Geospatial Approach of Land Use/Land Cover Studies on Swarnamukhi River Basin, Andhra Pradesh

DOI : 10.17577/IJERTV5IS100114

Download Full-Text PDF Cite this Publication

  • Open Access
  • Total Downloads : 156
  • Authors : Vani Timmapuram, Kalyan Yakkala, Gangadhar Battala, Ramakrishna Naidu Gurijala
  • Paper ID : IJERTV5IS100114
  • Volume & Issue : Volume 05, Issue 10 (October 2016)
  • DOI : http://dx.doi.org/10.17577/IJERTV5IS100114
  • Published (First Online): 17-10-2016
  • ISSN (Online) : 2278-0181
  • Publisher Name : IJERT
  • License: Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License

Text Only Version

Geospatial Approach of Land Use/Land Cover Studies on Swarnamukhi River Basin, Andhra Pradesh

Vani Timmapuram1

Research Scholar, Department of Environmental Sciences, S.V.University, Tirupati, India1

Kalyan Yakkala2,

Young Scientist, Department of Environmental Sciences, S.V.University, Tirupati, India2

Gangadhar Battala3,

Post Doctoral Fellow, Department of Environmental Sciences, S.V.University, Tirupati, India3 Ramakrishna Naidu Gurijala4

Professor Emeritus, Department of Environmental Sciences, S.V.University, Tirupati, India4

Abstract – Spatial information on land use/land cover is a necessary prerequisite in planning, utilizing and management of natural resources. In the current days context of development planning, information on land use/land cover and the changes over a period of time attain prominence because of its primary requirement in all the planning activities. Information on land use/land cover in the form of maps and statistical data is very vital for spatial planning, management and utilization of land for agricultural, forestry, pasture, urban-industrial, Environmental studies, economic production, etc. Keeping this in view, land use categories are mapped by using on-screen visual interpretation techniques in Arc GIS environment. For detection of land use classes taken the multi dated satellite data of Land-sat has been used for the study. Change map has been generated for change analysis.

Keywords- Land Use/Land Cover, Change matrix, Remote sensing and GIS, Arc GIS.

  1. INTRODUCTION

    The Land Use/Land Cover patterns of a region are an outcome of natural and socio-economic factors and their utilization by man in time and space. The terms Land Use and Land Cover are often used simultaneously to describe maps that provide information about the types of features found on the earths surface is called as land cover and the human activity that is associates with them. Land cover is an important input parameter for a number of agricultural hydrological and ecological models, which constitute necessary tools for development planning and management of natural resources in the territory. [1, 2, 3] In order to use the land optimally and to provide as input data in modelling studies, it is not only necessary to have information on existing land use/land cover but also the capability to monitor the dynamics of land use resulting out of changing demands. [4,5,6] If the site is small and easily accessible a suitable land cover may be based on ground observation and surveys. However such methods are quickly become less feasible, if the site is large or difficult to access. Toposheets may be useful for reference but are generally outdated and too coarse for detailed analysis. With the improvement in software and decrease in the cost of imagery, satellite remote sensing is being used for more and more studies particularly at the landscape level [7,8,9]

    Another important purpose of the analysis of land use change is impact assessment. The contemporary interest is not so much on land use change itself as is on its various environmental and socio-economic impacts at all spatial levels. In addition, as policies are designed to address several of the environmental and socio-economic problems in which land use change contributes in one way or another, policy impact assessment has emerged as a significant scientific activity. The recent policy interest, specifically, is on the broader issue of sustainability of development as it is impacted by land use change triggered by proposed or implemented policies. Land use changes with adverse impacts such as land degradation, desertification, population etc, contribute negatively to the achievement of long term sustainability as they reduce the natural, economic, human, and social capital available to future generation. [10, 11, 12, 13]

    The rapidly developing technology of remote sensing offers an efficient and timely approach to the mapping and collection of basic land use/land cover data over large area. The remote sensing data is potentially more amenable to digital processing because the remote sensor output can be obtained in digital format as a more expedient means to map land use and land cover [14,15].

