Skip to main content

Advertisement

SpringerLink
Log in

RETRACTED ARTICLE: Green urban vegetation planning and economic efficiency based on remote sensing images and grid geographic space

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

This article was retracted on 25 November 2021

An Editorial Expression of Concern to this article was published on 28 September 2021

This article has been updated

Abstract

As we all know, China is currently in a critical period of economic and social structural transformation. The most crucial point in social change is taking urban-rural integration as the fundamental goal and increasing vegetation coverage in urban infrastructure construction. This is also the current urban greening work, the top priority. Green vegetation, as an indispensable part of the urban greening process, makes relevant greening work begin to flourish in China. At the same time, the progress of this work also urgently needs professional planning guidance. Although this work is complex, with all parties’ efforts, a suitable method has finally been found, which is the application of green city planning mentioned in this article. This paper conducts an in-depth discussion on the theories and research related to urban planning based on the planning and design of urban greening. It uses remote sensing images and the overall summary of the urban green space planning model through the GIS system and the related urban greening issues, carried out characterization analysis. By discussing the green circle problems involved in the current urban planning process and a systematic summary of the relevant scientific knowledge, with the subject knowledge, social practice, and the status quo of urban planning as the research background, urban planning is further discussed. The Chinese greening project’s importance and rationality finally determined the research direction of this article and the problems that need to be solved in the research process. Besides, this paper further combines remote sensing images with GIS system technology. It uses a series of analytical investigation methods, such as the literature research method, field inspection method, landscape interval index method, etc. and conduct research and analysis in the region. By studying the dynamic evolution process of the greening pattern in this area, based on the ecological fragility, a series of evolving urban vegetation laws in the greening area after being destroyed by the outside world are explained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Change history

References

  • Adhikary B, Kulkarni S, Dallura A, Tang Y, Chai T, Leung LR, Qian Y, Chung CE, Ramanathan V, Carmichael GR (2008) A regional-scale chemical transport modelling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique. Atmos Environ 42:8600–8615 https://doi-org.proxy2.cl.msu.edu/10.1016/j.atmosenv.2008.08.031

    Article  Google Scholar 

  • Ali MA, Assiri M (2019) Analysis of AOD from MODIS-merged DT–DB products over the Arabian Peninsula. Earth Syst Environ 3:625–636 https://doi-org.proxy2.cl.msu.edu/10.1007/s41748-019-00108-x

    Article  Google Scholar 

  • Benedetti A, Morcrette J, Boucher O, Dethof A, Engelen R, Fisher M, Flentje H, Huneeus N, Jones L, Kaiser J (2009) Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 2. Data assimilation. J Geophys Res 114:D13205 https://doi-org.proxy2.cl.msu.edu/10.1029/2008JD011115

    Article  Google Scholar 

  • Bergamaschi P, Hein R, Heimann M, Crutzen PJ (2000) Inverse modeling of the global CO cycle 1: inversion of CO mixing ratio. J Geophys Res 105:1909–1927

    Article  Google Scholar 

  • Bilal M, Nichol JE, Nazeer M (2016) Validation of aqua-MODIS C051 and C006 operational aerosol products using AERONET measurements over Pakistan. IEEE J Sel Top Appl Earth Obs Remote Sens 9:2074–2080

    Article  Google Scholar 

  • Carrassi A, Weber R, Guemas V, Doblas-Reyes F, Asif M, Volpi D (2014) Full-field and anomaly initialization using a low-order climate model: a comparison and proposals for advanced formulations. Nonlinear Process Geophys 21:521–537

    Article  Google Scholar 

  • Chandra V, Michael B (2018) Source influences emission pathways and ambient PM2.5 pollution over India (2015–2050). Atmos Chem Phys 18:8017–8039

    Article  Google Scholar 

  • Cohen A, Brauer M, Burnett R, Anderson H, Frostad J, Estep K, Balakrishnan K, Brunekreef B, Dandona L (2017) Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015. Lancet 389:1907–1918

    Article  Google Scholar 

  • Collins WD, Rasch PJ, Eaton BE, Khattatov BV, Lamarque J (2001) Simulating aerosols using a chemical transport model to assimilate satellite aerosol retrievals: a methodology for INDOEX. J Geophys Res 106:7313–7336

    Article  Google Scholar 

  • Dai T, Schutgens N, Goto D, Shi GY, Nakajima T (2014) Improvement of aerosol optical properties modelling over Eastern Asia with MODIS AOD assimilation globally non-hydrostatic icosahedral aerosol transport model. Environ Pollut 195:319–329

