Abstract
Urban Green Spaces (UGS) are an integral part of urban environment and act as lungs for rejuvenating the urban environment and improving the quality of life and health of residents. UGS assist in regulating urban microclimate, biodiversity conservation, alleviating floods, enhancing air quality, and also promotes physical and mental wellbeing of urban populace. They also provide spaces for improved social environment and are considered highly beneficial for physical, social and cognitive development of urban children. UGS exists in diverse shape, size, vegetation cover and types and includes parks, gardens, railway corridors, road side green, derelict monument sites, etc. [1] defined UGS as urban land that consists of unsealed, permeable, soft surfaces such as soil, grass, shrubs and trees. Chandigarh is the first planned city of modern India and is known for its uniformly distributed and ample UGS within its boundaries. However, only quantification of amount of UGS is not sufficient to harness the full range of benefits from UGS for smart urban environment. The UGS should be accessible, uniformly distributed and maintained for its daily use by urban population. Although, there are restrictions on development within the Chandigarh, the high pace of urbanization, population increase and economic development in surrounding area is creating a pressure on infrastructure of Chandigarh as well. Chandigarh is also facing the issues of traffic congestion, air pollution and environmental degradation which were unheard before. The multi-faceted issues and benefits of UGS call for smart approaches for their effective utilization and management. Geospatial technologies integrated with Information and Communication Technology (ICT) tools provide a useful and smart tool in the hand of planners for quantification, assessment and evaluation of UGS for smart management. They can help in identifying the vulnerable areas as well as to assess the accessibility and distribution of UGS. They can be used for quantitative as well as qualitative analysis of UGS by applying a range of remote sensing data sets and geo-analysis. Many indices have been developed using these datasets for evaluation and monitoring of UGS. With the growing technological advancements in smart web-based tools, these technologies can also be used effectively for monitoring and management of UGS through integration of ICT tools and citizen centric services. This study demonstrates various innovative geospatial and ICT tools for evaluation and monitoring of UGS.
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References
Dunnett N, Swanwick C, Woolley H (2002) Improving urban parks, play areas and green spaces, urban research report, Department of Landscape, University of Sheffield, Department for Transport, Local Government and the Regions: London, http://publiekeruimte.info/Data/Documents/e842aqrm/53/Improving-Urban-Parks.pdf. Last accessed on 5 Nov 2018
Panagopoulos T, Gonzalez-Duque JA, Dan MB (2016) Urban planning with respect to environmental quality and human well-being. Environ Pollut 208:137–144
Nijkamp P, Leventa TB (2004) Urban green space policies: a comparative study on performance and success conditions in European cities. In: Proceedings of the 44th European congress of the European regional science association, 25–29 Aug 2004, Porto, Portugal. http://dare.ubvu.vu.nl/bitstream/1871/8932/1/20040022.pdf
Dole J (1989) Greenscape 5: green cities. Architects’ J 61–69
Lee ACK, Maheswaran R (2010) The health benefits of urban green spaces: a review of the evidence. J Public Health 33(2):212–222
Ulrich R, Simons R, Losito B, Fiorito E, Miles M, Zelson M (1991) Stress recovery during exposure to natural and urban environments. J Environ Psychol 11:201–230
Grahn P, Stigsdotter U (2003) Landscape planning and stress. Urban For Urban Green 2:1–18
Gupta K, Roy A, Luthra K, Maithani S, Mahavir (2016) GIS based analysis for assessing the accessibility at hierarchical levels of urban green spaces. Urban For Urban Green 18:198–211
McMahon ET (1996) Green enhances growth. http://plannersweb.com/1996/04/green-enhances-growth/. Last accessed on 5 Nov 2018
European Commission. Directive, 2008, 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe
World Health Organisation (2014) Burden of disease from ambient air pollution for 2012
De Ridder K (2003) Benefits of urban green space, EVK4-CT-2000-00041, Belgium
Mcpherson EG (1992) Accounting for benefits and costs of urban green space. Landsc Urban Plan 22:41–51
Coles R, Caserio M (2001) Social criteria for evaluation and development of urban green spaces. URGE-improving the quality of life in urban regions through urban green initiative, Project Deliverable 7. Pdf. Retrieved from http://www.urgeproject.ufz.de/PDF/D7SocialReport.pdf
Buff City Status Report (2003) Urban green structure. Baltic University Urban Forum City Status Report V. Retrieved from http://www.balticuniv.uu.se/index.php/component/docman/docdownload/229-urban-green-structure