    To understand how changes in climate and bio- chemistry affect both land use and land cover, in a region, it is important to differentiate between land cover and land use when measuring patterns of changes are dealt. Simple land use classification is not sufficient for analysis of changes but land use functions of a land cover type needs to be known to understand the changes in land cover. [16,17]

    The present research work deals with the EIA (Environmental Impact Assessment) on Swarnamukhi River basin using Spatio multi-Temporal satellite data. Spatial means geographical distribution of features on the earth surface. Temporal means using different time period satellites data for this study. Land use and Land cover and changes between them over the years have been mapped and its impact has been studied.

  2. STUDY AREA

    Swarnamukhi River is one of the major 13 rivers flowing in Chittoor District, Andhra Pradesh, India. It is originated from Seshachalam Hills, Chandragiri mandal and passing nearly 83 Km length in the District. Whereas, the total length of the river is approximately 142 Km, in which the course of the river starts through a valley between Tirupati and Chandragiri towns and meets the Srikalahasti town, thereby the river finally joins into the Bay of Bengal at Siddavaram village in Nellore District.

    River Swarnamukhi, an ephemeral river, is the east flowing river between Pennar and Cauvery Rivers. The basin is bounded by North Latitude 130 25 30 and 140 28 30 and East Longitude 790 08 39 and 800 11 00, in Chittoor and Nellore Districts in. One hydrological observation station in the Swarnamukhi River basin is located in Naidupet and an average rainfall is 1,000 mm. The basin covers 5 watersheds and each watershed covers 5000 Ha. The river basin is occupied by rocks of granite and granitic gneisses. Swarnamukhi River basin location map is showing in figure1.

    Fig 1: Location map of Swarnamukhi River basin

  3. METHODOLOGY

    Mainly the methodology follows using of geo referenced Landsat data (Landsat-4,5,7,8) 2000-01, 2005-

    06, 2011-12 and 2014-15 respectively. The spatial resolution of Landsat data is 30 meters. The above satellite data has taken for both Kharif and Rabi seasons for generate of LU/LC map. The scale of mapping is 1:50,000 for the study area. ERDAS 2015 software is used for satellite data rectification and mosaicking. ARC GIS 10.3 software is used for creating of LU/LC vector data. The detailed methodology is given in Figure2.

    Fig 2: Methodology

  4. RESULTS AND DISCUSSONS

    Land use/Land cover maps from using of four different years with two seasons of 2000-01, 2005-06, 2011-12 and 2014-15 of satellite remote sensing data has been prepared based on onscreen digitization of visual interpretation with ground truth. Preparation of past and present LULC inventories and changes using multi-periods satellite data and to studied the impact of Swarnamukhi River basin. The rainfall map is showing in figure 3. The Land Use/Land Cover map of Swarnamukhi River is for the 2000, 2005, 2011 and 2014 are showing in figure 4,5, 6 and 7. The area statistics are given in table 1.

    The dominant land use / land cover categories in 2000-01 were built-up land occupied 78 sq.km (2.35%), agricultural land occupied 1308 sq.km (39.59%), forest area 1027 sq.km (31,08%), wastelands area 554 sq.km

    (16.78%), waterbodies area 319 sq.km (9.68%) and

    wetlands area 16 sq.km (0.49%).

    The land use / land cover categories in 2005-06 were built-up land occupied 93 sq.km (2.84%), agricultural land occupied 1297 sq.km (39.25%), forest area 1026

    sq.km (31.06%), wastelands area 558 sq.km (16.88%),

    waterbodies area 320 sq.km (9.70%) and wetlands area 8

    sq.km (0.24%).

