| Full text | |
| Author(s): |
Helen Cristina Dias
;
Lucas Henrique Sandre
[2]
;
Diego Alejandro Satizábal Alarcón
[3]
;
Carlos Henrique Grohmann
;
José Alberto Quintanilha
[5]
Total Authors: 5
|
| Document type: | Journal article |
| Source: | BRAZILIAN JOURNAL OF GEOLOGY; v. 51, n. 4 2021-12-17. |
| Abstract | |
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 compares three image classifiers (Maximum Likelihood, Random Forest, and Support Vector Machines (SVM)) used in identifying landslides in Itaóca (São Paulo, Brazil). Two datasets were used: a RapidEye-5 (5 m) image and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) (12.5 m). Seven pixel-based classifications were produced, two for each classifier and a binary class that identified only landslides and non-landslides. One classification contained five spectral bands (5B), while the other contained six bands (6B) and included the slope derived from the DEM. The results were validated using Kappa index and F1 score. The SVM 6B classification achieved the best results among the validation indices used herein. It identified a landslide area of 399,325 m². The results contribute to landslide mapping in tropical environments using pixel-based classifiers. However, although the SVM classification was successful, only landslides with larger areas were captured by the algorithms, confirming the importance of conducting further analyses using images with finer spatial resolution. (AU) | |
| FAPESP's process: | 19/26568-0 - High-resolution remote sensing, deep learning and geomorphometry in analyses of mass movements and geological risk |
| Grantee: | Carlos Henrique Grohmann de Carvalho |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 19/17261-8 - Analysis of manual and semi-automatic shallow landslides inventories and its suitability in predictive models |
| Grantee: | Helen Cristina Dias |
| Support Opportunities: | Scholarships in Brazil - Doctorate |