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Titel
Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography
VerfasserHölbling, Daniel ; Betts, Harley ; Spiekermann, Raphael ; Phillips, Chris
Erschienen in
Geosciences, Basel, 2016, Jg. 6, H. 4, S. 1-15
ErschienenMDPI, 2016
SpracheEnglisch
DokumenttypAufsatz in einer Zeitschrift
Schlagwörter (EN)Landslides / Object-based Image Analysis (OBIA) / Aerial Photography / Visual Interpretation / Remote Sensing / Spatio-temporal Hotspot Mapping
Projekt-/ReportnummerFFG-ASAP-847970
ISSN2076-3263
URNurn:nbn:at:at-ubs:3-1856 Persistent Identifier (URN)
DOI10.3390/geosciences6040048 
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 Das Werk ist frei verfügbar
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Identifying Spatio-Temporal Landslide Hotspots on North Island, New Zealand, by Analyzing Historical and Recent Aerial Photography [7.53 mb]
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Zusammenfassung (Englisch)

Accurate mapping of landslides and the reliable identification of areas most affected by landslides are essential for advancing the understanding of landslide erosion processes. Remote sensing data provides a valuable source of information on the spatial distribution and location of landslides. In this paper we present an approach for identifying landslide-prone “hotspots” and their spatio-temporal variability by analyzing historical and recent aerial photography from five different dates, ranging from 1944 to 2011, for a study site near the town of Pahiatua, southeastern North Island, New Zealand. Landslide hotspots are identified from the distribution of semi-automatically detected landslides using object-based image analysis (OBIA), and compared to hotspots derived from manually mapped landslides. When comparing the overlapping areas of the semi-automatically and manually mapped landslides the accuracy values of the OBIA results range between 46% and 61% for the producers accuracy and between 44% and 77% for the users accuracy. When evaluating whether a manually digitized landslide polygon is only intersected to some extent by any semi-automatically mapped landslide, we observe that for the natural-color images the landslide detection rate is 83% for 2011 and 93% for 2005; for the panchromatic images the values are slightly lower (67% for 1997, 74% for 1979, and 72% for 1944). A comparison of the derived landslide hotspot maps shows that the distribution of the manually identified landslides and those mapped with OBIA is very similar for all periods; though the results also reveal that mapping landslide tails generally requires visual interpretation. Information on the spatio-temporal evolution of landslide hotspots can be useful for the development of location-specific, beneficial intervention measures and for assessing landscape dynamics.

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CC-BY-Lizenz (4.0)Creative Commons Namensnennung 4.0 International Lizenz