    The land use / land cover categories in 2011-12 were built-up land occupied 127 sq.km (3.85%), agricultural land occupied 1303 sq.km (39.44%), forest area 1026 sq.km (31.04%), wastelands area 517 sq.km

    (15.65%), waterbodies area 305 sq.km (9.24%) and

    wetlands area 25 sq.km (0.75%).

    The dominant land use / land cover categories in 2014-15 were built-up land occupied 142 sq.km (4.31%), agricultural land occupied 1320 sq.km (39.93%), forest area 1024 sq.km (30.99%), wastelands area 489 sq.km

    (14.81%), waterbodies area 319 sq.km (9.65%) and wetlands area 9 sq.km (0.28%). The land use/land cover maps of the study area for 2000-01, 2005-06, 2011-12 and 2014-15 respectively given below.

    Thus, this study concludes that the remote sensing and GIS have a pivotal role in assessing the various parameters of land use / land cover analysis.

    Rainfall map of Swarnamukhi River basin

    Rainfall map of Swarnamukhi River basin

    2500

    The two crop area has been decreased from 24.20% to 10.89% for 2000-01 to 2014-15. Kharif crop has been decreased from 6.91% to 7.56%, Rabi crop has been increased 2.91% to 12.11%, and agricultural plantation increased from 0.99% to 2.63%, fallow land has been increased 4.14% to 5.53%, aquaculture land increased from 0.44% to 1.21%.

    There has been increased the built up compact 0.62% to 0.93%, built up sparse increased from 0.53% to 0.99%, rural area has been increased from 1.09% to 1.57%

    2000

    1500

    1000

    500

    0

    2000

    2005

    2010

    2013

    and the development area for built up it has been increased to0.07% . Under the quarry area increased from 0.04% to 0.09% and the active mining area has not been seen in the 2000-01 it has observed on 2011-12 satellite data and it is increased 0.10% to0.12% under silica mining near the coast area of the study area. Under the development activities in the study area it has been increased from 0.08% to 0.51%.

    There has been decreased the deciduous forest from 22.14% to 21.99% and increased the scrub forest area from 7.83% to 7.91%.

    Under the waste land category salt affected land has been decreased for developmental activities from 2.62% to 2.10%. Scrub land has been decreased from 12.30% to 11.21%. The sandy area has been decreased from 1.22% to 0.86%.

    There has been decreased the tank areas from 7.38% to 7.22%, canal area has been increased from 0.13% to 0.19%. Under the wetland areas it is decreased from 0.49% to 0.28%.

    The study successfully shows that the change detection technique can be applied for land use / land cover using remote sensing data. Based on the land use / land cover analysis, along with the change detection matrix analysis of the study area, it is found that the land use /land cover change trend varied during the period of 15 years of study.

    This research has brought out that the land use which spatially reflects the Swarnamukhi River basin can be done on a rational and scientific basis by evolving land use.

    Remote Sensing is not an end in itself for any resources study. It is only an effective tool for acquiring authentic comprehensive timely data, especially for land use study. But field checks are most essential for confirming the result derived from Remote Sensing data products.

    Fig 3 : Map showing the rainfall distribution in Swarnamukhi River basin

    Last 15 years (2000-2015) significant changes in various LULC are observed in the studied in Swarnamukhi River basin and this information would provide useful inputs to LULC planners for effective management of the basin. Remarkable increase in built up area, industrial area from fallow land, salt affected land and scrub land in the basin. Aquaculture area ha s been increased from agricultural crop land besides the river channel at the coast of the basin.

    The major causes of negative LULC changes are deforestation activities, due to built up land has been identified, soil erosion in sloping mountainous areas, rapid urbanization, unplanned infrastructure development such as road and building etc and population pressure.

    The other important limitation of this study was that the social and economic drivers of land use land cover change like population pressure and economic status etc. were not considered in the LULC change and impact assessment. Figure 8 is showing the land use change between 2000-01 and 2014-15. Table 2 is showing the error matrix or change matrix for 2000-01 to 2014-15. Areas are showing in hectares.

