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

Author(s)

Soares, Victoria Beatriz ; Parreiras, Taya Cristo ; Furuya, Danielle Elis Garcia ; Bolfe, Edson Luis ; Nechet, Katia de Lima

• Document type: Journal article
• Source: AGRICULTURE-BASEL; v. 15, n. 19, p. 19-pg., 2025-09-30.

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. (AU)

• FAPESP's process: 25/01750-0 - Detecting Diversified Agricultural Systems with High-Resolution Imaging and Deep Learning
• Grantee: Victória Beatriz Soares Leandro
• Support Opportunities: Scholarships in Brazil - Master
• FAPESP's process: 22/09319-9 - Center of Science for Development in Digital Agriculture - CCD-AD/SemeAr
• Grantee: Silvia Maria Fonseca Silveira Massruhá
• Support Opportunities: Research Grants - Science Centers for Development
• FAPESP's process: 24/13150-5 - Spatiotemporal analysis of agricultural dynamics in a region of high production diversity using multisensor imagery and machine learning
• Grantee: Taya Cristo Parreiras
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 24/05205-4 - Development of land use mapping methods based on multi-sensor data and machine learning algorithms
• Grantee: Danielle Elis Garcia Furuya
• Support Opportunities: Scholarships in Brazil - Post-Doctoral