@InProceedings{UeharaSoQuKöFoAd:2020:LaCoCl,
author = "Uehara, Tatiana Dias Tardelli and Soares, Anderson Reis and
Quevedo, Renata Pacheco and K{\"o}rting, Thales Sehn and Fonseca,
Leila Maria Garcia and Adami, Marcos",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Land cover classification of an area susceptible to landslides
using random forest and NDVI time series data",
year = "2020",
organization = "IEEE International Geoscience and Remote Sensing Symposium
(IGARSS)",
keywords = "landslide, time series, Random Forest, land cover, disasters.",
abstract = "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.",
conference-location = "Virtual Symposium",
conference-year = "26 Sept. - 02 Oct.",
language = "en",
targetfile = "uehara_land.pdf",
urlaccessdate = "20 set. 2024"
}