  5. CONCLUSIONS:

Land is the most vulnerable natural resource for production of food, fibre, fuel and many other essential goods to meet human and animal needs. However, it is facing serious threats of deterioration due to simmering human pressure and utilization incompatible with its capacity. The success of any impact assessment study depends mainly on two factors. One is the assessment of the environmental condition and the second one is estimation of impact from proposed project on the environment. Both are key factors to arrive at the post project scenario.

Remote Sensing study indicates the various land Use/Land Cover patterns. In this paper the methodology followed by detailed results derived from medium resolution satellite data. This analysis shows that the LULC in Swarnamukhi River basin has undergone medium changes between 2000 and 2015.

Urban area in the present research area has been increased from 77 sq.kms to 94 sq kms. During the 15 years period from 2000-2015 was observed. This increase is mainly due to increase in population especially urban population in the research area, it is observed that urban area increased from Tirupati town to Tiruchanur village at a 4 Kilometres distance. Tirupati Town is a change in the central place activity from pilgrim cum educational cum industrial sector.

Cultivation and aquaculture are the major activities in this region. Between 2000 and 2015 of the 22 major LULC classes, considerable changes has been observed in agriculture (39.5% to 40%) and built-up areas (2.32% to 4.10%), area under forest (31.09% to 30%) marginally changed. Other LULC classes including barren land (0.43% to 0.41%), sandy area (1.22% to 0.86%) and scrub land (12.30 to 11.21%) also recorded marginal changes in this basin. Mining or quarry has been occurred in coastal sandy areas (0.04% to 0.21%).

It is observed that, due to not proper rainfall in the research area the tanks are dried up in over the year. Canal has been 430 ha in 2000-01 in the study area, it is observed the canal has been increased in 2014-15 nearly 612 ha.

Using of above data it will be investigate the Environmental Impact Assessment of Land Use/Land Cover in Swarnamukhi River Basin. Furthermore, these maps will enable identification of vulnerable areas of land degradation process, changing patterns of precipitation and temperature.

Fig 4:Map showing the LULC in 2000-01

Fig 5:Map showing the LULC in 2005-06

Fig 6:Map showing the LULC in 2011-12

Fig 7:Map showing the LULC in 2014-15

REFERNCES

  1. E.R. Lambin, M.D.A. Rounsevell and H.J.Geist, Are agricultural land-use Models able to predict changes in land use intensity? Agriculture, Ecosystems and Environment, Vol. 82, 2000, pp.321-331.

  2. G.D. Squires, Urban Sprawl and the Uneven Development of Metropolitan America, Urban Responses, Urban Institute Press, Washington, D.C. 2002, pp.1-22.

  3. Fei Yuan, Kali E. Sawaya, Brian C. Loeffelholz and Marvin

    E. Batter Land cover classification and Change analysis of the Twin Cities (Minnesota) Metropolitan Area bymultitemporal Landsat remote sensing, Remote Sensing of Envoronment, Vol.99, August 2005, pp.317-327.

  4. Lillesand, T M and Kiefer, R.W. (2000) Remote Sensing and Image Interpretation.

  5. National Land Use/Land Cover classification by N C Gautam, Centre for Land Use Management, 2004

  6. Priyanka Patel N. Janardhana Raju. B. C. Sundara Raja Reddy. U. Suresh, Wolfgang Gossel, Peter Wycisk, Geochemical processes and multivariate statistical analysis for the assessment of groundwater quality in the Swarnamukhi River basin, Andhra Pradesh, India Environ Earth Scince (2016) 75:611, pp. 610-611, December 2015.

  7. Verburg, P.H., Paul, S., Yaqui Chen, Soepboer, W., and Veldkamp, T.A. 2000. GIS-Based Modeling of Human- Environment Interactions for Natural Resource Management Applications in Asia. In 4th International Conference on Integrating GIS and Environmental modeling (GIS/EM4): Problems , Prospects and Research Needs, Banff, Alberta, Canada. Boulder: Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado. http://www.colorado.edu/research/cires/banff/upload/337/.

