Rainfall-Induced Shallow Landslide Recognition and Transferability Using Object-Based Image Analysis in Brazil (2023)
- Authors:
- USP affiliated authors: CARVALHO, CARLOS HENRIQUE GROHMANN DE - IEE ; DIAS, HELEN CRISTINA - IEE
- Unidade: IEE
- Assunto: SENSORIAMENTO REMOTO
- Language: Inglês
- Imprenta:
- Source:
- Título: Remote Sensing
- Volume/Número/Paginação/Ano: v. 15,n.21,p.art.5137/1-16, Oct.2023
-
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: 27 dez. 2025. -
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 2025 dez. 27 ] 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 2025 dez. 27 ] Available from: https://doi.org/10.3390/rs15215137 - Statistical-based shallow landslide susceptibility assessment for a tropical environment: a case study in the southeastern Brazilian coast
- Landslide Susceptibility Mapping in Brazil: a review
- An object-based approach for semi-automated shallow landslide mapping: suitability and comparison in Itaóca (SP) and Nova Friburgo (RJ), southeastern Brazil
- Examining the Influence of Different Inventories on Shallow Landslide Susceptibility Modeling: an assessment using machine learning and statistical approaches
- Standards for shallow landslide identification in Brazil: Spatial trends and inventory mapping
- Distinction between watersheds prone to debris flow, debris flood, and flood using morphometry in Serra do Mar, Brazil (São Paulo State North shore)
- Rainfall-induced debris flows and shallow landslides in Ribeira Valley, Brazil: main characteristics and inventory mapping
- Landslide Segmentation with Deep Learning: evaluating model generalization in rainfall-induced landslides in Brazil
- Application of Object-Based Image Analysis for Detecting and Differentiating between Shallow Landslides and Debris Flows
- Modelagem da suscetibilidade a escorregamentos rasos com base em análises estatísticas
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