Modelling the future vulnerability of urban green space for priority-based management and green prosperity strategy planning in Kolkata, India: a PSR-based analysis using AHP-FCE and ANN-Markov model

References

• Abass K, Afriyie K, Gyasi RM. 2019. From green to grey: the dynamics of land use/land cover change in urban Ghana. Landscape Res. 44(8):909–921..
   Web of Science ®Google Scholar
• Anguluri R, Narayanan P. 2017. Role of green space in urban planning: outlook towards smart cities. Urban For Urban Greening. 25:58–65.
   Web of Science ®Google Scholar
• Ali SB, Patnaik S. 2019. Assessment of the impact of urban tree canopy on microclimate in Bhopal: a devised low-cost traverse methodology. Urban Clim. 27:430–445..
   Web of Science ®Google Scholar
• Arsanjani JJ, Helbich M, Kainz W, Boloorani AD. 2013. Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Obs Geoinf. 21:265–275.
   Web of Science ®Google Scholar
• Asian Green City Index. 2011. Assessing the environmental performance of Asia's major cities. Munich: Siemens AG.
   Google Scholar
• Azari M, Tayyebi A, Helbich M, Reveshty MA. 2016. Integrating cellular automata, artificial neural network, and fuzzy set theory to simulate threatened orchards: application to Maragheh, Iran. GISci Remote Sens. 53(2):183–205.
   Web of Science ®Google Scholar
• Banerjee P, Ghose MK, Pradhan R. 2020. Analytic hierarchy process based spatial biodiversity impact assessment model of highway broadening in Sikkim Himalaya. Geocarto Int. 35(5):470–493.
   Web of Science ®Google Scholar
• Banti M, Kiachidis K, Gemitzi A. 2019. Estimation of spatio-temporal vegetation trends in different land use environments across Greece. J Land Use Sci. 14(1):21–36..
   Web of Science ®Google Scholar
• Bardhan R, Debnath R, Bandopadhyay S. 2016. A conceptual model for identifying the risk susceptibility of urban green spaces using geo-spatial techniques. Model Earth Syst Environ. 2(3):144.
   Web of Science ®Google Scholar
• Behera MD, Borate SN, Panda SN, Behera PR, Roy PS. 2012. Modelling and analyzing the watershed dynamics using Cellular Automata (CA)–Markov model–a geo-information-based approach. J Earth Syst Sci. 121(4):1011–1024.
   Web of Science ®Google Scholar
• Bhatta B. 2009. Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. International Journal of Remote Sensing. 30(18):4733–4746.
   Web of Science ®Google Scholar
• Bihamta N, Soffianian A, Fakheran S, Gholamalifard M. 2015. Using the SLEUTH urban growth model to simulate future urban expansion of the Isfahan metropolitan area. J Indian Soc Remote Sens. 43(2):407–414.
   Web of Science ®Google Scholar
• Census. 2011. Census of India 2011. Government of India; [archived 5 February 2021]. http://censusindia.gov.in/DigitalLibrary/Archive_home.aspx.
   Google Scholar
• Chakraborti S, Das DN, Mondal B, Shafizadeh-Moghadam H, Feng Y. 2018. A neural network and landscape metrics to propose a flexible urban growth boundary: a case study. Ecol Indic. 93:952–965.
   Web of Science ®Google Scholar
• Chen WY. 2015. The role of urban green infrastructure in offsetting carbon emissions in 35 major Chinese cities: a nationwide estimate. Cities. 44:112–120.
   Web of Science ®Google Scholar
• Chen Y, Yu J, Khan S. 2010. Spatial sensitivity analysis of multi-criteria weights in GIS-based land suitability evaluation. Environ Modell Software. 25(12):1582–1591.
   Web of Science ®Google Scholar
• Chiu RH, Lin LH, Ting SC. 2014. Evaluation of green port factors and performance: a fuzzy AHP analysis. Math Prob Eng. 2014:1–12.
   Web of Science ®Google Scholar
• Congalton RG, Green K. 2009. Assessing the accuracy of remotely sensed data: principles and practices. 2nd ed., Lewis Publishers, Boca Raton.
   Google Scholar
• Dahal KR, Benner S, Lindquist E. 2018. Analyzing spatiotemporal patterns of urbanization in Treasure Valley, Idaho, USA. Appl Spat Anal. 11(2):205–226.
   Web of Science ®Google Scholar
• Daniels B, Zaunbrecher BS, Paas B, Ottermanns R, Ziefle M, Roß-Nickoll M. 2018. Assessment of urban green space structures and their quality from a multidimensional perspective. Sci Total Environ. 615:1364–1378.
