Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives

Miet Van Den Eeckhout, N. Kerle, Javier Hervas, Robert Supper

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

2 Citations (Scopus)

Abstract

Light Detection and Ranging (LiDAR) and its wide range of derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides, only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. So far, it has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study focuses on the application of OOA using LiDAR derivatives such as slope gradient, curvature, and difference in elevation (2m resolution). More specifically, the focus is on the possible use for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide inventory mapping. The test areas are the Flemish Ardennes (Belgium) and Vorarlberg (Austria). In a first phase, a relatively qualitative procedure based on expert-knowledge and basic statistical analysis was developed for a test area in the Flemish Ardennes. The procedure was then applied without further modification to a validation area in the same region. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides, because approximately 70% of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bed rock outcrops like Vorarlberg, on the other hand, it is more difficult to create a transferable model.
Original languageEnglish
Title of host publicationLandslide science and practice : volume 1 : Landslide inventory and susceptibility and hazard zoning
EditorsC. Margottini, P. Canuti, K. Sassa
Place of PublicationBerlin
PublisherSpringer
Pages103-109
ISBN (Print)978-3-642-31325-7
DOIs
Publication statusPublished - 2013

Publication series

NameLandslide science and practice
PublisherSpringer
Number1

Fingerprint

vegetation cover
landslide
analysis
detection
segmentation
curvature
pixel
statistical analysis
outcrop
mountain
rock

Keywords

  • METIS-302134
  • IR-96827
  • ITC-GOLD

Cite this

Van Den Eeckhout, M., Kerle, N., Hervas, J., & Supper, R. (2013). Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives. In C. Margottini, P. Canuti, & K. Sassa (Eds.), Landslide science and practice : volume 1 : Landslide inventory and susceptibility and hazard zoning (pp. 103-109). (Landslide science and practice; No. 1). Berlin: Springer. https://doi.org/10.1007/978-3-642-31325-7
Van Den Eeckhout, Miet ; Kerle, N. ; Hervas, Javier ; Supper, Robert. / Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives. Landslide science and practice : volume 1 : Landslide inventory and susceptibility and hazard zoning. editor / C. Margottini ; P. Canuti ; K. Sassa. Berlin : Springer, 2013. pp. 103-109 (Landslide science and practice; 1).
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abstract = "Light Detection and Ranging (LiDAR) and its wide range of derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides, only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. So far, it has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study focuses on the application of OOA using LiDAR derivatives such as slope gradient, curvature, and difference in elevation (2m resolution). More specifically, the focus is on the possible use for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide inventory mapping. The test areas are the Flemish Ardennes (Belgium) and Vorarlberg (Austria). In a first phase, a relatively qualitative procedure based on expert-knowledge and basic statistical analysis was developed for a test area in the Flemish Ardennes. The procedure was then applied without further modification to a validation area in the same region. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides, because approximately 70{\%} of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bed rock outcrops like Vorarlberg, on the other hand, it is more difficult to create a transferable model.",
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Van Den Eeckhout, M, Kerle, N, Hervas, J & Supper, R 2013, Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives. in C Margottini, P Canuti & K Sassa (eds), Landslide science and practice : volume 1 : Landslide inventory and susceptibility and hazard zoning. Landslide science and practice, no. 1, Springer, Berlin, pp. 103-109. https://doi.org/10.1007/978-3-642-31325-7

Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives. / Van Den Eeckhout, Miet; Kerle, N.; Hervas, Javier; Supper, Robert.

Landslide science and practice : volume 1 : Landslide inventory and susceptibility and hazard zoning. ed. / C. Margottini; P. Canuti; K. Sassa. Berlin : Springer, 2013. p. 103-109 (Landslide science and practice; No. 1).

Research output: Chapter in Book/Report/Conference proceedingChapterAcademicpeer-review

TY - CHAP

T1 - Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives

AU - Van Den Eeckhout, Miet

AU - Kerle, N.

AU - Hervas, Javier

AU - Supper, Robert

PY - 2013

Y1 - 2013

N2 - Light Detection and Ranging (LiDAR) and its wide range of derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides, only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. So far, it has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study focuses on the application of OOA using LiDAR derivatives such as slope gradient, curvature, and difference in elevation (2m resolution). More specifically, the focus is on the possible use for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide inventory mapping. The test areas are the Flemish Ardennes (Belgium) and Vorarlberg (Austria). In a first phase, a relatively qualitative procedure based on expert-knowledge and basic statistical analysis was developed for a test area in the Flemish Ardennes. The procedure was then applied without further modification to a validation area in the same region. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides, because approximately 70% of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bed rock outcrops like Vorarlberg, on the other hand, it is more difficult to create a transferable model.

AB - Light Detection and Ranging (LiDAR) and its wide range of derivative products have become a powerful tool in landslide research, particularly for landslide identification and landslide inventory mapping. In contrast to the many studies that use expert-based analysis of LiDAR derivatives to identify landslides, only few studies, all pixel-based, have attempted to develop computer-aided methods for extracting landslides from LiDAR. So far, it has not been tested whether object-oriented analysis (OOA) could be an alternative. Therefore, this study focuses on the application of OOA using LiDAR derivatives such as slope gradient, curvature, and difference in elevation (2m resolution). More specifically, the focus is on the possible use for segmentation and classification of slow-moving landslides in densely vegetated areas, where spectral data do not allow accurate landslide inventory mapping. The test areas are the Flemish Ardennes (Belgium) and Vorarlberg (Austria). In a first phase, a relatively qualitative procedure based on expert-knowledge and basic statistical analysis was developed for a test area in the Flemish Ardennes. The procedure was then applied without further modification to a validation area in the same region. The results obtained show that OOA using LiDAR derivatives allows recognition and characterization of profound morphologic properties of deep-seated landslides, because approximately 70% of the landslides of an expert-based inventory were also included in the object-oriented inventory. For mountain areas with bed rock outcrops like Vorarlberg, on the other hand, it is more difficult to create a transferable model.

KW - METIS-302134

KW - IR-96827

KW - ITC-GOLD

UR - https://ezproxy2.utwente.nl/login?url=http://dx.doi.org/10.1007/978-3-642-31325-7_13

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U2 - 10.1007/978-3-642-31325-7

DO - 10.1007/978-3-642-31325-7

M3 - Chapter

SN - 978-3-642-31325-7

T3 - Landslide science and practice

SP - 103

EP - 109

BT - Landslide science and practice : volume 1 : Landslide inventory and susceptibility and hazard zoning

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PB - Springer

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ER -

Van Den Eeckhout M, Kerle N, Hervas J, Supper R. Mapping of landslides under dense vegetation cover using object - oriented analysis and LiDAR derivatives. In Margottini C, Canuti P, Sassa K, editors, Landslide science and practice : volume 1 : Landslide inventory and susceptibility and hazard zoning. Berlin: Springer. 2013. p. 103-109. (Landslide science and practice; 1). https://doi.org/10.1007/978-3-642-31325-7