LAND COVER CLASSIFICATION OF AN AREA SUSCEPTIBLE TO LANDSLIDES USING RANDOM FOREST AND NDVI TIME SERIES DATA

被引：3

作者：

Tardelli Uehara, Tatiana Dias ^{[1
]}

Soares, Anderson Reis ^{[1
]}

Quevedo, Renata Pacheco ^{[1
]}

Korting, Thales Sehn ^{[1
]}

Garcia Fonseca, Leila Maria ^{[1
]}

Adami, Marcos ^{[1
]}

机构：

[1] Brazils Natl Inst Space Res INPE, Gen Coordinat Earth Observat OBT, Av Astronautas 1758, Sao Jose Dos Campos, SP, Brazil

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

基金：

巴西圣保罗研究基金会;

关键词：

landslide; time series; Random Forest; land cover; disasters;

D O I：

10.1109/IGARSS39084.2020.9324108

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1345 / 1348

页数：4

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