Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

Burns are common practices in Brazil and cause major fires, especially in the Legal Amazon. This study evaluated the dynamics of the fire foci in the Legal Amazon in Brazil and their consequences on environmental degradation, particularly in the transformation of the forest into pasture, in livestock and agriculture areas, mining activities and urbanization. The fire foci data were obtained from the reference satellites of the BDQueimadas of the CPTEC/INPE for the period June 1998–May 2022. The data obtained were subjected to descriptive and exploratory statistical analysis, followed by a comparison with the PRODES data during 2004–2021, the DETER data (2016–2019) and the ENSO phases during the ONI index for the study area. Biophysical parameters were used in the assessment of environmental degradation. The results showed that El Niño’s years of activity and the years of extreme droughts (2005, 2010 and 2015) stand out with respect to significant increase in fire foci. Moreover, the significant numbers of fire foci indices during August, September, October and November were recorded as 23.28%, 30.91%, 15.64% and 10.34%, respectively, and these were even more intensified by the El Niño episodes. Biophysical parameters maps showed the variability of the fire foci, mainly in the south and west part of the Amazon basin referring to the Arc of Deforestation. Similarly, the states of Mato Grosso, Pará and Amazonas had the highest alerts from PRODES and DETER, and in the case of DETER, primarily mining and deforestation (94.3%) increased the environmental degradation. The use of burns for agriculture and livestock, followed by mining and wood extraction, caused the degradation of the Amazon biome.

[1]  Dimas de Barros Santiago,et al.  Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil , 2022, Sustainability.

[2]  Peng Han,et al.  Landscape context determines soil fungal diversity in a fragmented habitat , 2022, CATENA.

[3]  Sen Liu,et al.  The Distribution Characteristics and Human Health Risks of High- Fluorine Groundwater in Coastal Plain: A Case Study in Southern Laizhou Bay, China , 2022, Frontiers in Environmental Science.

[4]  Carlos Antonio da Silva Junior,et al.  Using Remote Sensing to Quantify the Joint Effects of Climate and Land Use/Land Cover Changes on the Caatinga Biome of Northeast Brazilian , 2022, Remote. Sens..

[5]  João Paulo Assis Gobo,et al.  Comparison between Air Temperature and Land Surface Temperature for the City of São Paulo, Brazil , 2022, Atmosphere.

[6]  G. Du,et al.  Differential Mechanisms Drive Species Loss Under Artificial Shade and Fertilization in the Alpine Meadow of the Tibetan Plateau , 2022, Frontiers in Plant Science.

[7]  Dong Wang,et al.  Effects of long-term grazing exclusion on plant and soil properties vary with position in dune systems in the Horqin Sandy Land , 2022, CATENA.

[8]  S. Freitas,et al.  Improving the south America wildfires smoke estimates: Integration of polar-orbiting and geostationary satellite fire products in the Brazilian biomass burning emission model (3BEM) , 2022, Atmospheric Environment.

[9]  C. Gao,et al.  Simulation and design of joint distribution of rainfall and tide level in Wuchengxiyu Region,China , 2021, Urban Climate.

[10]  G. Lyra,et al.  Fire foci in South America: Impact and causes, fire hazard and future scenarios , 2021, Journal of South American Earth Sciences.

[11]  S. Fu,et al.  Fine root biomass and morphology in a temperate forest are influenced more by the nitrogen treatment approach than the rate , 2021 .

[12]  J. F. Oliveira‐Júnior,et al.  Spatiotemporal climatic analysis in Pernambuco State, Northeast Brazil , 2021 .

[13]  Yaochen Qin,et al.  Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery , 2021, Remote. Sens..

[14]  Z. Niu,et al.  Summer Maize Mapping by Compositing Time Series Sentinel-1A Imagery Based on Crop Growth Cycles , 2021, Journal of the Indian Society of Remote Sensing.

[15]  Chao Li,et al.  Exploring the utility of radar and satellite-sensed precipitation and their dynamic bias correction for integrated prediction of flood and landslide hazards , 2021, Journal of Hydrology.

[16]  Quansheng Zhao,et al.  A preliminary study on the eco-environmental geological issue of in-situ oil shale mining by a physical model. , 2021, Chemosphere.

[17]  S. J. Mayor,et al.  Dry corridors opened by fire and low CO2 in Amazonian rainforest during the Last Glacial Maximum , 2021, Nature Geoscience.