  8. Raskin, P., Banuri. T., Gallopin, Gutman, P., Hammond, A. et al. 2002. Great transition: the promise and lure of the times ahead. Glob. Scenar. Group, SEI Pole Star Ser. Rep. 10, Stockholm Environ. Inst. Boston.

  9. Martens. P, and Rotman, J. (Ed). 2002. Transition in a Globalizing World. Lisse, Neth: Swets and Zeitlinger, 135 pp.

  10. Manual LULC 50K 2005 (write complete name)

  11. National Land Use/Land Cover classification by N C Gautam, Centre for Land Use Management, 2004.

  12. R. Nagaraja, Appraisal and evaluation of land and water resources for integrated land use planning-A Remote Sensing approach, Ph.D Thesis, 1989.

  13. Anderson, J.R., Hardy, E.E., Roach, J.T. and Witmer, R.E. 1976. A land use and land cover classification system for use with remote sensor data. U.S. Geological Survey Professional Paper, No. 964. USGS, Washington, D.C.

  14. Darwin, R., Tsigas, M., Lewandrowski, J. & Raneses, A. 1996. Land use and cover in ecological economics. Ecological Economics, 17: 157181.

  15. Meyer, W.B. & Turner II, B.L. 1992. Human population growth and global land use/land cover change. pp. 3961, in: Annual Review of Ecology and Systematics, No. 23.

  16. Di Gregorio, A. 1991. Technical report on the land cover mapping of Lebanon. FAO Project NECP/LEB/001/SAU.

  17. Di Gregorio, A. 1995. FAO land use statistics: A case study for three countries using remote sensing and GIS technology. Consultancy Report for FAO Statistics Division, Rome.

Table 1: Overall extent of land use and land cover classes in Swarnamukhi river basin