Yigitcanlar T (2006) Australian local governments’ practice and prospects with online planning. URISA J 18(2):7–17
Jong M, Joss S, Schraven D, Zhan C, Weijnen M (2015) Sustainable–smart–resilient–low carbon–eco–knowledge cities; making sense of a multitude of concepts promoting sustainable urbanization. J Clean Prod 109:25–38
Yigitcanlar T (2016) Technology and the city: systems, applications and implications. Routledge, New York
Angelidou M (2015) Smart cities: a conjuncture of four forces. Cities 47:95–106
Hortz T (2016) The smart state test: a critical review of the smart state strategy 2005–2015’s knowledge-based urban development. Int J Knowl Based Dev 7(1):75–101
Yigitcanlar T, Baum S (2008) Benchmarking local e-government. In: Electronic government: concepts, methodologies, tools, and applications. IGI Global, pp 371–378
Caragliu A, Del Bo C, Nijkamp P (2011) Smart cities in Europe. J Urban Technol 18(2):65–82
Angelidou M (2014) Smart city policies: a spatial approach. Cities 41:S3–S11
Gudes O, Kendall E, Yigitcanlar T, Pathak V, Baum S (2010) Rethinking health planning: a framework for organising information to underpin collaborative health planning. Health Inf Manag J 39(2):18–29
Lara A, Costa E, Furlani T, Yigitcanlar T (2016) Smartness that matters: comprehensive and human-centred characterisation of smart cities. J Open Innov 2(8):1–13
Gupta K, Kumar P, Pathan SK, Sharma KP (2012) Urban neighborhood green index—a measure of green spaces in urban areas. Landsc Urban Plan 105(3):325–335
Jat MK, Garg PK, Khareb D (2008) Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int J Appl Earth Obs Geoinformation 10(1):26–43
Gupta K (2013) Unprecedented growth of Dehradun urban area: a spatio-temporal analysis. Int J Adv Remote Sens GIS Geogr 1(2):6–15
Sarika B, Gupta K, Kumar P (2015) Smart planning through geo-enabling of digital database for planning authorities: a case study of regional park zone (RPZ) pockets in Navi Mumbai notified area. In: ISG 2015 conference at Jaipur, 16–18 Dec 2015
Chowdhury PKR, Maithani S (2014) Modelling, urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata. Int J Appl Earth Obs Geoinformation 33(1):155–165
Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(2003):370–384
Vaidya G, Pawar AS, Gupta K (2017) Mapping urban green spaces for neighbourhood sustainability by using urban neighbourhood green index, Spandrel. Int J SPA Bhopal 1(12):27–39
Huete A, Didan K, Miura T, Rodriguez EP, Gao X, Ferreira LG (2002) Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ 83(1–2):195–213
Wu Z, Zhang Y (2018) Spatial variation of urban thermal environment and its relation to green space patterns: implication to sustainable landscape planning. Sustainability 10:2249. https://doi.org/10.3390/su10072249
Lee W-J, Lee C-W (2018) Forest canopy height estimation using multiplatform remote sensing dataset. J Sens. https://doi.org/10.1155/2018/1593129
Dhanda P, Nandy S, Kushwaha SPS, Ghosh S, Krishna Murthy YVN, Dadhwal VK (2017) Optimising spaceborne LiDAR and very high resolution optical sensor parameters for biomass estimation at ICESat/GLAS footprint level using regression algorithms. Prog Phys Geogr 41(3):247–267
Lang S, Blaschke T (2006) Bridging remote sensing and GIS—what are the main supporting pillars? In: International archives of photogrammetry, remote sensing and spatial information sciences; ISPRS: Vienna, Austria, vol XXXVI-4/C42
ThiLoi D, Tuan PA, Gupta K (2015) Development of an index for assessment of urban green Spacesat city level. Int J Remote Sens Appl 5(1):78
Clark RN (1999) Spectroscopy of rocks and minerals and principles of spectroscopy. In: Rencz AN (ed) Manual of remote sensing, vol 1. Wiley, New York, pp 3–58
Singh D, Singh A, Sharma AK, Sodhi L (1998) Burn mortality in Chandigarh zone: 25 years autopsy experience from a tertiary care hospital of India. Burns 24(2):150–156
Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS symposium, NASA SP-351 I, pp 309–317
Holme AM, Burnside DG, Mitchell AA (1987) The development of a system for monitoring trend in range condition in the arid shrublands of Western Australia. Aust Rangel J 9:14–20
Jansson M, Persson B (2010) Playground planning and management: an evaluation of standard-influenced provision through user needs. Urban For Urban Green 9:33–42
Grahn P, Stigsdotter UK (2010) The relation between perceived sensory dimensions of urban green space and stress restoration. Landsc Urban Plan 94:264–275
Hostetler M, Escobedo F (2016) What types of urban greenspace are better for carbon dioxide sequestration? WEC279, Wildlife Ecology and Conservation Department, UF/IFAS Extension. Retrieved from https://edis.ifas.ufl.edu/pdffiles/UW/UW32400.pdf
Jim CY, Chen WY (2009) Value of scenic views: hedonic assessment of private housing in Hong Kong. Landsc Urban Plan 91:226–234
Troy A, Grove JM (2008) Property values, parks, and crime: a hedonic analysis in Baltimore, MD. Landsc Urban Plan 87:233–245
