An attempt for automatic detection and visualization of talus cones from digital elevation data

licoricehealthΤεχνίτη Νοημοσύνη και Ρομποτική

14 Νοε 2013 (πριν από 3 χρόνια και 5 μήνες)

74 εμφανίσεις

An attempt for automatic detection and visualization of talus
cones from digital elevation data


Balázs Székely
1,2

and Tomaž Podobnikar
1,3




Dr Balázs Székely and
Dr
Tomaž Podobnikar

1
Christian Doppler Laboratory Spatial Data from Laser Scanning and Remot
e Sensing,
Institute of Photogrammetry and Remote Sensing, Vienna University of Technology

Gußhausstraße 27
-
29, A
-
1040 Vienna, Austria

Tel: +43 1 58801 12210, Fax: +43 1 58801 12299

2
Space Research Group, Department of Geophysics and Space Science, Eötvös
University

Pázmány Péter sétány 1/a
,

HU
-
1117
Budapest, Hungary
, bz@ipf.tuwien.ac.at

3
Scientific Research Centre of the Slovenian Academy of Sciences and Arts

Novi trg 2, SI
-
1000 Ljubljana, Slovenia
, tp@ipf.tuwien.ac.at



Abstract


The importance of
methods

for
automated feature extraction from digital elevation data is increasing as
the amount of data to be processed multiplies in size. Landscape elements can be extracted from the data
if the comm
on characteristic parameters
can be
identified

and
an effecti
ve

searching algorithm can be
found to identify
the
feature
.

In our approach we focus on actively evolving slopes in alpine areas, where sediment
redeposition

takes
place in res
ponse to postglacial processes. S
cree
-
covered
valley slopes, oversteepened at p
laces,

may
become unstable in long
-
term due to the active geomorphic
evolution
. Mass movements,
partly

induced
by seismic activity, may cause significant damages, therefore automated delineation of talus
surfaces

is

importance for assessment purposes.

In o
ur pilot study we developed tools for automatic identification of
scree slopes

based on sectorial
analysis of slope histograms. The preliminary results are
promising;

however, the parameter selection
can only be done

yet

manually
.



1.
INTRODUCTION


1.1 Si
gnificance of the automated topographic feature extraction


The development of current technologies of data acquisition multiplied the amount of available accurate
digital elevation data.

Photogrammetric, LiDAR
-
based
(
W
AGNER

et al
.

2006,
R
ONCAT

et al
. 2007
, in press)

and other techniques provide hundreds of millions of measured surface points; t
he
horizontal
resolution of
such datasets reached now the sub
-
meter range
. Consequently,
the size of the digital database of a project
exceeds the order of magnitude

of terabytes.

Of course the data acquisition is carried out to achieve a
certain goal of the project; on the other hand, the re
-
use and re
-
evaluation of the expensively obtained data
is more and more common, as new aspects of analysis appear (e.g.,
P
IKE

2
000
,

T
IMÁR

et al. 2005
).

Although the manual feature extraction remains important for the next decades as well, t
hese vast datasets
generate imminent need for fast and automated processing techniques
, especially in the field of feature
identification
. Feat
ure extraction are typically intended to get vectorized in formation of the geomorphic
features like sinuous parts of a river (e.g.,

T
IMÁR

et al. 2005,

Z
ÁMOLYI

et al
.

2007
) or peaks (
e.g,
S
ZÉKELY

and
K
ARÁTSON

2004,
C
HANG

and
S
INHA

2007)
.

Feature extraction

in this sense

can be split into to phases: an identification phase when the possible
feature
candidates are marked by an automated process, and a verification and extraction phase in which the
preselected feature set is manually or semi
-
automatically redu
ced to the features that we are looking for.
Feature extraction methods are applied widely in geomorphic, natural hazard assessment, archaeological
and other studies, since they provide preprocessed feature sets that allow focused research work.