   PubMed Web of Science ®Google Scholar
• Dinda S, Chatterjee ND, Ghosh S. 2021. An integrated simulation approach to the assessment of urban growth pattern and loss in urban green space in Kolkata, India: a GIS-based analysis. Ecol Indic. 121:107178..
   Web of Science ®Google Scholar
• Dinda S, Das K, Chatterjee ND, Ghosh S. 2019. Integration of GIS and statistical approach in mapping of urban sprawl and predicting future growth in Midnapore town. Model Earth Syst Environ. 5(1):331–352.
   Web of Science ®Google Scholar
• Dutta S, Ghosh S, Dinda S. 2020. Urban air-quality assessment and inferring the association between different factors: A comparative study among Delhi, Kolkata and Chennai megacity of India. Aerosol Sci Eng. 87:1–19.
   Google Scholar
• Feng Y, Liu Y, Tong X, Liu M, Deng S. 2011. Modeling dynamic urban growth using cellular automata and particle swarm optimization rules. Landscape Urban Plann. 102(3):188–196.
   Web of Science ®Google Scholar
• Gan M, Deng J, Zheng X, Hong Y, Wang K. 2014. Monitoring urban greenness dynamics using multiple endmember spectral mixture analysis. PloS One. 9(11):e112202.
   PubMed Web of Science ®Google Scholar
• Gao P, Niu X, Wang B, Zheng Y. 2015. Land use changes and its driving forces in hilly ecological restoration area based on GIS and RS of northern China. Sci Rep. 5:11038.
   PubMed Web of Science ®Google Scholar
• Gaur S, Mittal A, Bandyopadhyay A, Holman I, Singh R. 2020. Spatio-temporal analysis of land use and land cover change: a systematic model inter-comparison driven by integrated modelling techniques. Int J Remote Sens. 41(23):9229–9255.
   Web of Science ®Google Scholar
• Gavrilidis AA, Niță MR, Onose DA, Badiu DL, Năstase II. 2019. Methodological framework for urban sprawl control through sustainable planning of urban green infrastructure. Ecol Indic. 96:67–78.
   Web of Science ®Google Scholar
• Ghosh S, Chatterjee ND, Dinda S. 2019. Relation between urban biophysical composition and dynamics of land surface temperature in the Kolkata metropolitan area: a GIS and statistical based analysis for sustainable planning. Model Earth Syst Environ. 5(1):307–329.
   Web of Science ®Google Scholar
• Ghosh S, Chatterjee ND, Dinda S. 2021. Urban ecological security assessment and forecasting using integrated DEMATEL-ANP and CA-Markov models: a case study on Kolkata Metropolitan Area, India. Sustain Cities Soc. 68:102773..
   Web of Science ®Google Scholar
• Ghosh S, Dinda S, Chatterjee ND, Das K. 2018. Analyzing risk factors for shrinkage and transformation of East Kolkata Wetland. Spat Inf Res. 26(6):661–677.
   Web of Science ®Google Scholar
• Gidey E, Dikinya O, Sebego R, Segosebe E, Zenebe A. 2017. Cellular automata and Markov Chain (CA_Markov) model-based predictions of future land use and land cover scenarios (2015–2033) in Raya, northern Ethiopia. Model Earth Syst Environ. 3(4):1245–1262.
   Web of Science ®Google Scholar
• Goodarzi S. 2017. Measuring the effect of an ongoing urbanization process on biodiversity conservation suitability index: integrating scenario-based urban growth modelling with Conservation Assessment and Prioritization System (CAPS). Geocarto Int. 32(8):834–852.
   Google Scholar
• Gupta K, Kumar P, Pathan SK, Sharma KP. 2012. Urban Neighborhood Green Index–a measure of green spaces in urban areas. Landscape Urban Plann. 105(3):325–335.
   Web of Science ®Google Scholar
• Hadjimitsis DG, Papadavid G, Agapiou A, Themistocleous K, Hadjimitsis MG, Retalis A, Michaelides S, Chrysoulakis N, Toulios L, Clayton CRI. 2010. Atmospheric correction for satellite remotely sensed data intended for agricultural applications: impact on vegetation indices. Nat Hazards Earth Syst Sci. 10(1):89–95.
   Web of Science ®Google Scholar
• Hamdy O, Zhao S, Salheen MA, Eid YY. 2017. Analyses the driving forces for urban growth by using IDRISI® Selva models Abouelreesh-Aswan as a case study. IJET. 9(3):226–232.
   Google Scholar
• Hou K, Tao W, Wang L, Li X. 2020. Study on hierarchical transformation mechanisms of regional ecological vulnerability and its applicability. Ecol Indic. 114:106343.
