A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives
Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/11/3/303 |
| _version_ | 1828142893326401536 |
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| author | Michael D. Bunn Ben A. Leshchinsky Michael J. Olsen Adam Booth |
| author_facet | Michael D. Bunn Ben A. Leshchinsky Michael J. Olsen Adam Booth |
| author_sort | Michael D. Bunn |
| collection | DOAJ |
| description | Landslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km<sup>2</sup> of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m<sup>2</sup>; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature. |
| first_indexed | 2024-04-11T19:49:17Z |
| format | Article |
| id | doaj.art-6974f128ba6f4ac18c551efcd1c6eff3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:49:17Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6974f128ba6f4ac18c551efcd1c6eff32022-12-22T04:06:21ZengMDPI AGRemote Sensing2072-42922019-02-0111330310.3390/rs11030303rs11030303A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model DerivativesMichael D. Bunn0Ben A. Leshchinsky1Michael J. Olsen2Adam Booth3School of Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331, USASchool of Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331, USASchool of Civil and Construction Engineering, Oregon State University, 101 Kearney Hall, Corvallis, OR 97331, USADepartment of Geology, Portland State University, P.O. Box 751, Portland, OR 97207, USALandslide inventory maps are critical to understand the factors governing landslide occurrence and estimate hazards or sediment delivery to channels. Numerous semi-automated approaches for landslide inventory mapping have been proposed to improve the efficiency and objectivity of the process, but these methods have not been widely adopted by practitioners because of the use of input parameters without physical meaning, a lack of transparency in machine-learning based mapping techniques, and limitations in resulting products, which are not ordinarily designed or tested on a large-scale or in diverse geologic units. To this end, this work presents a new semi-automated method, called the Scarp Identification and Contour Connection Method (SICCM), which adapts to diverse geologic settings automatically or semi-automatically using interventions driven by simple inputs and interpretation from an expert mapper. The applicability of SICCM for use in landslide inventory mapping is demonstrated for three diverse study areas in western Oregon, USA by assessing the utility of the results as a landslide inventory, evaluating the sensitivity of the algorithm to changes in input parameters, and exploring how geology influences the resulting landslide inventory results. In these case studies, accuracies exceed 70%, with reliability and precision of nearly 80%. Conclusions of this work are that (1) SICCM efficiently produces meaningful landslide inventories for large areas as evidenced by mapping 216 km<sup>2</sup> of landslide deposits with individual deposits ranging in size from 58 to 1.1 million m<sup>2</sup>; (2) results are predictable with changes to input parameters, resulting in an intuitive approach; (3) geology does not appear to significantly affect SICCM performance; and (4) the process involves simplifications compared with more complex alternatives from the literature.https://www.mdpi.com/2072-4292/11/3/303landslide inventoryLIDARContour Connection Methodsemi automatedBig Elk CreekDixie MountainGales Creek |
| spellingShingle | Michael D. Bunn Ben A. Leshchinsky Michael J. Olsen Adam Booth A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives Remote Sensing landslide inventory LIDAR Contour Connection Method semi automated Big Elk Creek Dixie Mountain Gales Creek |
| title | A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives |
| title_full | A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives |
| title_fullStr | A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives |
| title_full_unstemmed | A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives |
| title_short | A Simplified, Object-Based Framework for Efficient Landslide Inventorying Using LIDAR Digital Elevation Model Derivatives |
| title_sort | simplified object based framework for efficient landslide inventorying using lidar digital elevation model derivatives |
| topic | landslide inventory LIDAR Contour Connection Method semi automated Big Elk Creek Dixie Mountain Gales Creek |
| url | https://www.mdpi.com/2072-4292/11/3/303 |
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