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Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil

Author

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  • José Francisco de Oliveira-Júnior

    (Laboratório de Meteorologia Aplicada e Meio Ambiente (LAMMA), Insstituto de Ciências Atmosféricas (ICAT), Universidade Federal do Alagoas (UFAL), Maceió 57072-260, Brazil)

  • Munawar Shah

    (GNSS and Space Education Research Lab, Department of Space Science, National Center of GIS and Space Applications, Institute of Space Technology, Islamabad 44000, Pakistan)

  • Ayesha Abbas

    (Department of Petroleum Engineering, NED University of Engineering and Technology, Karachi 75270, Pakistan)

  • Washington Luiz Félix Correia Filho

    (Institute of Mathematics, Statistics and Physics, Federal University of Rio Grande, Rio Grande 96203-900, Brazil)

  • Carlos Antonio da Silva Junior

    (Geotechonology Applied in Agriculture and Forest (GAAF), State University of Mato Grosso (UNEMAT), Sinop 78555-000, Brazil)

  • Dimas de Barros Santiago

    (Postgraduate Program in Meteorology, Unidade Acadêmica de Ciências Atmosféricas (UACA), Federal University of Campina Grande (UFCG), Campina Grande 58429-140, Brazil)

  • Paulo Eduardo Teodoro

    (Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Brazil)

  • David Mendes

    (Post-Graduate Program in Aerospace Engineering (PPGEA), Federal University of Rio Grande do Norte (UFRN), Campus Universitário Lagoa Nova, Natal 59056-000, Brazil)

  • Amaury de Souza

    (Physics Institute, Federal University of Mato Grosso do Sul (UFRN), C.P. 549, Campo Grande 79070-900, Brazil)

  • Elinor Aviv-Sharon

    (Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel)

  • Vagner Reis Silveira

    (Departamento PROEAD, Centro Universitário de Goiás, Goiânia 74423-115, Brazil)

  • Luiz Claudio Gomes Pimentel

    (Department of Meteorology, Institute of Geosciences, Federal University of Rio de Janeiro, Av. Athos da Silveira Ramos, 274 Cidade Universitária—Ilha do Fundão, Rio de Janeiro 21941-916, Brazil)

  • Elania Barros da Silva

    (Pós-Graduação em Engenharia de Biossistemas (PGEB), Universidade Federal Fluminense (UFF), Niterói 24210-240, Brazil)

  • Mohd Anul Haq

    (Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah 11952, Saudi Arabia)

  • Ilyas Khan

    (Department of Electrical Engineering, Department of Mathematics, College of Science Al-Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia)

  • Abdullah Mohamed

    (University Research Centre, Future University in Egypt, New Cairo 11745, Egypt)

  • El-Awady Attia

    (Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
    Mechanical Engineering Department, Faculty of Engineering (Shoubra), Benha University, Cairo 11511, Egypt)

Abstract

Forest fires destroy productive land throughout the world. In Brazil, mainly the Northeast of Brazil (NEB) is strongly affected by forest fires and bush fires. Similarly, there is no adequate study of long-term data from ground and satellite-based estimation of fire foci in NEB. The objectives of this study are: (i) to evaluate the spatiotemporal estimation of fires in NEB biomes via environmental satellites during the long term over 1998–2018, and (ii) to characterize the environmental degradation in the NEB biomes via orbital products during 1998–2018, obtained from the Burn Database (BDQueimadas) for 1794 municipalities. The spatiotemporal variation is estimated statistically (descriptive, exploratory and multivariate statistics) from the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Standardized Precipitation Index (SPI) through the Climate Hazards Group InfraRed Precipitation Station (CHIRPS). Moreover, we identify 10 homogeneous groups of fire foci (G1–G10) with a total variance of 76.5%. The G1 group is the most extended group, along with the G2 group, the exception being the G3 group. Similarly, the G4–G10 groups have a high percentage of hotspots, with more values in the municipality of Grajaú, which belongs to the agricultural consortium. The gradient of fire foci from the coast to the interior of the NEB is directly associated with land use/land cover (LULC) changes, where the sparse vegetation category and areas without vegetation are mainly involved. The Caatinga and Cerrado biomes lose vegetation, unlike the Amazon and Atlantic Forest biomes. The fires detected in the Cerrado and Atlantic Forest biomes are the result of agricultural consortia. Additionally, the two periods 2003–2006 and 2013–2018 show periods of severe and prolonged drought due to the action of El Niño.

Suggested Citation

  • José Francisco de Oliveira-Júnior & Munawar Shah & Ayesha Abbas & Washington Luiz Félix Correia Filho & Carlos Antonio da Silva Junior & Dimas de Barros Santiago & Paulo Eduardo Teodoro & David Mendes, 2022. "Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil," Sustainability, MDPI, vol. 14(11), pages 1-19, June.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6935-:d:832706
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    References listed on IDEAS

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    1. Ghafar Salavati & Ebrahim Saniei & Ebrahim Ghaderpour & Quazi K. Hassan, 2022. "Wildfire Risk Forecasting Using Weights of Evidence and Statistical Index Models," Sustainability, MDPI, vol. 14(7), pages 1-15, March.
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    Cited by:

    1. Helvécio de Oliveira Filho & José Francisco de Oliveira-Júnior & Marcos Vinícius da Silva & Alexandre Maniçoba da Rosa Ferraz Jardim & Munawar Shah & João Paulo Assis Gobo & Claudio José Cavalcante Bl, 2022. "Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation," Sustainability, MDPI, vol. 14(15), pages 1-21, August.
    2. Muhammad Muzamil Khan & Bushra Ghaffar & Rasim Shahzad & M. Riaz Khan & Munawar Shah & Ali H. Amin & Sayed M. Eldin & Najam Abbas Naqvi & Rashid Ali, 2022. "Atmospheric Anomalies Associated with the 2021 M w 7.2 Haiti Earthquake Using Machine Learning from Multiple Satellites," Sustainability, MDPI, vol. 14(22), pages 1-17, November.

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