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Artigo de periodico
Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping (2024)
Dias, Helen Cristina ; Grohmann, Carlos Henrique
Fonte: Journal of South American Earth Sciences. Unidade: IEE
Assunto: DESLIZAMENTO DE TERRA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Helen Cristina e GROHMANN, Carlos Henrique. Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping. Journal of South American Earth Sciences, v. 135, p. art.104805/1-10, 2024Tradução . . Acesso em: 06 maio 2024.
APA
Dias, H. C., & Grohmann, C. H. (2024). Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping. Journal of South American Earth Sciences, 135, art.104805/1-10.
NLM
Dias HC, Grohmann CH. Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping. Journal of South American Earth Sciences. 2024 ;135 art.104805/1-10.[citado 2024 maio 06 ]
Vancouver
Dias HC, Grohmann CH. Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping. Journal of South American Earth Sciences. 2024 ;135 art.104805/1-10.[citado 2024 maio 06 ]
Artigo de periodico
Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows (2023)
Dias, Helen Cristina ; Holbling, Daniel ; Dias, Vivian Cristina ; Grohmann, Carlos Henrique
Fonte: GI_Forum. Unidade: IEE
Assunto: DESLIZAMENTO DE TERRA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Helen Cristina et al. Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows. GI_Forum, v. 11, n. 1, p. 34-44, 2023Tradução . . Disponível em: https://austriaca.at/0xc1aa5576_0x003e555a.pdf. Acesso em: 06 maio 2024.
APA
Dias, H. C., Holbling, D., Dias, V. C., & Grohmann, C. H. (2023). Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows. GI_Forum, 11( 1), 34-44. Recuperado de https://austriaca.at/0xc1aa5576_0x003e555a.pdf
NLM
Dias HC, Holbling D, Dias VC, Grohmann CH. Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows [Internet]. GI_Forum. 2023 ;11( 1): 34-44.[citado 2024 maio 06 ] Available from: https://austriaca.at/0xc1aa5576_0x003e555a.pdf
Vancouver
Dias HC, Holbling D, Dias VC, Grohmann CH. Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows [Internet]. GI_Forum. 2023 ;11( 1): 34-44.[citado 2024 maio 06 ] Available from: https://austriaca.at/0xc1aa5576_0x003e555a.pdf
Trabalho de evento
RPA flight pattern and GCP influence on SfMMVS modeling of a stable landslide in SE Brazil (2023)
Grohmann, Carlos Henrique ; Garcia, Guilherme Pereira Bento ; Viana, Camila Duelis ; Dias, Helen Cristina ; Coelho, Rebeca Durço
Fonte: Proceedings. Geomorphometry 2023. Nome do evento: International Conference on Geomorphometry. Unidade: IEE
Assunto: GEOMORFOMETRIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GROHMANN, Carlos Henrique et al. RPA flight pattern and GCP influence on SfMMVS modeling of a stable landslide in SE Brazil. 2023, Anais.. [S.l.]: International Society for Geomorphometry, 2023. Disponível em: https://doi.org/10.5281/zenodo.7779223. Acesso em: 06 maio 2024.