   Web of Science ®Google Scholar
• Hyandye C, Martz LW. 2017. A Markovian and cellular automata land-use change predictive model of the Usangu Catchment. Int J Remote Sens. 38(1):64–81.
   Web of Science ®Google Scholar
• Ibrahim I, Samah AA, Fauzi R. 2014. Biophysical factors of remote sensing approach in urban green analysis. Geocarto Int. 29(7):807–818.
   Web of Science ®Google Scholar
• Islam K, Rahman MF, Jashimuddin M. 2018. Modeling land use change using cellular automata and artificial neural network: the case of Chunati Wildlife Sanctuary, Bangladesh. Ecol Indic. 88:439–453..
   Web of Science ®Google Scholar
• Khan AA, Shameem M, Nadeem M, Akbar MA. 2021. Agile trends in Chinese global software development industry: fuzzy AHP based conceptual mapping. Appl Soft Comput. 102:107090.
   Web of Science ®Google Scholar
• Kim I, Kwon H. 2021. Assessing the impacts of urban land use changes on regional ecosystem services according to urban green space policies via the patch-based cellular automata model. Environ Manage. 67(1):192–204..
   PubMed Web of Science ®Google Scholar
• KMC. 2020. Basic statistics of Kolkata; [accessed 15 January 2020] https://www.kmcgov.in/KMCPortal/jsp/KolkataStatistics.jsp.
   Google Scholar
• Li F, Sun Y, Li X, Hao X, Li W, Qian Y, Liu H, Sun H. 2016. Research on the sustainable development of green-space in Beijing using the dynamic systems model. Sustainability. 8(10):965.
   Web of Science ®Google Scholar
• Lin C, Kou G. 2021. A heuristic method to rank the alternatives in the AHP synthesis. Appl Soft Comput. 100:106916.
   Web of Science ®Google Scholar
• Lin M, Lin T, Sun C, Jones L, Sui J, Zhao Y, Liu J, Xing L, Ye H, Zhang G, et al. 2020. Using the Eco-Erosion Index to assess regional ecological stress due to urbanization–a case study in the Yangtze River Delta urban agglomeration. Ecol Indic. 111:106028.
   Web of Science ®Google Scholar
• Liu L, Wang Z, Zhang H. 2018. Neural-network-based robust optimal tracking control for MIMO discrete-time systems with unknown uncertainty using adaptive critic design. IEEE Trans Neural Netw Learn Syst. 29(4):1239–1251.
   PubMed Web of Science ®Google Scholar
• Liu Y, Fang P, Bian D, Zhang H, Wang S. 2014. Fuzzy comprehensive evaluation for the motion performance of autonomous underwater vehicles. Ocean Eng. 88:568–577.
   Web of Science ®Google Scholar
• Lu Y, Wang X, Xie Y, Li K, Xu Y. 2016. Integrating future land use scenarios to evaluate the spatio-temporal dynamics of landscape ecological security. Sustainability. 8(12):1242.
   Web of Science ®Google Scholar
• Malekmohammadi B, Jahanishakib F. 2017. Vulnerability assessment of wetland landscape ecosystem services using driver-pressure-state-impact-response (DPSIR) model. Ecol Indic. 82:293–303.
   Web of Science ®Google Scholar
• Mohamed A, Worku H. 2020. Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Clim. 31:100545..
   Web of Science ®Google Scholar
• Moghadam HS, Helbich M. 2013. Spatiotemporal urbanization processes in the megacity of Mumbai, India: a Markov chains-cellular automata urban growth model. Appl Geogr. 40:140–149.
   Web of Science ®Google Scholar
• Mondal B, Das DN, Bhatta B. 2017. Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration. Geocarto Int. 32(4):401–419.
   Web of Science ®Google Scholar
• Monserud RA, Leemans R. 1992. Comparing global vegetation maps with the Kappa statistic. Ecol Modell. 62(4):275–293..
   Web of Science ®Google Scholar
• MoUD. 2014. Urban and Regional Development Plans Formulation & Implementation Guidelines (URDPFI), 2014. Vol. 1, Draft Report. New Delhi: Ministry of Urban Development, Government of India, Nirman Bhavan.
   Google Scholar
• Munthali MG, Mustak S, Adeola A, Botai J, Singh SK, Davis N. 2020. Modelling land use and land cover dynamics of Dedza district of Malawi using hybrid Cellular Automata and Markov model. Remote Sens Appl: Soc Environ. 17:100276.
   Google Scholar
• Musa SI, Hashim M, Reba MNM. 2017. A review of geospatial-based urban growth models and modelling initiatives. Geocarto Int. 32(8):813–833.
