Skip to main content

Advertisement

Log in

Spatiotemporal ecological quality assessment of metropolitan cities: a case study of central Iran

  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

The present study used the recently developed Remote Sensing-Based Ecological Index (RSEI) to assess the temporal-spatial variation of ecological quality in the metropolitan city of Isfahan (Iran) as a member of the UNESCO Creative Cities Network. This study was conducted from the Landsat TM/OLI satellite images of 2004, 2009, 2014 and 2019. The RSEI was synthesized by principal component analysis for four indices of Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Land Surface Moisture (LSM) and Normalized Differential Build-up, and Bare Soil Index (NDBSI) based on the framework of the Pressure-State-Response (PSR) in the aforementioned years. The ecological quality of the city was assessed by RSEI over a 15-year period. The index has a value range of 0 (completely poor ecological quality) to 1 (completely desirable). In addition, the spatial heterogeneity of RSEIs at different intervals was assessed by the Moran index. The results showed that the RSEI value was always less than 0.4, which indicated the unfavourable ecological quality of the city. This index was 0.34, 0.37, 0.26 and 0.30 in 2004, 2009, 2014 and 2019, respectively. Therefore, the ecological quality of the city did not have a constant trend during the studied period and had several fluctuations, which could be attributed to the natural and anthropogenic changes in the studied period. Additionally, the results of the Moran index showed a steady decline, which indicated a declining homogeneity during this period. Matching the calculated RSEIs with the realities of the region at each time interval suggested that the index could be a useful tool for assessing urban ecological quality.

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

Access this article

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

Instant access to the full article PDF.

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

Similar content being viewed by others

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Akbari, M., & Rezaey, M. (2018). Assessment of land use changes in the 3rd zone of Isfahan Metropolis. Journal of Urban Research and Planning, 9(34), 93–104

    Google Scholar 

  • Amaral, P. V., & Anselin, L. (2014). Finite sample properties of Moran’s I test for spatial autocorrelation in tobit models: Properties of Moran’s I test in tobit models. Papers in Regional Science, 93(4), 773–781. https://doi.org/10.1111/pirs.12034

    Article  Google Scholar 

  • Ameen, R. F. M., & Mourshed, M. (2017). Urban environmental challenges in developing countries—A stakeholder perspective. Habitat International, 64, 1–10. https://doi.org/10.1016/j.habitatint.2017.04.002

    Article  Google Scholar 

  • Anselin, L., Syabri, I., & Kho, Y. (2006). GeoDa: An introduction to spatial data analysis. Geographical Analysis, 38(1), 5–22. https://doi.org/10.1111/j.0016-7363.2005.00671.x

    Article  Google Scholar 

  • Ariken, M., Zhang, F., Liu, K., Fang, C., & Kung, H.T. (2020). Coupling coordination analysis of urbanization and eco-environment in Yanqi Basin based on multi-source remote sensing data. Ecological Indicators, 114, 106331. https://doi.org/10.1016/j.ecolind.2020.106331

    Article  Google Scholar 

  • Asgarian, A., Amiri, B. J., & Sakieh, Y. (2015). Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosystems, 18(1), 209–222. https://doi.org/10.1007/s11252-014-0387-7

    Article  Google Scholar 

  • Assari, A., & Mahesh, T. M. (2011). Urbanization process in Iranian cities. Asian Journal of Development Matters, 5(1), 151–154

    Google Scholar 

  • Assari, A., Maghreby, S., & Nik, M. M. (2017). Investigation of smart growth in traditional Islamic culture: Case study of Isfahan city in Iran. Journal of Geography and Regional Planning, 10(4), 47–56

    Article  Google Scholar 

  • Atitar, M., & Sobrino, J. A. (2009). A split-window algorithm for estimating LST from Meteosat 9 Data: Test and comparison with data and MODIS LSTs. IEEE Geoscience and Remote Sensing Letters, 6(1), 122–126. https://doi.org/10.1109/LGRS.2008.2006410

    Article  Google Scholar 

  • Bai, X., Du, P., Guo, S., Zhang, P., Lin, C., Tang, P., & Zhang, C. (2019). Monitoring land cover change and disturbance of the Mount Wutai World Cultural Landscape Heritage Protected Area, based on remote sensing time-series images from 1987 to 2018. Remote Sensing, 11(11), 1332. https://doi.org/10.3390/rs11111332