One
of the

most frequently
applied

and even more rapidly expanding use of the digital elevation data is the
assessment of the stability of slopes. This analysis is especially crucial in mountainous environment, where
rapid evolution of topography
is due to the on
-
go
ing orogeny, the effects of the deglaciation, climate
change

(Fig. 1)
. I
n alpine settings
where the topographic evolution is mostly
driven by climate change
, the
analysis of digital elevation data

has attracted much interest, because unstable valley slopes
, often covered
by post
-
glacial talus cones, may suffer various
-
scale mass movements including
rock avalanches,
rock and
landslides.
In
these areas

t
he scree slopes are often unvegetated, however in some settings young forests or
shrubs may cover them. Sin
ce these slopes are mostly of loose
, poorly sorted

material, decadal
meteorological events, or excess discharge from glacial or snow meltwater may lead to sudden incision.





Figure 1 A glacially overdeepened valley in Vorarlberg (Austria) in the East
ern Alps. The oversteepened
valley slopes start to be reshaped by rock falls, rock avalanches and other mass movements forming a talus
coverage of various thickness. It is important to note that the age of the talus surface is different: the age of
the veg
etation gives hints for the sequence of events, from bare rock surface (very young) to forest covered
(several decade
s

old).


Such suddenly incised valleys (that actually cause local base level drops) then may pave the way for
medium
-
sized or major mass mo
vements. The slopes slightly steeper than the angle of repose, under certain
circumstances, may remain temporarily metastable, especially if they are vegetated. Removal of vegetation
(deforestation or other change in the land use) may change their stabilit
y situation. If such slopes are also
connected with outlets of hanging valleys that are common in post
-
glacial geomorphic settings, the surplus
in sediment discharge from the upstream area is completely deposited at the slopes of the talus cones. In
long r
un the slopes become even more oversteepened, therefore increasingly unstable.

Thus, t
he identification and monitoring of such talus cones on decadal scale are of major importance
. Hill
foot areas
often host man
-
made structures as well
, so the potential da
mage of mass movements are typically
high

(Fig. 2).

In this contribution we intend to tackle the problem of the automated extraction of such
surfaces in order to be able to create maps and analyse the properties of these slopes.
With unconventional
process
ing techniques applied on high
-
resolution, high
-
accuracy digital terrain models (DTM)
we intend
to
detect automat
icall
y well developed talus cones.
In this paper the results of the first successful experiments
are presented.




Figure 2 A densely popul
ated postglacial talus cone in Gaschurn, Vorarlberg (Austria). The smooth and
wavy surface abruptly changes on the right side of the image to a steeper slope angle. This is due to the
rapid incision of the river Ill. In long term this incision will lead to

the metastable status of the talus cone
that may endanger the build
-
up structures. Automated recognition may help to identify such places that can
be affected by incision
-
induced
mass movements.




1.2 Theoretical approach


The most common spatial analyse
s in geographical information systems (GIS) include topologic and
cartographic modelling, modelling of networks, automated cartography, map algebra and spatial statistics.
A GIS is considered as a suitable tool
for the environmental
modelling
,

designed for

creating, storing,
updating, analysing, displaying, and managing (manipulating) spatial data and associated attributes (ESRI
1997). GIS applications are common in business, government, research, the internet, etc. While it has been
truly operational since

mid 1960s, its usage has grown significantly since the 1980s. The strength of GIS
lies in its ability to visualise and analyse spatial patterns (
T
OMLINSON

2003), as well as to allow exploration
of more complex interactions between social and natural space
s.
Spatial analysis that build
s

the models
,

could be classified as descriptive, explanatory, predictive and normative (
C
HOU

1997)
.

In our case, a
descriptive

analysis evaluate
s

the data and their suitability for explanatory models that are goal of this stu
dy.
The other approach to spatial modelling (
A
NSELIN

2005) starts with exploratory analysis to find interesting
patterns, continue with visualisation for showing the patterns and then with spatial modelling for explaining
the patterns that is also part of
the study procedure. As part of the modelling procedure other type of
models could be involved, e.g. regression, Boolean, empirical

ones
. Therefore
,

spatial modelling c
ombine
different spatial analyse
s regarding of decision
-
making process and even more, th
e data (variables) and
models could be part of environmental decision support system (
K
ANEVSKI

and
M
AIGNAN

2004) for
estimation of selected environmental phenomena, e.g. erosion
, potential mass movements
.

