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Evaluation of Classification Techniques for Land Use Change Mapping of Indian Cities

  • Research Article
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Abstract

This study looks into the development of multi-level classification approach for land use change mapping in Indian cities using Landsat imageries. In this study, we mapped 47 Indian cities at different time frames 1990, 2000, 2010, and 2017. We started with traditional classification methods, but results provided unsatisfactory accuracy levels. Thus, we employed multiple classification techniques to achieve results with higher accuracy. The paper captures the evaluation of different classification techniques—hybrid, unsupervised, decision tree classification (DTC), and object-based image analysis (OBIA). The results suggest improvement in accuracy levels by using multi-level classification for different cities at different stages of the classification process. The most prominent is the hybrid classification technique; 14 cities out of 47 reached to accuracy above 72% through hybrid classification. For problematic classes, we used DTC, OBIA, and unsupervised classification techniques after masking the datasets. DTC was used in cities with a greater number of problems in datasets. For example, in the case of Kochi City, the accuracy at the initial level was reported 51% through unsupervised classification which improved to 77% (supervised classification), and finally, it reached 90% by DTC technique. The overall accuracy achieved through the multi-level classification approach described in this paper for the 47 Indian cities ranges from 81 to 93%.

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Notes

  1. Stripping effect is observed in the images when data is loss by sensor while viewing the geometry. Band stripping is caused by miscalibration of sensor either at the detector level or at scan level.

  2. A mixed pixel issue occurs when image element signifies properties of more than one surface land cover type. Mixed pixels are found at two concerns, firstly at “edges of large objects” and objects with smaller dimensions for instance agricultural fields, rivers or highways, farms or ponds, or even bushes and trees in sparsely vegetated cover. Secondly appear when imaged objects are smaller in proportion as compared to spatial resolution of the satellite. Landsat TM images reported mixed pixels issues in water 29.6% and 68.3% in vegetation cover (Klein-Gebbinck 1998).

  3. The Landsat calibration refers to procedures that convert from pixel value to radiance value of biophysical cover of the earth surface (Chavez 1989).

  4. Image registration is the process of transforming datasets into geographic coordinate system acquired from different satellite, sensors, and timeframe.

  5. The signature value of the area is altered by suspension of fine solid or liquid particles in the air. Aerosols can be natural or anthropogenic. Naturally formed aerosols are fog, soil dust, sea salt, volcanoes, botanical debris, forest fires. Direct emission is particulate air pollution and smoke, haze (Lioy and Kneip 1980).

  6. “Shadow occurs when an object totally or partially occludes light directly from the light source. Shadows can be divided into two classes: cast and self” (Arevalo et al. 2005). In remote sensing, shadowing occurs in the images by different objects such as “cloud (cloud shadow), mountain (topographic shadow), and urban material (urban shadow)” (Shahtahmassebi et al. 2013).

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Acknowledgements

This research study was partly funded by the Indo-Norwegian Cooperation Project (INCP-2014/10093), as well as DDP Initiative (ddpinitiative.org) and the International Climate Initiative (IKI) (18_I_326_Global_A_Climate Action After Paris). INCP was jointly undertaken by the Governments of India and Norway through University Grants Commission (UGC) India and The Norwegian Centre for International Cooperation in Education (SIU). The Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) supports the DDP initiative on the basis of a decision adopted by the German Bundestag under the project titled “From NDCs to Pathways and Policies: Transformative Climate Action After Paris.”

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Appendices

Appendix 1: Land Use Shares for 47 Cities for 1990, 2000, 2010, and 2017

Agartala

figure a

Agra

figure b

Ahmedabad

figure c

Allahabad

figure d

Amritsar

figure e

Asansol

figure f

Aurangabad

figure g

Bengaluru

figure h

Bhopal

figure i

Chandigarh

figure j

Chennai

figure k

Dehradun

figure l

Delhi

figure m

Dhanbad

figure n

Durg-Bhilainagar

figure o

Faridabad

figure p

Gangtok

figure q

Ghaziabad

figure r

Guwahati

figure s

Hyderabad

figure t

Indore

figure u

Jabalpur

figure v

Jaipur

figure w

Jodhpur

figure x

Kanpur

figure y

Kochi

figure z

Kolkata

figure aa

Kota

figure ab

Lucknow

figure ac

Ludhiana

figure ad

Madurai

figure ae

Mumbai

figure af

Mysuru

figure ag

Nagpur

figure ah

Nashik

figure ai

Panaji

figure aj

Patna

figure ak

Pune

figure al

Rajkot

figure am

Shimla

figure an

Srinagar

figure ao

Surat

figure ap

Tiruchirapalli

figure aq

Vadodara

figure ar

Varanasi

figure as

Vasai-Virar

figure at

Vishakhapatnam

figure au

Appendix 2: Accuracy Assessment for all 47 Cities

S. No.

Cities

Accuracy (in percentage)

