![](https://csdnimg.cn/release/download_crawler_static/9958813/bgb.jpg)
as possible in both space and time, and which follows
international nomenclature (IAEG Commission on Land-
slides 1990).
Landslide inventories can be carried out using a variety
of techniques. A recent overview of the methods used for
landslide inventory mapping is given by Guzzetti et al.
(2012). Visual interpretation of stereoscopic imagery
(either aerial photographs or very high resolution optical
satellite images) remains the most widely used method, and
results in inventories of high resolution (Cardinali 2002)
when specific local conditions (such as vegetation limita-
tions) are met and when it is carried out by expert inter-
preters. Nowadays, the use of Google Earth data is a good
alternative for many areas, and many parts of the world are
covered by high-resolution imagery which can be down-
loaded and combined in GIS with a digital elevation model
to generate stereoscopic images, which are essential in
landslide interpretation. One of the most important devel-
opments is the use of shaded relief images produced from
LiDAR DEMs, from which the objects (e.g. vegetation) on
the Earth’s surface have been removed, for the visual
interpretation of landslide phenomena (Haugerud et al.
2003; Ardizzone et al. 2007; Van Den Eeckhaut et al. 2009;
Razak et al. 2011).
Landslide inventory mapping using visual stereo image
interpretation is a time-consuming task, and requires
extensive skills, training and perseverance. In many cases,
such skilled interpreters are not available, or landslide
inventories have to be produced within a short period of
time after the occurrence of a triggering event, requiring
the application of automated detection methods based on
remote sensing. Michoud et al. (2010) and Stumpf et al.
(2011) provide complete overviews of the various remote
sensing methods and tools that can be used for (semi-)
automated landslide mapping and monitoring. A large
number of methods make use of passive optical remote
sensing tools, such as pixel-based classification of or
change detection from spaceborne images (Herva
´
s et al.
Table 3 continued
Main source Group of data Examples M Scale Relevance
NRLS RS L
Archive studies
and ancillary
data
Past landslide events Historical information on location, date of occurrence,
triggering mechanism, size, volume, runout length
Af
Pf
ssddHHC
Damage data Historical information on economic losses and
population affected with dates, location and
characterisation
Pf ssssHHH
Meteorological data Precipitation (continuous or daily), temperature,
windspeed, humidity
Pn ddddHHH
Changes in land use Historical maps of land use/land cover for different
periods
Ac ddddMH H
Elements at risk Historical maps of buildings, transportation
infrastructure, economic activities and population
characteristics
Af
L
ddddHHH
Digital elevation Topographic maps with contour lines, digital elevation
models from existing catalogues
Ac ddddHHH
Thematic maps Geological, geomorphological, drainage network and
other existing thematic maps
Ac ddddHHH
Remote sensing Aerial photographs
and high-resolution
satellite images
Image interpretation for mapping and characterising
landslide locations, geomorphology, faults and
lineaments, land use/land cover, elements-at-risk
mapping
Af
Ac
sdddCCC
Multi-spectral
imagery
Image classification methods for mapping of landslides,
land use/land cover, normalised difference vegetation
index, leaf area index
Af
Ac
ddddMH M
Digital elevation data Airborne stereophotogrammetry, spaceborne stereo-
photogrammetry, LiDAR, InSAR
Ac ddddCCC
The relevance is indicated as C (crucial), H (highly important), M (moderately important), and L (less important). The potential for this
information to be collected at different scales is also indicated by: d = possible, s = difficult, 9=not possible. The scales are N (national
scale), R (regional scale), L (local scale), and S (site-specific scale). M indicates the method used for spatial data collection, with Pf = point data
linked to specific features (e.g. landslides), Ps
= sample points characterising spatial units (e.g. soil types, vegetation types), Pn = points in a
network which need to be interpolated, Af = area-based feature data (e.g. landslide polygons, buildings), Ac = complete area coverage, L = line
data
Quantitative analysis of landslide risk 219
123