Land Use/Land Cover Classes

Area

2000_01

2005_06

2011_12

2014_15

Area

%

Area

%

Area

%

Area

%

Urban

3799.38

1.15

4661.42

1.41

5610.38

1.70

6589.14

1.99

Rural

3596.99

1.09

4103.75

1.24

5065.59

1.53

5190.31

1.57

Industrial

264.46

0.08

431.35

0.13

1433.28

0.43

1767.46

0.53

Mining/Quarry

133.51

0.04

195.45

0.06

631.38

0.19

699.06

0.21

Cropland

112448.34

34.02

109583.21

33.16

109571.48

33.15

101004.09

30.56

Fallow land

13685.17

4.14

13591.22

4.11

10762.61

3.26

18278.72

5.53

Agricultural Plantation

3281.29

0.99

3528.47

1.07

6710.05

2.03

8706.15

2.63

Aquaculture

1445.63

0.44

3051.17

0.92

3319.51

1.00

4012.99

1.21

Deciduous forest

73184.10

22.14

73166.60

22.14

72979.27

22.08

72691.56

21.99

Forest Plantation

770.85

0.23

770.85

0.23

719.50

0.22

781.29

0.24

Scrub Forest

25893.70

7.83

25835.26

7.82

26047.41

7.88

26158.97

7.91

Tree Clad Area

2895.09

0.88

2895.09

0.88

2863.40

0.87

2798.92

0.85

Salt Affected land

8657.34

2.62

7309.28

2.21

7112.81

2.15

6945.24

2.10

Gullied /Ravinous land

739.82

0.22

777.63

0.24

751.97

0.23

751.97

0.23

Scrub land

40636.33

12.30

42370.71

12.82

39284.11

11.89

37052.31

11.21

Sandy area

4021.73

1.22

3995.95

1.21

3233.39

0.98

2853.43

0.86

Barren rocky

1408.80

0.43

1357.94

0.41

1357.94

0.41

1347.44

0.41

River

7171.48

2.17

7264.99

2.20

5605.92

1.70

7438.78

2.25

Canal

429.91

0.13

429.91

0.13

612.34

0.19

612.34

0.19

Tank

24392.98

7.38

24370.74

7.37

24324.93

7.36

23867.33

7.22

Coastal Inland

1628.05

0.49

793.95

0.24

2487.67

0.75

931.39

0.28

Inland

19.87

0.01

19.87

0.01

19.87

0.01

25.92

0.01

Grand Total

330504.83

100.00

330504.83

100.00

330504.83

100.00

330504.83

100.00

Table 2: Showing the error matrix or change marix for 2000-01 to 2014-15

In_2014_II

In_200 0_II

1

2

3

4

5

6

7

8

9

1

0

11

12

13

1

4

15

16

17

18

1

9

20

2

1

2

2

Gra nd Tota l

1

37

99

379

9

2

17

2

34

25

359

7

3

21

9

4

5

264

4

29

9

2

3

10

134

5

18

01

15

82

20

8

2

7

933

02

963

0

36

40

13

96

17

7

447

80

47

5

6

49

6

112

448

6

26

4

45

31

9

455

6

795

0

76

6

16

8

4

179

26

2

4

136

85

7

29

11

13

8

4

263

69

23

88

77

2

204

70

1

7

1

7

328

1

8

355

26

17

10

48

144

5

9

20

14

4

51

726

19

1

7

3

248

1

1

731

84

10

3

7

8

393

771

11

2

5

10

23

62

2

3

0

255

18

1

41

258

94

12

40

10

5

27

99

41

289

5

13

90

10

29

6

510

81

10

27

67

37

3

8

581

5

12

865

7

14

2

6

7

4

0

740

15

33

9

65

78

8

7

2

160

7

298

16

65

28

9

14

353

80

6

8

5

2

2

7

406

36

16

15

13

4

1

4

63

142

11

7

44

2

159

26

26

10

402

2

17

5

6

8

11

33

13

47

140

9

18

2

38

5

16

75

7

6

11

69

21

9

1

717

1

19

4

3

0

430

20

34

10

46

4

3

75

57

1

12

4

35

1

3

237

42

1

5

243

93

21

2

225

1

9

48

1

11

34

10

8

21

7

3

6

162

8

22

2

0

20

Grand Total

65

89

51

91

17

67

6

9

9

101

004

182

78

87

06

40

13

726

92

7

8

1

261

59

27

99

69

45

7

5

2

370

52

28

54

13

47

74

39

6

1

2

238

67