    Article  Google Scholar 

  • Binh, T. N. K. D., Vromant, N., Hung, N. T., Hens, L., & Boon, E. K. (2005). Land Cover Changes Between 1968 and 2003 In Cai Nuoc, Ca Mau Peninsula. Vietnam. Environment, Development and Sustainability, 7(4), 519–536. https://doi.org/10.1007/s10668-004-6001-z

    Article  Google Scholar 

  • Campbell-Lendrum, D., & Corvalán, C. (2007). Climate change and developing-country cities: Implications for environmental health and equity. Journal of Urban Health, 84(S1), 109–117. https://doi.org/10.1007/s11524-007-9170-x

    Article  Google Scholar 

  • Casey Keat-Chuan, N. G. (2020). The Avifauna-Based Biophysical Index (ABI) approach for assessing and planning ecological landscaping in tropical cities. Urban Forestry & Urban Greening, 55, 126850. https://doi.org/10.1016/j.ufug.2020.126850

    Article  Google Scholar 

  • Chatterjee, R. S., Singh, N., Thapa, S., Sharma, D., & Kumar, D. (2017). Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs. International Journal of Applied Earth Observation and Geoinformation, 58, 264–277. https://doi.org/10.1016/j.jag.2017.02.017

    Article  Google Scholar 

  • Chen, Y. (2013). New approaches for calculating Moran’s Index of spatial autocorrelation. PLoS ONE, 8(7), e68336. https://doi.org/10.1371/journal.pone.0068336

    Article  CAS  Google Scholar 

  • Chen, X., Li, F., Li, X., Hu, Y., & Wang, Y. (2020). Mapping ecological space quality changes for ecological management: A case study in the Pearl River Delta urban agglomeration. China. Journal of Environmental Management, 267, 110658. https://doi.org/10.1016/j.jenvman.2020.110658

    Article  Google Scholar 

  • Das, M., Das, A., & Mandal, A. (2020). Research note: Ecosystem Health (EH) assessment of a rapidly urbanizing metropolitan city region of eastern India – A study on Kolkata Metropolitan Area. Landscape and Urban Planning, 204, 103938. https://doi.org/10.1016/j.landurbplan.2020.103938

    Article  Google Scholar 

  • Essa, W., Verbeiren, B., van der Kwast, J., Van de Voorde, T., & Batelaan, O. (2012). Evaluation of the DisTrad thermal sharpening methodology for urban areas. International Journal of Applied Earth Observation and Geoinformation, 19(1), 163–172. https://doi.org/10.1016/j.jag.2012.05.010

    Article  Google Scholar 

  • Flies, E. J., Skelly, C., Negi, S. S., Prabhakaran, P., Liu, Q., Liu, K., Goldizen, F. C., Lease, C., & Weinstein, P. (2017). Biodiverse green spaces: A prescription for global urban health. Frontiers in Ecology and the Environment, 15(9), 510–516. https://doi.org/10.1002/fee.1630

    Article  Google Scholar 

  • Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). Ndvi: Vegetation change detection using remote sensing and Gis – A case study of Vellore District. Procedia Computer Science, 57, 1199–1210. https://doi.org/10.1016/j.procs.2015.07.415

    Article  Google Scholar 

  • Ghahraei, H., Ziari, K., & Pourahamd, A. (2019). Urban land policies and its impact on the physical development of Isfahan. Human Geography Research, 51, 211–227

    Google Scholar 

  • Gorgani, S.A., Panahi, M., & Rezaei, F. (2013). The relationship between NDVI and LST in the urban area of Mashhad, Iran. International Conference on Civil Engineering Architecture & Urban Sustainable Development, Tabriz, Iran.

  • Guo, H., Zhang, B., Bai, Y., & He, X. (2017). Ecological environment assessment based on Remote Sensing in Zhengzhou. IOP Conference Series: Earth and Environmental Science, 94, 012190. https://doi.org/10.1088/1755-1315/94/1/012190

    Article  Google Scholar 

  • Hang, X., Li, Y., Luo, X., Xu, M., & Han, X. (2020). Assessing the ecological quality of Nanjing during its urbanization process by using satellite, meteorological, and socioeconomic data. Journal of Meteorological Research, 34(2), 280–293. https://doi.org/10.1007/s13351-020-9150-6

