Cover Change and the advance of the Cerrado biome on the edge of the Brazilian Amazonia. • Biophysical-atmospheric aspects altered with the anthropic advance and its influence on public environmental policies. The Amazonia is the largest tropical forest in the world, with approximately 5 million km2 occupying about 49.3% of Brazil in the North and Central-West regions and bordering the Cerrado sensu lato (Brazilian savannah). The border area between these two biomes is called the Cerrado-Amazonia transition that extends approximately 6240 km. Currently, these limits, as well as other areas of ecotonal stress, are not clearly defined mainly due to intense Land Use and Land Cover Change (LULCC). Remote sensing is a tool that tries to define and update these limits. In this context, the objective of this study was to use remote sensing tools to determine the transition between the two large Brazilian biomes. The study area comprised the transition between the Cerrado-Amazonia biomes in Brazil and an area composed of a buffer of 300 km was created around the official line of the abovementioned biomes. We used data from some remote sensors to calculate the composition of the previous year's pixel means for the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) (-1˜1), CO 2 Flux (μmol m−2 s-1), evaporative efficiency (mm yr-1), and Gross Primary Production (GPP) (g C m−2 d−1) variables. The statistical analyses were performed by softening the pixels of all the variables by means of the Kernel density (KD). Later, the analysis of the main components and the cluster between the biomes and the remote sensing variables were applied. Therefore, we sought to standardize the results and to find an overall mean between the standards to delimit a new area of the Amazonia and Cerrado biomes, called the Transition Area between Biomes (TAB). The variables analyzed by remote sensing were effective for identifying the Amazonia and Cerrado biomes characteristics that highlighted the evaporative efficiency variable in relation to other variables in both biomes. Thus, from the results achieved, a new boundary was determined that delimits the transition of the biomes that is different than the official government boundary. The analyses used in this study constitute important tools to foment the creation of public policies able to curb the anthropic actions on these biomes. [ABSTRACT FROM AUTHOR]

Abstract Brazil is the second largest soybean producer in the world with a planted area in the crop year 2017/18 of 33.347 million hectares, distributed in the Pampa, Atlantic Forest, Cerrado, and Amazon biomes. Through remote sensing techniques we show that the new agricultural frontier of soy is no longer in the Amazon, but in the last continuous areas of Cerrado, present in the region known as MATOPIBA. The soybean production chain has been striving to present to its overseas customers a soy produced in a sustainable way, without the removal of forests. Our data challenge its main program, the Amazonian Soy Moratorium, and we call attention to the conservation need of the MATOPIBA Cerrado, which is not monitored by Soy Moratorium. [ABSTRACT FROM AUTHOR]

The failure of the Fundão Dam in Mariana, more precisely in the subdistrict of Bento Rodrigues, state of Minas Gerais (Brazil) on November 5th, 2015, is considered to be "the biggest environmental tragedy in the country's history." About thirty-four million cubic meters of tailings were dumped into the river where, another 16 million continued to reach the Atlantic ocean. This disaster seriously affected the flora, fauna, economic activities and people's lives, including the loss of human lives. Remote sensing allows mapping the variability of terrain properties, such as vegetation, water and geology, both in space and time, offering a synoptic view and useful environmental information in future decision making. In this way, this research aims to analyze the impacts of the failure of the Fundão Dam in the municipality of Mariana-MG on the vegetation cover, by means of remote sensing techniques and analysis of digital processing of orbital optical images. In order to analyze the soil cover, Unmixing Espectral Linear Model (UELM) was used in order to separate soil, shade and vegetation classes. Subsequently, Artificial Neural Network (ANN) classification method was applied, followed by Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI). The results showed a loss of 13.02% of the vegetation, about 1289 ha, and a reduction of 68.57% of shade (water), approximately 1347 ha. The UELM showed to be effective in the separation of each image-fraction, being an important stage for the success of the classification. The EVI was the index that best described the vegetation deficit in the affected areas spilling the sludge from waste. [ABSTRACT FROM AUTHOR]

