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Artigo de periodico
The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication (2022)
Demattê, José Alexandre Melo ; Paiva, Ariane Francine da Silveira ; Poppiel, Raul Roberto ; Rosin, Nícolas Augusto ; Ruiz, Luis Fernando Chimelo ; Mello, Fellipe Alcantara de Oliveira ; Minasny, Budiman ; Grunwald, Sabine ; Ge, Yufeng; Ben Dor, Eyal ; Gholizadeh, Asa ; Gomez, Cecile ; Chabrillat, Sabine; Francos, Nicolas ; Ayoubi, Shamsollah; Fiantis, Dian; Biney, James Kobina Mensah ; Wang, Changkun; Belal, Abdelaziz ; Naimi, Salman ; Hafshejani, Najmeh Asgari; Bellinaso, Henrique ; Moura-Bueno, Jean Michel; Silvero, Nélida E. Q
Source: Remote Sensing. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, ANÁLISE DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, SOLOS
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ABNT
DEMATTÊ, José Alexandre Melo et al. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication. Remote Sensing, v. 14, p. 1-27, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14030740. Acesso em: 06 jul. 2022.
APA
Demattê, J. A. M., Paiva, A. F. da S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. de O., et al. (2022). The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication. Remote Sensing, 14, 1-27. doi:10.3390/rs14030740
NLM
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication [Internet]. Remote Sensing. 2022 ; 14 1-27.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14030740
Vancouver
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. The Brazilian Soil Spectral Service (BraSpecS): a User-Friendly System for Global Soil Spectra Communication [Internet]. Remote Sensing. 2022 ; 14 1-27.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14030740
Artigo de periodico
A conceptual model for detecting small-scale forest disturbances based on ecosystem worphological traits (2022)
Stoddart, Jaz; Almeida, Danilo Roberti Alves de ; Silva, Carlos Alberto; Görgens, Eric Bastos; Keller, Michael; Valbuena, Ruben
Source: Remote Sensing. Unidade: ESALQ
Subjects: CARBONO, CORTE (PLANTAS), DOSSEL (BOTÂNICA), ECOSSISTEMAS FLORESTAIS, FLORESTAS TROPICAIS, MODELAGEM DE DADOS, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
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ABNT
STODDART, Jaz et al. A conceptual model for detecting small-scale forest disturbances based on ecosystem worphological traits. Remote Sensing, v. 14, p. 1-20, 2022Tradução . . Disponível em: https://www.mdpi.com/2072-4292/14/4/933/html. Acesso em: 06 jul. 2022.
APA
Stoddart, J., Almeida, D. R. A. de, Silva, C. A., Görgens, E. B., Keller, M., & Valbuena, R. (2022). A conceptual model for detecting small-scale forest disturbances based on ecosystem worphological traits. Remote Sensing, 14, 1-20. doi:10.3390/rs14040933
NLM
Stoddart J, Almeida DRA de, Silva CA, Görgens EB, Keller M, Valbuena R. A conceptual model for detecting small-scale forest disturbances based on ecosystem worphological traits [Internet]. Remote Sensing. 2022 ; 14 1-20.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/14/4/933/html
Vancouver
Stoddart J, Almeida DRA de, Silva CA, Görgens EB, Keller M, Valbuena R. A conceptual model for detecting small-scale forest disturbances based on ecosystem worphological traits [Internet]. Remote Sensing. 2022 ; 14 1-20.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/14/4/933/html
Artigo de periodico
Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 (2022)
Demattê, José Alexandre Melo ; Paiva, Ariane Francine da Silveira ; Poppiel, Raul Roberto ; Rosin, Nícolas Augusto ; Ruiz, Luis Fernando Chimelo ; Mello, Fellipe Alcantara de Oliveira ; Minasny, Budiman ; Grunwald, Sabine ; Ge, Yufeng; Ben Dor, Eyal ; Gholizadeh, Asa ; Gomez, Cecile ; Chabrillat, Sabine; Francos, Nicolas ; Ayoubi, Shamsollah; Fiantis, Dian; Biney, James Kobina Mensah ; Wang, Changkun; Belal, Abdelaziz ; Naimi, Salman ; Hafshejani, Najmeh Asgari; Bellinaso, Henrique ; Moura-Bueno, Jean Michel; Silvero, Nélida E. Q
Source: Remote Sensing.
Subjects: AGRICULTURA DE PRECISÃO, ANÁLISE DO SOLO, ESPECTROSCOPIA INFRAVERMELHA, QUALIDADE DO SOLO
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ABNT
DEMATTÊ, José Alexandre Melo et al. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740. Remote Sensing, v. 14, p. 1-2, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14061459. Acesso em: 06 jul. 2022.
