Mapping Banana and Peach Palm in Diversified Landscapes in the Brazilian Atlantic Forest with Sentinel-2

Victória Beatriz Soares (), Taya Cristo Parreiras, Danielle Elis Garcia Furuya, Édson Luis Bolfe and Katia de Lima Nechet
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Victória Beatriz Soares: Institute of Geosciences, State University of Campinas, Campinas 13083-970, São Paulo, Brazil
Taya Cristo Parreiras: Institute of Geosciences, State University of Campinas, Campinas 13083-970, São Paulo, Brazil
Danielle Elis Garcia Furuya: Embrapa Digital Agriculture, Brazilian Agricultural Research Corporation, Campinas 13083-886, São Paulo, Brazil
Édson Luis Bolfe: Institute of Geosciences, State University of Campinas, Campinas 13083-970, São Paulo, Brazil
Katia de Lima Nechet: Embrapa Meio Ambiente, Brazilian Agricultural Research Corporation, Jaguariúna 13820-000, São Paulo, Brazil

Agriculture, 2025, vol. 15, issue 19, 1-19

Abstract: Mapping banana and peach palm in heterogeneous landscapes remains challenging due to spatial heterogeneity, spectral similarities between crops and native vegetation, and persistent cloud cover. This study focused on the municipality of Jacupiranga, located within the Ribeira Valley region and surrounded by the Atlantic Forest, which is home to one of Brazil’s largest remaining continuous forest areas. More than 99% of Jacupiranga’s agricultural output in the 21st century came from bananas ( Musa spp.) and peach palms ( Bactris gasipaes ), underscoring the importance of perennial crops to the local economy and traditional communities. Using a time series of vegetation indices from Sentinel-2 imagery combined with field and remote data, we used a hierarchical classification method to map where these two crops are cultivated. The Random Forest classifier fed with 10 m resolution images enabled the detection of intricate agricultural mosaics that are typical of family farming systems and improved class separability between perennial and non-perennial crops and banana and peach palm. These results show how combining geographic information systems, data analysis, and remote sensing can improve digital agriculture, rural management, and sustainable agricultural development in socio-environmentally important areas.

Keywords: agriculture; rural communities; Ribeira Valley; multitemporal; Brazil (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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