    Article  Google Scholar 

  • He, C., Gao, B., Huang, Q., Ma, Q., & Dou, Y. (2017). Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data. Remote Sensing of Environment, 193, 65–75. https://doi.org/10.1016/j.rse.2017.02.027

    Article  Google Scholar 

  • Hosseiniebalam, F., & Ghaffarpasand, O. (2015). The effects of emission sources and meteorological factors on sulphur dioxide concentration of Great Isfahan. Iran. Atmospheric Environment, 100, 94–101. https://doi.org/10.1016/j.atmosenv.2014.10.012

    Article  CAS  Google Scholar 

  • Hu, X., & Xu, H. (2018). A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City. China. Ecological Indicators, 89(8), 11–21. https://doi.org/10.1016/j.ecolind.2018.02.006

    Article  Google Scholar 

  • Hua, L., Shao, G., & Zhao, J. (2017). A concise review of ecological risk assessment for urban ecosystem application associated with rapid urbanization processes. International Journal of Sustainable Development & World Ecology, 24(3), 248–261. https://doi.org/10.1080/13504509.2016.1225269

    Article  Google Scholar 

  • Huang, J., Wang, R., Li, F., Yang, W., Zhou, C., Jin, J., & Shi, Y. (2009). Simulation of thermal effects due to different amounts of urban vegetation within the built-up area of Beijing, China. International Journal of Sustainable Development & World Ecology, 16(1), 67–76. https://doi.org/10.1080/13504500902772113

    Article  Google Scholar 

  • Isfahan Municipality (2016). Atlas of Isfahan metropolitan.

  • Isfahan Municipality. (2018). Isfahan City Statistics. Isfahan Municipality’s Deputy for Planning and Human Capital Development.

  • Javadzarin, I., Damavandi, A., Gorji, M., Jamshidi, M., & Eftekhari, K. (2018). Study of indexes changes of NDVI, NDMI and NDSI in two time sections, within 30 years by using satellite images Landsat. Arvand Kennar Region.

    Google Scholar 

  • Jing, Y., Zhang, F., He, Y., Kung, H., Johnson, V. C., & Arikena, M. (2020). Assessment of spatial and temporal variation of ecological environment quality in Ebinur Lake Wetland National Nature Reserve, Xinjiang. China. Ecological Indicators, 110, 105874. https://doi.org/10.1016/j.ecolind.2019.105874

    Article  Google Scholar 

  • Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. https://doi.org/10.1098/rsta.2015.0202

    Article  Google Scholar 

  • Kafy, A.A., Rahman, Md. S., & Faisal, A.A., Hasan, M. M., & Islam, M. . (2020). Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment, 18, 100314. https://doi.org/10.1016/j.rsase.2020.100314

    Article  Google Scholar 

  • Liu, X. Y., Zhang, X. X., He, Y. R., and Luan, H. J. (2020). Monitoring and assessment of ecological change in coastal cities based on RSEI. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-3/W10, 461–470. 10.5194/isprs-archives-XLII-3-W10-461-2020

  • Mahato, S., & Pal, S. (2018). Changing land surface temperature of a rural Rarh tract river basin of India. Remote Sensing Applications: Society and Environment, 10, 209–223. https://doi.org/10.1016/j.rsase.2018.04.005

    Article  Google Scholar 

  • Mahmoudian, H., & Ghassemi-Ardahaee, A. (2014). Internal Migration and Urbanization in I.IR.Iran (p. 111).

  • Mahmoudian, H., & Ghassemi-Ardehayi, A. (2014). Internal migration and urbanization in I.R. Iran. Payame Noor University.

  • Meng, F., Guo, J., Guo, Z., Lee, J. C. K., Liu, G., & Wang, N. (2021). Urban ecological transition: The practice of ecological civilization construction in China. Science of The Total Environment, 755, 142633. https://doi.org/10.1016/j.scitotenv.2020.142633

    Article  CAS  Google Scholar 

  • Mishra, S. prasad, Taraphder, S., Swain, D., & Laishram, M. (2017). Multivariate statistical data analysis-principal component analysis (PCA). Int J Liv Res, 60–78.

  • Niu, X., and Li, Y. (2020). Remote sensing evaluation of ecological environment of Anqing City based on remote sensing ecological index. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020, 733–737. 10.5194/isprs-archives-XLIII-B3-2020-733-2020

  • Regional Water Company of Isfahan (2019). Water resources.