   Web of Science ®Google Scholar
• Nasehi S, Imanpour Namin A, Salehi E. 2019. Simulation of land cover changes in urban area using CA-MARKOV model (case study: zone 2 in Tehran, Iran). Model Earth Syst Environ. 5(1):193–202.
   Web of Science ®Google Scholar
• Nath B, Wang Z, Ge Y, Islam K, P Singh R, Niu Z. 2020. Land use and land cover change modeling and future potential landscape risk assessment using Markov-CA model and analytical hierarchy process. IJGI. 9(2):134..
   Google Scholar
• Nath TK, Han SSZ, Lechner AM. 2018. Urban green space and well-being in Kuala Lumpur, Malaysia. Urban For Urban Greening. 36:34–41..
   Web of Science ®Google Scholar
• OECD. 1993. Core set of indicators for environmental performance. Paris (France): Organization for Economic Co-Operation and Development; [accessed 15 January 2021] http://enrin.grida.no/htmls/armenia/soe2000/eng/oecdind.pdf.
   Google Scholar
• Otukei JR, Blaschke T. 2010. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. Int J Appl Earth Obs Geoinf. 12(Suppl. 1):S27–S31..
   Google Scholar
• Paliwal M, Kumar UA. 2009. Neural networks and statistical techniques: a review of applications. Expert Syst Appl. 36(1):2–17.
   Web of Science ®Google Scholar
• Pontius RG, Jr, Millones M. 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens. 32(15):4407–4429.
   Web of Science ®Google Scholar
• Rahman MTU, Esha EJ. 2020. Prediction of land cover change based on CA-ANN model to assess its local impacts on Bagerhat, southwestern coastal Bangladesh. Geocarto Int. 1–23.
   Web of Science ®Google Scholar
• Saaty TL. 1980. The analytical hierarchy process: planning, priority setting, resource allocation, 1–287. New York: McGraw-Hill.
   Google Scholar
• Saaty TL. 2008. Decision making with the analytic hierarchy process. IJSSCI. 1(1):83–98.
   Google Scholar
• Saeidi S, Mirkarimi SH, Mohammadzadeh M, Salmanmahiny A, Arrowsmith C. 2018. Designing an integrated urban growth prediction model: a scenario-based approach for preserving scenic landscapes. Geocarto Int. 33(12):1381–1397.
   Web of Science ®Google Scholar
• Sahana M, Hong H, Sajjad H. 2018. Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: a study on Kolkata urban agglomeration, India. Sci Total Environ. 628:1557–1566.
   PubMed Web of Science ®Google Scholar
• Shao Z, Huq ME, Cai B, Altan O, Li Y. 2020. Integrated remote sensing and GIS approach using fuzzy-AHP to delineate and identify groundwater potential zones in semi-arid Shanxi Province, China. Environ Modell Software. 134:104868.
   Web of Science ®Google Scholar
• Sharma R, Chakraborty A, Joshi PK. 2015. Geospatial quantification and analysis of environmental changes in urbanizing city of Kolkata (India). Environ Monit Assess. 187(1):4206.
   PubMed Web of Science ®Google Scholar
• Shishir S, Tsuyuzaki S. 2018. Hierarchical classification of land use types using multiple vegetation indices to measure the effects of urbanization. Environ Monit Assess. 190(342):1–15.
   Google Scholar
• Singh SK, Mustak S, Srivastava PK, Szabó S, Islam T. 2015. Predicting spatial and decadal LULC changes through cellular automata Markov chain models using earth observation datasets and geo-information. Environ Process. 2(1):61–78.
   Google Scholar
• Stehman S. 1996. Estimating the kappa coefficient and its variance under stratified random sampling. Photogramm Eng Remote Sens. 62(4):401–407.
   Web of Science ®Google Scholar
• Sun B, Tang J, Yu D, Song Z, Wang P. 2019. Ecosystem health assessment: a PSR analysis combining AHP and FCE methods for Jiaozhou Bay, China. Ocean Coast Manage. 168:41–50.
   Web of Science ®Google Scholar
• Taheri K, Gutiérrez F, Mohseni H, Raeisi E, Taheri M. 2015. Sinkhole susceptibility mapping using the analytical hierarchy process (AHP) and magnitude–frequency relationships: a case study in Hamadan province, Iran. Geomorphology. 234:64–79.
   Web of Science ®Google Scholar
• Taylor L, Hochuli DF. 2017. Defining greenspace: multiple uses across multiple disciplines. Landscape Urban Plann. 158:25–38..
