| Texto completo | |
| Autor(es): |
Tardelli Uehara, Tatiana Dias
;
Soares, Anderson Reis
;
Quevedo, Renata Pacheco
;
Korting, Thales Sehn
;
Garcia Fonseca, Leila Maria
;
Adami, Marcos
;
IEEE
Número total de Autores: 7
|
| Tipo de documento: | Artigo Científico |
| Fonte: | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM; v. N/A, p. 4-pg., 2020-01-01. |
| Resumo | |
Landslides are a natural, gravity driven phenomena which can cause great economic and human losses. To prevent them, Land Use and Land Cover (LULC) maps are essential to identify areas of high susceptibility and to detect landslide scars. This paper presents results of a classification of a landslide susceptible area, using Random Forest algorithm and time series. The time series dataset is composed by the Normalized Difference Vegetation Index (NDVI) values and 16 metrics derived from the time series. The best performance was achieved using 14 metrics plus the NDVI values, with overall accuracy of 93.23% and kappa equals to 0.8937. The metrics revealed a great capability for landslides detection. (AU) | |
| Processo FAPESP: | 17/24086-2 - Gerenciamento de metadados de grandes volumes de dados de sensoriamento remoto |
| Beneficiário: | Thales Sehn Körting |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |