Título:	Spatial patterns of cattle densities across the Brazilian Amazon revealed by very high-resolution satellite imagery.
Autor:	HODEL, L. WEGNER, J. D. GARNOT, V. S. F. GOMES, F. C. da R. VALENTIM, J. F. GARRETT, R. D.
Afiliación:	LEONIE HODEL, UNIVERSITY OF CAMBRIDGE; JAN D. WEGNER, UNIVERSITAT ZURICH; VIVIEN SAINTE FARE GARNOT, UNIVERSITAT ZURICH; FRANCISCO CARLOS DA ROCHA GOMES, CPAF-AC; JUDSON FERREIRA VALENTIM, CPAF-AC; RACHAEL D. GARRETT, UNIVERSITY OF CAMBRIDGE.
Año:	2026
Referencia:	Communications Sustainability, n. 1, article 98, 2026.
Descripción:	Cattle ranching is a sustainability challenge worldwide, and in the Amazon, the planet’s largest tropical forest, it remains the main driver of deforestation. Yet, cattle numbers have typically been estimated from coarse census data or indirect proxies, limiting our ability to monitor land-use change at finer scales. Here, we introduce a novel approach that applies deep learning-based density estimation to very high-resolution satellite imagery to detect individual animals across the Brazilian Amazon.
Thesagro:	Pecuária Bovinocultura Taxa de Lotação Uso da Terra Sensoriamento Remoto Satélite
NAL Thesaurus:	Livestock Cattle production Population density Stocking rate Land use Remote sensing Satellites
Palabras clave:	Deep learning Ganado Producción de ganado bovino Densidad poblacional Densidad de pastoreo Uso de la tierra Teledetección Brazilian Amazon Amazônia brasileira
ISSN:	3059-4308
DOI:	https://doi.org/10.1038/s44458-026-00082-2
Tipo de Material:	Artigo de periódico
Acceso:	openAccess
Aparece en las colecciones:	Artigo em periódico indexado (CPAF-AC)