APA
Grohmann, C. H., Garcia, G. P. B., Viana, C. D., Dias, H. C., & Coelho, R. D. (2023). RPA flight pattern and GCP influence on SfMMVS modeling of a stable landslide in SE Brazil. In Proceedings. Geomorphometry 2023. International Society for Geomorphometry. doi:10.5281/zenodo.7779223
NLM
Grohmann CH, Garcia GPB, Viana CD, Dias HC, Coelho RD. RPA flight pattern and GCP influence on SfMMVS modeling of a stable landslide in SE Brazil [Internet]. Proceedings. Geomorphometry 2023. 2023 ;[citado 2024 maio 06 ] Available from: https://doi.org/10.5281/zenodo.7779223
Vancouver
Grohmann CH, Garcia GPB, Viana CD, Dias HC, Coelho RD. RPA flight pattern and GCP influence on SfMMVS modeling of a stable landslide in SE Brazil [Internet]. Proceedings. Geomorphometry 2023. 2023 ;[citado 2024 maio 06 ] Available from: https://doi.org/10.5281/zenodo.7779223
Trabalho de evento-resumo
An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil (2023)
Dias, Helen Cristina ; Holbling,Daniel ; Grohmann, Carlos Henrique
Fonte: Abstract EGU23. Nome do evento: EGU General Assembly 2023. Unidade: IEE
Assunto: GEOMORFOMETRIA
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. An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil. 2023, Anais.. Viena: Instituto de Energia e Ambiente, Universidade de São Paulo, 2023. Disponível em: https://doi.org/10.5194/egusphere-egu23-158. Acesso em: 06 maio 2024.
APA
Dias, H. C., Holbling, D., & Grohmann, C. H. (2023). An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil. In Abstract EGU23. Viena: Instituto de Energia e Ambiente, Universidade de São Paulo. doi:10.5194/egusphere-egu23-158
NLM
Dias HC, Holbling D, Grohmann CH. An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil [Internet]. Abstract EGU23. 2023 ;[citado 2024 maio 06 ] Available from: https://doi.org/10.5194/egusphere-egu23-158
Vancouver
Dias HC, Holbling D, Grohmann CH. An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil [Internet]. Abstract EGU23. 2023 ;[citado 2024 maio 06 ] Available from: https://doi.org/10.5194/egusphere-egu23-158
Trabalho de evento-resumo
Distinction between watersheds prone to debris flow, debris flood, and flood using morphometry in Serra do Mar, Brazil (São Paulo State North shore) (2023)
Dias, Vivian Cristina ; Dias, Helen Cristina ; Grohmann, Carlos Henrique
Fonte: Abstract EGU23. Nome do evento: EGU General Assembly 2023. Unidade: IEE
Assunto: GEOMORFOMETRIA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Vivian Cristina e DIAS, Helen Cristina e GROHMANN, Carlos Henrique. Distinction between watersheds prone to debris flow, debris flood, and flood using morphometry in Serra do Mar, Brazil (São Paulo State North shore). 2023, Anais.. Viena: Instituto de Energia e Ambiente, Universidade de São Paulo, 2023. Disponível em: https://doi.org/10.5194/egusphere-egu23-1052. Acesso em: 06 maio 2024.
APA
Dias, V. C., Dias, H. C., & Grohmann, C. H. (2023). Distinction between watersheds prone to debris flow, debris flood, and flood using morphometry in Serra do Mar, Brazil (São Paulo State North shore). In Abstract EGU23. Viena: Instituto de Energia e Ambiente, Universidade de São Paulo. doi:10.5194/egusphere-egu23-1052
NLM
Dias VC, Dias HC, Grohmann CH. Distinction between watersheds prone to debris flow, debris flood, and flood using morphometry in Serra do Mar, Brazil (São Paulo State North shore) [Internet]. Abstract EGU23. 2023 ;[citado 2024 maio 06 ] Available from: https://doi.org/10.5194/egusphere-egu23-1052
Vancouver
Dias VC, Dias HC, Grohmann CH. Distinction between watersheds prone to debris flow, debris flood, and flood using morphometry in Serra do Mar, Brazil (São Paulo State North shore) [Internet]. Abstract EGU23. 2023 ;[citado 2024 maio 06 ] Available from: https://doi.org/10.5194/egusphere-egu23-1052
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
Fonte: Remote Sensing. Unidades: IEE, IGC
Assunto: DESLIZAMENTO DE TERRA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 06 maio 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 maio 06 ] 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 maio 06 ] Available from: https://doi.org/10.3390/rs14092237
Artigo de periodico
Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil (2021)
Dias, Helen Cristina ; Sandre, Lucas Henrique ; Satizábal Alarcón, Diego Alejandro ; Grohmann, Carlos Henrique ; Quintanilha, José Alberto
Fonte: Brazilian Journal of Geology. Unidades: IEE, IGC
Assunto: MUDANÇA CLIMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Helen Cristina et al. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil. Brazilian Journal of Geology, v. 51, n. 4. p. e20200105/1-10, 2021Tradução . . Disponível em: https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en. Acesso em: 06 maio 2024.
