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Applied Earth Science
Transactions of the Institutions of Mining and Metallurgy
Volume 132, 2023 - Issue 3-4
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Articles

The near real-time deforestation detection system: case study of the DETER system for the Cerrado Biome

ORCID Icon, , , , , , & show all
Pages 271-280 | Received 02 Mar 2023, Accepted 27 Sep 2023, Published online: 09 Oct 2023
 

ABSTRACT

Less than half of the original two million km2 of the Brazilian Savanna natural vegetation, called the Cerrado Biome, remains standing. Given its climate and socio-biodiversity importance, more effective public policies are needed to protect the remaining natural areas. In this paper, we present the methodology and results of the DETER Cerrado, an early warning deforestation system within the Cerrado region. The findings support that DETER is effective in detecting a wider range of deforestation patch sizes, from the larger patches, heavily associated with agricultural expansion, to the smaller areas (>1 ha <10 ha). Nevertheless, 80% of the deforestation is concentrated in the 10-km radius zone from the DETER Alerts. This area was later detected by the Cerrado Deforestation Monitoring Project (PRODES), the system that accounts for the annual deforestation rate assessment, which highlights the capability of the DETER system to provide support to the surveillance of deforestation in the Cerrado.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Geolocation information

Bounding Box - X: -41.512, Y: -2.699; X: -61.071, Y: -23.793.

Data availability statement

The data that support the findings of this study are openly available in the TerraBrasilis platform at http://terrabrasilis.dpi.inpe.br/en/download-2/, reference number e6e15388-4ca9-49b9-aec9-03891339a35e.

Additional information

Funding

This work was supported by the World Bank Group under Grant P143185; and the Conselho Nacional de Desenvolvimento Científico e Tecnológico under Grant 444418/2018-0, 306334/2020-8, 381125/2023-8, 381120/2023-6. We thank Laboratório de Processamento de Imagens (LAPIG/UFG) for assistance in the field, the anonymous reviewers, and Prof. Dr. Camilo Rennó for suggestions in the manuscript.

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