APA
Demattê, J. A. M., Paiva, A. F. da S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. de O., et al. (2022). Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740. Remote Sensing, 14, 1-2. doi:10.3390/rs14061459
NLM
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 [Internet]. Remote Sensing. 2022 ; 14 1-2.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14061459
Vancouver
Demattê JAM, Paiva AF da S, Poppiel RR, Rosin NA, Ruiz LFC, Mello FA de O, Minasny B, Grunwald S, Ge Y, Ben Dor E, Gholizadeh A, Gomez C, Chabrillat S, Francos N, Ayoubi S, Fiantis D, Biney JKM, Wang C, Belal A, Naimi S, Hafshejani NA, Bellinaso H, Moura-Bueno JM, Silvero NEQ. Correction: Demattê et al.: The Brazilian soil spectral service (BraSpecS): a user-friendly system for global soil spectra communication. Remote Sens. 2022, 14, 740 [Internet]. Remote Sensing. 2022 ; 14 1-2.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14061459
Artigo de periodico
Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence (2022)
Albuquerque, Rafael Walter ; Vieira, Daniel Luis Mascia; Ferreira, Manuel Eduardo; Soares, Lucas Pedrosa; Olsen, Søren Ingvor; Araujo, Luciana Spinelli; Vicente, Luiz Eduardo; Tymus, Julio Ricardo Caetano; Balieiro, Cintia Palheta; Matsumoto, Marcelo Hiromiti ; Grohman, Carlos Henrique
Source: Remote Sensing. Unidades: ESALQ, IEE, IGC
Subjects: AEROFOTOGRAMETRIA, AERONAVES NÃO TRIPULADAS, APRENDIZADO COMPUTACIONAL, ÁRVORES FLORESTAIS, DOSSEL (BOTÂNICA), ECOLOGIA DA RESTAURAÇÃO, HETEROGENEIDADE
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ALBUQUERQUE, Rafael Walter et al. Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence. Remote Sensing, v. 14, p. 1-28, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14040830. Acesso em: 06 jul. 2022.
APA
Albuquerque, R. W., Vieira, D. L. M., Ferreira, M. E., Soares, L. P., Olsen, S. I., Araujo, L. S., et al. (2022). Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence. Remote Sensing, 14, 1-28. doi:10.3390/rs14040830
NLM
Albuquerque RW, Vieira DLM, Ferreira ME, Soares LP, Olsen SI, Araujo LS, Vicente LE, Tymus JRC, Balieiro CP, Matsumoto MH, Grohman CH. Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence [Internet]. Remote Sensing. 2022 ; 14 1-28.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14040830
Vancouver
Albuquerque RW, Vieira DLM, Ferreira ME, Soares LP, Olsen SI, Araujo LS, Vicente LE, Tymus JRC, Balieiro CP, Matsumoto MH, Grohman CH. Mapping key indicators of forest restoration in the Amazon using a low-cost drone and artificial intelligence [Internet]. Remote Sensing. 2022 ; 14 1-28.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14040830
Artigo de periodico
Mapping fire susceptibility in the brazilian Amazon forests using multitemporal remote sensing and time-varying unsupervised anomaly detection (2022)
Luz, Andréa Eliza O ; Negri, Rogério Galante ; Massi, Klécia Gili ; Colnago, Marilaine ; Silva, Erivaldo Antonio da ; Casaca, Wallace Correa de Oliveira
Source: Remote Sensing. Unidade: ICMC
Subjects: SENSORIAMENTO REMOTO, REDES NEURAIS, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ANÁLISE DE SÉRIES TEMPORAIS, INCÊNDIOS FLORESTAIS
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ABNT
LUZ, Andréa Eliza O et al. Mapping fire susceptibility in the brazilian Amazon forests using multitemporal remote sensing and time-varying unsupervised anomaly detection. Remote Sensing, v. 14, n. 10, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14102429. Acesso em: 06 jul. 2022.