9

3

1

2

6

330

499

Legend

Figure 8: Showing change map of 2000-01 to 2014-15

1

Urban

2

Rural

3

Industrial

4

Mining/Quarry

5

Cropland

6

Fallow land

7

Agricultural Plantation

8

Aquaculture

9

Deciduous forest

10

Forest Plantation

11

Scrub Forest

12

Tree Clad Area

13

Salt Affected land

14

Gullied /Ravinous land

15

Scrub land

16

Sandy area

17

Barren rocky

18

River

19

Canal

20

Tank

21

Coastal Inland

22

Inland

1

Urban

2

Rural

3

Industrial

4

Mining/Quarry

5

Cropland

6

Fallow land

7

Agricultural Plantation

8

Aquaculture

9

Deciduous forest

10

Forest Plantation

11

Scrub Forest

12

Tree Clad Area

13

Salt Affected land

14

Gullied /Ravinous land

15

Scrub land

16

Sandy area

17

Barren rocky

18

River

19

Canal

20

Tank

21

Coastal Inland

22

Inland

No Change

7 to 3

13 to 1

17 to 4

2 to 1

7 to 4

13 to 2

17 to 5

3 to 21

7 to 5

13 to 3

17 to 6

4 to 3

7 to 6

13 to 5

17 to 9

4 to 7

7 to 8

13 to 6

17 to 15

4 to 15

7 to 13

13 to 7

18 to 2

5 to 1

7 to 15

13 to 8

18 to 5

5 to 2

7 to 16

13 to 15

18 to 6

5 to 3

7 to 18

13 to 19

18 to 7

5 to 4

5 to 6

5 to 7

  1. to 8 5 to 13 5 to 14 5 to 15 5 to 16 5 to 18 5 to 19 5 to 20 5 to 22

  2. to 1

6 to 2

6 to 3

6 to 4

7 to 19

  1. to 21

  2. to 5

8 to 6

  1. to 7

  2. to 1

9 to 2

9 to 4

  1. to 7 9 to 10 9 to 11

  2. to 11

  3. to 1

11 to 4

11 to 6

  1. to 20

  2. to 4

  3. to 1

15 to 2

15 to 3

15 to 4

15 to 5

15 to 6

15 to 7

15 to 8

15 to 13

15 to 16

15 to 17

15 to 19

15 to 20

15 to 21

18 to 8

18 to 13

18 to 15

18 to 16

18 to 21

20 to 1

20 to 2

20 to 3

20 to 4

20 to 5

20 to 8

20 to 13

20 to 19

  1. to 21

  2. to 2

6 to 5

11 to 7

16 to 2

21 to 5

6 to 7

11 to 9

16 to 3

21 to 7

6 to 8

11 to 10

16 to 4

21 to 8

6 to 13

11 to 19

16 to 5

21 to 15

6 to 15

11 to 20

16 to 6

21 to 16

6 to 16

12 to 1

16 to 7

21 to 18

6 to 19

12 to 2

16 to 8

21 to 20

7 to 1

12 to 5

16 to 15

7 to 2

12 to 15

16 to 18

No Change

7 to 3

13 to 1

17 to 4

2 to 1

7 to 4

13 to 2

17 to 5

3 to 21

7 to 5

13 to 3

17 to 6

4 to 3

7 to 6

13 to 5

17 to 9

4 to 7

7 to 8

13 to 6

17 to 15

4 to 15

7 to 13

13 to 7

18 to 2

5 to 1

7 to 15

13 to 8

18 to 5

5 to 2

7 to 16

13 to 15

18 to 6

5 to 3

7 to 18

13 to 19

18 to 7

5 to 4

5 to 6

5 to 7

  1. to 8 5 to 13 5 to 14 5 to 15 5 to 16 5 to 18 5 to 19 5 to 20 5 to 22

  2. to 1

6 to 2

6 to 3

6 to 4

7 to 19

  1. to 21

  2. to 5

8 to 6

  1. to 7

  2. to 1

9 to 2

9 to 4

  1. to 7 9 to 10 9 to 11

  2. to 11

  3. to 1

11 to 4

11 to 6

  1. to 20

  2. to 4

  3. to 1

15 to 2

15 to 3

15 to 4

15 to 5

15 to 6

15 to 7

15 to 8

15 to 13

15 to 16

15 to 17

15 to 19

15 to 20

15 to 21

18 to 8

18 to 13

18 to 15

18 to 16

18 to 21

20 to 1

20 to 2

20 to 3

20 to 4

20 to 5

20 to 8

20 to 13

20 to 19

  1. to 21

  2. to 2

6 to 5

11 to 7

16 to 2

21 to 5

6 to 7

11 to 9

16 to 3

21 to 7

6 to 8

11 to 10

16 to 4

21 to 8

6 to 13

11 to 19

16 to 5

21 to 15

6 to 15

11 to 20

16 to 6

21 to 16

6 to 16

12 to 1

16 to 7

21 to 18

6 to 19

12 to 2

16 to 8

21 to 20

7 to 1

12 to 5

16 to 15

7 to 2

12 to 15

16 to 18

20 to 6

Leave a Reply