  • Rizwan, A. M., Dennis, L. Y. C., & Liu, C. (2008). A review on the generation, determination and mitigation of Urban Heat Island. Journal of Environmental Sciences, 20(1), 120–128. https://doi.org/10.1016/S1001-0742(08)60019-4

    Article  CAS  Google Scholar 

  • SCI (2017). General Census of Population and Housing 2016. Statistical Centre of Iran.

  • SCI (2020). Statistical data and information. Statistical Centre of Iran, https://www.amar.org.ir/

  • Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D., & Willis, K. J. (2016). Sensitivity of global terrestrial ecosystems to climate variability. Nature, 531(7593), 229–232. https://doi.org/10.1038/nature16986

    Article  CAS  Google Scholar 

  • Setturu, B., KS, R., & TV, R. (2013). Land surface temperature responses to land use land cover dynamics. Geoinformatics & Geostatistics: An Overview, 01(04). https://doi.org/10.4172/2327-4581.1000112

  • Shan, W., Jin, X., Ren, J., Wang, Y., Xu, Z., Fan, Y., Gu, Z., Hong, C., Lin, J., & Zhou, Y. (2019). Ecological environment quality assessment based on remote sensing data for land consolidation. Journal of Cleaner Production, 239, 118126. https://doi.org/10.1016/j.jclepro.2019.118126

    Article  Google Scholar 

  • Shirani-bidabadi, N., Nasrabadi, T., Faryadi, S., Larijani, A., & Shadman Roodposhti, M. (2019). Evaluating the spatial distribution and the intensity of urban heat island using remote sensing, case study of Isfahan city in Iran. Sustainable Cities and Society, 45, 686–692. https://doi.org/10.1016/j.scs.2018.12.005

    Article  Google Scholar 

  • Singh, P., Kikon, N., & Verma, P. (2017). Impact of land use change and urbanization on urban heat island in Lucknow city, Central India. A remote sensing based estimate. Sustainable Cities and Society, 32, 100–114. https://doi.org/10.1016/j.scs.2017.02.018

    Article  Google Scholar 

  • Solanky, V., Singh, S., & Katiyar, S. K. (2018). Land surface temperature estimation using remote sensing data. In V. P. Singh, S. Yadav, & R. N. Yadava (Eds.), Hydrologic Modeling (Vol. 81, pp. 343–351). Springer Singapore. https://doi.org/10.1007/978-981-10-5801-1_24

  • Stewart, I. D. (2011). A systematic review and scientific critique of methodology in modern urban heat island literature. International Journal of Climatology, 31(2), 200–217. https://doi.org/10.1002/joc.2141

    Article  Google Scholar 

  • Su, M., Xie, H., Yue, W., Zhang, L., Yang, Z., & Chen, S. (2019). Urban ecosystem health evaluation for typical Chinese cities along the Belt and Road. Ecological Indicators, 101, 572–582. https://doi.org/10.1016/j.ecolind.2019.01.070

    Article  Google Scholar 

  • Sun, C., Li, X., Zhang, W., & Li, X. (2020). Evolution of ecological security in the tableland region of the Chinese Loess Plateau using a remote-sensing-based index. Sustainability, 12(8), 3489. https://doi.org/10.3390/su12083489

    Article  Google Scholar 

  • Tomlinson, C. J., Chapman, L., Thornes, J. E., & Baker, C. (2011). Remote sensing land surface temperature for meteorology and climatology: A review: Remote sensing land surface temperature. Meteorological Applications, 18(3), 296–306. https://doi.org/10.1002/met.287

    Article  Google Scholar 

  • Tuvdendorj, B., Wu, B., Zeng, H., Batdelger, G., & Nanzad, L. (2019). Determination of appropriate remote sensing indices for spring wheat yield estimation in Mongolia. Remote Sensing, 11(21), 2568. https://doi.org/10.3390/rs11212568

    Article  Google Scholar 

  • USGS (2019). Real-time Data. https://www.usgs.gov/products/data-and-tools/real-time-data

  • Vlassova, L., Perez-Cabello, F., Nieto, H., Martín, P., Riaño, D., & de la Riva, J. (2014). Assessment of methods for land surface temperature retrieval from Landsat-5 TM images applicable to multiscale tree-grass ecosystem modeling. Remote Sensing, 6(5), 4345–4368. https://doi.org/10.3390/rs6054345