APA
Luz, A. E. O., Negri, R. G., Massi, K. G., Colnago, M., Silva, E. A. da, & Casaca, W. C. de O. (2022). Mapping fire susceptibility in the brazilian Amazon forests using multitemporal remote sensing and time-varying unsupervised anomaly detection. Remote Sensing, 14( 10), 1-17. doi:10.3390/rs14102429
NLM
Luz AEO, Negri RG, Massi KG, Colnago M, Silva EA da, Casaca WC de O. Mapping fire susceptibility in the brazilian Amazon forests using multitemporal remote sensing and time-varying unsupervised anomaly detection [Internet]. Remote Sensing. 2022 ; 14( 10): 1-17.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14102429
Vancouver
Luz AEO, Negri RG, Massi KG, Colnago M, Silva EA da, Casaca WC de O. Mapping fire susceptibility in the brazilian Amazon forests using multitemporal remote sensing and time-varying unsupervised anomaly detection [Internet]. Remote Sensing. 2022 ; 14( 10): 1-17.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs14102429
Artigo de periodico
Mapping particle size and soil organic matter in tropical soil based on hyperspectral imaging and non-imaging sensors (2021)
Nanni, Marcos Rafael; Demattê, José Alexandre Melo ; Rodrigues, Marlon; Santos, Glaucio Leboso Alemparte Abrantes dos; Reis, Amanda Silveira; Oliveira, Karym Mayara de; Cezar, Everson; Furlanetto, Renato Herrig; Crusiol, Luís Guilherme Teixeira; Sun, Liang
Source: Remote Sensing. Unidade: ESALQ
Subjects: GRANULOMETRIA DO SOLO, IMAGEM (CARTOGRAFIA), MAPEAMENTO DO SOLO, MATÉRIA ORGÂNICA DO SOLO, SENSORES DE AERONAVES, SOLO TROPICAL
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NANNI, Marcos Rafael et al. Mapping particle size and soil organic matter in tropical soil based on hyperspectral imaging and non-imaging sensors. Remote Sensing, v. 13, n. 9, p. 1-19, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13091782. Acesso em: 06 jul. 2022.
APA
Nanni, M. R., Demattê, J. A. M., Rodrigues, M., Santos, G. L. A. A. dos, Reis, A. S., Oliveira, K. M. de, et al. (2021). Mapping particle size and soil organic matter in tropical soil based on hyperspectral imaging and non-imaging sensors. Remote Sensing, 13( 9), 1-19. doi:10.3390/rs13091782
NLM
Nanni MR, Demattê JAM, Rodrigues M, Santos GLAA dos, Reis AS, Oliveira KM de, Cezar E, Furlanetto RH, Crusiol LGT, Sun L. Mapping particle size and soil organic matter in tropical soil based on hyperspectral imaging and non-imaging sensors [Internet]. Remote Sensing. 2021 ; 13( 9): 1-19.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13091782
Vancouver
Nanni MR, Demattê JAM, Rodrigues M, Santos GLAA dos, Reis AS, Oliveira KM de, Cezar E, Furlanetto RH, Crusiol LGT, Sun L. Mapping particle size and soil organic matter in tropical soil based on hyperspectral imaging and non-imaging sensors [Internet]. Remote Sensing. 2021 ; 13( 9): 1-19.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13091782
Artigo de periodico
Effects of beach nourishment project on coastal geomorphology and mangrove dynamics in southern Louisiana, USA (2021)
Cohen, Marcelo Cancela Lisboa ; Souza, Adriana Vivan de ; Liu, Kam-Biu ; Rodrigues, Erika ; Yao, Qiang ; Pessenda, Luiz Carlos Ruiz ; Rossetti, Dilce ; Ryu, Junghyung ; Dietz, Marianne
Source: Remote Sensing. Unidade: CENA
Subjects: DELTAS, AERONAVES NÃO TRIPULADAS, IMAGEAMENTO DE SATÉLITE, NÍVEL DO MAR, ECOSSISTEMAS COSTEIROS
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COHEN, Marcelo Cancela Lisboa et al. Effects of beach nourishment project on coastal geomorphology and mangrove dynamics in southern Louisiana, USA. Remote Sensing, v. 12, 2021Tradução . . Acesso em: 06 jul. 2022.
APA
Cohen, M. C. L., Souza, A. V. de, Liu, K. -B., Rodrigues, E., Yao, Q., Pessenda, L. C. R., et al. (2021). Effects of beach nourishment project on coastal geomorphology and mangrove dynamics in southern Louisiana, USA. Remote Sensing, 12. doi:10.3390/rs13142688
NLM
Cohen MCL, Souza AV de, Liu K-B, Rodrigues E, Yao Q, Pessenda LCR, Rossetti D, Ryu J, Dietz M. Effects of beach nourishment project on coastal geomorphology and mangrove dynamics in southern Louisiana, USA. Remote Sensing. 2021 ; 12[citado 2022 jul. 06 ]
Vancouver
Cohen MCL, Souza AV de, Liu K-B, Rodrigues E, Yao Q, Pessenda LCR, Rossetti D, Ryu J, Dietz M. Effects of beach nourishment project on coastal geomorphology and mangrove dynamics in southern Louisiana, USA. Remote Sensing. 2021 ; 12[citado 2022 jul. 06 ]
Artigo de periodico
Digital Elevation Models: terminology and definitions (2021)
Guth, Peter L ; Van Niekerk, Adriaan ; Grohmann, Carlos Henrique ; Muller, Jan-Peter ; Hawker, Laurence ; Florinsky, Igor ; Gesch, Dean B. ; Reuter, Hannes Isaak ; Herrera-Cruz, Virginia ; Riazanoff, Serge ; López-Vázquez, Carlos ; Carabajal, Claudia ; Albinet, Clement ; Strobl, Peter A
Source: Remote Sensing. Unidade: IEE
Subject: GEOMORFOMETRIA
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ABNT
GUTH, Peter L et al. Digital Elevation Models: terminology and definitions. Remote Sensing, v. 13, n. 18, p. se 2021, 2021Tradução . . Disponível em: https://www.mdpi.com/1263464. Acesso em: 06 jul. 2022.