Bao T, Li X, Zhang J, Zhang Y, Tian S (2016). Assessing the distribution of urban green spaces and its anisotropic cooling distance on urban heat island pattern in Baotou, China. Int J Geo Inf 5(12). https://doi.org/10.3390/ijgi5020012
Toftager M, Ekholm O, Schipperijn J, Stigsdotter U, Bentsen P, Gronbaek M, Randrup TB, Kamper-Jorgensen F (2011) Distance to green space and physical activity: a Danish national representative survey. J Phys Act Health 8(6):741–749
Meng Q-Y, Liu Y-Q, Li X-J (2014) Urban green space remote sensing retrieval with LIDAR and multi-spectral satellite data. In: Open GIS conference, Szekesfehervar, Hungary
Pitt R, Bannerman R, Clark S, Williamson D (2004) Sources of pollutants in urban areas (part 2)—recent sheetflow monitoring. J Water Manag Model, Jan 2005. https://doi.org/10.14796/jwmm.r223-24
Haboudane D, Miller JR, Tremblay N, Zarco-tejada P, Dextraze L (2002) Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture. Remote Sens Environ 81:416–426
Van Herzele A, Wiedemann T (2003) A monitoring tool for the provision of accessible and attractive urban green spaces. Landsc Urban Plan 63(2):109–126
Norberg-Schulz C (1979) Genius loci: towards a phenomenology of architecture. Rizzoli, New York
Caserio M (2001) User perceptions of landscape in the Val Fontanabuona, research proposal. UCE, Birmingham, UK
Sipilä M, Tyrväinen L (2005) Evaluation of collaborative urban forest planning in Helsinki, Finland. Urban For Urban Green 4(1):1–12
Brown S, Gillespie AJR, Lugo AE (1989) Biomass estimation methods for tropical forests with applications to forest inventory data. For Sci 35:881–902
Negi JDS, Manhas RK, Chauhan PS (2003) Carbon allocation in different components of some tree species of India: a new approach for carbon estimation. Curr Sci 85(11):1528–1531
Chavan BL, Rasal GB (2010) Sequestered standing carbon stock in selective tree species grown in University Campus at Aurangabad, Maharashtra, India. Int J Eng Sci Technol 2:3003–3007
Brown S (1997) Estimating biomass and biomass change of tropical forests: a primer. FAO Forestry Paper-134. Retrieved from http://www.fao.org/docrep/w4095e/w4095e00.htm
Yin W, Yin M, Zhao L, Yang L (2012) Research on the measurement of carbon storage in plantation tree trunks based on the carbon storage dynamic analysis method. Int J For Res 2012:10 p (Article ID 626149). https://doi.org/10.1155/2012/626149
Ruangrit V, Sokhi BS (2004) Remote sensing and GIS for urban green space analysis—a case study of Jaipur city, Rajasthan, India. J Inst Town Plan India 1(2):55–67
Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309
Gao BC (1996) NDWI—a normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266
Hunt ERJR, Rock BN (1989) Detection of changes in leaf water content using near-and middle-infrared reflectances. Remote Sens Environ 30:43–54
Penuelas J, Baret F, Filella I (1995) Semi-empirical indices to assess carotenoids/chlorophyll—a ratio from leaf spectral reflectance. Photosynthetica 31(2):221–230
Gamon J, Serrano L, Surfus J (1997) The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levels. Oecologia 112(4):492–501. Retrieved from http://www.jstor.org/stable/4221805
Guyot G, Baret F (1988) Utilisation de la Haute Resolution Spectrale pour Suivre L’etat des Couverts Vegetaux. In: Proceedings of the 4th international colloquium on spectral signatures of objects in remote sensing, Aussois, France, Jan 18–22 1988. Retrieved from https://www.researchgate.net/publication/234432105_Utilisation_de_la_Haute_Resolution_Spectrale_pour_Suivre_L’etat_des_Couverts_Vegetaux
Eschenbach C, Kappen L (1996) Leaf area index determination in an alder forest: a comparison of three methods. J Exp Bot 47(9):1457–1462. https://doi.org/10.1093/jxb/47.9.1457
Haboudane D, Miller JR, Pattey E, Zarco-tejada PJ, Strachan IB (2004) Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: modeling and validation in the context of precision agriculture. Remote Sens Environ 90:337–352. https://doi.org/10.1016/j.rse.2003.12.013
Vogelmann JE, Rock BN, Moss DM (1993) Red edge spectral measurements from sugar maple leaves. Int J Remote Sens 14(8):1563–1575. https://doi.org/10.1080/01431169308953986
Daughtry C (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74(2):229–239. https://doi.org/10.1016/S0034-4257(00)00113-9
http://chandigarh.gov.in/greencap/gcap-2009/gap-protect-imp2009.pdf
Acknowledgements
The authors acknowledge the encouragement provided by Director, IIRS and Dean (Academics), IIRS for carrying out these studies. The authors are also thankful to all the students of IIRS and CSSTEAP, Dehradun and SPA, Bhopal who have worked under the guidance of authors for their support in data generation and processing.
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Gupta, K., Puntambekar, K., Roy, A., Pandey, K., Mahavir, Kumar, P. (2020). Smart Environment Through Smart Tools and Technologies for Urban Green Spaces. In: Vinod Kumar, T. (eds) Smart Environment for Smart Cities. Advances in 21st Century Human Settlements. Springer, Singapore. https://doi.org/10.1007/978-981-13-6822-6_5
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