Título:	Putting abandoned farmlands in the legend of land use and land cover maps of the Brazilian tropical savanna.
Autor:	MAGALHÃES, I. A. L. SANO, E. E. BOLFE, E. L. SILVA, G. B. S. da
Afiliación:	IVO AUGUSTO LOPES MAGALHÃES, UNIVERSIDADE DE BRASÍLIA; EDSON EYJI SANO, CPAC; EDSON LUIS BOLFE, CNPTIA; GUSTAVO BAYMA SIQUEIRA DA SILVA, CNPMA.
Año:	2026
Referencia:	Land, v. 15, n. 1, 53, Jan. 2026.
Descripción:	Farmland abandonment is becoming a growing land use challenge in the Brazilian Cerrado, yet its extent, spatial distribution, and underlying drivers remain poorly understood. This study addresses the following question: Can deep learning methods reliably identify abandoned farmlands in tropical savanna environments using multispectral satellite images? To answer this question, we used a Fully Connected Neural Network (FCNN) classifier to map abandoned farmlands in the municipality of Buritizeiro, Minas Gerais State, Brazil, using Sentinel-2 images acquired in 2018 and 2022. Seven land use and land cover (LULC) classes were mapped using visible and near-infrared bands, spectral indices, spectral mixture components, and principal components as input parameters for the CNN. The LULC map for 2022 achieved high classification performance (overall accuracy = 94.7%; Kappa coefficient = 0.93). Agricultural areas classified in 2018 as annual croplands, cultivated pastures, eucalyptus plantations, or harvested eucalyptus that transitioned to grasslands or shrublands in 2022 were considered abandoned. Based on this definition, we identified 13,147 hectares of abandoned land in 2022, representing 4.7% of the municipality’s agricultural area in 2018. Most abandoned areas corresponded to eucalyptus plantations established for charcoal production. This study provides the first deep learning-based assessment of farmland abandonment in the Cerrado. Our findings demonstrated the potential of FCNN classifiers for detecting abandoned farmlands in this biome and provide important contribution for public policies focused on ecological restoration, carbon sequestration, and sustainable agricultural planning.
Thesagro:	Sensoriamento Remoto Uso da Terra
NAL Thesaurus:	Remote sensing Land use Land cover
Palabras clave:	Cobertura da terra Savana tropical Aprendizado profundo Redes neurais Tropical savanna Deep learning
ISSN:	2073-445X
DOI:	https://doi.org/10.3390/land15010053
Notas:	Na publicação: Gustavo Bayma.
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CPAC)