Abstract
Most noticeable and direct impacts of urbanization on the environment is the changes in daily and seasonal thermal variable and increase the hydraulic stress. Both are cumulatively formed the urban heat island (UHI) effects. From the sustainability point of view, it is very crucial for research to focus on how to mitigate UHI. Therefore, objective of this study is to evaluate the cooling effects of different types of urban greenery at local and park level. For land surface temperature, Landsat 8—TIRS and for greenery mapping Sentinel 2A imageries have been used. LST was derived using the single channel algorithm, and greenery was mapping using combination of unsupervised (i.e., ISODATA) and supervised (i.e., maximum likelihood) image classification techniques. Numbers of class level metrics like PLAND PD; LPI; AREA_MN etc. used to measure composition and configuration of urban greenery. Ordinary least square regression, multiple linear regression, and spatial auto-regression model were used to measure the cooling effects of urban green space quantitatively. Besides, park level analysis also was done using park cooling intensity (PCI) and its relationship with park land use. Results shows that amount of urban greenery in terms of percentage of land cover or average size of the green patch is very important to reduce the UHI. PLAND, LPI, AREA_MN and COHESION negatively related with LST whereas PD, LSI, SHAPE_MN, and ENN_MN positively related with LST. So, fragmented vegetation area, green space with complex shape could increase the LST. If we look at the vegetation types and relation with LST, only dense vegetation and trees and plantation can effectively reduce the LST. Besides PCI negatively related with amount of built-up area and open space within parks. Finally, this study can help to the urban and land use planning to build a heat resilience city.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- PLAND:
-
Percentage of land cover class
- PD:
-
Patch density
- LPI:
-
Largest patch index
- AREA_MN:
-
Mean area
- ED:
-
Edge density
- LSI:
-
Landscape shape index
- SHAPE_MN:
-
Mean shape index
- COHESION:
-
Cohesion
- ENN_MN:
-
Mean Euclidian distance
- GCI:
-
Green park cooling intensity
- PCI:
-
Park cooling intensity
- CR:
-
Cooling range
- TA:
-
Temperature amplitude
- TG:
-
Temperature gradient.
References
Ahmedabad Municipal Corporation (2013) Ahmedabad Heat Action Plan, pp 1–24. https://doi.org/10.1371/journal.pone.0091831
Alavipanah S, Wegmann M, Qureshi S, Weng Q, Koellner T (2015) The role of vegetation in mitigating urban land surface temperatures: a case study of Munich, Germany during the warm season. Sustainability (Switzerland) 7(4):4689–4706. https://doi.org/10.3390/su7044689
Anselin L (2004) Exploring spatial data with GeoDaTM: a workbook. Urbana 51(61801):309
Anselin L, Syabri I, Kho Y (2006) GeoDa: an introduction to spatial data analysis. Geogr Anal 38(1):5–22
Buyadi SNA, Mohd WMNW, Misni A (2014) Quantifying green space cooling effects on the urban microclimate using remote sensing and GIS techniques. In: FIG congress, Kuala Lumpur, Malaysia, pp 1–16. http://www.fig.net/resources/proceedings/fig_proceedings/fig2014/papers/ts09g/TS09G_buyadi_wan_mohd_et_al_6897.pdf. Accessed 14 Sept 2018
Cao X, Onishi A, Chen J, Imura H (2010) Quantifying the cool island intensity of urban parks using ASTER and IKONOS data. Landsc Urban Plan 96(4):224–231. https://doi.org/10.1016/j.landurbplan.2010.03.008
Chaudhary J (2018) India needs strategies to combat urban heat stress. India Climate Dialogue. https://indiaclimatedialogue.net/2018/03/09/india-needs-strategies-combat-urban-heat-stress/. Accessed 11 Sept 2018
Chen A, Yao L, Sun R, Chen L (2014) How many metrics are required to identify the effects of the landscape pattern on land surface temperature? Ecol Indic 45:424–433
Cinar I (2015) Assessing the correlation between land cover conversion and temporal climate change—a pilot study in coastal Mediterranean city, Fethiye Turkey. Atmosphere 6(8):1102–1118. https://doi.org/10.3390/atmos6081102
Congedo L (2016) Semi-automatic classification plugin documentation. Release 4(0.1):29
