Monitoring subsidence effects in the urban area of Zonguldak

Transcription

Nat. Hazards Earth Syst. Sci., 10, 1807–1814, 2010
www.nat-hazards-earth-syst-sci.net/10/1807/2010/
doi:10.5194/nhess-10-1807-2010
© Author(s) 2010. CC Attribution 3.0 License.
Natural Hazards
and Earth
System Sciences
Monitoring subsidence effects in the urban area of Zonguldak
Hardcoal Basin of Turkey by InSAR-GIS integration
H. Akcin1 , H. S. Kutoglu1 , H. Kemaldere1 , T. Deguchi2 , and E. Koksal1
1 Zonguldak
2 Nitetsu
Karaelmas University, Department of Geodesy and Photogrammetry Engineering, 67100, Zonguldak, Turkey
Mining Consultants Co., Ltd., Tokyo, Japan
Received: 23 November 2009 – Revised: 1 July 2010 – Accepted: 29 July 2010 – Published: 1 September 2010
Abstract. Zonguldak Hardcoal Basin is the largest bituminous coal region in Turkey where extensive underground
mining activity exists. Because of this activity subsidence effects have been experienced in different locations of the city.
In this study, surface deformations caused by the subsidence
have been observed by D-InSAR technique using C-Band
RADARSAT data. InSAR data process of 16 RADARSAT
images acquired between 24 July 2005–23 October 2006 has
resulted in significant deformations in the order of about 6 cm
in the most populated region of the city. The deformation
map obtained has been integrated with digitized mine production maps and Quickbird Orthoimage into GIS. According to GIS analysis, there are three mine seams at different
levels driven below the deformed zone. Many governmental
and private buildings located in this area have a high potential risk of subsidence damage. Also, this area covers approximately 12 km of transportation routes.
1
Introduction
Zonguldak is the capital city of the Zonguldak province, located at the Western Black Sea coast of Turkey, 360 km east
of Istanbul and 270 km north of Ankara (Fig. 1). The topography around the city is very undulating and steep; 56% of
the city’s land is surrounded by mountains, and only 29%
with the slope with less than 20% suitable for settlement
and agriculture. Also, very heavy forests cover the land surface in the immediate vicinity of the city centre (Zonguldak
Province Environment and Forestry Directorate, 2006).
Forestry and fishing are among the important sources of
income in the city with a population of 200 thousand while
the major industry is the hard coal mining, so that ZongulCorrespondence to: H. S. Kutoglu
([email protected])
dak is the most famous mining region of Turkey. Underground coal mine extraction in the basin was initiated in 1848
when the Ottoman Empire, before Republic of Turkey, ruled
the land. Now, Turkish Hardcoal Enterprises (TTK) is the
company authorized for mine production in the basin. According to the official records of TTK, the hard coal production is about 2.5 million tons per year, and has totally
reached 400 million tons since 1848. There are widespread
coal seams located between the levels of +155 m and −550 m
under the city (Turkish Hardcoal Enterprise, 2008).
Due to the above-mentioned mining activities, major subsidence events occurred at some locations of the city in the
past (Fig. 2). Some locations are still suffering from subsidence phenomena, and some locations are under the risk of
subsidence damage. For the safety of human life and property, monitoring the temporal evolution of the subsidence
effects for the city of Zonguldak is very crucial. Also, such
a monitoring program can facilitate defining the locations at
risk, early warning before major occurrences, and proper urban planning for the future.
Regarding the deformation monitoring, GPS is the most
powerful geodetic technique producing the most precise, reliable and exact results to detect pointwise surface deformations. However, to keep wide grounds under control, Differential InSAR is today’s most useful geodetic technique. GPS
may need thousands of site points to monitor an area of interest which can be controlled only through a pair of InSAR
images.
Therefore, D-InSAR was decided as the main technique
for this study, since subsidence coming out in the basin is
formerly not known. GPS was preferred for validation of
the results obtained from InSAR. The surface deformation
maps obtained from D-InSAR analysis were integrated with
QuickBird Ortho-image into Geographical Information System (GIS). Finally, the deformation areas in different levels
were determined, and buildings, roads etc. located in those
areas were documented by a GIS analysis.
