Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Carlos Henrique Grohmann"'
Publikováno v:
Brazilian Journal of Geology, Vol 54, Iss 2 (2024)
Abstract In February 2023, anomalously heavy rainfall caused widespread landslides in the coastal city of São Sebastião (Southeastern Brazil). This report describes the first version of a landslide inventory dataset for this event. The inventory is
Externí odkaz:
https://doaj.org/article/ab39f0aa2ed0477bbd0f2763e52d54c8
Beach surface model construction: A strategy approach with structure from motion - multi-view stereo
Autor:
A.T.S. Ferreira, Carlos Henrique Grohmann, Maria Carolina Hernandez Ribeiro, Marcelo Soares Teles Santos, Regina Célia de Oliveira, Eduardo Siegle
Publikováno v:
MethodsX, Vol 12, Iss , Pp 102694- (2024)
In contrast to traditional beach profiling methods like topographic surveys and GNSS, which pose significant challenges in terms of cost and time, this research underscores the efficiency, cost-effectiveness, and simplicity of terrestrial photogramme
Externí odkaz:
https://doaj.org/article/5f08499323164021a6653d099e5895db
Autor:
Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Maria Carolina Hernandez Ribeiro, Carlos Henrique Grohmann, Eduardo Siegle
Publikováno v:
Coasts, Vol 3, Iss 3, Pp 160-174 (2023)
As the interface between land and water, coastlines are highly dynamic and intricately tied to the sediment budget. These regions have a high functional diversity and require enlightened management to preserve their value for the future. In this stud
Externí odkaz:
https://doaj.org/article/8cf0439f2423450fbf728cadcef89b8a
Publikováno v:
Brazilian Journal of Geology, Vol 53, Iss 3 (2023)
Abstract On February 6, 2023, two earthquakes shook the southern and central Türkiye causing significant loss of human life and devastating many cities. These are related to the active East Anatolian Fault (EAF). In this study, the digital image cor
Externí odkaz:
https://doaj.org/article/541f492c62df4586bf4867e3c38fd67e
Publikováno v:
Remote Sensing, Vol 15, Iss 21, p 5137 (2023)
Landslides are among the most frequent hazards in Latin America and the world. In Brazil, they occur every year and cause economic and social loss. Landslide inventories are essential for assessing susceptibility, vulnerability, and risk. Over the de
Externí odkaz:
https://doaj.org/article/e4f2dc6a6c964cc7b2286a066b4e4afc
Autor:
Amanda Mendes de Sousa, Camila Duelis Viana, Guilherme Pereira Bento Garcia, Carlos Henrique Grohmann
Publikováno v:
Remote Sensing, Vol 15, Iss 12, p 3028 (2023)
This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named “CEU P
Externí odkaz:
https://doaj.org/article/fbdfddb624a847409f871c65a147a5b4
Autor:
Helen Cristina Dias, Lucas Henrique Sandre, Diego Alejandro Satizábal Alarcón, Carlos Henrique Grohmann, José Alberto Quintanilha
Publikováno v:
Brazilian Journal of Geology, Vol 51, Iss 4 (2021)
Abstract Landslide identification is important for understanding their conditioning factors, and for constructing susceptibility, risk, and vulnerability maps. In remote sensing this can be accomplished manually or through classifiers. This study com
Externí odkaz:
https://doaj.org/article/15258a968d1041239eb9f221df0ad0bb
Autor:
Lucas Pedrosa Soares, Helen Cristina Dias, Guilherme Pereira Bento Garcia, Carlos Henrique Grohmann
Publikováno v:
Remote Sensing, Vol 14, Iss 9, p 2237 (2022)
Automatic landslide mapping is crucial for a fast response in a disaster scenario and improving landslide susceptibility models. Recent studies highlighted the potential of deep learning methods for automatic landslide segmentation. However, only a f
Externí odkaz:
https://doaj.org/article/3363474487f84ba6b0eeb21c0099c23e
Autor:
Rafael Walter Albuquerque, Daniel Luis Mascia Vieira, Manuel Eduardo Ferreira, Lucas Pedrosa Soares, Søren Ingvor Olsen, Luciana Spinelli Araujo, Luiz Eduardo Vicente, Julio Ricardo Caetano Tymus, Cintia Palheta Balieiro, Marcelo Hiromiti Matsumoto, Carlos Henrique Grohmann
Publikováno v:
Remote Sensing, Vol 14, Iss 4, p 830 (2022)
Monitoring the vegetation structure and species composition of forest restoration (FR) in the Brazilian Amazon is critical to ensuring its long-term benefits. Since remotely piloted aircrafts (RPAs) associated with deep learning (DL) are becoming pow
Externí odkaz:
https://doaj.org/article/5a961217f9ab45b085c619d61660352c
Publikováno v:
Geosciences, Vol 11, Iss 10, p 425 (2021)
Landslide susceptibility studies are a common type of landslide assessment. Landslides are one of the most frequent hazards in Brazil, resulting in significant economic and social losses (e.g., deaths, injuries, and property destruction). This paper
Externí odkaz:
https://doaj.org/article/f3ef22e954b94f1bbb9b525a08bc6342