In this study the primarily interest is to apply th
e raster
-
based methods on continuous surfaces


digital
terrain models (DTMs). Spatial analyses are mostly analytical operations of continuous surfaces
(classifying and reclassifying, overlaying, geometrical operations, neighbourhood operations) and spatia
l
interpolations (deterministic and stochastic, global and local methods, etc.).
We apply here c
lassifying
,

an
operation for merging attributes to selected classes. Continuous values
have to be

classified to
form
categori
es, and then re
classification
is of
ten needed,
an operation whe
re more classes are reduced

to fewer.


It is usually used for converting nominal or ordinal values to a Boolean binary form.


Since we intend to have one single class (the slope features to be found) and another non
-
classified g
roup at
the end of the processing reclassification has to be applied. To achieve that, however, previous data
reduction steps are needed.

With o
verlaying
, that

is an analytical operation that combines two or more data
s
ets to a new one on a same area,

the
data sets are compared by logical operations (Boolean algebra: AND,
OR, NOT, etc.) on binary data sets, or processed by arit
hmetical operators +,

, ·, /, etc. and relational
operators (>, =, etc.).
In our case the numerous indicator functions can be merged this way.

Last, but not least, a
nalytically important processing in GIS that is related with classification and
overlaying

operations is map algebra (
T
OMLIN

1990).
Sophisticated g
eometrical operations offer
calculations of distances, areas, connections, directions, etc., for example Euclidean distances and surfaces,
buffer zones, cost distances, Thiessen polygons, etc.
Instea
d of that, for simplicity and for a better
performance n
eighbourhood operations
have been used that operate with

windows of different sizes and
forms (e.g. square window 3 x 3 cells) for calculation of slope, aspect, hill shading, visibility analysis, etc.

This analysis is usually
carried out
on a DTM.

In a later stage, if the behaviour of the data is better known
in various environments (e.g., crystalline, calcareous rocks), more sophisticated and computer
-
exhaustive
processing techniques can be applied to

minimize the manual input and heuristic parameter settings.



2. DATA AND METHODS


2.1
The study area


Our

primary selection criterion for the study area was that
,

from geomorphic point of view
,

the
digital
elevation data of the area

s
hould be as accurate

as it can

be.

Another important quality requirement was the
homogeneity of the data set, especially concerning the error level.
As it was mentioned above, due to the
various
resolutions

of the DTMs
,

the calculated slope angle may vary and


depending on t
he actual relief
of the area


the DTM
may turn to be less suitable for the slope angle calculation. For test purposes,
therefore, a small part of the upper valley of the
Sava
D
olinka

river was chosen in the
NW

part of Slovenia

near to
the
border with Aust
ria and Italy

(Fig.
3
)
. T
he high geomorphological quality Digital Terrain Model
of Slovenia (DTM of Slovenia; © 2005, Surveying and Mapping Authority
of
R
epublic of
S
lovenia
)
has
been compiled
from various data sets

using high quality control techniques

(
P
ODOBNIKAR

2005)
.

DTM of
Slovenia is available in resolution of 12.5, 25 and 100 m.
Of these resolutions the higher ones have been
used. For slope angle applications the optimal resolution can be determined (
H
UTCHINSON

and
G
ALLANT

2000). It is found that no
t always the highest resolution is the optimal for this purpose (e.g.,
Z
ÁMOLYI

2006).




Figure
3

A study area of the
upper valley of the Sava
D
olinka river in the NW part of Slovenia

with
dimensions of 39 km by 15 km
.


The extent of the
study
area is
approximately 39 km by 15 km. The Sava river
drains

there
the
mountain
range of Karavanke
(
in the
north
)

and Julian Alps
(
in the
south
)
.

Due to the different lithological properties
of these two regions, the drainage pattern, the denudation history, the se
diment production,
and all related
phenomena

can be very different. As a consequence the river find
s its thalweg according
to these properties.
However, the karstification
of
the calcareous lithological units considerably modifies the drainage pattern.