Published by Copernicus Publications on behalf of the European Geosciences Union.
locations
of the city around
in the past (Fig. 2). Some locations are still suffering from subsidence
360 km east of Istanbul and 270 km north of Ankara (Fig. 1). The
topography
phenomena, and some locations are under the risk of subsidence damage. For the safety of
very undulating and steep; 56% of the city lands are surrounded
by
mountains,
and the temporal evolution of the subsidence effects for the city
human life and property, monitoring
of
Zonguldak
is
very
crucial.
with the slope of less than %20 is suitable for settlement and agriculture. Also, veryAlso, such a monitoring program can facilitate definition of the
locations under the risk, early warning before major occurrences, and proper urban planning for
H. Akcin
et city
al.: Monitoring
s cover the1808
land surface in the immediate vicinity
of the
center
[1].subsidence effects in Zonguldak, Turkey, by InSAR-GIS
the future.
Figure 2. Subsidence scenes from different places of the city
Fig. 2. Subsidence scenes from different places of the city.
As regards deformation monitoring, GPS is the most powerful geodetic technique producing the
most precise, reliable and exact results to detect pointwise surface deformations. However, to
keep wide grounds under control Differential InSAR is today’s most useful geodetic technique.
GPS2.1
may need
thousands
Data
choiceof site points to monitor an area of interest which can be controlled
through only a pair of InSAR images.
Surface
deformations
heavily
forested
cansince
be where
de- subsidence
Therefore,
D-InSAR
was decided in
as the
main technique
for areas
this study
Figure 1. Location
Zonguldak
city and TTK
GoogleEarth)
Fig. 1. ofLocation
of Zonguldak
cityhard
and coal
TTKbasin
hard (source:
coal basin
comes
out in best
the basin
is formerly
not known.
waswhich
preferred
for penetrate
validation of the results
tected
through
L-band
SARGPS
data,
can
(source: GoogleEarth).
2
Detecting surface deformations using
InSAR technique
InSAR technique has found a wide use since the late 1990s
for monitoring ground deformations caused by landslide,
tectonic motions, natural and mining-induced subsidence
events, and etc. InSAR applications for landslides can
be exemplified by Colesanti et al. (2003), Colesanti and
Wasowski (2006), Hilley et al. (2004), Kimura and Yamaguchi (2000). This technique has been mostly applied
for monitoring tectonic motions (Kimura and Yamaguchi,
2000; Bürgmann et al., 2006; Delouis et al., 2002; Johanson and Bürgmann, 2005; Wright et al., 2004; Bürgmann
et al., 2002). It has also been used successfully for surface effects of subsidence induced by natural causes or man
made activities (Poland et al., 2006; Amelung et al., 1999;
Tomas et al., 2005; Dixon et al., 2006; Fernandez et al.,
2009). However, InSAR technique, for deformation monitoring, depends a lot on local conditions such as topography,
vegetation, atmosphere as well as resolution of data. Therefore, each study can be regarded unique. For instance, in
Turkey, many successful studies were carried out for monitoring tectonic movements, however, no successful study to
detect mining-induced deformations has been carried out yet.
obtained from InSAR. The surface deformation maps obtained from D-InSAR analysis were
vegetation (Gens, 2006; European Space Agency, 2009). For
that reason, L-band ALOS Palsar data was considered first
to detect the surface deformations in the Zonguldak basin.
However, sufficient data pairs having a baseline less than
500 m could not be found. Therefore, it was decided to utilize the data from C-band RADARSAT, because orbit stability is quite good. Figure 4 shows the acquisition dates of
16 RADARSAT images used for the study.
The image dated 24 July 2005 was chosen as the master image and others were considered slave images (Fig. 3).
Therefore, 15 InSAR data pairs were composed to detect surface deformations in relation to the master image. Each pair
has the baseline less than 500 m.
2.2
InSAR processing for deformation monitoring
InSAR uses phase information of SAR data. The distance
change between a sensor and the ground can be measured
from phase difference of two observations using phase property in slant-range length:
φ = φorbit + φtopo + φatm + φdef + φnoise
(1)
where φorbit is the orbit fringe caused by baseline distance
obtained by two observations while φtopo is the topographic
fringe with respect to terrain. These are described by the
following equations
φorbit =
4π Bpara
λ
(2)
φtopo =
4π hBperp
.
