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Artigo de periodico
Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil (2023)
Dias, Helen Cristina ; Holbling,Daniel ; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidade: IEE
Assunto: SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Helen Cristina e HOLBLING, Daniel e GROHMANN, Carlos Henrique. Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil. Remote Sensing, v. 15, n. 21, p. art.5137/1-16, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15215137. Acesso em: 16 nov. 2024.
APA
Dias, H. C., Holbling, D., & Grohmann, C. H. (2023). Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil. Remote Sensing, 15(21), art.5137/1-16. Recuperado de https://doi.org/10.3390/rs15215137
NLM
Dias HC, Holbling D, Grohmann CH. Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil [Internet]. Remote Sensing. 2023 ; 15(21):art.5137/1-16.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs15215137
Vancouver
Dias HC, Holbling D, Grohmann CH. Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil [Internet]. Remote Sensing. 2023 ; 15(21):art.5137/1-16.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs15215137
Artigo de periodico
Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models (2023)
Sousa, Amanda Mendes de ; Viana, Camila Duelis ; Garcia, Guilherme Pereira Bento ; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidades: IGC, IEE
Subjects: GEOLOGIA AMBIENTAL, MONITORAMENTO AMBIENTAL, DESLIZAMENTO DE TERRA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUSA, Amanda Mendes de et al. Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models. Remote Sensing, v. 15, n. 12, p. 3028-, 2023Tradução . . Disponível em: https://doi.org/10.3390/rs15123028. Acesso em: 16 nov. 2024.
APA
Sousa, A. M. de, Viana, C. D., Garcia, G. P. B., & Grohmann, C. H. (2023). Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models. Remote Sensing, 15( 12), 3028-. doi:10.3390/rs15123028
NLM
Sousa AM de, Viana CD, Garcia GPB, Grohmann CH. Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models [Internet]. Remote Sensing. 2023 ; 15( 12): 3028-.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs15123028
Vancouver
Sousa AM de, Viana CD, Garcia GPB, Grohmann CH. Monitoring geological risk areas in the city of São Paulo based on multi-temporal high-resolution 3D models [Internet]. Remote Sensing. 2023 ; 15( 12): 3028-.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs15123028
Artigo de periodico
Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil (2022)
Soares, Lucas Pedrosa ; Dias, Helen Cristina ; Garcia, Guilherme Pereira Bento ; Grohmann, Carlos Henrique
Source: Remote Sensing. Unidades: IEE, IGC
Assunto: DESLIZAMENTO DE TERRA
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ABNT
SOARES, Lucas Pedrosa et al. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil. Remote Sensing, v. 14, n. 9, p. e2237/1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/rs14092237. Acesso em: 16 nov. 2024.
APA
Soares, L. P., Dias, H. C., Garcia, G. P. B., & Grohmann, C. H. (2022). Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil. Remote Sensing, 14( 9), e2237/1-17. doi:10.3390/rs14092237
NLM
Soares LP, Dias HC, Garcia GPB, Grohmann CH. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil [Internet]. Remote Sensing. 2022 ; 14( 9):e2237/1-17.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs14092237
Vancouver
Soares LP, Dias HC, Garcia GPB, Grohmann CH. Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil [Internet]. Remote Sensing. 2022 ; 14( 9):e2237/1-17.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs14092237
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 ; Grohmann, 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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ABNT
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: 16 nov. 2024.
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, Grohmann 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 2024 nov. 16 ] 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, Grohmann 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 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs14040830
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
Assunto: GEOMORFOMETRIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 16 nov. 2024.
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 2024 nov. 16 ] 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 2024 nov. 16 ] Available from: https://www.mdpi.com/1263464
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: 16 nov. 2024.
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: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 2024 nov. 16 ] 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 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs13122401
Artigo de periodico
Aboveground biomass estimation in Amazonian tropical forests: A comparison of aircraft-and gatoreye UAV-borne LIDAR data in the Chico mendes extractive reserve in Acre, Brazil (2020)
d'Oliveira, M. V. N; Broadbent, E. N; Oliveira, L. C; Almeida, Danilo Roberti Alves de ; Papa, D. A; Ferreira, M. E; Zambrano, A. M. A; Silva, C. A; Avino, F. S; Prata, G. A; Mello, R. A; Figueiredo, E. O; Jorge, L. A. de Castro; Junior, L; Albuquerque, Rafael Walter ; Brancalion, Pedro Henrique Santin ; Wilkinson, B; Oliveira-da-Costa, M
Source: Remote Sensing. Unidades: ESALQ, IEE
Subjects: AERONAVES NÃO TRIPULADAS, BIOMASSA, FLORESTAS TROPICAIS, INVENTÁRIO FLORESTAL, MONITORAMENTO AMBIENTAL, SENSORIAMENTO REMOTO, TECNOLOGIA LIDAR
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ABNT
D'OLIVEIRA, M. V. N et al. Aboveground biomass estimation in Amazonian tropical forests: A comparison of aircraft-and gatoreye UAV-borne LIDAR data in the Chico mendes extractive reserve in Acre, Brazil. Remote Sensing, v. 12, p. 1-19 , 2020Tradução . . Disponível em: https://doi.org/10.3390/rs12111754. Acesso em: 16 nov. 2024.
