Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Inacio T. Bueno"'
Autor:
João P. S. Werner, Mariana Belgiu, Inacio T. Bueno, Aliny A. Dos Reis, Ana P. S. G. D. Toro, João F. G. Antunes, Alfred Stein, Rubens A. C. Lamparelli, Paulo S. G. Magalhães, Alexandre C. Coutinho, Júlio C. D. M. Esquerdo, Gleyce K. D. A. Figueiredo
Publikováno v:
Remote Sensing, Vol 16, Iss 8, p 1421 (2024)
Integrated crop–livestock systems (ICLS) are among the main viable strategies for sustainable agricultural production. Mapping these systems is crucial for monitoring land use changes in Brazil, playing a significant role in promoting sustainable a
Externí odkaz:
https://doaj.org/article/484b040de1fb46ecb851cc7798aab45e
Autor:
Ana P. S. G. D. D. Toro, Inacio T. Bueno, João P. S. Werner, João F. G. Antunes, Rubens A. C. Lamparelli, Alexandre C. Coutinho, Júlio C. D. M. Esquerdo, Paulo S. G. Magalhães, Gleyce K. D. A. Figueiredo
Publikováno v:
Remote Sensing, Vol 15, Iss 4, p 1130 (2023)
Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increa
Externí odkaz:
https://doaj.org/article/8070c2d50868467b93fef9874a4aff57
Autor:
Inacio T. Bueno, Greg J. McDermid, Eduarda M. O. Silveira, Jennifer N. Hird, Breno I. Domingos, Fausto W. Acerbi Júnior
Publikováno v:
Remote Sensing, Vol 12, Iss 18, p 2948 (2020)
Detecting disturbances in native vegetation is a crucial component of many environmental management strategies, and remote sensing-based methods are the most efficient way to collect multi-temporal disturbance data over large areas. Given that there
Externí odkaz:
https://doaj.org/article/f69563307fd4412e993227d7a14bf0c0
Autor:
Inacio T. Bueno, Fausto W. Acerbi Júnior, Eduarda M. O. Silveira, José M. Mello, Luís M. T. Carvalho, Lucas R. Gomide, Kieran Withey, José Roberto S. Scolforo
Publikováno v:
Remote Sensing, Vol 11, Iss 5, p 570 (2019)
Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to im
Externí odkaz:
https://doaj.org/article/e9d7f47d857f4b9a8011ee3c13ff8b89