Multi-Scale Geomorphometric Analysis of Mass Movements in São Sebastião (SP, Brazil)

• Grant number: 23/11197-1
• Support Opportunities: Regular Research Grants
• Start date: February 01, 2024
• End date: January 31, 2026
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geology
• Principal Investigator: Carlos Henrique Grohmann de Carvalho
• Grantee: Carlos Henrique Grohmann de Carvalho
• Host Institution: Instituto de Astronomia, Geofísica e Ciências Atmosféricas (IAG). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated researchers: Camila Duelis Viana ; John Lindsay ; Mihai Niculita ; Vivian Cristina Dias
• Associated scholarship(s): 24/18298-0 - Analysis of the influence of topography parameters on landslide susceptibility using machine learning: A case study in the municipality of São Sebastião - SP, BP.IC

Abstract

The development of remote sensing technologies in the last decade has lead to an exponential growth of available information about the Earth's surface. Among such advances, one can mention high-resolution orbital imagery with stereoscopic geometry allowing the generation of Digital Elevation Models (DEMs), airborne or terrestrial LiDAR (Light Detection And Ranging), and, more recently, applications of Structure from Motion--Multi View Stereo (SfM-MVS) to images acquired by Remotely Piloted Aircrafts (RPAs).In this research project, we propose the application of modern geo-spatial tools -- high-resolution Remote Sensing based on RPAs, airborne LiDAR, SfM-MVS, Deep Learning -- in the analysis of mass movements (landslides) and geological risk.The study area comprises the municipality of São Sebastião (SP), which suffered a humantarian tragedy in February 2023, after anomalous rainfall caused hundreds of shallow landslides and mudflows. The subjects selected for study include: multiscale analysis backed by machine learning of the influence of lands surface parameters in landslide susceptbility; SfM-MVS 3D modelling of landslides areas; comparative analysis of SfM-MVS and lidar data over the region; temporal mapping of a landslide area active for over 20 years.The project, with a two-year schedule, will be conducted by the principal researcher and his students, in collaboration with professors from Guelph (Canada) and Iasi (Romenia) Universities. (AU)

Scientific publications

(4)

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

COELHO, REBECA DURCO; VIANA, CAMILA DUELIS; DIAS, VIVIAN CRISTINA; GROHMANN, CARLOS HENRIQUE. Landslides of the 2023 summer event of Sao Sebastiao, southeastern Brazil: spatial dataset. BRAZILIAN JOURNAL OF GEOLOGY, v. 54, n. 2, p. 5-pg., 2024-01-01. (23/11197-1, 19/26568-0, 22/04233-9)

DIAS, HELEN CRISTINA; HOELBLING, DANIEL; GROHMANN, CARLOS HENRIQUE. Examining the Influence of Different Inventories on Shallow Landslide Susceptibility Modeling: An Assessment Using Machine Learning and Statistical Approaches. GEOSCIENCES, v. 15, n. 3, p. 17-pg., 2025-02-20. (22/01534-8, 23/11197-1, 19/26568-0, 19/17261-8)

GUTH, PETER L.; TREVISANI, SEBASTIANO; GROHMANN, CARLOS H.; LINDSAY, JOHN; GESCH, DEAN; HAWKER, LAURENCE; BIELSKI, CONRAD. Ranking of 10 Global One-Arc-Second DEMs Reveals Limitations in Terrain Morphology Representation. REMOTE SENSING, v. 16, n. 17, p. 31-pg., 2024-09-01. (23/11197-1)

ALVIOLI, MASSIMILIANO; LOCHE, MARCO; JACOBS, LIESBET; GROHMANN, CARLOS H.; ABRAHAM, MINU TREESA; GUPTA, KUNAL; SATYAM, NEELIMA; SCARINGI, GIANVITO; BORNAETXEA, TXOMIN; ROSSI, MAURO; et al. A benchmark dataset and workflow for landslide susceptibility zonation. EARTH-SCIENCE REVIEWS, v. 258, p. 26-pg., 2024-09-18. (23/11197-1)