   Web of Science ®Google Scholar
• Tong F, Liu X. 2005. Samples selection for artificial neural network training in preliminary structural design. Tinshhua Sci Technol. 10(2):233–239.
   Google Scholar
• Tripathy P, Kumar A. 2019. Monitoring and modelling spatio-temporal urban growth of Delhi using cellular automata and geoinformatics. Cities. 90:52–63.
   Web of Science ®Google Scholar
• UN. 2019. World population prospects. United Nations: Department of economic and social affairs. https://population.un.org/wpp.
   Google Scholar
• USGS. 2016. Landsat 8 (L8) Data Users Handbook. Department of the Interior US Geological Survey; [accessed 19 December 2020] http://landsat.usgs.gov/sites/default/fles/documents/LSDS-1574_L8_Data_Users_Handbook.pdf.
   Google Scholar
• Václavík T, Rogan J. 2009. Identifying trends in land use/land cover changes in the context of post-socialist transformation in central Europe: a case study of the greater Olomouc region, Czech Republic. GISci Remote Sens. 46(1):54–76.
   Web of Science ®Google Scholar
• Vermote EF, Tanré D, Deuze JL, Herman M, Morcette JJ. 1997. Second simulation of the satellite signal in the solar spectrum, 6S: an overview. IEEE Trans Geosci Remote Sensing. 35(3):675–686.
   Web of Science ®Google Scholar
• Vigneshwaran S, Vasantha Kumar S. 2021. Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery. Geocarto Int. 36(13):1–14.
   Web of Science ®Google Scholar
• Wang J, Zhou W, Qian Y, Li W, Han L. 2018. Quantifying and characterizing the dynamics of urban green space at the patch level: a new approach using object-based image analysis. Remote Sens Environ. 204:94–108..
   Web of Science ®Google Scholar
• Weng Q, Lo CP. 2001. Spatial analysis of urban growth impacts on vegetative greenness with Landsat TM data. Geocarto Int. 16(4):19–28.
   Google Scholar
• WHO. 2012. Health indicators of sustainable cities in the context of the Rio + 20 UN Conference on Sustainable Development. Geneva (Switzerland): World Health Organization.
   Google Scholar
• WEF. 2015. The Future of Urban Development & Services: Urban Development Recommendations for the Government of India. Geneva (Switzerland): World Economic Forum.
   Google Scholar
• Wu J, Wang X, Zhong B, Yang A, Jue K, Wu J, Zhang L, Xu W, Wu S, Zhang N, et al. 2020. Ecological environment assessment for Greater Mekong Subregion based on pressure-state-response framework by remote sensing. Ecol Indic. 117:106521.
   Web of Science ®Google Scholar
• Xu C, Haase D, Pauleit S. 2018. The impact of different urban dynamics on green space availability: a multiple scenario modeling approach for the region of Munich, Germany. Ecol Indic. 93:1–12.
   Web of Science ®Google Scholar
• Xu T, Gao J, Coco G. 2019. Simulation of urban expansion via integrating artificial neural network with Markov chain–cellular automata. Int J Geogr Inform Sci. 33(10):1960–1983.
   Web of Science ®Google Scholar
• Yang W, Zhang Z, Sun T, Liu H, Shao D. 2021. Marine ecological and environmental health assessment using the pressure-state-response framework at different spatial scales, China. Ecol Indic. 121:106965.
   Web of Science ®Google Scholar
• Yu L, Wu X, Zheng X, Zheng T, Xin J, Walther M. 2019. An index system constructed for ecological stress assessment of the coastal zone: a case study of Shandong, China. J Environ Manage. 232:499–504.
   PubMed Web of Science ®Google Scholar
• Zhao J, Chen S, Jiang B, Ren Y, Wang H, Vause J, Yu H. 2013. Temporal trend of green space coverage in China and its relationship with urbanization over the last two decades. Sci Total Environ. 442:455–465.
   PubMed Web of Science ®Google Scholar
• Zhou W, Huang G, Pickett ST, Cadenasso ML. 2011. 90 years of forest cover change in an urbanizing watershed: spatial and temporal dynamics. Landscape Ecol. 26(5):645–659.
   Web of Science ®Google Scholar
• Zhu R, Liang Q, Zhan H. 2017. Analysis of aero-engine performance and selection based on fuzzy comprehensive evaluation. Procedia Eng. 174:1202–1207.
   Google Scholar
• Zhu Z, Wulder MA, Roy DP, Woodcock CE, Hansen MC, Radeloff VC, Healey SP, Schaaf C, Hostert P, Strobl P, et al. 2019. Benefits of the free and open Landsat data policy. Remote Sens Environ. 224:382–385..
   Web of Science ®Google Scholar