    Article  Google Scholar 

  • Danny ML, Amos PK (2018) Synoptic meteorological modes of variability for fine particulate matter air quality in major metropolitan regions of China. Atmos Chem Phys 18:6733–6748

    Article  Google Scholar 

  • Fan Y, Li C (2015) Analysis of the influence of meteorological factor for the air quality forecasting in Xuzhou City (in Chinese) [J]. Environ Sci Technol 2:54–56

    Article  Google Scholar 

  • Goudarzi G, Rashidi R, Keishams F, Moradi M, Sadeghi S, Masihpour F, Shegerd M, Mehrizi EA, Shikhrobat MV, Khaniabadi YO (2017) An assessment on dispersion of carbon monoxide from a cement factory. Environ Health Eng Manag 4(3):163–168 https://doi-org.proxy2.cl.msu.edu/10.15171/ehem.2017.23

    Article  Google Scholar 

  • Guenther A, Baugh B, Brasseur G, Greenberg J, Harley P, Klinger L, Serca D, Vierling L (1999) Isoprene emission estimates and uncertainties for the Central African EXPRESSO study domain. J Geophys Res 104:30 625–30 639

    Article  Google Scholar 

  • Guenther A, Hewitt CN, Erickson D, Fall R, Geron C, Graedel T, Harley P, Klinger L, Lerdau M, Mckay WA, Pierce T, Scholes B, Steinbrecher R, Tallamraju R, Taylor J, Zimmerman P (1995) A global model of natural volatile organic compound emissions. J Geophys Res 100:8873–8892

    Article  Google Scholar 

  • Hartley DE, Prinn RG (1993) On the feasibility of determining surface emissions of trace gases using an inverse method in a three-dimensional chemical transport model. J Geophys Res 98:5183–5198

    Article  Google Scholar 

  • He KB, Yang FM, Ma YL, Zhang Q, Yao X, Chan CK, Cadle S, Chan T, Mulawa P (2001) The characteristics of PM2.5 in Beijing, China. Atmos Environ 35(29):4959–4970

    Article  Google Scholar 

  • Hein R, Crutzen PJ, Heimann M (1997) An inverse modeling approach to investigate the global atmospheric methane cycle. Glob Biogeochem Cycles 11:43–76

    Article  Google Scholar 

  • Horowitz LW, Walters S, Mauzerall DL, Emmons LK, Rasch PJ, Granier C, Tie X, Lamarque JF, Schultz MG, Tyndall GS, Orlando JJ, Brasseur GP (2003) A global simulation of tropospheric ozone and related tracers: description and evaluation of MOZART , version 2. J Geophys Res 108(D24):4784 https://doi-org.proxy2.cl.msu.edu/10.1029/2002JD002853

    Google Scholar 

  • Ide K, Courtier P, Ghil M, Lorenc AC (1997) Unified notation for data assimilation: operational, sequential and variational. J Meteorol Soc Jpn 75(1B):181–189

    Article  Google Scholar 

  • Islam MN, Ali MA, Islam MM (2019) Spatiotemporal investigations of aerosol optical properties over Bangladesh for the period 2002–2016. Earth Syst Environ 3:563–573 https://doi-org.proxy2.cl.msu.edu/10.1007/s41748-019-00120-1

    Article  Google Scholar 

  • Ji Q, Bo Z et al (2017) A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China. Atmos Environ 170(2017):156–168

    Google Scholar 

  • Jia MW, Zhao TL, Cheng X, Gong S, Zhang X, Tang L, Liu D, Wu X, Wang L, Chen Y (2017) Inverse relations of PM2.5 and O3 in air compound pollution between cold and hot seasons over an urban area of East China. Atmosphere 8:59 https://doi-org.proxy2.cl.msu.edu/10.3390/atmos8030059

    Article  Google Scholar 

  • Kahnert M (2008) Variational data analysis of aerosol species in a regional CTM: background error covariance constraint and aerosol optical observation operators. Tellus B 60:753–770 https://doi-org.proxy2.cl.msu.edu/10.1111/j.1600-0889.2008.00377.x

    Article  Google Scholar 

  • Kukkonen J, Olsson T et al (2012) Operational, regional-scale, chemical weather forecasting models in Europe. Atmos Chem Phys Discuss 2011(11):5985–6162

    Google Scholar 

  • Lee EH, Ha JC, Lee SS, Chun Y (2013) PM10 data assimilation over South Korea to Asian dust forecasting model with the optimal interpolation method. Asia-Pac J Atmos Sci 49:73–85 https://doi-org.proxy2.cl.msu.edu/10.1007/s13143-013-0009-y

