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

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 Silva Barbosa, Carlos Leandro Cordeiro, Nerivaldo Afonso Santos, Rafael Oliveira Franca Rocha, Deorgia T. M. Souza and Washington J. S. Franca Rocha
Additional contact information
Diego Pereira Costa: Postgraduate Program in Energy and Environment (PGEnAm), 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
André T. Cunha Lima: Postgraduate Program in Energy and Environment (PGEnAm), Federal University of Bahia (UFBA), Salvador 40170-115, BA, Brazil
Soltan Galano Duverger: GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, BA, Brazil
Rodrigo N. Vasconcelos: GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, 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
Rafael Oliveira Franca Rocha: GEODATIN—Data Intelligence and Geoinformation, Bahia Technological Park Rua Mundo, 121—Trobogy, Salvador 41301-110, 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

Sustainability, 2024, vol. 16, issue 20, 1-21

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.

Keywords: deforestation; drylands; Caatinga biome; change detection; remote sensing; spectral mixture model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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