λρ sinα
(3)
H. Akcin et al.:
Surface deformations in heavily forested areas can be detected best through L-band SAR data,
which can penetrate vegetation [17, 18]. For that reason, L-band ALOS Palsar data was
considered first to detect the surface deformations in the Zonguldak basin. However, sufficient
data pairs having baseline less than 500 m could not be found. Therefore, it was decided to
utilizesubsidence
the data fromeffects
C-band in
RADARSAT,
of which
orbitby
stability
is quite good. Figure 4 shows
Monitoring
Zonguldak,
Turkey,
InSAR-GIS
the acquisition dates of 16 RADARSAT images used for the study.
RADARSAT
1809
16 images
05/8/17
06/4/14 populated
06/6/1
06/7/19 06/9/5
education campus located
in 05/10/4
the 05/11/21
most densely
area06/10/23
of the city. Also, the university
05/7/24 05/9/10 05/10/28
06/3/21
06/5/8 06/6/25 06/8/12 06/9/29
campus is located in the blue zone. The maximum rate of the deformation is obtained as 5.6 cm
No observation (snow season)
per 15 months. A detailed temporal development
of the deformation, detected from the each pair,
Figure
3.
Radarsat
data
used for the study
Fig. 3.in
Radarsat
usedthe
for the
study.
is given
Fig. data
6 for
foregoing
area.
The image dated 2005/7/24 was chosen as the master image and others were considered slave
images (Fig 3). Therefore, 15 InSAR data pairs were composed to detect surface deformations in
relation to the master image. Each pair has the baseline less than 500m.
InSAR processing for deformation monitoring
InSAR uses phase information of SAR data. The change of distance between a sensor and the
ground can be measured from phase difference of two observations using phase property in
slant-range length:
φ = φorbit + φtopo + φ atm + φ def + φ noise
(1)
Master image
where φ orbit is the orbit fringe caused by baseline distance obtained by two observations while
φtopo is the topographic fringe with respect to terrain. These are described by the following
equations
Slave image
4. used
D-InSAR
processing
Fig. 4. D-InSAR processingFigure
procedure
in this study
(Deguchi etprocedure
al. 2006).
In these equations, Bpara and Bperp represent parallel and perpendicular components of baseline, respectively. Also h, λ,
ρ and α represent elevation, wavelength, slant-range length
and incidence angle, respectively. Finally, φatm is a phase
delay caused by the reflection of microwaves in the water
vapor layer, φnoise is the error component caused by thermal noise, or temporal and spatial decorrelation associated
with baseline distance or scattering characteristic change,
and φdef represents the amount of surface deformation during the period between two observations (see Massonnet and
Feigl, 1998; Franceschetti and Lanari, 1999; Hanssen, 2001).
Radar Interferometry - Data Interpretation and Error Analysis, Springer Verlag, New York. The whole process in which
the deformation interferograms are obtained is explained by
a scheme in Deguchi et al. (2006), illustrated in Fig. 4.
Black Sea
used in this study [22].
In order to measure the time series of deformation, we applied the smoothness-constrained least-squares method to the
pixels satisfying (a) and (b) below:
Kilimli
(a) capable of phase unwrapping,
(b) coherence values greater than 0.1.
The amount of deformation since 24 July 2005 was solved
as an unknown parameter under the condition that U in the
Eq. (4) was minimized.
X (k) (k) 2
φi,j − ti,j
X
(k) 2
+ α2
first or second difference ofti,j
U=
(4)
(k)
where φi,j is the amount of deformation of k-th periodic obCity centre
servation since 24 July 2005. The pixel location is indicated
1810
H. Akcin et al.: Monitoring subsidence effects in Zonguldak, Turkey, by InSAR-GIS
Figure 4. D-InSAR processing procedure used in this study [22].
Kilimli
Black Sea
City centre
Kozlu
5.6 cm
Figure 5. Final deformation map around Zonguldak Urban Area for 15 months
Fig. 5. Final deformation map around Zonguldak Urban Area for
15 months.
using i and j . The amount of deformation of k-th periFigure 6. Temporal deformation developments from the D-InSAR pairs
odic observation estimated by smoothness-constrained conFig. 6. Temporal deformation developments from the D-InSAR
(k)
2
dition is described in ti,j . Finally, α is a parameter to 3.
conpairs.
GPS Validation
(k)
trol the smoothness of ti,j and the optimum value isD-InSAR
decan reveal areal deformations over resolution limits of data depending on the factors
cided by minimizing Akaike’s Bayesian Information Critestated above. InSAR technique can catch an existing deformation, but can not verify non-existent
15 months.