APA
Dias, H. C., Sandre, L. H., Satizábal Alarcón, D. A., Grohmann, C. H., & Quintanilha, J. A. (2021). Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil. Brazilian Journal of Geology, 51( 4. p. e20200105/1-10). Recuperado de https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en
NLM
Dias HC, Sandre LH, Satizábal Alarcón DA, Grohmann CH, Quintanilha JA. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil [Internet]. Brazilian Journal of Geology. 2021 ; 51( 4. p. e20200105/1-10):[citado 2024 maio 06 ] Available from: https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en
Vancouver
Dias HC, Sandre LH, Satizábal Alarcón DA, Grohmann CH, Quintanilha JA. Landslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaóca, southeastern Brazil [Internet]. Brazilian Journal of Geology. 2021 ; 51( 4. p. e20200105/1-10):[citado 2024 maio 06 ] Available from: https://www.scielo.br/j/bjgeo/a/Y6s5whm57BV9cgrMDWcJvgp/?format=pdf&lang=en
Artigo de periodico
Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast (2021)
Dias, Helen Cristina ; Gramani, Marcelo Fischer ; Grohmann, Carlos Henrique ; Bateira, Carlos ; Vieira, Bianca Carvalho
Fonte: Natural Hazards. Unidade: IEE
Assunto: DESLIZAMENTO DE TERRA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
DIAS, Helen Cristina et al. Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast. Natural Hazards, v. 108, n. 1, p. 205-223, 2021Tradução . . Acesso em: 06 maio 2024.
APA
Dias, H. C., Gramani, M. F., Grohmann, C. H., Bateira, C., & Vieira, B. C. (2021). Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast. Natural Hazards, 108( 1), 205-223.
NLM
Dias HC, Gramani MF, Grohmann CH, Bateira C, Vieira BC. Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast. Natural Hazards. 2021 ;108( 1): 205-223.[citado 2024 maio 06 ]
Vancouver
Dias HC, Gramani MF, Grohmann CH, Bateira C, Vieira BC. Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast. Natural Hazards. 2021 ;108( 1): 205-223.[citado 2024 maio 06 ]
Artigo de periodico
Landslide Susceptibility Mapping in Brazil: a review (2021)
Dias, Helen Cristina ; Holbling,Daniel ; Grohmann, Carlos Henrique
Fonte: Geosciences. Unidade: IEE
Assunto: DESLIZAMENTO DE TERRA
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. Landslide Susceptibility Mapping in Brazil: a review. Geosciences, v. 11, n. 10, p. art.425/1-15, 2021Tradução . . Disponível em: https://doi.org/10.3390/geosciences11100425. Acesso em: 06 maio 2024.
APA
Dias, H. C., Holbling, D., & Grohmann, C. H. (2021). Landslide Susceptibility Mapping in Brazil: a review. Geosciences, 11( 10), art.425/1-15. doi:10.3390/geosciences11100425
NLM
Dias HC, Holbling D, Grohmann CH. Landslide Susceptibility Mapping in Brazil: a review [Internet]. Geosciences. 2021 ; 11( 10): art.425/1-15.[citado 2024 maio 06 ] Available from: https://doi.org/10.3390/geosciences11100425
Vancouver
Dias HC, Holbling D, Grohmann CH. Landslide Susceptibility Mapping in Brazil: a review [Internet]. Geosciences. 2021 ; 11( 10): art.425/1-15.[citado 2024 maio 06 ] Available from: https://doi.org/10.3390/geosciences11100425

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