APA
Guth, P. L., Van Niekerk, A., Grohmann, C. H., Muller, J. -P., Hawker, L., Florinsky, I., et al. (2021). Digital Elevation Models: terminology and definitions. Remote Sensing, 13( 18), se 2021. Recuperado de https://www.mdpi.com/1263464
NLM
Guth PL, Van Niekerk A, Grohmann CH, Muller J-P, Hawker L, Florinsky I, Gesch DB, Reuter HI, Herrera-Cruz V, Riazanoff S, López-Vázquez C, Carabajal C, Albinet C, Strobl PA. Digital Elevation Models: terminology and definitions [Internet]. Remote Sensing. 2021 ;13( 18): se 2021.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/1263464
Vancouver
Guth PL, Van Niekerk A, Grohmann CH, Muller J-P, Hawker L, Florinsky I, Gesch DB, Reuter HI, Herrera-Cruz V, Riazanoff S, López-Vázquez C, Carabajal C, Albinet C, Strobl PA. Digital Elevation Models: terminology and definitions [Internet]. Remote Sensing. 2021 ;13( 18): se 2021.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/1263464
Artigo de periodico
A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery (2021)
Martins, Rodrigo Nogueira; Pinto, Francisco de Assis de Carvalho; Queiroz, Daniel Marçal de; Valente, Domingos Sárvio Magalhães; Rosas, Jorge Tadeu Fim
Source: Remote Sensing. Unidade: ESALQ
Subjects: AEROFOTOGRAMETRIA, AERONAVES NÃO TRIPULADAS, CAFÉ, COLHEITA, MATURAÇÃO VEGETAL, SENSORIAMENTO REMOTO
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ABNT
MARTINS, Rodrigo Nogueira et al. A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery. Remote Sensing, v. 13, p. 1-13, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13020263. Acesso em: 06 jul. 2022.
APA
Martins, R. N., Pinto, F. de A. de C., Queiroz, D. M. de, Valente, D. S. M., & Rosas, J. T. F. (2021). A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery. Remote Sensing, 13, 1-13. doi:10.3390/rs13020263
NLM
Martins RN, Pinto F de A de C, Queiroz DM de, Valente DSM, Rosas JTF. A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery [Internet]. Remote Sensing. 2021 ; 13 1-13.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13020263
Vancouver
Martins RN, Pinto F de A de C, Queiroz DM de, Valente DSM, Rosas JTF. A Novel Vegetation Index for Coffee Ripeness Monitoring Using Aerial Imagery [Internet]. Remote Sensing. 2021 ; 13 1-13.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13020263
Artigo de periodico
Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations (2021)
Alvim, Débora Souza ; Chiquetto, Júlio Barboza ; D'Amelio, Monica Tais Siqueira ; Khalid, Bushra; Herdies, Dirceu Luis ; Pendharkar, Jayant ; Capistrano, Vinicius B ; Boian, Claudia; Corrêa, Sérgio M; Figueroa, Silvio Nilo ; Frassoni, Ariane ; Kubota, Paulo Yoshio; Nobre, Paulo
Source: Remote Sensing. Unidade: EEL
Subject: MONÓXIDO DE CARBONO
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ALVIM, Débora Souza et al. Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations. Remote Sensing, v. 13, p. 36, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13112231. Acesso em: 06 jul. 2022.
