http://www.iisc.ernet.in/
Modelling urban dynamics in rapidly urbanising Indian cities

a Energy & Wetlands Research Group, Center for Ecological Sciences [CES], Indian Institute of Science, Bangalore, Karnataka 560 012, India
b Centre for Sustainable Technologies (astra), Indian Institute of Science, Bangalore, Karnataka 560 012, India
c Centre for infrastructure, Sustainable Transportation and Urban Planning [CiSTUP], Indian Institute of Science, Bangalore, Karnataka 560 012, India
d RCGSIDM, IIT Kharagpur, Kharagpur 721302, India

http://wgbis.ces.iisc.ernet.in/energy/

Results and discussion

Geo-visualisation of urbanisation of five tier I cities are depicted in Fig. 3–7 and results are as given in Tables 2–6. The cities on an average would grow by 1.5 to over 2 times the current state in next decade. Prediction reveals that built up area in these cities and surroundings, grows over 57% (Delhi), 27% (Mumbai), 45.8% (Chennai), 50% (Pune) and 37% (Coimbatore) respectively by 2025. The various drivers of growth for different cities are listed in Annexure 1. In all these cases, if CDP (City Development Plan) implemented in true spirit, which would help in regulating the unsustainable growth within the city though still some growth takes place in peri-urban regions. Prime agents of growth include the transportation network, industrialisation, educational sector, etc.

3.1. Validation and calibration of land use data

Validation and calibration of land use data: Land use was calibrated by predicting for year land use has already been calculated. If T0, T1 and T2 are three different years of known land use. T0 and T1 were used to calibrate and match the T3 image. Error was calculated using Kappa statistics (Pontius and Malanson, 2005) as a measure of agreement. Based on the accuracy agreement, the combination of T2 and T3 were used in already calibrated method, constraints and values of CA-Markov to predict for 2020, 2025 as per the case of temporal data availability. Validation of data for Delhi with optimal parameters was observed with value of kappa of 0.91 and similarly for Mumbai kappa obtained was 0.93, Chennai kappa value was 0.94, Pune kappa value was 0.89 and Coimbatore kappa value was 0.96. Future scenarios was compared to data obtained by developmental plans and other plans based on various sectorial growth. Models predicted growth showed a great coherence to these plans and specified growth areas.

3.2. Delhi

Spatial analysis and modelling reveals of same pattern of urban densification covering about 57% and 70% of the landscape by 2025 and 2035 respectively. Delhi is one among the fastest and largest growing global cities with huge expansion in both industrial and housing sectors. The neighbouring regions would also experience unprecedented growth with escalations in land values. This would lead to an increased outward expansion in almost all directions. Regions such as Ghaziabad, Noida, Bahadurgarh, Sonipat, Dadri, Modinagar, Bhagpur and Pataudi would experience urban growth/expansions in the next decade. Visualisation also highlights the need for an immediate policy intervention for regulating haphazard growth at outskirts

Fig. 3. Prediction land use of Delhi.

Year Built up Vegetation Water Others

2016

5.80

17.98

1.25

34.97

2025

57.37

8.77

1.18

32.68

2030

70.86

3.76

1.19

24.18

Table 2. Predicted landscape dynamics of Delhi.(All units as percentage area.)

3.3. Mumbai

Being the business capital of India, Mumbai has been experiencing urbanisation with ever increasing urban footprint. During the past four decades (1973–2009), urbanisation has significantly modified the landscape structure of Mumbai city and its outskirts. The built-up area has significantly increased by 155% in past four decades, at the expense of non-forest land in the study region (Mumbai metropolitan area with 10 km buffer). Urban sprawl is seen toward the southwest and northeast sectors of the metropolitan area. This trend would continue considering that major driving force behind the urban growth and sprawl in Mumbai as per the analysis is setting up major industrial parks and corridors along with increase in the population density mainly for employment opportunities. It can be noted that major growth would happen in regions of Navi Mumbai region, Bhiwandi, Badlapur, Matheran. The developments majorly are in towards Raigad district with Rasayani and Khalapur would be next urban centre of development in current trends. Mumbai and Navi Mumbai would also see huge infilling growth and reach saturation of horizontal development considering resources. Considering the study region, the urban would cover about 31 percent of the total region though 44% is water. With Pune also gaining massive urban development the requirement of land mass for urban region would grow in Mumbai in coming years. Government of India declaring Navi Mumbai,Greater Mumbai, Thane, Kaylan as some of the cities that wouldtranslate as smart cities would also pressurise the developmentof Mumbai and its core with requirements of Basic services andamenities. Land use predicted is given in Fig. 4 and category wiseland use percentage is listed in Table 3.

Fig. 4. Predicted land use of Mumbai.

Year Built up Vegetation Water Others

2020

25.83

9.09

44.52

20.56

2030

31.27

6.33

44.52

17.88

Table 3. Predicted landscape dynamics of Mumbai.(All units as percentage area.)

3.4. Chennai

Urban growth in 2026 predicted considering agents is given in Fig. 5 and category wise details are provided in Table 4. This shows an increase in built-up areas by two folds with decrease in vegetation. Significant changes can be seen in areas which falls within the CMDA boundary such as Korattur and Cholavaram lake bed, Redhills catchment area, forests at Perungalathur, wetland in Sholinganallur, etc. The regions closer to Chennai boundary lying in peri urban region (such as Kanchipuram, towards Pulicat, Kavaraipettai, Vellore and towards Krishnagiri) have also shown significant urban expansions by 2026.

Fig. 5. Predicted landscape use of Chennai.

Year Built up Vegetation Water Others

2026

45.80

17.98

1.25

34.97

Table 4. Predicted landscape dynamics of Chennai.(All units as percentage area.)

3.5. Pune

Model (Fig. 6, Table 5) predicts that the Localities such as Markal, Lonikand, Dattwade, Girinagar, Lavale, Pimpri, Chinchwad, Khadakwasla, Dhayari phata, Katruj, Yerwada, Pashan in and around Pune would experience a large scale land use change. These regions are the ones that are offering better lands for industriagrowth in and around Pune and have been considered as sprawling areas with associated problems such as lack of basic amenities, etc. Pimpri Chinchwad was established in 1988 and developed to cater the requirement of industrial needs (Ramachandra et al., 2014aModel shows that the region would experience an unprecedented urbanisation and built-up land use would dominate with 50% the total land use in the study region. There would be higher pressure in the city boundaries since infilling would be very high and also towards regions connecting Mumbai show a huge spurt urban.

Fig. 6. Predicted land use of Pune.

Year Built up Vegetation Water Others

2015

37.78

1.75

16.37

44.11

2020

41.64

1.75

20.16

36.45

2025

50.02

1.75

20.16

28.06

Table 5 Predicted landscape dynamics of Pune. (All units as percentage area.)

3.6. Coimbatore

Coimbatore is facing an unprecedented urban growth with likely increase of about 42% by 2035 (Fig. 7 and tabulated in Table 6). Coimbatore would develop towards Salem, Sulur, Tiruppur, Annur, Kanathur, etc.

Fig. 7. Predicted land use of Coimbatore.

Year Built up Vegetation Water Others

2025

32.64

0.29

17.14

49.94

2035

42.92

0.29

17.58

39.21

Table 6 Predicted landscape dynamics of Coimbatore.(All units as percentage area.)

Citation : H.A. Bharath, M.C. Chandan, S. Vinay, T.V. Ramachandra, 2018. Modelling urban dynamics in rapidly urbanising Indian cities. The Egyptian Journal of Remote Sensing and Space Science, Volume 19, Issue 2, December 2016, Pages 175-193, https://doi.org/10.1016/j.ejrs.2016.09.001

    * Corresponding author

    H.A. Bharath
    Ranbir and Chitra Gupta School of Infrastructure
    Design and Management (RCGSIDM), Twin Science Block, IIT-Kharagpur, Kharagpur,
    West Bengal 721302, India.
    E-mail : bharathhaithal@gmail.com
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