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All Deforestation Matters: Deforestation Alert System for the Caatinga Biome in South America’s Tropical Dry Forest

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  • Diego Pereira Costa

    (Postgraduate Program in Energy and Environment (PGEnAm), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil
    GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
    Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil
    Interdisciplinary Center for Energy and Environment (CIEnAm), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil)

  • Carlos A. D. Lentini

    (Postgraduate Program in Energy and Environment (PGEnAm), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil
    Interdisciplinary Center for Energy and Environment (CIEnAm), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil
    Department of Earth and Environment Physics, Physics Institute, Federal University of Bahia (UFBA), Campus Ondina, Salvador 40170-280, BA, Brazil
    Postgraduate Program in Geochemistry: Oil and Environment (POSPETRO), Geosciences Institute (IGEO), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil)

  • André T. Cunha Lima

    (Postgraduate Program in Energy and Environment (PGEnAm), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil
    Interdisciplinary Center for Energy and Environment (CIEnAm), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil
    Department of Earth and Environment Physics, Physics Institute, Federal University of Bahia (UFBA), Campus Ondina, Salvador 40170-280, BA, Brazil)

  • Soltan Galano Duverger

    (GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
    Multidisciplinary and Multi-Institutional Postgraduate Program in Knowledge Diffusion (DMMDC), Federal University of Bahia (UFBA), Salvador 40110-100, BA, Brazil)

  • Rodrigo N. Vasconcelos

    (GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
    Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil)

  • Stefanie M. Herrmann

    (School of Natural Resources and the Environment (SNRE), The University of Arizona, 1064 E. Lowell St, Tucson, AZ 85721, USA)

  • Jefferson Ferreira-Ferreira

    (World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, Sao Paulo 05422-030, SP, Brazil)

  • Mariana Oliveira

    (World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, Sao Paulo 05422-030, SP, Brazil)

  • Leonardo da Silva Barbosa

    (World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, Sao Paulo 05422-030, SP, Brazil)

  • Carlos Leandro Cordeiro

    (World Resources Institute Brasil, Rua Cláudio Soares, 72 Cj. 1510, Sao Paulo 05422-030, SP, Brazil)

  • Nerivaldo Afonso Santos

    (GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
    Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil)

  • Rafael Oliveira Franca Rocha

    (GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
    Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil)

  • Deorgia T. M. Souza

    (Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil)

  • Washington J. S. Franca Rocha

    (Postgraduate Program in Earth Modeling and Environmental Sciences—PPGM, State University of Feira de Santana—UEFS, Feira de Santana 44036-900, BA, Brazil)

Abstract

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome.

Suggested Citation

  • Diego Pereira Costa & Carlos A. D. Lentini & André T. Cunha Lima & Soltan Galano Duverger & Rodrigo N. Vasconcelos & Stefanie M. Herrmann & Jefferson Ferreira-Ferreira & Mariana Oliveira & Leonardo da, 2024. "All Deforestation Matters: Deforestation Alert System for the Caatinga Biome in South America’s Tropical Dry Forest," Sustainability, MDPI, vol. 16(20), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:20:p:9006-:d:1501061
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    References listed on IDEAS

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    1. A. L. Burrell & J. P. Evans & M. G. De Kauwe, 2020. "Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
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