APA
Alvim, D. S., Chiquetto, J. B., D'Amelio, M. T. S., Khalid, B., Herdies, D. L., Pendharkar, J., et al. (2021). Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations. Remote Sensing, 13, 36. doi:10.3390/rs13112231
NLM
Alvim DS, Chiquetto JB, D'Amelio MTS, Khalid B, Herdies DL, Pendharkar J, Capistrano VB, Boian C, Corrêa SM, Figueroa SN, Frassoni A, Kubota PY, Nobre P. Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations [Internet]. Remote Sensing. 2021 ; 13 36.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13112231
Vancouver
Alvim DS, Chiquetto JB, D'Amelio MTS, Khalid B, Herdies DL, Pendharkar J, Capistrano VB, Boian C, Corrêa SM, Figueroa SN, Frassoni A, Kubota PY, Nobre P. Evaluating Carbon Monoxide and Aerosol Optical Depth Simulations from CAM-Chem Using Satellite Observations [Internet]. Remote Sensing. 2021 ; 13 36.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13112231
Artigo de periodico
Evaluating the latest IMERG products in a subtropical climate: The Case of Paraná State, Brazil (2021)
Nascimento, Jéssica Garcia ; Althoff, Daniel ; Bazame, Helizani Couto ; Neale, Christopher M. U; Duarte, Sergio Nascimento; Ruhoff, Anderson L ; Gonçalves, Ivo Zution
Source: Remote Sensing. Unidade: ESALQ
Subjects: CHUVA, CLIMA, DISTRIBUIÇÃO ESPACIAL, HIDROLOGIA, IMAGEAMENTO DE SATÉLITE, PLUVIOMETRIA, PRECIPITAÇÃO ATMOSFÉRICA, SENSORIAMENTO REMOTO
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NASCIMENTO, Jéssica Garcia et al. Evaluating the latest IMERG products in a subtropical climate: The Case of Paraná State, Brazil. Remote Sensing, v. 13, p. 1-18, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13050906. Acesso em: 06 jul. 2022.
APA
Nascimento, J. G., Althoff, D., Bazame, H. C., Neale, C. M. U., Duarte, S. N., Ruhoff, A. L., & Gonçalves, I. Z. (2021). Evaluating the latest IMERG products in a subtropical climate: The Case of Paraná State, Brazil. Remote Sensing, 13, 1-18. doi:10.3390/rs13050906
NLM
Nascimento JG, Althoff D, Bazame HC, Neale CMU, Duarte SN, Ruhoff AL, Gonçalves IZ. Evaluating the latest IMERG products in a subtropical climate: The Case of Paraná State, Brazil [Internet]. Remote Sensing. 2021 ; 13 1-18.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13050906
Vancouver
Nascimento JG, Althoff D, Bazame HC, Neale CMU, Duarte SN, Ruhoff AL, Gonçalves IZ. Evaluating the latest IMERG products in a subtropical climate: The Case of Paraná State, Brazil [Internet]. Remote Sensing. 2021 ; 13 1-18.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13050906
Artigo de periodico
Terrain slope effect on forest height and wood volume estimation from GEDI data (2021)
Fayad, Ibrahim; Baghdadi, Nicolas; Alcarde Alvares, Clayton; Stape, Jose Luiz ; Bailly, Jean Stéphane; Scolforo, Henrique Ferraço; Cegatta, Italo Ramos ; Zribi, Mehrez; Le Maire, Guerric
Source: Remote Sensing. Unidade: ESALQ
Subjects: DENDROMETRIA, DOSSEL (BOTÂNICA), FLORESTAS, MADEIRA, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
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FAYAD, Ibrahim et al. Terrain slope effect on forest height and wood volume estimation from GEDI data. Remote Sensing, v. 13, n. 11, 2021Tradução . . Disponível em: https://www.mdpi.com/2072-4292/13/11/2136. Acesso em: 06 jul. 2022.
APA
Fayad, I., Baghdadi, N., Alcarde Alvares, C., Stape, J. L., Bailly, J. S., Scolforo, H. F., et al. (2021). Terrain slope effect on forest height and wood volume estimation from GEDI data. Remote Sensing, 13( 11). doi:doi.org/10.3390/rs13112136
NLM
Fayad I, Baghdadi N, Alcarde Alvares C, Stape JL, Bailly JS, Scolforo HF, Cegatta IR, Zribi M, Le Maire G. Terrain slope effect on forest height and wood volume estimation from GEDI data [Internet]. Remote Sensing. 2021 ; 13( 11):[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/13/11/2136
Vancouver
Fayad I, Baghdadi N, Alcarde Alvares C, Stape JL, Bailly JS, Scolforo HF, Cegatta IR, Zribi M, Le Maire G. Terrain slope effect on forest height and wood volume estimation from GEDI data [Internet]. Remote Sensing. 2021 ; 13( 11):[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/13/11/2136
Artigo de periodico
Strategies for the Development of Spectral Models for Soil Organic Matter Estimation (2021)
Cezar, Everson; Nanni, Marcos Rafael; Crusiol, Luís Guilherme Teixeira; Sun, Liang; Chicati, Mônica Sacioto; Furlanetto, Renato Herrig; Rodrigues, Marlon; Sibaldelli, Rubson Natal Ribeiro; Silva, Guilherme Fernando Capristo; Oliveira, Karym Mayara de; Demattê, Jose Alexandre Melo
Source: Remote Sensing. Unidade: ESALQ
Subjects: AMOSTRAGEM, ESPECTROSCOPIA INFRAVERMELHA, MATÉRIA ORGÂNICA DO SOLO, MODELOS MATEMÁTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CEZAR, Everson et al. Strategies for the Development of Spectral Models for Soil Organic Matter Estimation. Remote Sensing, v. 13, p. 1-16, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13071376. Acesso em: 06 jul. 2022.