A detailed
deformation.per
Moreover,
deformation
rates cantemporal
be slightlydevelopment
different point of
to the
point in equirion (ABIC) given below.
deformation
area
detected
by
InSAR.
Last,
but
not
least
deformations
from
InSAR
are in
deformation,
detected
from
the
each
pair,
is
given
in
Fig.
6
offor
slant
Therefore,
the InSAR results must be validated through a more exact
therange.
foregoing
area.
ABIC = nlog2π + nlogσ 2 + n + 2nlogdet I + α 2 DT D direction
method such as GPS.
− nlogα 2
In(5)
this study, a geodetic network with fifteen site points was established in the study area
(Fig.10). This
was observed 4 times through GPS technique, which is the most effective
3 network
GPS validation
2
where n is the number of unknown parameters and σ issurveying
demethod for monitoring pointwise deformations. Fig. 7 shows the time schedule for the
GPS observations.
fined by U/n. Also, I is the identity matrix and D is the matrix
(k)
of first or second difference of ti,j (Goldstein and Werner,
1998; Akaike and Kitagawa, 1999).
2.3
Results
Applying the above-mentioned procedure for the
RADARSAT data pairs have resulted in the deformation map given by Fig. 5. Since RADARSAT is a C-band
microwave sensor, it is difficult to provide high coherence
between master and slave images in densely, vegetated
areas. Consequently, fringe with spatial continuity was
observed only in the urban district. As seen from the figure,
there are significant surface deformations near Kozlu district
(Fig. 5). The core (maximum) area of the deformation in
red, coincides with the secondary and high school education
campus located in the most densely populated area of the
city. Also, the university campus is located in the blue zone.
The maximum rate of the deformation is obtained as 5.6 cm
D-InSAR can reveal areal deformations over resolution limits of data depending on the factors stated above. InSAR
technique can catch an existing deformation, but can not verify non-existent deformation. Moreover, deformation rates
can be slightly different point to point in equi-deformation
area detected by InSAR. Last, but not least deformations
from InSAR are in the direction of slant range. Therefore,
the InSAR results must be validated through a more exact
method such as GPS.
In this study, a geodetic network with fifteen site points
was established in the study area (Fig. 10). This network
was observed 4 times through GPS technique, which is the
most effective surveying method for monitoring pointwise
deformations. Figure 7 shows the time schedule for the GPS
observations.
The GPS observations were conducted in static mode
with one hour sessions. For datum definition, the network
points were connected to the four points of Turkish National
Fundamental GPS Network (TUTGA). The observations in
2005/10
2006/3
2006/6
2006/10
Figure 7. Time schedule for GPS campaigns
As seen from Fig. 10, there are mining activities at three different mining levels (-425, -485 a
meters) under
theby
deformed
zone. This might be the main reason of 1811
the subsidence ef
H. Akcinwere
et al.:conducted
Monitoring
subsidence
in560
Zonguldak,
Turkey,
InSAR-GIS
e GPS observations
in static
mode effects
with one
hour sessions.
datum
monitored
on the For
ground.
inition, the network points were connected to the four points of Turkish National
ndamental GPSGPS
Network
(TUTGA). The observations in all the periods were processed into a
campaign 4 times
ch adjustment with respect to the principle of free network adjustment to obtain the temporal
As seen from Fig. 10, there are mining activities at three different mining levels (-425, -485 and 2005/10
2006/6
2006/10
ordinates of the site points.
In the first 2006/3
process, two
TUTGA points
were found
unstable.
Then,
560 meters)
under
the deformed zone. This might be the main reason of the subsidence effects
Figure 7.
Time schedule
for GPS campaigns
se points were removed from the
datum
definition,
and the final results
wereon obtained
monitored
the ground.
arding the other
two
TUTGA
points.
The GPS
observations
were conducted
static mode with one hour sessions. For datum
Fig.
7. Time
schedule
for GPSin campaigns.
-6
06/10
06/10
06/6
-8
-10
06/6
-10
-4
06/3
-8
0
-2
06/3
-6
2
05/10
-4
Deformation rate in cm
-2
05/10
Deformation rate in cm
definition, the network points were connected to the four points of Turkish National
Fundamental GPS Network (TUTGA). The observations in all the periods were processed into a
batch adjustment with respect to the principle of free network adjustment to obtain the temporal
2 site points. In the first process, two TUTGA points were found unstable. Then,
coordinates of the
these points were removed from the datum definition, and the final results were obtained
0 two TUTGA points.
regarding the other
Period
Period
113
113
114
114
115
115
Figure 8. Temporal deformations at GPS site points in the significant deformation area. The point 112 is not listed
here because it was destroyed during a building construction after the second GPS observation period.
gure 8. Temporal deformations at GPS site points in the significant deformation area. The point 112 is not listed
Fig.
