Analysis of manual and semi-automatic shallow landslides inventories and its suitability in predictive models

• Grant number: 19/17261-8
• Support Opportunities: Scholarships in Brazil - Doctorate
• Effective date (Start): December 01, 2019
• Status: Discontinued
• Field of knowledge: Physical Sciences and Mathematics - Geosciences - Geology
• Principal Investigator: Carlos Henrique Grohmann de Carvalho
• Grantee: Helen Cristina Dias
• Host Institution: Instituto de Energia e Ambiente (IEE). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• Associated scholarship(s): 22/01534-8 - Creation and assessment of shallow landslides inventories based on the Object-Based Image Analysis (OBIA) method, BE.EP.DR

Abstract

Shallow landslides are a frequent kind of mass movement in Brazilian territory. The creation of shallow landslides inventory is important to conditioning factors studies. They are important to analysis of susceptibility, vulnerability and risk, besides being useful for urban planning. Thus, the general aim of this research is to demonstrate how construction of shallow landslides inventories from distinct methods and database may influence their accuracy and suitability in predictive mapping. The following procedures were adopted: (1) manual mapping of shallow landslides based on two distinct databases, Google Earth Pro images and Rapid Eye (5 m); (2) semi-automatic mapping of shallow landslides by OBIA method based on Rapid Eye (5 m); (3) comparison of manual and semi-automatic inventories; and (4) definition of susceptibility to shallow landslides through statistical analysis using the informative value of a watershed. The main product of this project will be the definition of the suitability of shallow landslides inventories from distinct methods and database, it will be possible to infer which methodology is most efficient for mapping past landslides events. (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)

DIAS, HELEN CRISTINA; HOELBLING, DANIEL; GROHMANN, CARLOS HENRIQUE. Landslide Susceptibility Mapping in Brazil: A Review. GEOSCIENCES, v. 11, n. 10, OCT 2021. (19/17261-8, 19/26568-0)

DIAS, HELEN CRISTINA; GRAMANI, MARCELO FISCHER; GROHMANN, CARLOS HENRIQUE; BATEIRA, CARLOS; VIEIRA, BIANCA CARVALHO. 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, AUG 2021. (19/17261-8)

DIAS, HELEN CRISTINA; SANDRE, LUCAS HENRIQUE; SATIZABAL ALARCON, DIEGO ALEJANDRO; GROHMANN, CARLOS HENRIQUE; QUINTANILHA, JOSE ALBERTO. andslide recognition using SVM, Random Forest, and Maximum Likelihood classifiers on high-resolution satellite images: A case study of Itaoca, southeastern Brazi. BRAZILIAN JOURNAL OF GEOLOGY, v. 51, n. 4, 2021. (19/26568-0, 19/17261-8)

HELEN CRISTINA DIAS; LUCAS HENRIQUE SANDRE; DIEGO ALEJANDRO SATIZÁBAL ALARCÓN; CARLOS HENRIQUE GROHMANN; JOSÉ ALBERTO QUINTANILHA. 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, 2021-12-17. (19/17261-8, 19/26568-0)