Skip to main content

Advertisement

SpringerLink
Log in

RETRACTED ARTICLE: Evaluation of urban land green utilization efficiency with a view of GIS images

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

This article was retracted on 01 December 2021

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

This article has been updated

Abstract

The city is at the center of the growing strategy. There are high-cost issues with the production of energy and the environment. The strategy for environmentally friendly and sustainable development is limited. Therefore, if we assess the productivity of urban land use, it is of far-reaching value to use the idea of green growth as an assessment index. On the basis of input, planned production, and unforeseen output, nine indicators, including urban fixed capital stock, secondary and tertiary value-added, and SO2 emissions, are selected to create a research framework and related assessment indicators for the green use of urban land. Related data on the efficiency of urban land use in 12 districts and counties were collected from 2012 to 2018 to assess the efficiency of green land use in urban areas. The findings show that the productivity of green land use decreased significantly during the study period. Many cities in the province have set up a large number of industries, including those with high emissions and high usage. These industries have relocated inland from the eastern coastal regions, resulting in a decline in the rate of urban land use of green land in many towns. If we want to solve this issue, we need to change the framework of land use to follow empirical and fair methods for the rational use of land, to promote the green use of land, and to increase the level of assessment in order to improve the efficiency of green land use.

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

Similar content being viewed by others

Change history

References

  • Bobkov A, Panidi E, Torlopova N, Tsepelev V (2015) NDVI dynamics of the taiga zone in connection with modern climate changes. Int Arch Photogramm Remote Sens Spat Inf Sci XL-7(W3):157–163. https://doi.org/10.5194/isprsarchives-XL-7-W3-157-2015

    Article  Google Scholar 

  • Delbart NJ-P, Kergoats L, Le Toan T, Lhermitte J, Picard G (2005) Determination of phenological dates in boreal regions using normalized difference water index. Remote Sens Environ 97(1):26–38. https://doi.org/10.1016/j.rse.2005.03.011

    Article  Google Scholar 

  • Ferreira D, Simões M, Reboredo F, Pessoa F, Almeida AS, Daradzhanska I, Lidon F (2018) Effects of climate change (precipitation variations), on rice crop yields in alluvial plains of the Tejo and Sado rivers. Emir J Food Agr 30(6):488–496. https://doi.org/10.9755/ejfa.2018.v30.i6.1719

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Goetz SJ, Mack MC, Gurney KP, Randerson JT, Houghton RA (2007) Ecosystem responses to recent climate change and fire disturbance at northern high latitudes: observations and model results contrasting northern Eurasia and North America. Environ Res Lett 2(4):045031. https://doi.org/10.1088/1748-9326/2/4/045031

    Article  Google Scholar 

  • Gorelick N, Hancher M, Dixon M, Ilyushchenko S, Thau D, Moore R (2017) Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens Environ 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031

    Article  Google Scholar 

  • Jia GJ, Epstein HE, Walker DA (2009) Vegetation greening in the Canadian Arctic related to decadal warming. J Environ Monit 11(12):2231–2238. https://doi.org/10.1039/b911677j

    Article  Google Scholar 

  • Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Leetmaa A, Reynolds R, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 123. https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2

  • Medvedeva MA, Bartalev SA, Lupyan EA, Matveev AM, Tolpin VA, Poida AA (2008) The possibility of estimation of the growing season onset on the basing on satellite and meteorological data. Sovremennye Problemy Distantsionnogo Zondirovaniya Zemli iz Kosmosa, Contemp Probl of Remote Sens of the Earth from Space 5(2):313–321 (in Russian)

  • Mo Y, Kearney MS, Turner RE (2019) Feedback of coastal marshes to climate change: long-term phenological shifts. Ecol Evol 9(12):6785–6797. https://doi.org/10.1002/ece3.5215

    Article  Google Scholar 

  • Ols C, Kålås IH, Drobyshev I, Söderström L, Hofgaard A (2019) Spatiotemporal variation in the relationship between boreal forest productivity proxies and climate data. Dendrochronologia. 58:125648. https://doi.org/10.1016/j.dendro.2019.125648

    Article  Google Scholar 

  • Panidi E, Tsepelev V, Torlopova N, Bobkov A (2016) Mapping of the land cover spatiotemporal characteristics in northern Russia caused by climate change. Int Arch Photogramm Remote Sens Spat Inf Sci XLI-B8:997–1002. https://doi.org/10.5194/isprs-archives-XLI-B8-997-2016

    Article  Google Scholar 

  • Panidi E, Rykin I, Nico G, Tsepelev V (2019) Toward satellite-based estimation of growing season framing dates in conditions of unstable weather. In: Advances in remote sensing and geo informatics applications. Springer, pp. 131–133. https://doi.org/10.1007/978-3-030-01440-7_31

  • Ped DA (1951) About determination of dates of steady air temperature transition through certain values. Meteorol Gidrol [Meteorol Hydrol] 10:38–39 (in Russian)

    Google Scholar 

  • Sekhon NS, Hassan QK, Sleep RW (2010) A remote sensing based system to predict early spring phenology over boreal forest. Int Arch Photogramm Remote Sens Spat Inf Sci XXXVIII(1):5

    Google Scholar 

  • Semenova IG (2015) Synoptic and climatic conditions of formation of arid phenomena in Ukraine. Doctoral thesis, p. 296 (in Ukrainian)

  • Stow DA, Hopea A, McGuireb D, Verbylac D, Gamond J, Huemmriche F, Houstond S, Racinef C, Sturmg M, Tapeh K, Hinzman L, Yoshikawai K, Tweediej C, Noylek B, Silapaswanl C, Douglasm D, Grifthn B, Jiao G, Epsteino H, Walkerp D, Daeschnera S, Petersena A, Zhouq L, Myneni R (2004) Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems. Remote Sens Environ 89(3):281–308. https://doi.org/10.1016/j.rse.2003.10.018

    Article  Google Scholar 

  • Tsepelev V, Panidi E, Torlopova N, Bobkov A (2015) Study of climate change at the north of European Russia using land cover data. In: SGEM2015 Conference Proceedings, Book 3, vol 2, pp 529–536. https://doi.org/10.5593/SGEM2015/B32/S14.071

  • Vermote E (2015) MOD09A1 MODIS/Terra surface reflectance 8-day L3 global 500m SIN grid V006 [data set], NASA EOSDIS Land Processes DAAC doi:https://doi.org/10.5067/MODIS/MOD09A1.006

Download references

Funding

This work was supported by the Philosophical and Social Sciences Research Projects of Hubei Provincial Department of Education (No.20Q020), the Young Scientific and Technological Backbone Cultivation Project of Wuhan University of Science and Technology (No. 2017xz021) and Innovation and Entrepreneurship Training Program for College Students in Hubei Province in 2019 (NO. S201910488014).

Author information

Authors and Affiliations

  1. School of Urban Construction, Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China

    Jun Shao

  2. Wuhan University of Science and Technology, Wuhan, 430081, Hubei, China

    Huihui Yan

Authors
  1. Jun Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huihui Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huihui Yan.

Ethics declarations

Conflict of interest

The authors 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/s12517-021-09146-0

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shao, J., Yan, H. RETRACTED ARTICLE: Evaluation of urban land green utilization efficiency with a view of GIS images. Arab J Geosci 14, 629 (2021). https://doi.org/10.1007/s12517-021-06748-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12517-021-06748-6

Keywords

Search

Navigation