8 8.
shows
the surface
deformations
at the
pointssite
located
in
thesecond
significant
deformation
here becauseFig.
it was
destroyed
during
a building
construction
after
the
observation period.
Temporal
deformations
atsiteGPS
points
in
the GPS
significant
area determined through InSAR technique. The GPS results validate the existence of a vertical
Fig.Figure
9. Different
deformation
levels
InSARdata
data.
9. Different
deformation
levelsvectorized
vectorizedfrom
from InSAR
deformation
The
112 are
is -8.2
notcm/listed
here-7.3because
it was
deformation in thisarea.
area. The
total point
deformations
13 months,
cm/ 13 months
and
Figure 9. Different deformation levels vectorized from InSAR data
-4.4surface
cm/13 months
at the points 113,
andsite
115, respectively.
In this region,
the deformation
g. 8 shows the
deformations
at114
the
pointsafter
located
in the
significant
deformation
destroyed
during
a building
construction
the second
GPS
obrate was obtained 5.6 cm/15 months by InSAR. As stated above, the deformations obtained from
a determinedservation
through
InSAR
technique.
The
GPS For
results
validate
themethods,
existence
of a vertical
InSAR
are areal
while those
from GPS are
pointwise.
a comparison
of both
an
period.
average
deformation
rate is deformations
obtained 6.6 cm/13 are
months
for the
three
points. If -7.3
two months
ormation in this
area.
The total
-8.2
cm/
13GPS
months,
cm/ 13 months and
time difference is ignored coherence of the average GPS rate with InSAR result is %85. In this
4 cm/13 months
atthethe
points
113,
114 and
respectively.
respect,
results
from both
techniques
can be115,
regarded
as comparable. In this region, the deformation
e was obtained
5.6integration
cm/15 months
by InSAR. As stated above, the deformations obtained from
4.
GIS
and
analysis
all the periods were processed into a batch adjustment with
SAR are areal while those from GPS are pointwise. For a comparison of both methods, an
respect
principle
free
to area
obtain
After InSARtoprocessing,
the underground
minenetwork
production maps
for the related
were
erage deformation
isthe
obtained
6.6toof
cm/13
foradjustment
the
three
GPS
points.
digitizedrate
and incorporated
to GIS
establish months
the relationship
between
the obtained
surface If two months
temporal
of
the
site
points.
Inwas
the
first result
prodeformations
and
thecoordinates
mining activity.
the final
deformation
integrated
to the is %85. In this
me difference the
is ignored
coherence
of theAlso,
average
GPS
rate map
with
InSAR
system, and
equivalent
deformation
zoneswere
1 cm
intervalsunstable.
been vectorized
for these
defining
cess,
two
TUTGA
points
found
Then,
pect, the results
from
both
can9).with
beThen,
regarded
ashave
comparable.
different
levels
of techniques
deformation (Fig.
they were
both
displayed on QuickBird
Orthoimagewere
for the region
(Fig. 10).from the datum definition, and the fipoints
removed
GIS integration
analysis
nal and
results
were obtained regarding the other two TUTGA
points.
ter InSAR processing, the underground mine production maps for the related area were
Figure 8toshows
surface
at the the
siteobtained surface
itized and incorporated
GIS to the
establish
the deformations
relationship between
points
located activity.
in the significant
area determined
ormations and
the mining
Also, the deformation
final deformation
map was integrated to the
throughdeformation
InSAR technique.
The
GPS
resultshave
validate
ex- for defining
tem, and equivalent
zones with
1 cm
intervals
been the
vectorized
ferent levelsistence
of deformation
(Fig.
9). Then, in
they
of a vertical
deformation
thiswere
area.both
The displayed
total de- on QuickBird
thoimage for formations
the region (Fig.
are 10).
−8.2 cm/13 months, −7.3 cm/13 months and
−4.4 cm/13 months at the points 113, 114, and 115, respectively. In this region, the deformation rate was obtained
Fig. 10.