2.
2
DTM derivatives


The detection
o
f

the potential areas of the talus cones bases on overlays different independent variables that
produce a potential surface of the talus cones. The variables and their connection with the talus cones are
described

as follo
ws
.


Visibility simulation


The aim of this variable was to distinguish between the areas that are at the bottom of the relative relief and
those that are exposed to the simulated sun
-
motion (
P
ODOBNIKAR

2008). We suppose that the talus cones
are located ne
ar to the bottom of the relative relief. This variable was calculated from the DTM 12.5.
Calculating the exposedness of the terrain in different scales is the aim of this group of methods. The output
may be the visualisation of the terrain surface that sho
ws areas which are differently exposed to a virtual
light source thus allowing the distinction between more or less prominent topographic features

(Fig. 4a)
.


Curvature


The talus cone has a curvature over all of area, but it is very low. A positive curvat
ure indicates that the
surface is convex and a negative curvature that the surface is concave.

The talus cones have a slightly
convex curvature. This variable was calculated from the DTM 25 as a geomorphologically prec
ise and very
reliable data set (Fig. 4
b).


Slope


Talus cones have particular ranges of inclination angles along the gradients. This variable was calculated as
well as from the DTM 25

(Fig. 4c)
.

The importance of the local slope histogram is very high in the
geomorphometric studies, since it i
s typically invariant in case of tectonic movements, uplift, subsidence,
etc. (
S
ZÉKELY

et al
.

2002). The slope histogram can be considered as “topographic memory” for longer
periods of time.


Variables based on a local window


Additional variables that bas
e on a local window calculation were c
ompute
d. The local window has a shape
of an annular wedge. The wedge was constructed on such way that imitated the shape of the typical talus
cones with certain parameters: radius, wedge, and azimuths in four sectors:

-

east:


45º to 135º

-

south:

135º to 225º

-

west:

225º to 315º

-

north:

315º to 360º and 0º to 45º

Different operations on the local windows were calculated. If the values of the local windows fit well to the
talus cones, then low range and standard dev
iation between the windows’ values is expected. Of course, the
standard deviation should not be too low, that could mean completely flat areas

(Fig. 4d)
.


Aspect


The talus cones are
directed
toward
s a

particular direction
, unless

their horizontal radius i
s low. With
applying four sectors


azimuths toward east, south, west and north direction, the particular talus cones are
more precise
ly

classified considering of the all listed variables

(Fig. 4e)
.


2.3 Modelling


All of the described variables are classi
fied by tuning with some of selected talus cones in the study area.
The main criteria for typical talus zones are: talus zones are relatively in the bottom of the local relief, the
curvature is low and convex, the slope is regular, near to constant (not to
o low or too high), the azimuth is
mainly constant and the wedge is typical, too (less than 180º).

All of the listed variables were classified to particular range according the typical talus zone description.
With classifying were produced binary raster se
ts. They were then overlaid by logical operations “
AND
” of
Boolean algebra. The areas that survived operations with all of variables considered as talus cones

(Fig. 4f)
.
As the talus cones areas were produced for four sectors, the final results were mosaic
ed to a final potential
of talus cones

(Fig
5
)
.





a




b





c




d




e





f

Figure
4

Variables of
v
isibility simulation (a), curvature (b), slope (c), variable based on a local window
for the south sect
or
(d), aspect
for the south sector
(e) and
potential talus cones for the south sector

(f)
.
More blue
(than red)
areas mean lower relative relief

(as presented with variable a)
.

Presented is the area
around Mojstrana with dimensions of 7.9

km by 5
.
8

km.


3
. RESULTS


The preliminary results of calculations are promising. Map algebra operations of sectorial parameters
yielded the best results. Both in the wider valley of Sava
D
olinka river, and also in the tributary valleys
there are contiguous patches of cla
ssed that can be considered as talus slopes (Fig
5
). The vast majority of
the talus area

has been categorized by the method to the correct category. The misclassifications are

relatively rare, and they appear

rather
as
single points, than in extensive patc
hes. It seems that later they can
be removed applying a simple filter.




