Comparison and validation of per-pixel and object-based approaches for landslide susceptibility mapping

Thimmaiah Gudiyangada Nachappa, Stefan Kienberger, Sansar Raj Meena, Daniel Hölbling, Thomas Blaschke

Research output: Contribution to journalArticleAcademicpeer-review

25 Citations (Scopus)
31 Downloads (Pure)


Remote sensing and geographic information systems (GIS) are
widely used for landslide susceptibility mapping (LSM) to support
planning authorities to plan, prepare and mitigate the consequences
of future hazards. In this study, we compared the traditional perpixel
models of data-driven frequency ratio (FR) and expert-based
multi-criteria assessment, i.e. analytical hierarchical process (AHP),
with an object-based model that uses homogenous regions (‘geon’).
The geon approach allows for transforming continuous spatial information
into discrete objects. We used ten landslide conditioning
factors for the four models to produce landslide susceptibility maps:
elevation, slope angle, slope aspect, rainfall, lithology, geology, land
use, distance to roads, distance to drainage, and distance to faults.
Existing national landslide inventory data were divided into training
(70%) and validation data (30%). The spatial correlation between
landslide locations and the conditioning factors were identified
using GIS-based statistical models. Receiver operating characteristics
(ROC) and the relative landslide density index (R-index) were
used to validate the resulting susceptibility maps. The area under
the curve (AUC) was used to obtain the following values from ROC
for the per-pixel based FR approach (0.894) and the AHP (0.886)
compared with the object-based geon FR approach (0.905) and the
geon AHP (0.896). The object-based geon aggregation yielded a
higher accuracy than both per-pixel based weightings (FR and AHP).
We proved that the object-based geon approach creates meaningful
regional units that are beneficial for regional planning and hazard
Original languageEnglish
Pages (from-to)572-600
Number of pages29
JournalGeomatics, Natural Hazards and Risk
Issue number1
Early online date28 Mar 2020
Publication statusPublished - 2020




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