Classes

User

Producer

1

Agartala

Agriculture

96.39

67.80

Built-up

98.90

98.90

River

95.59

96.30

Urban open

96.77

95.24

Urban green

66.12

97.56

Waterbody

98.16

96.39

Overall accuracy

91.92

Kappa coefficient

90.24

2

Agra

Agriculture

91.60

82.76

Built-up

100.00

98.23

River

100.00

99.40

Urban open

87.88

98.31

Urban green

77.39

76.07

Waterbody

100.00

98.75

Forest

81.48

92.63

Overall accuracy

91.63

Kappa coefficient

90.10

3

Ahmedabad

Agriculture

84.75

90.91

Built-up

93.75

91.84

River

100.00

86.27

Urban open

93.02

88.64

Urban green

5.88

87.91

Waterbody

71.01

100.00

Overall accuracy

90.06

Kappa coefficient

0.88

4

Allahabad

Agriculture

93.10

98.18

Built-up

89.74

89.74

River

79.17

79.17

Urban open

97.50

97.50

Urban green

94.44

89.47

Waterbody

84.78

85.71

Overall accuracy

107.09

Kappa coefficient

0.88

5

Amritsar

Agriculture

72.31

85.45

Built-up

98.13

98.13

Urban open

97.40

87.21

urban green

91.67

91.67

Waterbody

100.00

97.30

Overall accuracy

92.10

Kappa coefficient

0.90

6

Asansol

Agriculture

88.96

78.38

Built-up

100.00

97.94

River

66.92

86.14

Urban open

95.24

100.00

Urban green

78.22

87.29

Waterbody

100.00

84.24

Overall accuracy

91.15

Kappa coefficient

0.89

7

Aurangabad

Agriculture

91.73

89.05

Built-up

89.38

100.00

River

93.48

87.76

Urban open

91.93

98.67

Urban green

82.39

77.06

Waterbody

95.29

98.78

Forest

98.10

97.64

Overall accuracy

91.81

Kappa coefficient

90.18

8

Bangalore

Agriculture

85.42

82.00

Built-up

83.33

62.50

River

100.00

100.00

Urban open

82.66

93.46

urban green

81.63

83.33

Waterbody

100.00

100.00

Forest

93.88

95.83

Overall accuracy

88.89

Kappa coefficient

86.81

9

Bhopal

Agriculture

91.11

91.11

Built-up

83.33

61.22

River

100.00

100.00

Urban open

88.27

95.33

Urban green

87.69

87.69

Waterbody

100.00

100.00

Forest

94.85

95.83

Overall accuracy

92.66

Kappa coefficient

91.19

10

Chandigarh

Agriculture

82.20

84.35

Built-up

98.36

98.36

River

100.00

91.57

Urban open

94.35

99.40

Urban green

72.88

66.15

Waterbody

81.58

100.00

Forest

91.49

86.00

Overall accuracy

91.62

Kappa coefficient

89.99

11

Chennai

Agriculture

82.95

84.88

Built-up

92.00

94.52

River

100.00

95.10

Urban open

95.83

81.18

Urban green

84.78

84.78

Waterbody

89.25

91.21

Bay

65.38

85.00

Overall accuracy

88.40

Kappa coefficient

86.59

12

Dehradun

Agriculture

90.45

90.96

Built-up

98.06

96.19

River

87.10

96.43

Urban open

98.77

81.63

Urban green

67.35

75.86

Waterbody

100.00

100.00

Forest

87.84

89.04

Overall accuracy

89.01

Kappa coefficient

86.70

13

Delhi (New Delhi)

