%0 Conference Proceedings
%4 sid.inpe.br/mtc-m21c/2020/09.28.12.49
%2 sid.inpe.br/mtc-m21c/2020/09.28.12.49.15
%T Land cover classification of an area susceptible to landslides using random forest and NDVI time series data
%D 2020
%A Uehara, Tatiana Dias Tardelli,
%A Soares, Anderson Reis,
%A Quevedo, Renata Pacheco,
%A Körting, Thales Sehn,
%A Fonseca, Leila Maria Garcia,
%A Adami, Marcos,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress
%@electronicmailaddress anderson.soares@inpe.br
%@electronicmailaddress renata.quevedo@inpe.br
%@electronicmailaddress thales.korting@inpe.br
%@electronicmailaddress leila.fonseca@inpe.br
%@electronicmailaddress marcos.adami@inpe.br
%B IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
%C Virtual Symposium
%8 26 Sept. - 02 Oct.
%K landslide, time series, Random Forest, land cover, disasters.
%X 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.
%@language en
%3 uehara_land.pdf