[18]  F. França,et al.  Tracking the impacts of El Niño drought and fire in human-modified Amazonian forests , 2021, Proceedings of the National Academy of Sciences.

[19]  M. Ehsan,et al.  Possible Thermal Anomalies Associated With Global Terrestrial Earthquakes During 2000–2019 Based on MODIS-LST , 2021, IEEE Geoscience and Remote Sensing Letters.

[20]  Carlos Antonio da Silva Junior,et al.  Recent trends in the fire dynamics in Brazilian Legal Amazon: Interaction between the ENSO phenomenon, climate and land use , 2021 .

[21]  Gui-zhou Wang,et al.  Monitoring Landsat Based Burned Area as an Indicator of Sustainable Development Goals , 2021, Earth's Future.

[22]  B. Soares-Filho,et al.  Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon , 2021, Nature Communications.

[23]  T. Jackson,et al.  Retrievals of soil moisture and vegetation optical depth using a multi-channel collaborative algorithm , 2021 .

[24]  T. Biggs,et al.  Forests Mitigate Drought in an Agricultural Region of the Brazilian Amazon: Atmospheric Moisture Tracking to Identify Critical Source Areas , 2021, Geophysical Research Letters.

[25]  A. M. D. R. F. Jardim,et al.  Genotypic differences relative photochemical activity, inorganic and organic solutes and yield performance in clones of the forage cactus under semi-arid environment. , 2021, Plant physiology and biochemistry : PPB.

[26]  Q. Quan,et al.  Assessment of the sustainability of Gymnocypris eckloni habitat under river damming in the source region of the Yellow River. , 2021, The Science of the total environment.

[27]  V. R. S. Cheela,et al.  Combating Urban Heat Island Effect—A Review of Reflective Pavements and Tree Shading Strategies , 2021, Buildings.

[28]  M. M. Rolim,et al.  Spatio-temporal monitoring of soil and plant indicators under forage cactus cultivation by geoprocessing in Brazilian semi-arid region , 2021 .

[29]  Dimas de Barros Santiago,et al.  Temporal record and spatial distribution of fire foci in State of Minas Gerais, Brazil. , 2020, Journal of environmental management.

[30]  L. Aragão,et al.  Drivers of Fire Anomalies in the Brazilian Amazon: Lessons Learned from the 2019 Fire Crisis , 2020, Land.

[31]  Zhijia Li,et al.  A hybrid runoff generation modelling framework based on spatial combination of three runoff generation schemes for semi-humid and semi-arid watersheds , 2020 .

[32]  E. Sills,et al.  Impacts of Protected Area Deforestation on Dry‐Season Regional Climate in the Brazilian Amazon , 2020, Journal of Geophysical Research: Atmospheres.

[33]  Joez André de Moraes Rodrigues,et al.  Pilot monitoring of caatinga spatial-temporal dynamics through the action of agriculture and livestock in the brazilian semiarid , 2020 .

[34]  Jiancheng Shi,et al.  Soil moisture experiment in the Luan River supporting new satellite mission opportunities , 2020 .

[35]  Junaid Ahmed,et al.  Possible ionosphere and atmosphere precursory analysis related to Mw > 6.0 earthquakes in Japan , 2020 .

[36]  Dimas de Barros Santiago,et al.  Fire foci related to rainfall and biomes of the state of Mato Grosso do Sul, Brazil , 2020 .

[37]  E. I. Fernandes-Filho,et al.  Fire foci assessment in the Western Amazon (2000–2015) , 2020, Environment, Development and Sustainability.

[38]  Michael T. Coe,et al.  Droughts Amplify Differences Between the Energy Balance Components of Amazon Forests and Croplands , 2020, Remote. Sens..

[39]  Ignácio Amigo,et al.  When will the Amazon hit a tipping point? , 2020, Nature.

[40]  Wade T. Crow,et al.  The Sensitivity of North American Terrestrial Carbon Fluxes to Spatial and Temporal Variation in Soil Moisture: An Analysis Using Radar‐Derived Estimates of Root‐Zone Soil Moisture , 2019, Journal of Geophysical Research: Biogeosciences.

[41]  J. F. Oliveira‐Júnior,et al.  Remote sensing for updating the boundaries between the brazilian Cerrado-Amazonia biomes , 2019, Environmental Science & Policy.

[42]  V. Bondur,et al.  Satellite Monitoring of Wildfire Impacts on the Conditions of Various Types of Vegetation Cover in the Federal Districts of the Russian Federation , 2019, Izvestiya, Atmospheric and Oceanic Physics.

[43]  Bergson G. Bezerra,et al.  Comparative analyzes and use of evapotranspiration obtained through remote sensing to identify deforested areas in the Amazon , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[44]  M. Andreae Emission of trace gases and aerosols from biomass burning – an updated assessment , 2019, Atmospheric Chemistry and Physics.

[45]  Y. Liu,et al.  Urban pollution greatly enhances formation of natural aerosols over the Amazon rainforest , 2019, Nature Communications.

[46]  O. Phillips,et al.  21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions , 2018, Nature Communications.

[47]  V. Bondur,et al.  Spacetime Distributions of Wildfire Areas and Emissions of Carbon-Containing Gases and Aerosols in Northern Eurasia according to Satellite-Monitoring Data , 2017, Izvestiya, Atmospheric and Oceanic Physics.

[48]  J. Randerson,et al.  A human-driven decline in global burned area , 2017, Science.

[49]  J. F. Oliveira‐Júnior,et al.  Spatiotemporal rainfall and temperature trends throughout the Brazilian Legal Amazon, 1973–2013 , 2017 .

[50]  Michael Brauer,et al.  Critical Review of Health Impacts of Wildfire Smoke Exposure , 2016, Environmental health perspectives.

[51]  C. Justice,et al.  The collection 6 MODIS active fire detection algorithm and fire products , 2016, Remote sensing of environment.

[52]  Shuanggen Jin,et al.  Statistical characteristics of seismo-ionospheric GPS TEC disturbances prior to global Mw ≥ 5.0 earthquakes (1998–2014) , 2015 .

[53]  Grant J. Williamson,et al.  Climate-induced variations in global wildfire danger from 1979 to 2013 , 2015, Nature Communications.

[54]  J. F. Oliveira‐Júnior,et al.  Overview of fire foci causes and locations in Brazil based on meteorological satellite data from 1998 to 2011 , 2015, Environmental Earth Sciences.

[55]  J. Stape,et al.  Köppen's climate classification map for Brazil , 2013 .

[56]  K. F. Boersma,et al.  Satellite observations indicate substantial spatiotemporal variability in biomass burning NO x emission factors for South America , 2013 .

[57]  J. Randerson,et al.  Satellite observations of terrestrial water storage provide early warning information about drought and fire season severity in the Amazon , 2013 .

[58]  C. Nobre,et al.  The droughts of 1997 and 2005 in Amazonia: floodplain hydrology and its potential ecological and human impacts , 2013, Climatic Change.

[59]  C. Nobre,et al.  The Drought of Amazonia in 2005 , 2008 .

[60]  R. Allen,et al.  At-Surface Reflectance and Albedo from Satellite for Operational Calculation of Land Surface Energy Balance , 2008 .

[61]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[62]  Jin Chen,et al.  Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction , 2006 .

[63]  D. Nepstad,et al.  MICROMETEOROLOGICAL AND CANOPY CONTROLS OF FIRE SUSCEPTIBILITY IN A FORESTED AMAZON LANDSCAPE , 2005 .

[64]  D. Nepstad,et al.  Frontier Governance in Amazonia , 2002, Science.

[65]  H. Kaiser The Application of Electronic Computers to Factor Analysis , 1960 .

[66]  K. M. Mohib,et al.  An integrated flood risk assessment approach based on coupled hydrological-hydraulic modeling and bottom-up hazard vulnerability analysis , 2022, Environ. Model. Softw..

[67]  Najam Abbas Naqvi,et al.  Possible Atmosphere and Ionospheric Anomalies of the 2019 Pakistan Earthquake Using Statistical and Machine Learning Procedures on MODIS LST, GPS TEC, and GIM TEC , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[68]  Jiahua Wei,et al.  Spatiotemporal characteristics and attribution of dry/wet conditions in the Weihe River Basin within a typical monsoon transition zone of East Asia over the recent 547 years , 2021, Environ. Model. Softw..

[69]  Carlos Antonio da Silva Junior,et al.  The forests in the indigenous lands in Brazil in peril , 2020 .

[70]  E. N. Stavros,et al.  Satellite Hydrology Observations as Operational Indicators of Forecasted Fire Danger across the Contiguous United States , 2019 .

[71]  B. Duncan,et al.  Vegetation fire emissions and their impact on air pollution and climate , 2009 .

[72]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .