Regional Sustainability ›› 2024, Vol. 5 ›› Issue (2): 100138.doi: 10.1016/j.regsus.2024.100138
• Full Length Article • Previous Articles Next Articles
Bubun MAHATAa,*(), Siba Sankar SAHUa, Archishman SARDARb, Laxmikanta RANAa, Mukul MAITYa
Received:
2023-06-25
Revised:
2024-03-13
Accepted:
2024-06-12
Published:
2024-06-30
Online:
2024-07-25
Contact:
Bubun MAHATA
E-mail:bubunmahata003@gmail.com
Bubun MAHATA, Siba Sankar SAHU, Archishman SARDAR, Laxmikanta RANA, Mukul MAITY. Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India[J]. Regional Sustainability, 2024, 5(2): 100138.
Table 1
Details about satellite data for land use/land cover (LULC) and land surface temperature (LST) analysis."
Satellite | Sensor | Path | Spatial resolution (m) | Cloud cover (%) | Date | Season | Purpose of use |
---|---|---|---|---|---|---|---|
Landsat 5 | TM | 138/44 | 30 | 9 | 17 Jan 1991 | Winter | LST |
Landsat 5 | TM | 138/44 | 30 | 2 | 6 Mar 1991 | Spring | LULC |
Landsat 5 | TM | 138/44 | 30 | 33 | 23 Apr 1991 | Summer | LST |
Landsat 5 | TM | 138/44 | 30 | 11 | 30 Sep 1991 | Autumn | LST |
Landsat 7 | ETM+ | 138/44 | 30 | 7 | 4 Jan 2001 | Winter | LST |
Landsat 5 | TM | 138/44 | 30 | 4 | 17 Mar 2001 | Spring | LULC |
Landsat 7 | ETM+ | 138/44 | 30 | 0 | 26 Apr 2001 | Summer | LST |
Landsat 7 | ETM+ | 138/44 | 30 | 11 | 19 Oct 2001 | Autumn | LST |
Landsat 7 | ETM+ | 138/44 | 30 | 13 | 16 Jan 2011 | Winter | LST |
Landsat 7 | ETM+ | 138/44 | 30 | 0 | 6 Apr 2011 | Summer | LST and LULC |
Landsat 7 | ETM+ | 138/44 | 30 | 6 | 31 Oct 2011 | Autumn | LST |
Landsat 8 | OLI and TIRS | 138/44 | 30 | 11 | 3 Jan 2021 | Winter | LST |
Landsat 8 | OLI and TIRS | 138/44 | 30 | 0 | 4 Feb 2021 | Spring | LULC |
Landsat 8 | OLI and TIRS | 138/44 | 30 | 0 | 25 Apr 2021 | Summer | LST |
Landsat 9 | OLI and TIRS | 138/44 | 30 | 0 | 7 Nov 2021 | Autumn | LST |
Fig. 2.
Methodological flow of this study. LST, land surface temperature; LULC, land use/land cover; NDBI, normalized difference built-up index; NDVI, normalized difference vegetation index; NDWI, normalized difference water index; UHI, urban heat island; UTFVI, urban thermal field variance index."
Table 2
Description of LULC types in this study."
LULC type | Description |
---|---|
Built-up land | Housing, roads, institutions, urban and manufacturing regions, etc. |
Dense vegetation | Trees with dense and confined canopy layers (Khwarahm, |
Sparse vegetation | Areas with sparse vegetation and open canopy layers (Khwarahm, |
Agricultural land | Land used for growing cultivated plants (Meyer and Turner, |
Fallow land | Unused farmland, and land with soil, sandy, rocky, or snowy condition with less than 10% natural vegetation throughout the year. |
Water body | Rivers, ponds, lakes, wetlands, etc. |
Table 3
Area changes of LULC types during 1991-2021."
LULC type | 1991 | 2001 | 2011 | 2021 | ||||
---|---|---|---|---|---|---|---|---|
Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | |
Agricultural land | 3.85 | 11.82 | 6.79 | 20.85 | 6.26 | 19.22 | 2.49 | 7.65 |
Built-up land | 7.13 | 21.91 | 6.19 | 19.02 | 9.21 | 28.29 | 14.85 | 45.63 |
Dense vegetation | 4.54 | 13.94 | 8.12 | 24.96 | 5.24 | 16.10 | 6.38 | 19.60 |
Fallow land | 6.84 | 21.01 | 3.59 | 11.02 | 4.17 | 12.82 | 5.64 | 17.34 |
Sparse vegetation | 9.40 | 28.86 | 6.44 | 19.78 | 6.04 | 18.54 | 0.92 | 2.82 |
Water body | 0.80 | 2.46 | 1.42 | 4.37 | 1.63 | 5.02 | 2.28 | 6.99 |
Table 4
Area growth rate of different LULC types during 1991-2021."
LULC type | Area growth rate during 1991-2001 (%) | Area growth rate during 2001-2011 (%) | Area growth rate during 2011-2021 (%) |
---|---|---|---|
Agricultural land | 76.37 | -7.79 | -60.22 |
Built-up land | -13.22 | 48.80 | 61.25 |
Dense vegetation | 79.02 | -35.48 | 21.68 |
Fallow land | -47.56 | 16.36 | 35.18 |
Sparse vegetation | -31.47 | -6.20 | -84.78 |
Water body | 78.09 | 14.76 | 39.22 |
Table 5
LULC conversion matrix during 1991-2021."
LULC type | Agricultural land (km2) | Built-up land (km2) | Dense vegetation (km2) | Fallow land (km2) | Sparse vegetation (km2) | Water body (km2) | Total area in 1991 (km2) | Area loss (km2) |
---|---|---|---|---|---|---|---|---|
Agricultural land | 0.24 | 1.99 | 0.72 | 0.69 | 0.06 | 0.15 | 3.85 | -3.61 |
Built-up land | 0.48 | 3.29 | 1.64 | 1.06 | 0.41 | 0.25 | 7.13 | -3.84 |
Dense vegetation | 0.47 | 2.22 | 0.26 | 0.93 | 0.07 | 0.59 | 4.54 | -4.28 |
Fallow land | 0.53 | 2.87 | 1.79 | 1.18 | 0.11 | 0.36 | 6.84 | -5.66 |
Sparse vegetation | 0.74 | 4.13 | 1.72 | 1.70 | 0.26 | 0.85 | 9.40 | -9.14 |
Water body | 0.03 | 0.35 | 0.25 | 0.08 | 0.01 | 0.08 | 0.80 | -0.72 |
Total area in 2021 (km2) | 2.49 | 14.85 | 6.38 | 5.64 | 0.92 | 2.28 | ||
Area gain (km2) | 2.25 | 11.56 | 6.12 | 4.46 | 0.66 | 2.20 |
Table 6
Seasonal variations of LST during 1991-2021."
LST in winter | 1991 | 2001 | 2011 | 2021 |
---|---|---|---|---|
Max (°C) | 18.39 | 21.82 | 23.36 | 26.01 |
Min (°C) | 10.93 | 16.55 | 16.55 | 18.57 |
Mean (°C) | 16.31 | 18.98 | 20.59 | 22.48 |
SD (°C) | 0.87 | 0.76 | 1.23 | 1.21 |
CV (%) | 5.31 | 4.01 | 5.97 | 5.36 |
LST in summer | 1991 | 2001 | 2011 | 2021 |
Max (°C) | 32.87 | 36.40 | 34.55 | 39.62 |
Min (°C) | 23.26 | 25.87 | 23.86 | 27.66 |
Mean (°C) | 29.18 | 30.10 | 29.31 | 34.61 |
SD (°C) | 1.44 | 1.64 | 1.95 | 1.84 |
CV (%) | 4.95 | 5.45 | 6.65 | 5.31 |
LST in autumn | 1991 | 2001 | 2011 | 2021 |
Max (°C) | 21.07 | 26.87 | 28.30 | 30.58 |
Min (°C) | 15.18 | 20.27 | 19.62 | 24.19 |
Mean (°C) | 19.18 | 24.02 | 23.29 | 27.11 |
SD (°C) | 0.84 | 1.23 | 1.30 | 1.01 |
CV (%) | 4.36 | 5.11 | 5.60 | 3.73 |
Table 7
Spatiotemporal changes of LST across various LULC types during 1991-2021."
LULC type | LST in summer of 1991 | LST in winter of 1991 | ||||||
---|---|---|---|---|---|---|---|---|
Max (°C) | Min (°C) | Mean (°C) | SD (°C) | Max (°C) | Min (°C) | Mean (°C) | SD (°C) | |
Agricultural land | 32.87 | 23.25 | 30.33 | 1.29 | 22.38 | 14.24 | 20.70 | 0.96 |
Built-up land | 32.87 | 23.26 | 30.04 | 1.27 | 22.82 | 14.71 | 20.80 | 0.81 |
Dense vegetation | 31.24 | 23.69 | 28.27 | 1.05 | 21.51 | 11.40 | 19.77 | 1.02 |
Sparse vegetation | 32.06 | 23.25 | 28.72 | 1.13 | 21.94 | 10.92 | 20.05 | 1.04 |
Water body | 31.24 | 23.25 | 28.72 | 1.11 | 21.94 | 13.78 | 20.14 | 0.94 |
Fallow land | 32.87 | 23.25 | 28.91 | 1.48 | 21.94 | 11.88 | 20.22 | 1.00 |
LST in summer of 2001 | LST in winter of 2001 | |||||||
Max (°C) | Min (°C) | Mean (°C) | SD (°C) | Max (°C) | Min (°C) | Mean (°C) | SD (°C) | |
Agricultural land | 33.60 | 27.36 | 30.65 | 1.04 | 21.82 | 17.09 | 19.23 | 0.75 |
Built-up land | 36.39 | 27.36 | 31.83 | 1.63 | 21.82 | 17.09 | 19.19 | 0.71 |
Dense vegetation | 32.19 | 26.37 | 29.28 | 0.90 | 21.82 | 16.55 | 18.66 | 0.68 |
Sparse vegetation | 32.19 | 26.37 | 28.88 | 1.13 | 21.82 | 17.09 | 19.21 | 0.70 |
Water body | 30.76 | 25.87 | 27.66 | 0.92 | 20.78 | 17.09 | 18.32 | 0.69 |
Fallow land | 34.54 | 27.36 | 31.06 | 0.95 | 21.82 | 17.09 | 18.80 | 0.67 |
LST in summer of 2011 | LST in winter of 2011 | |||||||
Max (°C) | Min (°C) | Mean (°C) | SD (°C) | Max (°C) | Min (°C) | Mean (°C) | SD (°C) | |
Agricultural land | 34.07 | 26.87 | 30.09 | 0.91 | 23.36 | 18.16 | 21.45 | 0.68 |
Built-up land | 34.55 | 24.87 | 30.26 | 1.42 | 23.36 | 17.09 | 20.94 | 0.99 |
Dense vegetation | 32.19 | 24.36 | 27.09 | 1.28 | 22.33 | 16.55 | 19.62 | 0.86 |
Sparse vegetation | 33.60 | 24.36 | 28.74 | 1.43 | 23.36 | 16.55 | 20.29 | 1.05 |
Water body | 30.76 | 23.86 | 26.23 | 1.20 | 22.84 | 16.55 | 18.39 | 1.07 |
Fallow land | 34.54 | 24.87 | 30.86 | 1.65 | 23.36 | 17.62 | 21.05 | 1.05 |
LST in summer of 2021 | LST in winter of 2021 | |||||||
Max (°C) | Min (°C) | Mean (°C) | SD (°C) | Max (°C) | Min (°C) | Mean (°C) | SD (°C) | |
Agricultural land | 38.95 | 30.53 | 35.18 | 1.52 | 25.43 | 19.68 | 23.28 | 1.02 |
Built-up land | 39.62 | 29.34 | 35.14 | 1.24 | 26.01 | 19.44 | 22.67 | 0.95 |
Dense vegetation | 37.98 | 28.49 | 33.71 | 1.29 | 24.56 | 19.38 | 21.73 | 0.81 |
Sparse vegetation | 36.64 | 28.54 | 33.68 | 1.40 | 23.89 | 19.59 | 21.22 | 0.73 |
Water body | 37.15 | 27.65 | 31.20 | 2.34 | 23.96 | 18.56 | 20.68 | 1.04 |
Fallow land | 39.30 | 29.60 | 35.60 | 1.47 | 25.80 | 19.67 | 23.40 | 0.89 |
Table 8
Areas under the various levels of LST during 1991-2021."
LST (°C) | 1991 | 2001 | 2011 | 2021 | ||||
---|---|---|---|---|---|---|---|---|
Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | Area (km2) | Percentage (%) | |
20.00-25.00 | 0.41 | 1.27 | 0.00 | 0.00 | 0.75 | 2.31 | 0.00 | 0.00 |
25.00-30.00 | 21.56 | 66.19 | 16.68 | 51.21 | 19.00 | 58.33 | 0.98 | 3.00 |
30.00-35.00 | 10.60 | 32.53 | 15.75 | 48.36 | 12.82 | 39.36 | 16.56 | 50.84 |
35.00-40.00 | 0.00 | 0.00 | 0.14 | 0.43 | 0.00 | 0.00 | 15.04 | 46.17 |
Table 9
Correlation between various spectral indicators (including NDVI, NDMI and NDWI) and LST in 1991, 2001, 2011, and 2021."
1991 | ||||
---|---|---|---|---|
LST | NDVI | NDBI | NDWI | |
LST | 1.000 | -0.017 | 0.426** | 0.027 |
NDVI | -0.017 | 1.000 | -0.630** | -0.949** |
NDBI | 0.426** | -0.630** | 1.000 | 0.472** |
NDWI | 0.027 | -0.949** | 0.472** | 1.000 |
2001 | ||||
LST | NDVI | NDBI | NDWI | |
LST | 1.000 | -0.520** | 0.845** | 0.320** |
NDVI | -0.520** | 1.000 | -0.653** | -0.945** |
NDBI | 0.845** | -0.653** | 1.000 | 0.418** |
NDWI | 0.320** | -0.945** | 0.418** | 1.000 |
2011 | ||||
LST | NDVI | NDBI | NDWI | |
LST | 1.000 | -0.472** | 0.804** | 0.305** |
NDVI | -0.472** | 1.000 | -0.576** | -0.964** |
NDBI | 0.804** | -0.576** | 1.000 | 0.387** |
NDWI | 0.305** | -0.964** | 0.387** | 1.000 |
2021 | ||||
LST | NDVI | NDBI | NDWI | |
LST | 1.000 | -0.047 | 0.697** | -0.132** |
NDVI | -0.047 | 1.000 | -0.385** | -0.965** |
NDBI | 0.697** | -0.385** | 1.000 | 0.181** |
NDWI | -0.132** | -0.965** | 0.181** | 1.000 |
Table 11
Hot spot and cold spot areas at various confidence levels in winter, summer, and autumn."
Categories | Percentage of area in winter (%) | Percentage of area in summer (%) | Percentage of area in autumn (%) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
1991 | 2001 | 2011 | 2021 | 1991 | 2001 | 2011 | 2021 | 1991 | 2001 | 2011 | 2021 | |
CS (99%) | 4.37 | 1.08 | 6.64 | 9.31 | 8.69 | 10.01 | 3.26 | 7.12 | 7.09 | 0.41 | 5.17 | 6.35 |
CS (95%) | 5.49 | 7.75 | 7.91 | 5.40 | 3.66 | 3.35 | 8.81 | 6.38 | 6.06 | 5.89 | 7.80 | 8.89 |
CS (90%) | 9.71 | 13.64 | 9.35 | 7.58 | 2.80 | 5.78 | 11.15 | 7.64 | 8.21 | 11.88 | 9.02 | 10.97 |
HS (90%) | 7.02 | 6.94 | 14.90 | 6.62 | 8.06 | 6.74 | 8.23 | 7.42 | 8.72 | 5.82 | 12.37 | 6.36 |
HS (95%) | 4.10 | 4.93 | 9.23 | 3.76 | 8.40 | 6.06 | 8.13 | 4.62 | 8.22 | 4.03 | 6.00 | 3.87 |
HS (99%) | 2.28 | 3.10 | 1.34 | 6.61 | 6.95 | 8.02 | 9.24 | 3.62 | 2.80 | 6.87 | 10.45 | 7.21 |
NS | 67.03 | 62.57 | 50.62 | 60.72 | 61.42 | 60.03 | 51.19 | 63.21 | 58.90 | 65.11 | 49.19 | 56.35 |
Table 12
LST changing pattern for each 100 m buffer zone for selected green space in 2001, 2011, and 2021."
Green space | Area of green space (m2) | Average LST of green space (°C) | Average LST for 0-100 m buffer zone (°C) | Average LST for 100-200 m buffer zone (°C) | Average LST for 200-300 m buffer zone (°C) | Cooling distance (m) |
---|---|---|---|---|---|---|
2001 | ||||||
Sample 1 | 175,070.00 | 28.94 | 31.53 | 32.67 | 32.60 | 82 |
Sample 2 | 648,622.00 | 29.14 | 30.85 | 31.27 | 31.29 | 56 |
2011 | ||||||
Sample 1 | 261,901.08 | 28.93 | 30.20 | 30.55 | 30.42 | 117 |
Sample 2 | 27,425.20 | 29.87 | 31.07 | 31.57 | 31.30 | 30 |
2021 | ||||||
Sample 1 | 25,110.87 | 34.06 | 35.59 | 36.60 | 36.30 | 138 |
Sample 2 | 36,209.57 | 33.12 | 35.09 | 36.15 | 36.45 | 60 |
[1] | Ara S., Alif M.A.U.J., Islam K.M.A., et al., 2021. Impact of tourism on LULC and LST in a coastal island of Bangladesh: A geospatial approach on St. Martin’s Island of Bay of Bengal. J. Indian Soc. Remote Sens. 49(10), 2329-2345. |
[2] | Ashwini K., Sil B.S., 2022. Impacts of land use and land cover changes on land surface temperature over Cachar Region, Northeast India—A case study. Sustainability. 14(21), 14087, doi: 10.3390/su142114087. |
[3] | Avdan U., Jovanovska G., 2016. Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. J. Sens. 2016, 1480307, doi: 10.1155/2016/1480307. |
[4] | Bajani S., Das D., 2020. Sustainable planning interventions in tropical climate for urban heat island mitigation—Case study of Kolkata. In: GhoshM., (ed.). Perception, Design and Ecology of the Built Environment. Cham: Springer, 167-182. |
[5] | Basu T., Das A., 2023. Urbanization induced degradation of urban green space and its association to the land surface temperature in a medium-class city in India. Sustain. Cities Soc. 90, 104373, doi: 10.1016/j.scs.2022.104373. |
[6] | Bera B., Shit P.K., Saha S., et al., 2021. Exploratory analysis of cooling effect of urban wetlands on Kolkata metropolitan city region, eastern India. Curr. Res. Environ. Sustain. 3, 100066, doi: 10.1016/j.crsust.2021.100066. |
[7] | Bhat P.A., ul Shafiq M., Mir A.A., et al., 2017. Urban sprawl and its impact on land use/land cover dynamics of Dehradun City, India. Int. J. Sustain. Built Environ. 6(2), 513-521. |
[8] | Biney E., Boakye E., 2021. Urban sprawl and its impact on land use land cover dynamics of Sekondi-Takoradi metropolitan assembly, Ghana. Environ. Chall. 4, 100168, doi: 10.1016/j.envc.2021.100168. |
[9] | Biswas A., Singh O., 2017. Rajarhat new town an urban perspective: A case study of urbanization, West Bengal, India. Int. J. New Technol. Res. 3(5), 39-44. |
[10] | Chakrabarty K., Mandal K., Srivastava J.K., et al., 2015. Land, People and Power:An Anthropological Study of Emerging Mega City of New Town, Rajarhat. New Delhi: Gyan Publishing House. |
[11] | Chatterjee U., Majumdar S., 2022. Impact of land use change and rapid Urbanization on urban heat island in Kolkata city: A remote sensing based perspective. J. Urban Manag. 11(1), 59-71. |
[12] | Choudhury D., Das A., Das M., 2021. Investigating thermal behavior pattern (TBP) of local climatic zones (LCZs): A study on industrial cities of Asansol-Durgapur development area (ADDA), eastern India. Urban Clim. 35, 10072, doi: 10.1016/j.uclim.2020.100727. |
[13] | Cohen B., 2004. Urban growth in developing countries: A review of current trends and a caution regarding existing forecasts. World Dev. 32(1), 23-51. |
[14] | Das N., Mondal P., Sutradhar S., et al., 2021. Assessment of variation of land use/land cover and its impact on land surface temperature of Asansol subdivision. Egypt. J. Remote Sens. Space Sci. 24(1), 131-149. |
[15] | Das S., Angadi D.P., 2020. Land use-land cover (LULC) transformation and its relation with land surface temperature changes: A case study of Barrackpore Subdivision, West Bengal, India. Remote Sens. Appl.: Soc. Environ. 19, 100322, doi: 10.1016/j.rsase.2020.100322. |
[16] | De B., Mukherjee M., 2018. Optimisation of canyon orientation and aspect ratio in warm-humid climate: Case of Rajarhat Newtown, India. Urban Clim. 24, 887-920. |
[17] | Deng Y.H., Wang S.J., Bai X.Y., et al., 2018. Relationship among land surface temperature and LUCC, NDVI in typical karst area. Sci. Rep. 8(1), 641, doi: 10.1038/s41598-017-19088-x. |
[18] | Deng Z., Zhu X., He Q., et al., 2019. Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area. Adv. Space Res. 63(7), 2144-2154. |
[19] | Dewan A.M., Yamaguchi Y., 2009. Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization. Appl. Geogr. 29(3), 390-401. |
[20] | Dey I., Samaddar R., Sen S.K., et al., 2016. Beyond Kolkata: Rajarhat and the Dystopia of Urban Imagination (1st ed.). New Delhi: Routledge India. |
[21] | Ding L., Chen K.L., Liu T., et al., 2015. Spatial-temporal hotspot pattern analysis of provincial environmental pollution incidents and related regional sustainable management in China in the period 1995-2012. Sustainability. 7(10), 14385-14407. |
[22] | Dong Y., Ren Z., Fu Y., et al., 2022. Decrease in the residents’ accessibility of summer cooling services due to green space loss in Chinese cities. Environ. Int. 158, 107002, doi: 10.1016/j.envint.2021.107002. |
[23] | Dou P., Chen Y., 2017. Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015. Int. J. Remote Sens. 38(19), 5388-5407. |
[24] | Dutta D., Rahman A., Paul S.K., et al., 2020., Estimating urban growth in peri-urban areas and its interrelationships with built-up density using earth observation datasets. Ann. Reg. Sci. 65(1), 67-82. |
[25] | Dwivedi A., 2019. Macro- and micro-level studies using Urban Heat Islands to simulate effects of greening, building materials and other mitigating factors in Mumbai city. Archit. Sci. Rev. 62(2), 126-144. |
[26] | Environmental Systems Research Institute, 2023. ArcGIS Desktop: Hot Spot Analysis (Getis-Ord Gi*). [2023-05-19]. https://desktop.arcgis.com/en/arcmap/10.3/tools/spatial-statisticstoolbox/hot-spot-analysis.htm. |
[27] | Gavsker K.K., 2023. Urban growth, changing relationship between biophysical factors and surface thermal characteristics: A geospatial analysis of Agra City, India. Sustain. Cities Soc. 94, 104542, doi: 10.2139/ssrn.4216250. |
[28] | Ghosh S., Das A., 2018. Modelling urban cooling island impact of green space and water bodies on surface urban heat island in a continuously developing urban area. Model. Earth Syst. Environ. 4(2), 501-515. |
[29] | Grover A., Singh R.B., 2015. Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): A comparative study of Delhi and Mumbai. Environments. 2(2), 125-138. |
[30] | Guchhait S., Das N., Dolui G., et al., 2023. Effects of land use and land cover on surface urban heat island (SUHI) in Durgapur-Asansol industrial region:A linear regression approach. In: SahuA.S., Das ChatterjeeN., (eds.). Environmental Management and Sustainability in India. Cham: Springer. |
[31] | Guha S., Govil H., 2022. Annual assessment on the relationship between land surface temperature and six remote sensing indices using Landsat data from 1988 to 2019. Geocarto Int. 37(15), 4292-4311. |
[32] | Halder B., Bandyopadhyay J., Banik P., et al., 2021. Monitoring the effect of urban development on urban heat island based on remote sensing and geo-spatial approach in Kolkata and adjacent areas, India. Sustain. Cities Soc. 74, 103186, doi: 10.1016/j.scs.2021.103186. |
[33] | Halder B., Bandyopadhyay J., Al-Hilali A.A., et al., 2022a. Assessment of urban green space dynamics influencing the surface urban heat stress using advanced geospatial techniques. Agronomy. 12(9), 2129, doi: 10.3390/agronomy12092129. |
[34] | Halder B., Bandyopadhyay J., Khedher K.M., et al., 2022b. Delineation of urban expansion influences urban heat islands and natural environment using remote sensing and GIS-based in industrial area. Environ. Sci. Pollut. Res. 29(48), 73147-73170. |
[35] | Haldar S., Mandal S., Bhattacharya S., et al., 2023. Dynamicity of land use/land cover (LULC): An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation (DMC) in India. Regional Sustainability. 4(2), 150-172. |
[36] | Hassan N.A., Hashim Z., Hashim J.H., et al., 2014. Impact of climate change on air quality and public health in urban areas. Asia-Pac. J. Public Health. 28(2_suppl), 38-48. |
[37] | Kafy A.A., Rahman M.S., Hasan M.M., et al., 2020. Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sens. Appl.: Soc. Environ. 18, 100314, doi: 10.1016/j.rsase.2020.100314. |
[38] | Kafy A.A., Dey N.N., Al Rakib A., et al., 2021a. Modeling the relationship between land use/land cover and land surface temperature in Dhaka, Bangladesh using CA-ANN algorithm. Environ. Chall. 4, 100190, doi: 10.1016/j.envc.2021.100190. |
[39] | Kafy A.A., Faisal A.A., Rahman M.S., et al., 2021b. Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh. Sustain. Cities Soc. 64, 102542, doi: 10.1016/j.scs.2020.102542. |
[40] | Kafy A.A., Al Rakib A., Akter K.S., et al., 2021c. Monitoring the effects of vegetation cover losses on land surface temperature dynamics using geospatial approach in Rajshahi City, Bangladesh. Environ. Chall. 4, 100187, doi: 10.1016/j.envc.2021.100187. |
[41] | Kayet N., Pathak K., Chakrabarty A., et al., 2016. Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Model. Earth Syst. Environ. 2, doi: 10.1007/s40808-016-0159-x. |
[42] | Khan M.S., Ullah S., Chen L., 2021. Comparison on land-use/land-cover indices in explaining land surface temperature variations in the city of Beijing, China. Land. 10(10), 1018, doi: 10.3390/land10101018. |
[43] |
Khwarahm N.R., 2021. Spatial modeling of land use and land cover change in Sulaimani, Iraq, using multitemporal satellite data. Environ. Monit. Assess. 193(3), 148, doi: 10.1007/s10661-021-08959-6.
pmid: 33638037 |
[44] | Koko A.F., Yue W., Abubakar G.A., et al., 2021. Spatiotemporal influence of land use/land cover change dynamics on surface urban heat island: A case study of Abuja metropolis, Nigeria. ISPRS Int. J. Geo-Inf. 10(5), 272, doi: 10.3390/ijgi10050272. |
[45] | Kumari B., Tayyab M., Shahfahad, et al., 2018. Satellite-driven land surface temperature (LST) using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) data and its association with built-up and green cover over urban Delhi, India. Remote Sens. Earth Syst. Sci. 1, 63-78. |
[46] | Kundu R., 2016. Making sense of place in Rajarhat New Town: The village in the urban and the urban in the village. Econ. Political Wkly. 17(51), 93-110. |
[47] | Li L., Zhao K., Wang X., et al., 2022. Spatio-temporal evolution and driving mechanism of Urbanization in small cities: Case study from Guangxi. Land. 11(3), 415, doi: 10.3390/land11030415. |
[48] | Liang X., Ji X., Guo N., et al., 2021. Assessment of urban heat islands for land use based on urban planning: A case study in the main urban area of Xuzhou City, China. Environ. Earth Sci. 80, 308, doi: 10.1007/s12665-021-09588-5. |
[49] | Liu K., Li X., Shi X., et al., 2008. Monitoring mangrove forest changes using remote sensing and GIS data with decision-tree learning. Wetlands. 28(2), 336-346. |
[50] | Lunetta R.S., Knight J.F., Ediriwickrema J., et al., 2006. Land-cover change detection using multi-temporal MODIS NDVI data. Remote Sens. Environ. 105(2), 142-154. |
[51] | Machiwa H., Mango J., Sengupta D., et al., 2021. Using time-series remote sensing images in monitoring the spatial-temporal dynamics of LULC in the Msimbazi basin, Tanzania. Land. 10(11), 1139, doi: 10.3390/land10111139. |
[52] | Mansourmoghaddam M., Rousta I., Cabral P., et al., 2023a. Investigation and prediction of the land use/land cover (LU/LC) and land surface temperature (LST) changes for Mashhad City in Iran during 1990-2030. Atmosphere. 14(4), 741, doi: 10.3390/atmos14040741. |
[53] | Mansourmoghaddam M., Naghipur N., Rousta I., et al., 2023b. Quantifying the effects of green-town development on land surface temperatures (LST) (A case study at Karizland (Karizboom), Yazd, Iran). Land. 12(4), 885, doi: 10.3390/land12040885. |
[54] | Mehrotra S., Bardhan R., Ramamritham K., et al., 2018. Urban informal housing and surface urban heat island intensity: Exploring spatial association in the city of Mumbai. Environ. Urban. ASIA. 9(2), 158-177. |
[55] | Meyer W.B., Turner B.L., 1992. Human population growth and global land-use/cover change. Annu. Rev. Ecol. Syst. 23(1), 39-61. |
[56] | Mitra D., Banerji S., 2018. Urbanisation and changing waterscapes: A case study of New Town, Kolkata, West Bengal, India. Appl. Geogr. 97, 109-118. |
[57] | Moazzam M.F.U., Doh Y.H., Lee B.G., 2022. Impact of urbanization on land surface temperature and surface urban heat Island using optical remote sensing data: A case study of Jeju Island, Republic of Korea. Build. Environ. 222, 109368, doi: 10.1016/j.buildenv.2022.109368. |
[58] | Mohammad P., Goswami A., Chauhan S., et al., 2022. Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India. Urban Clim. 42, 101116, doi: 10.1016/j.uclim. 2022.101116. |
[59] | Montanaro M., Gerace A., Rohrbach S., et al., 2015. Toward an operational stray light correction for the Landsat 8 Thermal Infrared Sensor. Appl. Opt. 54(13), 3963-3978. |
[60] | Njoku E.A., Tenenbaum D.E., 2022. Quantitative assessment of the relationship between land use/land cover (LULC), topographic elevation and land surface temperature (LST) in Ilorin, Nigeria. Remote Sens. Appl.: Soc. Environ. 27, 100780, doi: 10.1016/j.rsase.2022.100780. |
[61] | Ogashawara I., Bastos V., 2012. A quantitative approach for analyzing the relationship between urban heat islands and land cover. Remote Sens. 4(11), 3596-3618. |
[62] | Pal S., Ziaul S., 2017. Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egypt. J. Remote Sens. Space Sci. 20(1), 125-145. |
[63] | Patra S., Sahoo S., Mishra P., et al., 2018. Impacts of urbanization on land use/cover changes and its probable implications on local climate and groundwater level. J. Urban Manag. 7(2), 70-84. |
[64] | Rahaman S., Kumar P., Chen R., et al., 2020. Remote sensing assessment of the impact of land use and land cover change on the environment of Barddhaman district, West Bengal, India. Front. Environ. Sci. 8, 127, doi: 10.3389/fenvs.2020.00127. |
[65] | Rahman M.N., Rony M.R.H., Jannat F.A., et al., 2022. Impact of urbanization on urban heat island intensity in major districts of Bangladesh using remote sensing and geo-spatial tools. Climate. 10(1), 3, doi: 10.3390/cli10010003. |
[66] | Ramaiah M., Avtar R., Rahman M.M., 2020. Land cover influences on LST in two proposed smart cities of India: Comparative analysis using spectral indices. Land. 9(9), 292, doi: 10.3390/land9090292. |
[67] | Rousta I., Sarif M.O., Gupta R.D., et al., 2018. Spatiotemporal analysis of land use/land cover and its effects on surface urban heat Island using Landsat data: A case study of Metropolitan City Tehran (1988-2018). Sustainability. 10(12), 4433, doi: 10.3390/su10124433. |
[68] | Saha S., Saha A., Das M., et al., 2021. Analyzing spatial relationship between land use/land cover (LULC) and land surface temperature (LST) of three urban agglomerations (UAs) of Eastern India. Remote Sens. Appl.: Soc. Environ. 22, 100507, doi: 10.1016/j.rsase.2021.10050. |
[69] | Santhosh L.G., Shilpa D.N., 2023. Assessment of LULC change dynamics and its relationship with LST and spectral indices in a rural area of Bengaluru district, Karnataka India. Remote Sens. Appl.: Soc. Environ. 29, 100886, doi: 10.1016/j.rsase.2022.100886. |
[70] | Seyam M.M.H., Haque M.R., Rahman M.M., 2023. Identifying the land use land cover (LULC) changes using remote sensing and GIS approach: A case study at Bhaluka in Mymensingh, Bangladesh. Case Stud. Chem. Environ. Eng. 7, 100293, doi: 10.1016/j.cscee.2022.100293. |
[71] | Shahfahad, Talukdar, S., Rihan, M., et al., 2022. Modelling urban heat island (UHI) and thermal field variation and their relationship with land use indices over Delhi and Mumbai metro cities. Environ. Dev. Sustain. 24, 3762-3790. |
[72] | Shalaby A., Tateishi R., 2007. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Appl. Geogr. 27(1), 28-41. |
[73] | Singh A., 1989. Review article digital change detection techniques using remotely-sensed data. Int. J. Remote Sens. 10(6), 989-1003. |
[74] | Story M., Congalton R.G., 1986. Remote sensing brief accuracy assessment: A user’s perspective. Photogramm. Eng. Remote Sens. 52(3), 397-399. |
[75] | Sun Q., Zhang C., Liu M., et al., 2016. Land use and land cover change based on historical space-time model. Solid Earth. 7(5), 1395-1403. |
[76] | Tan J., Yu D., Li Q., et al., 2020. Spatial relationship between land-use/land-cover change and land surface temperature in the Dongting Lake area, China. Sci. Rep. 10, 9245, doi: 10.1038/s41598-020-66168-6. |