Connors JP, Galletti CS, Chow WTL (2013) Landscape configuration and urban heat island effects: assessing the relationship between landscape characteristics and land surface temperature in Phoenix, Arizona. Landsc Ecol 28(2):271–283. https://doi.org/10.1007/s10980-012-9833-1
Das Majumdar D, Biswas A (2016) Quantifying land surface temperature change from LISA clusters: an alternative approach to identifying urban land use transformation. Landsc Urban Plan 153:51–65. https://doi.org/10.1016/j.landurbplan.2016.05.001
Delhi Master Plan 2021 (2007). https://dda.org.in/planning/mpd_2021_environment.htm. Accessed 14 Aug 2018
De Knegt HJ, van Langevelde, F van, Coughenour MB, Skidmore AK, De Boer WF, Heitkönig IMA, De Boer WF, Prins HHT (2010) Spatial autocorrelation and the scaling of species–environment relationships. Ecology 91(8):2455–2465
Du H, Cai W, Xu Y, Wang Z, Wang Y, Cai Y (2017) Quantifying the cool island effects of urban green spaces using remote sensing Data. Urban For Urban Green 27(July):24–31. https://doi.org/10.1016/j.ufug.2017.06.008
EPA (2016) Heat island cooling strategies. https://www.epa.gov/heat-islands/heat-island-cooling-strategies. Retrieved 15 Dec 2018
Estoque RC, Murayama Y, Myint SW (2017) Effects of landscape composition and pattern on land surface temperature: an urban heat island study in the megacities of Southeast Asia. Sci Total Environ 577:349–359. https://doi.org/10.1016/j.scitotenv.2016.10.195
Fan C, Myint SW, Zheng B (2015) Measuring the spatial arrangement of urban vegetation and its impacts on seasonal surface temperatures. Prog Phys Geogr 39(2):199–219. https://doi.org/10.1177/0309133314567583
Gergel SE, Turner MG (2002) Learning landscape ecology. Libro. https://doi.org/10.1007/b97339
Giguère M (2007) Urban heat island mitigation strategies. Retrieved from https://www.inspq.qc.ca/pdf/publications/1513_UrbanHeatIslandMitigationStrategies.pdf. Accessed 12 Aug 2018
Gujarati DN (2009) Basic econometrics. Tata McGraw-Hill Education, New York
Hewitt V, Mackers E, Shickman K (2014) Cool policies for cool cities: best practices for mitigating urban heat islands in North American cities, Washington DC. https://www.coolrooftoolkit.org/wp-content/uploads/2014/06/ACEEE_GCCA-UHI-Policy-Survey-FINAL.pdf. Accessed 13 July 2017
Kim J, Gu D, Sohn W, Kil S, Kim H, Lee D (2016) Neighborhood landscape spatial patterns and land surface temperature: an empirical study on single-family residential areas in Austin, Texas. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph13090880
Lall BA, Talwar S, Shetty S, Singh M (2014) Scoping study for policy initiatives to minimise urban heat island effect for low carbon urban growth, New Delhi. https://shaktifoundation.in/wp-content/uploads/2017/06/Scoping-Study-for-Policy-Initiatives-to-Minimize-Urban-Heat-Island-effect1.pdf. Accessed 13 June 2018
Li J, Song C, Cao L, Zhu F, Meng X, Wu J (2011) Impacts of landscape structure on surface urban heat islands: a case study of Shanghai, China. Remote Sens Environ 115(12):3249–3263. https://doi.org/10.1016/j.rse.2011.07.008
Li X, Zhou W, Ouyang Z, Xu W, Zheng H (2012) Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China. Landsc Ecol 27(6):887–898. https://doi.org/10.1007/s10980-012-9731-6
McGarigal K, Cushman SA, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. Computer software program produced by the authors at the University of Massachusetts, Amherst. Available at: http://www.umass.edu/landeco/research/fragstats/fragstats.html
Rasul A, Balzter H, Smith C, Remedios J, Adamu B, Sobrino J, Srivanit M, Weng Q (2017) A review on remote sensing of urban heat and cool islands. Land 6(2):38. https://doi.org/10.3390/land6020038
Revi A, Satterthwaite DE, Aragón-Durand F, Corfee-Morlot J, Kiunsi RBR, Pelling M, Roberts DC, Solecki W (2014) Urban areas. In: Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, pp 535–612. https://doi.org/10.1017/CBO9781107415379.013
Sannigrahi S, Bhatt S, Rahmat S, Uniyal B, Banerjee S, Chakraborti S, Jha S, Lahiri S, Santra K, Bhatt A (2017) Analyzing the role of biophysical compositions in minimizing urban land surface temperature and urban heating. Urban Clim. https://doi.org/10.1016/j.uclim.2017.10.002