Agriculture

76.36

87.50

Built-up

95.00

93.44

River

89.41

96.20

Urban open

85.29

90.63

Urban green

96.67

69.88

Waterbody

74.07

95.24

Forest

97.06

95.65

Overall accuracy

88.84

Kappa coefficient

86.74

14

Dhanbad

Agriculture

86.67

96.30

Built-up

96.30

88.64

River

100.00

98.46

Urban open

96.59

77.98

Urban green

89.36

84.85

Waterbody

100.00

100.00

Forest

98.11

92.86

Mines

78.71

98.39

Overall accuracy

91.09

Kappa coefficient

89.65

15

Durg

Agriculture

90.97

78.77

Built-up

97.67

98.82

River

87.23

93.18

Urban open

97.17

92.79

Urban green

87.26

95.80

Waterbody

97.84

95.77

Overall accuracy

91.97

Kappa coefficient

89.70

16

Faridabad

Agriculture

92.59

76.92

Built-up

96.55

91.80

River

96.00

98.36

Urban open

90.91

85.71

Urban green

73.33

86.84

Waterbody

91.67

100.00

Forest

81.16

98.25

Overall accuracy

90.25

Kappa coefficient

88.61

17

Gangtok

Agriculture

81.63

95.24

Built-up

94.92

73.68

River

94.12

98.46

Urban open

98.88

90.72

Urban green

88.89

77.42

Waterbody

96.67

93.55

Forest

92.50

90.24

snow

73.33

91.67

Overall accuracy

88.55

Kappa coefficient

86.86

18

Ghaziabad

Agriculture

88.64

83.57

Built-up

97.73

97.73

River

79.31

88.46

Urban open

97.56

93.75

Urban green

84.80

91.77

Waterbody

95.71

91.78

Overall accuracy

91.03

Kappa coefficient

88.80

19

Guwahati

Agriculture

97.14

87.18

Built-up

95.74

97.83

River

93.48

96.63

Urban open

97.56

93.75

Urban green

85.56

96.86

Waterbody

79.31

83.13

Overall accuracy

91.75

Kappa coefficient

89.64

20

Hyderabad

Agriculture

91.67

77.88

Built-up

93.22

94.83

River

94.52

80.23

Urban open

90.74

97.03

Urban green

70.73

84.06

Waterbody

85.22

90.74

Forest

85.96

89.09

Overall accuracy

87.29

Kappa coefficient

85.02

21

Indore

Agriculture

94.71

90.45

Built-up

91.30

93.33

River

86.54

93.75

Urban open

96.00

92.31

Urban green

84.16

88.54

Waterbody

85.19

88.46

Overall accuracy

91.54

Kappa coefficient

88.88

22

Jabalpur

Agriculture

78.57

80.73

Built-up

90.32

93.33

River

100.00

88.89

Urban open

92.47

99.26

Urban green

92.73

89.08

Waterbody

82.67

100.00

Forest

94.37

90.54

Overall accuracy

90.94

Kappa coefficient

89.12

23

Jaipur

Agriculture

86.36

78.08

Built-up

96.08

96.08

River

91.67

81.48

Urban open

88.06

92.19

Urban green

84.11

84.91

Waterbody

78.00

90.70

Forest

89.74

92.11

Overall accuracy

87.37

Kappa coefficient

86.91

24

Jodhpur

Agriculture

86.67

85.53

Built-up

100.00

97.37

River

92.31

96.00

Urban open

95.31

92.42

Urban green

76.00

82.61

Waterbody

94.83

90.16

Overall accuracy

90.61

Kappa coefficient

80.15

25

Kanpur

Agriculture

75.44

97.73

Built-up

84.21

91.43

River

79.31

76.67

Urban open

95.45

84.00

Urban green

87.76

78.18

Waterbody

95.83

92.00

Overall accuracy

87.20

Kappa coefficient

84.39

26

Kochi

Agriculture

75.76

72.46

Built-up

94.74

98.36

River

93.75

82.19

Urban open

85.94

91.67

Urban green

73.42

71.60

Waterbody

81.94

93.65

Bay

80.65

55.56

Mangrove

89.61

94.52

Overall accuracy

86.47

Kappa coefficient

83.91

27

Kolkata

Agriculture

71.43

71.43

Built-up

96.23

96.23

Urban open

90.00

83.08

Urban green

78.72

90.24

Waterbody

100.00

97.96

Overall accuracy

88.98

Kappa coefficient

0.86

28

Kota

Agriculture

88.89

83.81

Built-up

91.80

94.92

River

97.78

91.67

Urban open

89.61

94.52

Urban green

82.76

88.89

Waterbody

90.59

97.47

Forest

87.50

80.00

Overall accuracy

89.88

Kappa coefficient

88.14

29

Lucknow

Agriculture

89.09

92.45

Built-up

91.30

91.30

Urban open

96.43

83.08

Urban green

88.64

95.12

Waterbody

100.00

95.24

Overall accuracy

91.25

Kappa coefficient

0.88

30

Ludhiana

Agriculture

83.61

86.44

Built-up

96.15

96.15

Urban open

95.83

80.70

Urban green

87.95

96.05

Waterbody

99.08

99.08

Overall accuracy

92.92

Kappa coefficient

0.91

31

Madurai

Agriculture

77.78

94.23

Built-up

97.06

97.06

River

85.37

92.11

Urban open

98.82

92.31

Urban green

92.55

87.88

Waterbody

96.39

93.02

Overall accuracy

92.00