[77] | Tarawally M., Xu W.B., Hou., W.M., et al., 2019. Land use/land cover change evaluation using land change modeller: A comparative analysis between two main cities in Sierra Leone. Remote Sens. Appl.: Soc. Environ. 16, 100262, doi: 10.1016/j.rsase.2019.100262. |
[78] | Tiando D.S., Hu S., Fan X., et al., 2021. Tropical coastal land-use and land cover changes impact on ecosystem service value during rapid urbanization of Benin, West Africa. Int. J. Environ. Res. Public Health. 18(14), 7416, doi: 10.3390/ijerph18147416. |
[79] | Tomlinson C.J., Chapman L., Thornes J.E., et al., 2011. Including the urban heat island in spatial heat health risk assessment strategies: A case study for Birmingham, UK. Int. J. Health Geogr. 10, 42, doi: 10.1186/1476-072X-10-42. |
[80] | Tran D.X., Pla F., Latorre-Carmona P., et al., 2017. Characterizing the relationship between land use land cover change and land surface temperature. ISPRS J. Photogramm. Remote Sens. 124, 119-132. |
[81] | Verma R., Garg P.K., 2021. Mapping the spatiotemporal changes of land use/land cover on the urban heat island effect by open source data: A case study of Lucknow, India. J. Indian Soc. Remote Sens. 49(11), doi: 10.1007/s12524-021-01421-7. |
[82] | Voogt J.A., Oke T.R., 2003. Thermal remote sensing of urban climates. Remote Sens. Environ. 86(3), 370-384. |
[83] | Wang C., Ren Z., Chang X., et al., 2023. Understanding the cooling capacity and its potential drivers in urban forests at the single tree and cluster scales. Sustain. Cities Soc. 93, 104531, doi: 10.1016/j.scs.2023.104531. |
[84] | Wang Q., Wang H., 2022. Spatiotemporal dynamics and evolution relationships between land-use/land cover change and landscape pattern in response to rapid urban sprawl process: A case study in Wuhan, China. Ecol. Eng. 182, 106716, doi: 10.1016/j.ecoleng.2022.106716. |
[85] | WBHIDCO (West Bengal Housing Infrastructure Development Corporation Ltd.), 2010. Annual Report 2010-2011. [2023-05-19]. https://www.wbhidcoltd.com/upload_file/report_publication/report2.pdf. |
[86] | WBHIDCO (West Bengal Housing Infrastructure Development Corporation Ltd.), 2015. Annual Report 2015-2016. [2023-05-19]. https://www.wbhidcoltd.com/upload_file/report_publication/HIDCO-AnualReport-2015-16.pdf. |
[87] | WBHIDCO (West Bengal Housing Infrastructure Development Corporation Ltd.), 2023. The Journey of New Town Kolkata. [2023-05-19]. https://www.wbhidcoltd.com/about-new-town. |
[88] | Yadav N., Sharma C., Peshin S.K., et al., 2017. Study of intra-city urban heat island intensity and its influence on atmospheric chemistry and energy consumption in Delhi. Sustain. Cities Soc. 32, 202-211. |
[89] | Yadav N., Sharma C., 2018. Spatial variations of intra-city urban heat island in megacity Delhi. Sustain. Cities Soc. 37, 298-306. |
[1] | Liton Chandra VOUMIK, Md. Hasanur RAHMAN, Md. Maznur RAHMAN, Mohammad RIDWAN, Salma AKTER, Asif RAIHAN. Toward a sustainable future: Examining the interconnectedness among Foreign Direct Investment (FDI), urbanization, trade openness, economic growth, and energy usage in Australia [J]. Regional Sustainability, 2023, 4(4): 405-415. |
[2] | LI Guoyi, LIU Jiahong, SHAO Weiwei. Urban flood risk assessment under rapid urbanization in Zhengzhou City, China [J]. Regional Sustainability, 2023, 4(3): 332-348. |
[3] | Subrata HALDAR, Somnath MANDAL, Subhasis BHATTACHARYA, Suman PAUL. Dynamicity of Land Use/Land Cover (LULC): An analysis from peri-urban and rural neighbourhoods of Durgapur Municipal Corporation (DMC) in India [J]. Regional Sustainability, 2023, 4(2): 150-172. |
[4] | WANG Yuchan, GAO Genghe, NING Xiaoju, LI Yuanzheng, NIU Ning, GUO Yaqi. Willingness of returning migrant workers to purchase houses: A case study of 45 villages in Henan Province, China [J]. Regional Sustainability, 2022, 3(2): 133-145. |
[5] | Ahmed Mohammed Sayed MOHAMMED, Tetsuya UKAI, Michael HALL. Towards a sustainable campus-city relationship: A systematic review of the literature [J]. Regional Sustainability, 2022, 3(1): 53-67. |
[6] | Yanhua He, Yi Lin, Guohua Zhou, Yixuan Zhu, Kai Tang. Spatial pattern and drivers of urbanization in China’s mid-level developing urban agglomeration: A case study of Chang-Zhu-Tan [J]. Regional Sustainability, 2021, 2(1): 83-97. |
[7] | Yupeng Liu, Jiajia Li, Linlin Duan, Min Dai, Wei-qiang Chen. Material dependence of cities and implications for regional sustainability [J]. Regional Sustainability, 2020, 1(1): 31-36. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||