Shih W (2017) Greenspace patterns and the mitigation of land surface temperature in Taipei metropolis. Habitat Int 60:69–80. https://doi.org/10.1016/j.habitatint.2016.12.006
Siderius C (2017) Will heat make life impossible in the Indus-Ganges plain? https://www.wur.nl/en/newsarticle/Will-heat-make-life-impossible-in-the-Indus-Ganges-plain.htm. Retrieved 29 Dec 2018
Sobrino JA, Jiménez-Muñoz JC, Paolini L (2004) Land surface temperature retrieval from LANDSAT TM 5. Remote Sens Environ 90(4):434–440. https://www.sciencedirect.com/science/article/pii/S0034425704000574. Accessed 24 Aug 2018
Su WZ, Gu CL, Yang GS (2010) Assessing the impact of land use/land cover on urban heat island pattern in Nanjing City, China. J Urban Plan Dev ASCE 136(4):365–372. https://doi.org/10.1061/(asce)up.1943-5444.0000033
Sun Q, Wu Z, Tan J (2012) The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environ Earth Sci 65(6):1687–1694. https://doi.org/10.1007/s12665-011-1145-2
Turner MG, O’Neill RV, Gardner RH, Milne BT (1989) Effects of changing spatial scale on the analysis of landscape pattern. Landsc Ecol 3(3–4):153–162
Voogt JA (2004) Urban heat islands: hotter cities. Am Inst Biol Sci. https://doi.org/10.1163/_q3_SIM_00374
Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384. https://doi.org/10.1016/S0034-4257(03)00079-8
Walawender JP, Hajto MJ, Iwaniuk P (2012) A new ArcGIS toolset for automated mapping of land surface temperature with the use of LANDSAT satellite data. In: 2012 IEEE international geoscience and remote sensing symposium (IGARSS). IEEE, pp 4371–4374
Watts N, Amann M, Ayeb-Karlsson S, Belesova K, Bouley T, Boykoff M, Costello A et al (2018) The Lancet Countdown on health and climate change: from 25 years of inaction to a global transformation for public health. Lancet 391(10120):581–630. https://doi.org/10.1016/S0140-6736(17)32464-9
Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89(4):467–483. https://doi.org/10.1016/j.rse.2003.11.005
Yang C, He X, Yu L, Yang J, Yan F, Bu K, Zhang S et al (2017a) The cooling effect of urban parks and its monthly variations in a snow climate city. Remote Sens 9(10):1066. https://doi.org/10.3390/rs9101066
Yang J, Sun J, Ge Q, Li X (2017b) Assessing the impacts of urbanization-associated green space on urban land surface temperature: a case study of Dalian, China. Urban For Urban Green 22:1–10. https://doi.org/10.1016/j.ufug.2017.01.002
Yokohari M, Brown RD, Kato Y, Moriyama H (1997) Effects of paddy fields on summertime air and surface temperatures in urban fringe areas of Tokyo, Japan. Landsc Urban Plan 38(1–2):1–11
Zhang Y, Odeh IOA, Han C (2009a) Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. Int J Appl Earth Observ Geoinf 11(4):256–264
Zhang X, Zhong T, Feng X, Wang K (2009b) Estimation of the relationship between vegetation patches and urban land surface temperature with remote sensing. Int J Remote Sens 30(8):2105–2118
Zhang X, Estoque RC, Murayama Y (2017) An urban heat island study in Nanchang City, China based on land surface temperature and social-ecological variables. Sustain Cities Soc 32(January):557–568. https://doi.org/10.1016/j.scs.2017.05.005
Zhibin R, Haifeng Z, Xingyuan H, Dan Z, Xingyang Y (2015) Estimation of the relationship between urban vegetation configuration and land surface temperature with remote sensing. J Indian Soc Remote Sens 43(1):89–100. https://doi.org/10.1007/s12524-014-0373-9
Zhou W, Huang G, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plan 102(1):54–63. https://doi.org/10.1016/j.landurbplan.2011.03.009
Acknowledgements
The authors would like to acknowledge to the Centre for the Study of Regional Development (CSRD), Jawaharlal Nehru University for necessary assistance and laboratory support during the period of research.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Pramanik, S., Punia, M. Assessment of green space cooling effects in dense urban landscape: a case study of Delhi, India. Model. Earth Syst. Environ. 5, 867–884 (2019). https://doi.org/10.1007/s40808-019-00573-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40808-019-00573-3