APA
Cezar, E., Nanni, M. R., Crusiol, L. G. T., Sun, L., Chicati, M. S., Furlanetto, R. H., et al. (2021). Strategies for the Development of Spectral Models for Soil Organic Matter Estimation. Remote Sensing, 13, 1-16. doi:10.3390/rs13071376
NLM
Cezar E, Nanni MR, Crusiol LGT, Sun L, Chicati MS, Furlanetto RH, Rodrigues M, Sibaldelli RNR, Silva GFC, Oliveira KM de, Demattê JAM. Strategies for the Development of Spectral Models for Soil Organic Matter Estimation [Internet]. Remote Sensing. 2021 ; 13 1-16.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13071376
Vancouver
Cezar E, Nanni MR, Crusiol LGT, Sun L, Chicati MS, Furlanetto RH, Rodrigues M, Sibaldelli RNR, Silva GFC, Oliveira KM de, Demattê JAM. Strategies for the Development of Spectral Models for Soil Organic Matter Estimation [Internet]. Remote Sensing. 2021 ; 13 1-16.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13071376
Artigo de periodico
Digital soil mapping using multispectral modeling with landsat time series cloud computing based (2021)
Novais, Jean J; Lacerda, Marilusa P. C; Sano, Edson E; Demattê, José Alexandre Melo ; Oliveira Junior, Manuel P
Source: Remote Sensing. Unidade: ESALQ
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, COMPUTAÇÃO EM NUVEM, ESPECTROSCOPIA, IMAGEAMENTO DE SATÉLITE, MAPEAMENTO DO SOLO, SENSORIAMENTO REMOTO
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ABNT
NOVAIS, Jean J et al. Digital soil mapping using multispectral modeling with landsat time series cloud computing based. Remote Sensing, v. 13, p. 1-18, 2021Tradução . . Disponível em: https://www.mdpi.com/2072-4292/13/6/1181. Acesso em: 06 jul. 2022.
APA
Novais, J. J., Lacerda, M. P. C., Sano, E. E., Demattê, J. A. M., & Oliveira Junior, M. P. (2021). Digital soil mapping using multispectral modeling with landsat time series cloud computing based. Remote Sensing, 13, 1-18. doi:doi.org/10.3390/rs13061181
NLM
Novais JJ, Lacerda MPC, Sano EE, Demattê JAM, Oliveira Junior MP. Digital soil mapping using multispectral modeling with landsat time series cloud computing based [Internet]. Remote Sensing. 2021 ; 13 1-18.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/13/6/1181
Vancouver
Novais JJ, Lacerda MPC, Sano EE, Demattê JAM, Oliveira Junior MP. Digital soil mapping using multispectral modeling with landsat time series cloud computing based [Internet]. Remote Sensing. 2021 ; 13 1-18.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/13/6/1181
Artigo de periodico
Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique (2021)
Canata, Tatiana Fernanda ; Wei, Marcelo Chan Fu ; Maldaner, Leonardo Felipe ; Molin, José Paulo
Source: Remote Sensing. Unidade: ESALQ
Subjects: AGRICULTURA DE PRECISÃO, APRENDIZADO COMPUTACIONAL, IMAGEAMENTO DE SATÉLITE, SENSORIAMENTO REMOTO, CANA-DE-AÇÚCAR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CANATA, Tatiana Fernanda et al. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sensing, v. 13, p. 1-14, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13020232. Acesso em: 06 jul. 2022.
APA
Canata, T. F., Wei, M. C. F., Maldaner, L. F., & Molin, J. P. (2021). Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique. Remote Sensing, 13, 1-14. doi:10.3390/rs13020232
NLM
Canata TF, Wei MCF, Maldaner LF, Molin JP. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique [Internet]. Remote Sensing. 2021 ; 13 1-14.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13020232
Vancouver
Canata TF, Wei MCF, Maldaner LF, Molin JP. Sugarcane Yield Mapping Using High-Resolution Imagery Data and Machine Learning Technique [Internet]. Remote Sensing. 2021 ; 13 1-14.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13020232
Artigo de periodico
Earth observation data-driven cropland soil monitoring: a review (2021)
Tziolas, Nikolaos; Van Wesemael, Bas; Zalidis, George; Tsakiridis, Nikolaos; Chabrillat, Sabine; Demattê, José Alexandre Melo ; Ben-Dor, Eyal; Gholizadeh, Asa
Source: Remote Sensing. Unidade: ESALQ
Subjects: ANÁLISE ESPECTRAL, APRENDIZADO COMPUTACIONAL, CARBONO, MAPEAMENTO DO SOLO, REVISÃO SISTEMÁTICA, SENSORIAMENTO REMOTO, SOLO AGRÍCOLA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TZIOLAS, Nikolaos et al. Earth observation data-driven cropland soil monitoring: a review. Remote Sensing, v. 13, p. 1-29, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13214439. Acesso em: 06 jul. 2022.