APA
d'Oliveira, M. V. N., Broadbent, E. N., Oliveira, L. C., Almeida, D. R. A. de, Papa, D. A., Ferreira, M. E., et al. (2020). Aboveground biomass estimation in Amazonian tropical forests: A comparison of aircraft-and gatoreye UAV-borne LIDAR data in the Chico mendes extractive reserve in Acre, Brazil. Remote Sensing, 12, 1-19 . doi:10.3390/rs12111754
NLM
d'Oliveira MVN, Broadbent EN, Oliveira LC, Almeida DRA de, Papa DA, Ferreira ME, Zambrano AMA, Silva CA, Avino FS, Prata GA, Mello RA, Figueiredo EO, Jorge LA de C, Junior L, Albuquerque RW, Brancalion PHS, Wilkinson B, Oliveira-da-Costa M. Aboveground biomass estimation in Amazonian tropical forests: A comparison of aircraft-and gatoreye UAV-borne LIDAR data in the Chico mendes extractive reserve in Acre, Brazil [Internet]. Remote Sensing. 2020 ; 12 1-19 .[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs12111754
Vancouver
d'Oliveira MVN, Broadbent EN, Oliveira LC, Almeida DRA de, Papa DA, Ferreira ME, Zambrano AMA, Silva CA, Avino FS, Prata GA, Mello RA, Figueiredo EO, Jorge LA de C, Junior L, Albuquerque RW, Brancalion PHS, Wilkinson B, Oliveira-da-Costa M. Aboveground biomass estimation in Amazonian tropical forests: A comparison of aircraft-and gatoreye UAV-borne LIDAR data in the Chico mendes extractive reserve in Acre, Brazil [Internet]. Remote Sensing. 2020 ; 12 1-19 .[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs12111754
Artigo de periodico
Estimating water pH using cloud-based landsat images for a new classification of the Nhecolândia lakes (Brazilian Pantanal) (2020)
Pereira, Osvaldo José Ribeiro ; Merino, Eder Renato ; Montes, Celia Regina ; Barbiero, Laurent ; Rezende Filho, Ary Tavares ; Lucas, Yves; Melfi, Adolpho José
Source: Remote Sensing. Unidades: CENA, ESALQ, IEE
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, COMPUTAÇÃO EM NUVEM, IMAGEAMENTO DE SATÉLITE, LAGOS, SENSORIAMENTO REMOTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PEREIRA, Osvaldo José Ribeiro et al. Estimating water pH using cloud-based landsat images for a new classification of the Nhecolândia lakes (Brazilian Pantanal). Remote Sensing, v. 12, p. 1-21, 2020Tradução . . Disponível em: https://doi.org/10.3390/rs12071090. Acesso em: 16 nov. 2024.
APA
Pereira, O. J. R., Merino, E. R., Montes, C. R., Barbiero, L., Rezende Filho, A. T., Lucas, Y., & Melfi, A. J. (2020). Estimating water pH using cloud-based landsat images for a new classification of the Nhecolândia lakes (Brazilian Pantanal). Remote Sensing, 12, 1-21. doi:10.3390/rs12071090
NLM
Pereira OJR, Merino ER, Montes CR, Barbiero L, Rezende Filho AT, Lucas Y, Melfi AJ. Estimating water pH using cloud-based landsat images for a new classification of the Nhecolândia lakes (Brazilian Pantanal) [Internet]. Remote Sensing. 2020 ; 12 1-21.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs12071090
Vancouver
Pereira OJR, Merino ER, Montes CR, Barbiero L, Rezende Filho AT, Lucas Y, Melfi AJ. Estimating water pH using cloud-based landsat images for a new classification of the Nhecolândia lakes (Brazilian Pantanal) [Internet]. Remote Sensing. 2020 ; 12 1-21.[citado 2024 nov. 16 ] Available from: https://doi.org/10.3390/rs12071090

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