InSAR
deformation
map, digitized
mine production
map in GIS
Figure 10. InSAR
deformation
map,
digitized mine production
map and Quickbird
Orthoimage combined
and Quickbird Orthoimage combined in GIS.
5.6 cm/15 months by InSAR. As stated above, the deformations obtained from InSAR are areal while those from GPS
are pointwise. For a comparison of both methods, an average
Figure 10. InSAR deformation map, digitized mine production map and Quickbird Orthoimage combined in G
deformation zones with 1 cm intervals have been vectorized
deformation rate is obtained 6.6 cm/13 months for the three
for defining different levels of deformation (Fig. 9). Then,
GPS points. If two months time difference is ignored coherthey were both displayed on QuickBird Orthoimage for the
ence of the average GPS rate with InSAR result is 85%. In
region (Fig. 10).
this respect, the results from both techniques can be regarded
As seen from Fig. 10, there are mining activities at three
as comparable.
different mining levels (−425, −485, and −560 m) under the
deformed zone. This might be the main reason for the subsi4 GIS integration and analysis
dence effects monitored on the ground.
After InSAR processing, the underground mine production
For the risk assessment, superstructure over the region
maps for the related area were digitized and incorporated to
such as buildings, roads etc. was extracted from the QuickGIS to establish the relationship between the obtained surbird image, thus, the final product was obtained for further
face deformations and the mining activity. Also, the final deGIS analysis (Fig. 11). All the GIS integration processes apformation map was integrated to the system, and equivalent
plied for the study is illustrated in Fig. 12.
Roads
Mine
1812tunnel (-200m)
tunnel (-300m)
tunnel (-425m)
tunnel (-485m)
Roads
tunnel
(-560m)
Mine
panel
tunnel (-200m)
tunnel (-300m)
Building
tunnel (-425m)
Government
tunnel (-485m)
Residence
tunnel (-560m)
panel
Building
Government
1st degree deformation zone
Residence
4th degree deformation zone
Finally, the buildings were investigated on site; the damaged ones were documented, and the
documents were relocated into the GIS (Fig. 14). In Fig 14, a high school damaged due to the
subsidence is illustrated from the designed GIS.
2nd degree deformation zone
Figure 11. Final GIS product
Figure 11. Final GIS product
Fig. 11. Final GIS product.
ZITRF96
ZITRF96
Finalproduct
product
Final
for
hazard
analysis
hazard
analysis
5th degree deformation zone
Table 1. Distribution of the structures into the deformation zones
Deformation zone
Area (m2)
Government
Private building
Road length (m)
building
st
1 degree
44 216.04
1 (High school)
26
525
2nd degree
83 805.19
4 (1 high school, 2
35
782
gyms)
3rd degree
138 937.46
2 (1 high school)
73
1 461
th
4 degree
180 140.77
6 (2 university
121
2 969
buildings, 1 primary
school, 1 hostel)
th degree deformation zone
th
3
6th degree deformation
5 degree
290 715.80
17(7 university
162zone
5 107
building
Figure 13. Structures located in the deformation zones
th
6 degree
1 094 307.47
21(12 university
623
7 239
Fig.
Structures
locatedbuildings,
in the
zones.
For
the13.
risk assessment,
superstructure
over
the region
roads etc. was extracted
1deformation
gym,such
4 as buildings,
from the (totally
Quickbird
image;
thus the final product
1 832
122.73)
hostelswas obtained for the further GIS analysis (Fig.
for
11). All the GIS integration process applied for the study is illustrated in Fig. 12.
Using the final product, statistics of the structures affected by the subsidence were studied by the
buffer zone analysis. Thus, buildings and roads located in the different degrees of the
deformation zones were determined (Fig. 13).
Extracted
buildings
Extracted
buildings
from Quick Birth
from
Quick
Birth
ortho-image
Based on the analysis, the size of the deformed area detected by InSAR is totally 1.83 km2. A
total of 51 government and 1040 private buildings (residence) are located in the detected
deformation zones. Also total length of the roads in these zones is 11.8 km. A detailed
distribution of the structures into the deformation zones can be seen in Table 1.
ortho-image
QuickBird
Orthoimage
QuickBird
Orthoimage
Z
ITRF96
ZITRF96
InSAR deformation map
InSAR deformation map
Mine production map
XITRF96
Mine production map
Figure 12. GIS integration process
Figure 14. An example of the damaged buildings documented on site (from a high school)
XITRF96
Figure 12.process.
