The Application of Remote Sensing Technology to the Interpretation of Land Use for Rainfall-Induced Landslides Based on Genetic Algorithms and Artificial Neural Networks

Author

Chen, Yie-Ruey ; Ni, Po-Ning ; Jing-Wen Chen ; Hsieh, Shun-Chieh

Author_Institution

Dept. of Land Manage. & Dev., Chang Jung Christian Univ., Tainan, Taiwan

Volume

2

Issue

2

fYear

2009

fDate

6/1/2009 12:00:00 AM

Firstpage

87

Lastpage

95

Abstract

In this paper, we explore the relationship between land use practices and landslides triggered by rainfall in eastern Taiwan. Before-and-after satellite images, combined with an artificial neural network method, enable the classification of land use and landslide zones. Genetic algorithms are used to evaluate the land use factors causing landslides. Using the geographic information system ArcGIS to support spatial reasoning, predictive maps are produced. The results suggest that the proposed method and procedures can be an effective tool for landslide monitoring and would be easily transferred to other similar applications.

Keywords

genetic algorithms; geographic information systems; geomorphology; geophysical techniques; geophysics computing; image classification; neural nets; remote sensing; ArcGIS; artificial neural network method; eastern Taiwan; genetic algorithms; geographic information system; land use classification; land use factors; landslide zones classification; predictive maps; rainfall-induced landslides; remote sensing technology; spatial reasoning; Algorithm design and analysis; Artificial neural networks; Artificial satellites; Earth; Genetic algorithms; Geographic Information Systems; Intelligent networks; Monitoring; Remote sensing; Terrain factors; Artificial neural networks; genetic algorithms; geographic information system; image classification; landslides;

fLanguage

English

Journal_Title

Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of

Publisher

ieee

ISSN

1939-1404

Type

jour

DOI

10.1109/JSTARS.2009.2023802

Filename

5161708