APA
Tziolas, N., Van Wesemael, B., Zalidis, G., Tsakiridis, N., Chabrillat, S., Demattê, J. A. M., et al. (2021). Earth observation data-driven cropland soil monitoring: a review. Remote Sensing, 13, 1-29. doi:10.3390/rs13214439
NLM
Tziolas N, Van Wesemael B, Zalidis G, Tsakiridis N, Chabrillat S, Demattê JAM, Ben-Dor E, Gholizadeh A. Earth observation data-driven cropland soil monitoring: a review [Internet]. Remote Sensing. 2021 ; 13 1-29.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13214439
Vancouver
Tziolas N, Van Wesemael B, Zalidis G, Tsakiridis N, Chabrillat S, Demattê JAM, Ben-Dor E, Gholizadeh A. Earth observation data-driven cropland soil monitoring: a review [Internet]. Remote Sensing. 2021 ; 13 1-29.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13214439
Artigo de periodico
Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach (2021)
Naimi, Salman ; Ayoubi, Shamsollah ; Zeraatpisheh, Mojtaba ; Dematte, Jose Alexandre Melo
Source: Remote Sensing. Unidade: ESALQ
Subjects: SALINIDADE DO SOLO, MAPEAMENTO DO SOLO, SENSORIAMENTO REMOTO, APRENDIZADO COMPUTACIONAL, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NAIMI, Salman et al. Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach. Remote Sensing, v. 13, p. 1-21, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13234825. Acesso em: 06 jul. 2022.
APA
Naimi, S., Ayoubi, S., Zeraatpisheh, M., & Dematte, J. A. M. (2021). Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach. Remote Sensing, 13, 1-21. doi:10.3390/rs13234825
NLM
Naimi S, Ayoubi S, Zeraatpisheh M, Dematte JAM. Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach [Internet]. Remote Sensing. 2021 ; 13 1-21.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13234825
Vancouver
Naimi S, Ayoubi S, Zeraatpisheh M, Dematte JAM. Ground observations and environmental covariates integration for mapping of soil salinity: a machine learning-based approach [Internet]. Remote Sensing. 2021 ; 13 1-21.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13234825
Artigo de periodico
Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest (2021)
Albuquerque, Rafael Walter ; Ferreira, Manuel Eduardo ; Olsen, Søren Ingvor ; Tymus, Julio Ricardo Caetano ; Balieiro, Cintia Palheta ; Mansur, Hendrik; Moura, Ciro José Ribeiro ; Costa, João Vitor Silva ; Branco,Maurício Ruiz Castello; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidade: IEE
Subjects: MONITORAMENTO AMBIENTAL, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ALBUQUERQUE, Rafael Walter et al. Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest. Remote Sensing, v. 13, n. 12, p. art.2401/1-21, 2021Tradução . . Disponível em: https://doi.org/10.3390/rs13122401. Acesso em: 06 jul. 2022.
APA
Albuquerque, R. W., Ferreira, M. E., Olsen, S. I., Tymus, J. R. C., Balieiro, C. P., Mansur, H., et al. (2021). Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest. Remote Sensing, 13( 12), art.2401/1-21. doi:https://doi.org/10.3390/rs13122401
NLM
Albuquerque RW, Ferreira ME, Olsen SI, Tymus JRC, Balieiro CP, Mansur H, Moura CJR, Costa JVS, Branco MRC, Grohmann CH. Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest [Internet]. Remote Sensing. 2021 ;13( 12): art.2401/1-21.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13122401
Vancouver
Albuquerque RW, Ferreira ME, Olsen SI, Tymus JRC, Balieiro CP, Mansur H, Moura CJR, Costa JVS, Branco MRC, Grohmann CH. Forest Restoration Monitoring Protocol with a Low-Cost Remotely Piloted Aircraft: lessons Learned from a Case Study in the Brazilian Atlantic Forest [Internet]. Remote Sensing. 2021 ;13( 12): art.2401/1-21.[citado 2022 jul. 06 ] Available from: https://doi.org/10.3390/rs13122401
Artigo de periodico
Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series (2021)
Tayebi, Mahboobeh ; Rosas, Jorge Tadeu Fim ; Mendes, Wanderson de Sousa; Poppiel, Raul Roberto ; Ostovari, Yaser; Ruiz, Luis Fernando Chimelo ; Santos, Natasha Valadares dos ; Cerri, Carlos Eduardo Pellegrino ; Silva, Sérgio Henrique Godinho; Curi, Nilton; Quiñonez Silvero, Nélida Elizabet ; Demattê, Jose Alexandre Melo
Source: Remote Sensing. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, CARBONO, IMAGEAMENTO DE SATÉLITE, MONITORAMENTO AMBIENTAL, SENSORIAMENTO REMOTO, USO DO SOLO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
TAYEBI, Mahboobeh et al. Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series. Remote Sensing, v. 13, p. 1-32, 2021Tradução . . Disponível em: https://www.proquest.com/docview/2539967894/fulltextPDF/4B6F6D53A45B4662PQ/1?accountid=14643. Acesso em: 06 jul. 2022.
