Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil (2026)
- Authors:
- USP affiliated authors: FERREIRA, MATHEUS PINHEIRO - ESALQ ; RODRIGUES, RICARDO RIBEIRO - ESALQ ; BRANCALION, PEDRO HENRIQUE SANTIN - ESALQ ; FUZA, MATHEUS SANTOS - ESALQ ; VIVEIROS, JOSÉ MATHEUS SEGRE MONEVA - ESALQ ; SANTORO, GIULIO BROSSI - ESALQ
- Unidade: ESALQ
- DOI: 10.1080/01431161.2026.2628300
- Subjects: ESPECTROSCOPIA; ECOSSISTEMAS FLORESTAIS; IMAGEAMENTO DE SATÉLITE; PAISAGEM; FLORESTAS TROPICAIS; REFLORESTAMENTO
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: International Journal of Remote Sensing
- ISSN: 0143-1161
- Volume/Número/Paginação/Ano: online, p. 1-26, 2026
- Status:
- Nenhuma versão em acesso aberto identificada
-
ABNT
FERREIRA, Matheus Pinheiro et al. Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil. International Journal of Remote Sensing, p. 1-26, 2026Tradução . . Disponível em: https://doi.org/10.1080/01431161.2026.2628300. Acesso em: 14 abr. 2026. -
APA
Ferreira, M. P., Fuza, M. S., Viveiros, J. M. S. M., Ferranti, T., Oliveira, D., Santoro, G. B., et al. (2026). Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil. International Journal of Remote Sensing, 1-26. doi:10.1080/01431161.2026.2628300 -
NLM
Ferreira MP, Fuza MS, Viveiros JMSM, Ferranti T, Oliveira D, Santoro GB, Molin PG, Almeida CT, Resende AF, Almeida DRA de, Zeng Y, Rodrigues RR, Brancalion PHS. Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil [Internet]. International Journal of Remote Sensing. 2026 ; 1-26.[citado 2026 abr. 14 ] Available from: https://doi.org/10.1080/01431161.2026.2628300 -
Vancouver
Ferreira MP, Fuza MS, Viveiros JMSM, Ferranti T, Oliveira D, Santoro GB, Molin PG, Almeida CT, Resende AF, Almeida DRA de, Zeng Y, Rodrigues RR, Brancalion PHS. Exploring EnMAP hyperspectral images and ensemble deep learning for classifying forest land-cover types in Brazil [Internet]. International Journal of Remote Sensing. 2026 ; 1-26.[citado 2026 abr. 14 ] Available from: https://doi.org/10.1080/01431161.2026.2628300 - Integrating UAV-borne LiDAR and deep learning for large-scale detection of productive macaw palms (Acrocomia aculeata)
- Solution for diagnostics of biological invasion in terrestrial ecosystems: how can deep learning help biodiversity conservation?
- Liana removal alters canopy chemistry more than structure in tropical seasonal forests: insights from UAV-borne hyperspectral and LiDAR data
- Monitoring the structure of restored forests and assessing aboveground carbon density through canopy metrics derived from digital aerial photogrammetry and LiDAR
- Análisis espacial y su importancia para la restauración: (Simposio)
- Protocol for monitoring tropical forest restoration: perspectives from the atlantic forest restoration pact in Brazil
- Engaging people for large-scale forest restoration: governance lessons from the Atlantic Forest of Brazil
- Functional traits and ecosystem services in ecological restoration
- Manual técnico de restauração ecológica para adequação ambiental de imóveis rurais do extremo sul da Bahia
- Optimizing seeding density of fast-growing native trees for restoring the Brazilian Atlantic Forest
Informações sobre a disponibilidade de versões do artigo em acesso aberto coletadas automaticamente via oaDOI API (Unpaywall).
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
