• Thomas BLASCHKE - Josef STROBL - Julia WEGMAYR (Eds.)

GI_Forum 2021, Volume 9, Issue 1

12th International Symposium on Digital Earth

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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.


Starting 2016, GI_Forum publishes two issues a Year.
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GI_Forum 2021, Volume 9, Issue 1

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8947-3
Online Edition



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Verlag der Österreichischen Akademie der Wissenschaften
Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2,
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400
https://verlag.oeaw.ac.at, e-mail: bestellung.verlag@oeaw.ac.at
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Automatic Landslide Detection Using Bi-Temporal Sentinel 2 Imagery

    Sepideh Tavakkoli Piralilou, Hejar Shahabi, Robert Pazur

GI_Forum 2021, Volume 9, Issue 1, pp. 39-45, 2021/06/29

12th International Symposium on Digital Earth

doi: 10.1553/giscience2021_01_s39

doi: 10.1553/giscience2021_01_s39


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doi:10.1553/giscience2021_01_s39



doi:10.1553/giscience2021_01_s39

Abstract

Landslide inventory data sets are required for any landslide susceptibility mapping and prediction approaches. However, generating accurate landslide inventory data sets depends on applied methods and quality of input data, for example spatial resolution for satellite imagery. Therefore, the accuracy and availability of inventories vary in different studies. This study evaluated a strategy of sudden landslide identification product (SLIP) for landslide detection using Bi-Temporal Sentinel 2 Imagery and ALOS Digital Elevation Model (DEM). The resulting landslide detection map was then compared with an improved version of SLIP based on a fuzzy overlay. The resulting probability map was classified into three classes using the natural breaks method; the third class with the highest probability was extracted as the final map. The accuracy assessment stage demonstrated that using the improved version increased the accuracy by 16% compared to the SLIP method.

Keywords: earth observation, sudden landslide identification product (SLIP), Sentinel 2

Published Online: 2021/06/29 09:31:43

Object Identifier: 0xc1aa5572 0x003c9b4e

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Rights: https://creativecommons.org/licenses/by-nd/4.0/