The importance of input data on landslide susceptibility mapping

Abstract Landslide detection and susceptibility mapping are crucial in risk management and urban planning. Constant advance in digital elevation models accuracy and availability, the prospect of automatic landslide detection, together with variable processing techniques, stress the need to assess th...

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Autores principales:	Krzysztof Gaidzik, María Teresa Ramírez-Herrera
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/25fc2d877b8c472097cf1510b231de5d
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spelling	oai:doaj.org-article:25fc2d877b8c472097cf1510b231de5d2021-12-02T17:37:12ZThe importance of input data on landslide susceptibility mapping10.1038/s41598-021-98830-y2045-2322https://doaj.org/article/25fc2d877b8c472097cf1510b231de5d2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-98830-yhttps://doaj.org/toc/2045-2322Abstract Landslide detection and susceptibility mapping are crucial in risk management and urban planning. Constant advance in digital elevation models accuracy and availability, the prospect of automatic landslide detection, together with variable processing techniques, stress the need to assess the effect of differences in input data on the landslide susceptibility maps accuracy. The main goal of this study is to evaluate the influence of variations in input data on landslide susceptibility mapping using a logistic regression approach. We produced 32 models that differ in (1) type of landslide inventory (manual or automatic), (2) spatial resolution of the topographic input data, (3) number of landslide-causing factors, and (4) sampling technique. We showed that models based on automatic landslide inventory present comparable overall prediction accuracy as those produced using manually detected features. We also demonstrated that finer resolution of topographic data leads to more accurate and precise susceptibility models. The impact of the number of landslide-causing factors used for calculations appears to be important for lower resolution data. On the other hand, even the lower number of causative agents results in highly accurate susceptibility maps for the high-resolution topographic data. Our results also suggest that sampling from landslide masses is generally more befitting than sampling from the landslide mass center. We conclude that most of the produced landslide susceptibility models, even though variable, present reasonable overall prediction accuracy, suggesting that the most congruous input data and techniques need to be chosen depending on the data quality and purpose of the study.Krzysztof GaidzikMaría Teresa Ramírez-HerreraNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Krzysztof Gaidzik María Teresa Ramírez-Herrera The importance of input data on landslide susceptibility mapping
description	Abstract Landslide detection and susceptibility mapping are crucial in risk management and urban planning. Constant advance in digital elevation models accuracy and availability, the prospect of automatic landslide detection, together with variable processing techniques, stress the need to assess the effect of differences in input data on the landslide susceptibility maps accuracy. The main goal of this study is to evaluate the influence of variations in input data on landslide susceptibility mapping using a logistic regression approach. We produced 32 models that differ in (1) type of landslide inventory (manual or automatic), (2) spatial resolution of the topographic input data, (3) number of landslide-causing factors, and (4) sampling technique. We showed that models based on automatic landslide inventory present comparable overall prediction accuracy as those produced using manually detected features. We also demonstrated that finer resolution of topographic data leads to more accurate and precise susceptibility models. The impact of the number of landslide-causing factors used for calculations appears to be important for lower resolution data. On the other hand, even the lower number of causative agents results in highly accurate susceptibility maps for the high-resolution topographic data. Our results also suggest that sampling from landslide masses is generally more befitting than sampling from the landslide mass center. We conclude that most of the produced landslide susceptibility models, even though variable, present reasonable overall prediction accuracy, suggesting that the most congruous input data and techniques need to be chosen depending on the data quality and purpose of the study.
format	article
author	Krzysztof Gaidzik María Teresa Ramírez-Herrera
author_facet	Krzysztof Gaidzik María Teresa Ramírez-Herrera
author_sort	Krzysztof Gaidzik
title	The importance of input data on landslide susceptibility mapping
title_short	The importance of input data on landslide susceptibility mapping
title_full	The importance of input data on landslide susceptibility mapping
title_fullStr	The importance of input data on landslide susceptibility mapping
title_full_unstemmed	The importance of input data on landslide susceptibility mapping
title_sort	importance of input data on landslide susceptibility mapping
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/25fc2d877b8c472097cf1510b231de5d
work_keys_str_mv	AT krzysztofgaidzik theimportanceofinputdataonlandslidesusceptibilitymapping AT mariateresaramirezherrera theimportanceofinputdataonlandslidesusceptibilitymapping AT krzysztofgaidzik importanceofinputdataonlandslidesusceptibilitymapping AT mariateresaramirezherrera importanceofinputdataonlandslidesusceptibilitymapping
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The importance of input data on landslide susceptibility mapping

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