    Article  Google Scholar 

  • Li Z, Zang Z, Li QB, Chao Y, Chen D, Ye Z, Liu Y, Liou KN (2013) A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction. Atmos Chem Phys 13:4265–4278 https://doi-org.proxy2.cl.msu.edu/10.5194/acp-13-4265-2013

    Article  Google Scholar 

  • Matthew OJ, Igbayo AN, Olise FS, Owoade KO, Abiye OE, Ayoola MA, Hopke PK (2019) Simulation of point source pollutant dispersion pattern: an investigation of effects of prevailing local weather conditions. Earth Syst Environ 3:215–230 https://doi-org.proxy2.cl.msu.edu/10.1007/s41748-019-00087-z

    Article  Google Scholar 

  • Pagowski M, Grell GA, McKeen SA, Peckham SE, Devenyi D (2010) Three-dimensional variational data assimilation of ozone and fine particulate matter observations: some results using the Weather Research and Forecasting–Chemistry model and Grid-point Statistical Interpolation. Q J R Meteorol Soc 136:2013–2024 https://doi-org.proxy2.cl.msu.edu/10.1002/qj.700

    Article  Google Scholar 

  • Qin Y, Oduyemi K (2003) Chemical composition of atmospheric aerosol in Dundee, UK. Atmos Environ 37(1):93–104 https://doi-org.proxy2.cl.msu.edu/10.1016/s1352-2310(02)00658-1

    Article  Google Scholar 

  • Reich SL, Gomez DR, Dawidowski LE (1999) Artificial neural network for the identification of unknown air pollution sources. Atmos Environ 33:3045–3052

    Article  Google Scholar 

  • Solazzo E, Bianconi R, Pirovano G, Matthias V, Vautard R, Moran MD, Wyat Appel K, Bessagnet B, Brandt J, Christensen JH, Chemel C, Coll I, Ferreira J, Forkel R, Francis XV, Grell G, Grossi P, Hansen AB, Miranda AI, Nopmongcol U, Prank M, Sartelet KN, Schaap M, Silver JD, Sokhi RS, Vira J, Werhahn J, Wolke R, Yarwood G, Zhang J, Rao ST, Galmarini S (2012) Operational model evaluation for particulate matter in Europe and North America in the context of AQMEII. Atmos Environ 53:75e92 https://doi-org.proxy2.cl.msu.edu/10.1016/j.atmosenv.2012.02.045

    Google Scholar 

  • Talagrand O (1997) Assimilation of observations an introduction. J Meteorol Soc Jpn Ser 75:81–99

    Article  Google Scholar 

  • Tombette M, Mallet V, Sportisse B (2009) PM10 data assimilation over Europe with the optimal interpolation method. Atmos Chem Phys 9:57–70 https://doi-org.proxy2.cl.msu.edu/10.5194/acp-9-57-2009

    Article  Google Scholar 

  • Wang SS, Zheng JY, Fu F, Yin SS, Zhong LJ (2011) Development of an emission processing system for the Pearl River Delta regional air quality modeling using the SMOKE model: methodology and evaluation. Atmos Environ 45:5079–5089 https://doi-org.proxy2.cl.msu.edu/10.1016/j.atmosenv.2011.06.037

    Article  Google Scholar 

  • Yumimoto K, Nagao TM, Kikuchi M, Sekiyama TT, Murakami H, Tanaka TY, Ogi A, Irie H, Khatri P, Okumura H, Arai K, Morino I, Uchino O, Maki T (2016) Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite. Geophys Res Lett 43:5886–5894

    Article  Google Scholar 

  • Zhang J, Reid JS, Westphal D, Baker N, Hyer E (2008) A system for operational aerosol optical depth data assimilation over global oceans. J Geophys Res 113:D10208 https://doi-org.proxy2.cl.msu.edu/10.1029/2007JD009065

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Management, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Yuan Lu & Lianghai Lei

Authors
  1. Yuan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lianghai Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lianghai Lei.

Ethics declarations

Conflict of interest

The author(s) declare that they have no competing interests.

Additional information

Responsible Editor: Ahmed Farouk

This article is part of the Topical Collection on Big Data and Intelligent Computing Techniques in Geosciences

This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s12517-021-09046-3

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Y., Lei, L. RETRACTED ARTICLE: Green urban vegetation planning and economic efficiency based on remote sensing images and grid geographic space. Arab J Geosci 14, 905 (2021). https://doi.org/10.1007/s12517-021-07146-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-021-07146-8

Keywords

Search

Navigation