Kappa coefficient

90.14

32

Mumbai

Agriculture

94.94

75.00

Built-up

94.92

56.00

River

100.00

100.00

Urban open

98.78

82.00

Urban green

96.80

133.00

Waterbody

100.00

175.00

Bay

73.56

64.00

Mangrove

92.31

64.00

Saltpan

71.52

136.00

Overall accuracy

90.40

Kappa coefficient

0.89

33

Mysore

Agriculture

86.15

82.35

Built-up

94.87

98.67

River

91.55

98.48

Urban open

96.83

83.56

Urban green

73.85

87.27

Waterbody

98.21

90.16

Overall accuracy

90.20

Kappa coefficient

88.23

34

Nagpur

Agriculture

86.15

90.32

Built-up

91.36

98.67

River

91.55

98.48

Urban open

96.00

77.42

Urban green

80.00

84.21

Waterbody

98.21

90.16

Overall accuracy

90.34

Kappa coefficient

88.38

35

Nashik

Agriculture

89.34

91.60

Built-up

93.33

100.00

River

93.62

96.70

Urban open

97.03

85.22

Urban green

77.59

84.91

Waterbody

95.89

92.11

Overall accuracy

91.82

Kappa coefficient

90.08

36

Panaji

Agriculture

93.85

98.39

Built-up

94.55

96.30

River

100.00

71.54

Urban open

95.70

98.89

Urban green

97.76

95.62

Waterbody

100.00

100.00

Bay

70.27

97.50

Mangrove

96.74

94.68

Saltpan

75.69

76.76

Overall accuracy

90.83

Kappa coefficient

0.90

37

Patna

Agriculture

81.16

83.58

Built-up

97.10

97.10

River

93.10

96.43

Urban open

91.53

87.10

Urban green

82.28

83.33

Waterbody

95.95

92.21

Overall accuracy

90.16

Kappa coefficient

88.16

38

Pune

Agriculture

87.12

93.50

Built-up

93.67

100.00

River

100.00

91.84

Urban open

92.68

88.37

Urban green

86.79

100.00

Waterbody

98.06

84.87

Forest

100.00

84.87

Overall accuracy

93.98

Kappa coefficient

0.93

39

Rajkot

Agriculture

76.47

85.53

Built-up

98.88

88.89

River

94.23

97.03

Urban open

94.55

81.25

Urban green

80.61

89.77

Waterbody

96.51

93.26

Overall accuracy

89.94

Kappa coefficient

87.87

40

Shimla

Agriculture

85.42

52.56

Built-up

91.84

97.83

River

93.55

100.00

Urban open

96.70

93.62

Forest

66.39

88.76

Waterbody

100.00

94.23

Overall accuracy

87.95

Kappa coefficient

85.39

41

Srinagar

Agriculture

70.97

90.41

Built-up

100.00

98.18

River

90.63

100.00

Urban open

96.25

92.77

Forest

89.90

77.39

Waterbody

100.00

90.32

Overall accuracy

89.69

Kappa coefficient

87.49

42

Surat

Agriculture

97.78

97.78

Built-up

98.89

96.74

River

100.00

84.42

Urban open

92.59

94.34

Urban green

93.33

88.29

Waterbody

97.09

100.00

Bay

75.58

97.01

Mangrove

87.76

94.51

Saltpan

82.86

72.50

Overall accuracy

91.85

Kappa coefficient

0.91

43

Trichy

Agriculture

85.22

83.76

Built-up

100.00

98.82

River

90.32

87.50

Urban open

96.08

90.74

Urban green

87.72

92.59

Waterbody

95.74

96.77

Overall accuracy

92.24

Kappa coefficient

90.44

44

Vadodara

Agriculture

86.41

79.46

Built-up

94.92

98.25

River

94.23

97.03

Urban open

96.70

90.72

Urban green

82.05

89.51

Waterbody

98.45

96.94

Overall accuracy

91.93

Kappa coefficient

90.04

45

Varanasi

Agriculture

83.19

88.39

Built-up

97.53

95.18

River

92.00

95.83

Urban open

96.74

81.65

Urban green

86.21

92.59

Waterbody

96.74

93.68

Overall accuracy

90.84

Kappa coefficient

0.89

46

Vasai-Virar

Agriculture

98.36

97.30

Built-up

98.04

97.09

River

100.00

88.00

Urban open

95.12

83.87

Urban green

97.06

79.52

Waterbody

100.00

99.38

Bay

50.86

94.68

Mangrove

100.00

95.15

Saltpan

90.91

78.43

Overall accuracy

90.24

Kappa coefficient

0.89

47

Vizag

Agriculture

93.94

97.48

Built-up

93.75

93.75

River

100.00

78.76

Urban open

97.96

84.21

Urban green

96.70

83.81

Waterbody

100.00

100.00

Bay

67.72

91.49

Mangrove

100.00

96.91

Saltpan

82.35

91.80

Overall accuracy

91.43

Kappa coefficient

0.90

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Avashia, V., Parihar, S. & Garg, A. Evaluation of Classification Techniques for Land Use Change Mapping of Indian Cities. J Indian Soc Remote Sens 48, 877–908 (2020). https://doi.org/10.1007/s12524-020-01122-7

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  • DOI: https://doi.org/10.1007/s12524-020-01122-7

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