    Article  Google Scholar 

  • Wen, X., Ming, Y., Gao, Y., & Hu, X. (2019). Dynamic monitoring and analysis of ecological quality of Pingtan Comprehensive Experimental Zone, a New Type of Sea Island City. Based on RSEI. Sustainability, 12(1), 21. https://doi.org/10.3390/su12010021

    Article  Google Scholar 

  • Xiao, H., & Weng, Q. (2007). The impact of land use and land cover changes on land surface temperature in a karst area of China. Journal of Environmental Management, 85(1), 245–257. https://doi.org/10.1016/j.jenvman.2006.07.016

    Article  Google Scholar 

  • Xiong, Y., Xu, W., Lu, N., Huang, S., Wu, C., Wang, L., Dai, F., & Kou, W. (2021). Assessment of spatial–temporal changes of ecological environment quality based on RSEI and GEE: A case study in Erhai Lake Basin, Yunnan province. China. Ecological Indicators, 125, 107518. https://doi.org/10.1016/j.ecolind.2021.107518

    Article  Google Scholar 

  • Xu, H., Wang, M., Shi, T., Guan, H., Fang, C., & Lin, Z. (2018). Prediction of ecological effects of potential population and impervious surface increases using a remote sensing based ecological index (RSEI). Ecological Indicators, 93, 730–740. https://doi.org/10.1016/j.ecolind.2018.05.055

    Article  Google Scholar 

  • Xu, H., Wang, Y., Guan, H., Shi, T., & Hu, X. (2019). Detecting ecological changes with a Remote Sensing Based Ecological Index (RSEI) produced time series and change vector analysis. Remote Sensing, 11(20), 2345. https://doi.org/10.3390/rs11202345

    Article  Google Scholar 

  • Yang, J., Wu, T., Pan, X., Du, H., Li, J., Zhang, L., Men, M., & Chen, Y. (2019). Ecological quality assessment of Xiongan New Area based on remote sensing ecological index. Chinese Journal of Applied Ecology, 30(1), 277–284. https://doi.org/10.13287/j.1001-9332.201901.017

  • Yin, H., Udelhoven, T., Fensholt, R., Pflugmacher, D., & Hostert, P. (2012). How Normalized Difference Vegetation Index (NDVI) Trends from Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) time series differ in agricultural areas: An Inner Mongolian case study. Remote Sensing, 4(11), 3364–3389. https://doi.org/10.3390/rs4113364

    Article  Google Scholar 

  • Yue, H., Liu, Y., Li, Y., & Lu, Y. (2019). Eco-environmental quality assessment in China’s 35 major cities based on remote sensing ecological index. IEEE Access, 7, 51295–51311. https://doi.org/10.1109/ACCESS.2019.2911627

    Article  Google Scholar 

  • Zawadzki, J., Przeździecki, K., & Miatkowski, Z. (2016). Determining the area of influence of depression cone in the vicinity of lignite mine by means of triangle method and LANDSAT TM/ETM+ satellite images. Journal of Environmental Management, 166, 605–614. https://doi.org/10.1016/j.jenvman.2015.11.010

    Article  Google Scholar 

  • Zhai, H., Xie, W., Li, S., & Zhang, Q. (2019). Urban ecological environment construction based on remote sensing ecological index. Ekoloji, 28(108), 1583–1588

    Google Scholar 

  • Zhu, D., Chen, T., Zhen, N., & Niu, R. (2020). Monitoring the effects of open-pit mining on the eco-environment using a moving window-based remote sensing ecological index. Environmental Science and Pollution Research, 27(13), 15716–15728. https://doi.org/10.1007/s11356-020-08054-2

    Article  Google Scholar 

  • Zhu, X., Wang, X., Yan, D., Liu, Z., & Zhou, Y. (2019). Analysis of remotely-sensed ecological indexes’ influence on urban thermal environment dynamic using an integrated ecological index: A case study of Xi’an. China. International Journal of Remote Sensing, 40(9), 3421–3447. https://doi.org/10.1080/01431161.2018.1547448

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Solmaz Amoushahi.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karbalaei Saleh, S., Amoushahi, S. & Gholipour, M. Spatiotemporal ecological quality assessment of metropolitan cities: a case study of central Iran. Environ Monit Assess 193, 305 (2021). https://doi.org/10.1007/s10661-021-09082-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-021-09082-2

Keywords

Navigation