GIS integration process
Fig. 12. GIS integration
Fig. 14. An example of the damaged buildings documented on site
(from a high school).
5. Conclusions
As stated previously, Zonguldak lands have very steep topography with heavy forests cover. In
Finally,not
theonly
buildings
were investigated
on out
site;
the dam-but also a
this difficult geography,
a heavy mining
activity is carried
underground
Using the final product, statistics of the structures affected
dense urban life
survives
the documented,
ground. In this context,
citywere
is a rare
place in the
aged
ones on
were
and theZonguldak
documents
reloby the subsidence were studied by the buffer zone analysis.
world. This is
the first
that (Fig.
the subsidence
effects
theschool
city were
successfully
cated
intostudy
the GIS
14). In Fig.
14,around
a high
damobserved
the InSAR technique and interpreted by GIS. At the same time, this is also the
Thus, buildings and roads located in the different degrees
of through
aged
to the in
subsidence
is illustrated
designed
GIS.
first successful
studydue
in Turkey
which D-InSAR
is applied by
for the
monitoring
the mining-induced
the deformation zones were determined (Fig. 13).
surface deformations. Furthermore, an extensive mining activity carried out under a densely
Based on the analysis, the size of the deformed area
de- city makes InSAR-GIS integration necessary. In this regard, the study is one of the
populated
5 Conclusions
first studies in which urban deformations monitored by InSAR results are analyzed within GIS.
tected by InSAR is totally 1.83 km2 . A total of 51 governinstance, a similar investigation was performed for the city of Naples, Italy [25].
ment and 1040 private buildings (residence) are locatedFor
in the
detected deformation zones. Also total length of the roads in
these zones is 11.8 km. A detailed distribution of the structures into the deformation zones can be seen in Table 1.
As stated previously, Zonguldak lands have very steep topography with heavy forest cover. In this difficult geography,
not only is a heavy mining activity carried out underground
but also a dense urban life survives on the ground. In this
context, Zonguldak city is a rare place in the world. This is
1813
Table 1. Distribution of the structures into the deformation zones.
Deformation zone
Area
(m2 )
Government building
Private building
Road length
(m)
1st degree
44 216.04
1 (high school)
26
525
2nd degree
83 805.19
4 (1 high school, 2 gyms)
35
782
3rd degree
138 937.46
2 (1 high school)
73
1461
4th degree
180 140.77
6 (2 university buildings, 1 primary school, 1 hostel)
121
2969
5th degree
290 715.80
17 (7 university building)
162
5107
6th degree
1 094 307.47
(totally 1 832 122.73)
21(12 university buildings, 1 gym, 4 hostels)
623
7239
the first study that the subsidence effects around the city were
successfully observed through the InSAR technique and interpreted by GIS. At the same time, this is also the first successful study in Turkey in which D-InSAR is applied for
monitoring the mining-induced surface deformations. Furthermore, an extensive mining activity carried out under a
densely populated city makes InSAR-GIS integration necessary. In this regard, the study is one of the first studies in
which urban deformations monitored by InSAR results are
analysed within GIS. For instance, a similar investigation
was performed for the city of Naples, Italy (Lanari, et al.,
2004).
In this study, processing of the 15 RADARSAT data pairs
resulted in subsidence of about 6 cm/15 months between
24 July 2005–5 September 2006. This is quite a rapid deformation rate. The deformation area monitored surrounds the
most populated area in the city, where many buildings and
roads are located. The mine production maps of TTK show
that there are mining activities at the three different mining levels (−425, −485, and −560 m) under the deformed
zone. These activities might be the main reason of the subsidence effects monitored on the ground. In addition, the
intense building stock in the region might be another factor
triggering the subsidence. According to the GIS analysis, it
was determined that 51 government, 1040 private buildings
and 11.8 km long roads are in the 1.8 km2 risky area. Those
statistics reveal the dimension of the problem experienced
in Zonguldak city. If the deformation rate progresses at the
same rate the structures in the zone can be exposed to destructive effects.
Acknowledgements. The authors thank the anonymous reviewers.
Their contributions greatly improved the paper. Another thank goes
to Hasan Gercek from Zonguldak Karaelmas University for editing
the language of the paper.
Edited by: G. G. R. Iovine
Reviewed by: M. Berber and four other anonymous referees
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Monitoring subsidence effects in the urban area of Zonguldak

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