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Titel |
Dealing with heterogeneous landslide information for landslide
susceptibility assessment: comparing a pixel-based and slope unit-based
approach |
VerfasserIn |
Liesbet Jacobs, Matthieu Kervyn, Jean Poesen, Paola Reichenbach, Mauro Rossi, Ivan Marchesini, Massimiliano Alvioli, Olivier Dewitte |
Konferenz |
EGU General Assembly 2017
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Medientyp |
Artikel
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Sprache |
en
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Digitales Dokument |
PDF |
Erschienen |
In: GRA - Volume 19 (2017) |
Datensatznummer |
250142619
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Publikation (Nr.) |
EGU/EGU2017-6260.pdf |
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Zusammenfassung |
In the Rwenzori Mountains, various multi-disciplinary data collection initiatives have
resulted in a heterogeneous database counting 247 fully characterized landslides with known
size and shape (polygon dataset) and 307 landslides represented as single points taken at an
unknown location within the landslide body (point dataset). While the polygon
dataset covers only 9% of the inhabited highlands, the point dataset extends the
total inventoried area to 18% of the entire inhabited highland region. A regional
susceptibility model for the total area should therefore include both information
from polygon- as well as point datasets. This involves two distinct methodological
challenges with regard to the use of points and polygons respectively. Firstly, the point
dataset, where the location of the point within the landslide body is unknown, may
not be fully representative for the spatial conditions under which the landslides
occurred. Here we aim to identify a robust approach, to limit this uncertainty and
maximize the point location representativeness. For this purpose, a pixel-based
approach is tested and compared to a slope unit-based approach. To mimic the
uncertainty related to the localization of the points, 50 random samplings of single
points within each landslide were performed and then fed into a logistic regression
model. The model was thus run 50 times using both the slope unit-based and the
pixel-based approach. The results show that the slope unit-based alternative has an overall
better performance than the pixel-based with comparable stability over the runs.
Based on these results, the slope unit seems a more appropriate mapping unit for a
susceptibility model based on point-data. A second significant methodological issue,
when using polygon-based models, concerns the decision on when a slope unit is
considered to be landslide-prone. A threshold representing the fraction of the slope unit
affected by landslides above which a slope unit is assigned to be landslide-prone
is often used for this purpose. The selection of this threshold is a trade-off: the
larger the threshold, the more slope units also containing landslides are considered
safe, while a small threshold will give more weight to mapping errors of landslide
polygons exceeding slope unit boundaries. Here, five different thresholds ranging from
0.0005 to 0.05 are compared with the repeated random sampling described above. A
threshold of 0.001 was found to provide the best model performances, while the
random sampling approach performed better than the models based on thresholds
larger than 0.001. This shows that a threshold approach can produce the best model
performances only if an optimal threshold selection is performed. Based on these findings, a
regional slope unit-based model was (i) calibrated using landslide polygon data and
performing an optimal threshold selection and (ii) validated using the point-data
achieving an AUCROC of 0.69. This experiment shows that although pixel-based
susceptibility mapping is by far the most common statistical approach, slope unit-based
modelling can represent a more powerful approach especially when dealing with
landslide point-data or a heterogeneous combination of point- and polygon data. |
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