The Amazon Basin is undergoing extensive environmental degradation as a result of deforestation and the rising occurrence of fires. The degradation caused by fires is exacerbated by the occurrence of anomalously dry periods in the Amazon Basin. The objectives of this study were: (i) to quantify the extent of areas that burned between 2001 and 2019 and relate them to extreme drought events in a 20-year time series; (ii) to identify the proportion of countries comprising the Amazon Basin in which environmental degradation was strongly observed, relating the spatial patterns of fires; and (iii) examine the Amazon Basin carbon balance following the occurrence of fires. To this end, the following variables were evaluated by remote sensing between 2001 and 2019: gross primary production, standardized precipitation index, burned areas, fire foci, and carbon emissions. During the examined period, fires affected 23.78% of the total Amazon Basin. Brazil had the largest affected area (220,087 fire foci, 773,360 km2 burned area, 54.7% of the total burned in the Amazon Basin), followed by Bolivia (102,499 fire foci, 571,250 km2 burned area, 40.4%). Overall, these fires have not only affected forests in agricultural frontier areas (76.91%), but also those in indigenous lands (17.16%) and conservation units (5.93%), which are recognized as biodiversity conservation areas. During the study period, the forest absorbed 1,092,037 Mg of C, but emitted 2908 Tg of C, which is 2.66-fold greater than the C absorbed, thereby compromising the role of the forest in acting as a C sink. Our findings show that environmental degradation caused by fires is related to the occurrence of dry periods in the Amazon Basin. [ABSTRACT FROM AUTHOR]

Wildfires in the Amazon biome of Mato Grosso cause extensive environmental, economic, and health damages, including biodiversity loss and high greenhouse gas emissions. This study used remote sensing to examine the relationship between fire severity and climatic factors, focusing on dNBR (Differenced Normalized Burn Ratio), precipitation, LST (Land Surface Temperature), SPI (Standardized Precipitation Index), NDVI (Normalized Difference Vegetation Index), and VCI (Vegetation Condition Index), analyzing data from 2001 to 2022. Statistical tests included Shapiro-Wilk, Tukey, regression kriging, Mann-Kendall for trend analysis, Pettitt for change points, and canonical variable tests. Regarding trends, only LST showed a significant trend starting in 2009, with the Northeast mesoregion showing the highest impact on temperature. dNBR correlated positively with NDVI and VCI, and negatively with precipitation and SPI. The northern mesoregion had a positive influence on dNBR and NDVI but negative for precipitation, SPI, and VCI. The southwestern mesoregion associated positively with dNBR and LST but negatively with the other variables. The Northeast and South-Central mesoregions showed positive correlations with most variables except dNBR and NDVI. These findings highlight the northern mesoregion's vulnerability due to its proximity to the central Amazon Forest and agri-cultural activity, indicating increased fire susceptibility with reduced humidity. • The northeast mesoregion of the Mato Grosso Amazon had the greatest impact on temperature. • There is vulnerability in the northern mesoregion due to its proximity to the central rainforest and agricultural activity. • The main positive correlations for dNBR were with the variables NDVI and VCI. [ABSTRACT FROM AUTHOR]

Ecological Niche models (ENMs) are tools that allow us to approximate the area of suitability for a species, thereby allowing elaboration of conservation strategies. The validation of these models in situ is not always possible due to costly access remote areas where conserved species are often found. The goal of our study was to provide a new validation concept for ENMs by applying remote sensing (SR) techniques, such as Geographic Object-Based Image Analysis (GEOBIA), which enables mapping of large areas and provides detailed information on land use. To assess the GEOBIA validation technique, we selected the species Bertholletia excelsa (Brazil nut), a tree that has great importance as a non-timber forest product and is considered vulnerable by the International Union for Conservation of Nature (IUCN). Models were built on the ‘biomod2’ package, and evaluation was conducted using the area under the receiver operating characteristic curve (AUC) and True Skill Statistics (TSS) metrics. Images were obtained from the orbital Operational Land Imager (OLI) on board the Landsat-8 satellite and the thematic maps were evaluated using Kappa and Overall Accuracy Statistics. We calculated vegetation indices (EVI, SAVI, LAI, and NDVI) and applied them to the GEOBIA technique. A total of 693 possible sites of B. excelsa were detected. Of these, 25 accessible sites were used for validation, and 45 new records of B. excelsa were added in the study area. GEOBIA was demonstrated to have high potential for validating ENMs, as well as in the extraction of arboreal species from medium-resolution spatial images. [ABSTRACT FROM AUTHOR]

Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO 2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study evaluates the CO 2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO 2) dry air mole fraction in the atmosphere, set as X CO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO 2 flux in the SMT. The results showed that the temporal variability of CO 2 flux is correlated positively with rainfall, while X CO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO 2 occurred in the dry and rainy months, respectively, for X CO2 , which might be the result of differences in the vertical resolution of the CO 2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only X CO2 but also CO 2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration. • X CO2 is inversely related to rainfall, with highest concentration in drier periods. • Human actions in land use and land cover change increase atmospherical CO 2. • Remote sensing to locate and understand the sources and sinks of carbon dioxide. [ABSTRACT FROM AUTHOR]