Figure
5

A

final potential of talus cones mosaiced for four sectors
. Yellow is talus cones for the south
sector, blue for north, green for east and pink for west.

Presented is th
e area around Mojstrana with
dimensions of 7.9

km by 5
.
8

km.


4. DISCUSSION


Although we present here the results of a relative small test area, the method works also in larger extents as
well.
On the other hand the processing of larger area needs more pre
paration time of the operator, since
misclassifications may occur. Especially if the various lithological classes are present in the area to be
processed, the results cannot remain always stable, because the modelling may turn to be selective in the
area o
f one lithology, and in the area of another rock type the classification selects to many or too few
points. This behaviour should be eliminated in operational environment.



5. CONCLUSIONS


S
ectorial summation and statistical analysis of slope angle histog
rams seem to be feasible techniques to
enhance even smaller talus cones. The outlined extent of the cone then can be analyzed for slope angles and
can be compared to angle of repose of the (stable) talus cones made of the same material in the vicinity.

Thi
s technique is found to be extendable partly to scree slopes with some restrictions.
However, in some
cases yet manual control is needed on the parameter set to achieve the optimal detection. Furthermore,
some sorts of ranking filtering seem to be also nee
d later as a final step of the processing.

The automated extraction of such features will also be used for visualization of geomorphic elements of the
landscape. Combination of numerical extraction with sophisticated visualization techniques is not only
im
portant in the geomorphic evaluation, but also in the communication of natural hazards to increase public
awareness.


ACKNOWLEDGEMENTS


In this study a portion of the Digital Terrain Model of Slovenia (© 2005, Surveying and Mapping Authority
of Republic of

Slovenia)
have
been used.

The
techniques for the detection of features have been carried out
in collaboration with the Mars related research project TMIS.plus.II (as part of “HRSC on Mars Express”)
which is funded by the Austrian Research Promotion Agency

in the frame of the ASAP program

and the
Scientific Research Centre of the Slovenian Academy of Sciences and Arts.


REFERENCES


A
NSELIN
, L. (2005):

Mapping and geovisualization (ACE 592SA


Spatial Analysis). Uni
versity of Illinois, Urbana

C
ampaign.

C
HANG
, Y.C.,

S
INHA
, G. (2007): A visual basic program for ridge axis picking on DEM data using the profile
-
recognition and polygon
-
breaking algorithm.
Computers and

Geosciences

33

(2), 229
-
237.

C
HOU
, Y.
-
H. (1997):

Exploring Spatial Analysis in Geographic Inform
ation Systems. OnWord Press, Santa Fe.

ESRI (1997):

Understanding GIS, The ARC/INFO method. John Wiley & Sons, Redlands.

H
UTCHINSON
, M. F.,
G
ALLANT
, J. C. (2000): Digital Elevation Models and representation of terrain shape.
In
:
W
ILSON

J.
P.,
G
ALLANT

J. C.

(eds.): Terrain Analysis: Principles and application. John Wiley & Sons, USA, pp. 29

50.

K
ANE
VSKI
, M.
,

M
AIGNAN
, M. (2004):

Analysis and modelling of spatial environmental data. EPFL Press, Lausanne.

P
IKE
, R. J. (2000): Geomorphometry


diversity in quanti
tative surface analysis.
Progress in Physical Geography
,
24
(1), 1

20.

P
ODOBNIKAR
, T.
(
2005
):
Production of integrated digital terrain model from multiple datasets of different quality.
International Journal of Geographical Information Science

19

(1): 69
-
89
.

P
ODOBNIKAR
, T. (2008):

Enhancing terrain features for improved cartographic visualization. International conference
on cartography and GIS, Borovets, Bulgaria
.

R
ONCAT
,

A.,

W
AGNER
,

W.,

M
ELZER
,

T
H
.,

U
LLRICH
,

A.

(2007): Echo detection and localization in fu
ll
-
waveform
airborne laser scanner data using the average difference function estimator. Photogr. J. Finland (in press).

S
ZÉKELY
, B.,
K
ARÁTSON
, D.