APA
Tayebi, M., Rosas, J. T. F., Mendes, W. de S., Poppiel, R. R., Ostovari, Y., Ruiz, L. F. C., et al. (2021). Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series. Remote Sensing, 13, 1-32. doi:10.3390/ rs13112223
NLM
Tayebi M, Rosas JTF, Mendes W de S, Poppiel RR, Ostovari Y, Ruiz LFC, Santos NV dos, Cerri CEP, Silva SHG, Curi N, Quiñonez Silvero NE, Demattê JAM. Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series [Internet]. Remote Sensing. 2021 ; 13 1-32.[citado 2022 jul. 06 ] Available from: https://www.proquest.com/docview/2539967894/fulltextPDF/4B6F6D53A45B4662PQ/1?accountid=14643
Vancouver
Tayebi M, Rosas JTF, Mendes W de S, Poppiel RR, Ostovari Y, Ruiz LFC, Santos NV dos, Cerri CEP, Silva SHG, Curi N, Quiñonez Silvero NE, Demattê JAM. Drivers of Organic Carbon Stocks in Different LULC History and Along Soil Depth for a 30 Years Image Time Series [Internet]. Remote Sensing. 2021 ; 13 1-32.[citado 2022 jul. 06 ] Available from: https://www.proquest.com/docview/2539967894/fulltextPDF/4B6F6D53A45B4662PQ/1?accountid=14643
Artigo de periodico
Single-pass UAV-Borne GatorEye LiDAR sampling as a rapid assessment method for surveying forest structure (2020)
Prata, Gabriel Atticciati; Broadbent, Eben North; Almeida, Danilo Roberti Alves de ; St. Peter, Joseph; Drake, Jason; Medley, Paul; Corte, Ana Paula Dalla; Vogel, Jason; Sharma, Ajay; Silva, Carlos Alberto; Zambrano, Angelica Maria Almeyda; Valbuena, Ruben; Wilkinson, Ben
Source: Remote Sensing. Unidade: ESALQ
Subjects: TECNOLOGIA LIDAR, AERONAVES NÃO TRIPULADAS, AMOSTRAGEM, ÁRVORES FLORESTAIS, DOSSEL (BOTÂNICA), SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PRATA, Gabriel Atticciati et al. Single-pass UAV-Borne GatorEye LiDAR sampling as a rapid assessment method for surveying forest structure. Remote Sensing, v. 12, p. 1-17, 2020Tradução . . Disponível em: https://www.mdpi.com/2072-4292/12/24/4111. Acesso em: 06 jul. 2022.
APA
Prata, G. A., Broadbent, E. N., Almeida, D. R. A. de, St. Peter, J., Drake, J., Medley, P., et al. (2020). Single-pass UAV-Borne GatorEye LiDAR sampling as a rapid assessment method for surveying forest structure. Remote Sensing, 12, 1-17. doi:doi.org/10.3390/rs12244111
NLM
Prata GA, Broadbent EN, Almeida DRA de, St. Peter J, Drake J, Medley P, Corte APD, Vogel J, Sharma A, Silva CA, Zambrano AMA, Valbuena R, Wilkinson B. Single-pass UAV-Borne GatorEye LiDAR sampling as a rapid assessment method for surveying forest structure [Internet]. Remote Sensing. 2020 ; 12 1-17.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/12/24/4111
Vancouver
Prata GA, Broadbent EN, Almeida DRA de, St. Peter J, Drake J, Medley P, Corte APD, Vogel J, Sharma A, Silva CA, Zambrano AMA, Valbuena R, Wilkinson B. Single-pass UAV-Borne GatorEye LiDAR sampling as a rapid assessment method for surveying forest structure [Internet]. Remote Sensing. 2020 ; 12 1-17.[citado 2022 jul. 06 ] Available from: https://www.mdpi.com/2072-4292/12/24/4111

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