Monitoring subsidence effects in the urban area of Zonguldak ...

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.

Nat. Hazards Earth Syst. Sci., 10, 1807–1814, 2010

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)

www.nat-hazards-earth-syst-sci.net/10/1807/2010/

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

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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 www.nat-hazards-earth-syst-sci.net/10/1807/2010/


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 Nat. Hazards Earth Syst. Sci., 10, 1807–1814, 2010

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 www.nat-hazards-earth-syst-sci.net/10/1807/2010/

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. www.nat-hazards-earth-syst-sci.net/10/1807/2010/


Roads Mine

1812tunnel (-200m)

H. Akcin et al.: Monitoring subsidence effects in Zonguldak, Turkey, by InSAR-GIS

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 www.nat-hazards-earth-syst-sci.net/10/1807/2010/


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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