(
2004
):

DEM
-
based morphometry as a tool for reconstructing primary volcanic landforms:
examples from the Börz
söny Mountains, Hungary.
Geomorphology

63
, 25

37.

S
ZÉKELY
,

B.,

R
EINECKER
,

J.,

D
UNKL
,

I.,

F
RISCH
,

W.,

K
UHLEMANN
,

J.

(2002): Neotectonic movements and their

geomorphic response as reflected in surface parameters and stress patterns in the Eastern Alps.
EGU

S
tephan
Mueller Special Publication Series
,
3
:149
-
166.

T
IMÁR
,

G.,

S
ÜMEGI
,

P.,

H
ORVÁTH
,

F.

(2005): Late Quaternary dynamics of the Tisza River: Evidence of climatic and
tectonic controls.
Tectonophysics
,
410
: 97
-
110.

T
OMLIN
, C.D. (1990)
:

Geographic informati
on systems and cartographic modeling. Prentice Hall, Englewood Cliffs,
New Jersey.

T
OMLINSON
, R.F. (2003)
:

Thinking about GIS: Geographic Information System planning for managers, ESRI Press,
Redlands.

W
AG
NER
,

W.,

U
LLRICH
,

A,

D
UČIĆ
,

V,

M
ELZER
,

T
H
.,

S
TUDNICKA
,

N. (2006): Gaussian decomposition and calibration of a
novel small
-
footprint full
-
waveform digitizing airborne laser scanner.
ISPRS J. Photogr. Rem. Sens.

60
:100
-
112.

Z
ÁMOLYI
, A. (2006): Nagy és kis re
liefenergiájú digitális domborzati modellek esettanulmánya (
Case studies of digital
elevation models with high and low relief energy;
in Hungarian with English abstract).
Geodézia és
Kartográfia

58
(11):24
-
30.

Z
ÁMOLYI
,

A.,

S
ZÉKELY
,

B.,

T
IMÁR
,

G.,

D
RAGANITS
,

E.

(2007): Quantitative river channel analysis based on

g
eoreferenced historical maps


documenting vertical movements in the Little Hungarian Plain.
Geophysical
Research Abstracts

9
: 06624.




BIOGRAPHY OF
THE
AUTHORS


Balázs
S
ZÉKELY
, Dr. rer. nat. (PhD)

Balázs has received his MSc in Geophysics and MSc in Astronomy at Eötvös University, Budapest,
Hungary (1989). He joined to the Space Research Group at the Department of Geophysics at Eötvös. He
studied solar
-
terrestrial relationships, worked in projects
of crop yield estimation based on satellite
imagery; later he started to analyse of digital elevation data. In 1998 he joined to a project at the Institute of
Geology and Palaeontology of the University of Tübingen (Germany), where he carried out morphomet
ric
analysis of the Eastern Alps. After having received his PhD in Geology in Tübingen (2001), he continued
to apply Surface Processes Modelling. In 2004 he returned to the Space Research Group at Eötvös to study
the neotectonics of the Pannonian basin. Re
cently he is focussing on tectonic geomorphology and interested
in archaeometry. He currently analyses LiDAR DTMs at the Institute of Photogrammetry and Remote
Sensing, Vienna University of Technology (Austria).



Tomaž
P
ODOBNIKAR
, PhD

Tomaž holds a BSc in geographical information systems and its applications in environment and
archaeology, MSc in Monte Carlo methods and its applications in geographical information systems, PhD
in digital terrain modelling from vari
ous data sources of different quality, all the University of Ljubljana.
He has authored several papers on GIS, DTM, and its applications in quality assessment, archaeology and
environment and numerous applications like production a DTM of Slovenia that is
currently widely
available from the government. He was research fellow at the Technical University Delft (The
Netherlands), Vienna University of Technology (Austria) and University of Franche
-
Comté (France).

He is
currently research
on geomorphological ana
lysis
at Institute of Photogrammetry and Remote Sensing,
Vienna University of Technology. He is involved to Scientific Research Centre of the Slovenian Academy
of Sciences and Arts and to University of Nova Gorica as well.