Skip to main content

Advertisement

Log in

Monitoring land cover changes in Isfahan Province, Iran using Landsat satellite data

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

Abstract

Changes in land cover and land use reveal the effects of natural and human processes on the Earth’s surface. These changes are predicted to exert the greatest environmental impacts in the upcoming decades. The purpose of the present study was to monitor land cover changes using Multispectral Scanner Sensor (MSS) and multitemporal Landsat Thematic Mapper (TM) data from the counties of Isfahan Province, Iran, during 1975, 1990, and 2010. The maximum likelihood supervised classification method was applied to map land cover. Postclassification change detection technique was also used to produce change images through cross-tabulation. Classification results were improved using ancillary data, visual interpretation, and local knowledge about the area. The overall accuracy of land cover change maps ranged from 88 to 90.6 %. Kappa coefficients associated with the classification were 0.81 for 1975, 0.84 for 1990, and 0.85 for 2010 images. This study monitored changes related to conversion of agricultural land to impervious surfaces, undeveloped land to agricultural land, agricultural land to impervious surfaces, and undeveloped land to impervious surfaces. The analyses of land cover changes during the study period revealed the significant development of impervious surfaces in counties of Isfahan Province as a result of population growth, traffic conditions, and industrialization. The image classification indicated that agricultural lands increased from 2520.96 km2 in 1975 to 4103.85 km2 in 2010. These land cover changes were evaluated in different counties of Isfahan Province.

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
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Alcantara, C., Kuemmerle, T., Prishchepov, V. A., & Radeloff, C. V. (2012). Mapping abandoned agriculture with multi-temporal MODIS satellite data. Remote Sensing of Environment, 124, 334–347.

    Article  Google Scholar 

  • Alphan, H. (2003). Land use change and urbanization in Adana, Turkey. Land Degradation and Development, 14(6), 575–586.

    Article  Google Scholar 

  • Anderson, J. R., Hardy, E. E., Roach, J. T., & Witmer, R. E. (1976). A land use/cover classification system for use with remote sensor data. Washington: United States Government Printing Office.

    Google Scholar 

  • Bateni, F., Fakheran, S., Soffianian, A. (2013). Assessment of land cover changes and water quality changes in the Zayandehroud River Basin between 1997–2008. Environmental Monitoring and Assessment, 185(8).

  • Bruzzone, L., & Serpico, S. B. (1997). An iterative technique for the detection of land- cover transitions in multitemporal remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing, 35(4), 858–867.

    Article  Google Scholar 

  • Bruzzone, L., Cossu, R., & Vernazza, G. (2004). Detection of land-cover transitions by combining multidate classifiers. Pattern Recogniton Letters, 25(13), 1491–1500.

    Article  Google Scholar 

  • Castellana, L., D’Addabbo, A., & Pasquariello, G. (2007). A composed supervised/unsupervised approach to improve change detection from remote sensing. Pattern Recogniton Letters, 28(4), 405–413.

    Article  Google Scholar 

  • Conese, C., & Maselli, F. (1992). Use of error matrices to improve area estimates with maximum likelihood classification procedures. Remote Sensing of Environment, 40(2), 113–124.

    Article  Google Scholar 

  • Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., & Lambin, E. (2004). Digital change detection methods in ecosystem monitoring: A review. International Journal of Remote Sensing, 25(9), 1565–1596.

    Article  Google Scholar 

  • Dai, X. L., & Khorram, S. (1998). The effects of image misregistration on the accuracy of remotely sensed change detection. IEEE Transactions on Geoscience and Remote Sensing, 36(5), 1566–1577.

    Article  Google Scholar 

  • Ediriwickrema, J., & Khorram, S. (1997). Hierarchical maximum-likelihood classification for improved accuracies. IEEE Transactions on Geoscience and Remote Sensing, 35(4), 810–816.

    Article  Google Scholar 

  • Falcucci, A., Maiorano, L., & Boitani, L. (2007). Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation. Landscape Ecology, 22, 617–631.

    Article  Google Scholar 

  • Fanni, Z. (2006). Cities and urbanization in Iran after the Islamic revolution. Cities, 23(6), 407–411.

    Article  Google Scholar 

  • Filippone, M., & Sanguinetti, G. (2010). Information theoretic novelty detection. Pattern Recognition, 43(3), 805–814.

    Article  Google Scholar 

  • Forzieri, G., & Catani, F. (2011). Scale-dependent relations in land cover biophysical dynamics. Ecological Modelling, 222(17), 3285–3290.

    Article  Google Scholar 

  • Forzieri, G., Gardenti, M., Caparrini, F., & Castelli, F. (2008). A methodology for the pre-selection of suitable sites for surface and underground small dams in arid areas: A case study in the region of Kidal, Mali. Physics and Chemistry of the Earth, Parts A/B/C, 33(1–2), 74–85.

    Article  Google Scholar 

  • Ghaed Rahmati, S., & Heydarinejad, N. (2009). Physical urban expansion and the necessity of providing safe boundary in urban area (a case study: Isfahan city). Geography and Environmental Studies, 1(1), 14–24.

    Google Scholar 

  • Haack, B. (1987). An assessment of Landsat MSS and TM for urban and near-urban digital classification. Remote Sensing of Environment, 21(2), 201–213.

    Article  Google Scholar 

  • Hall, F. G., Strebel, D. E., Nickeson, J. E., & Goetz, S. J. (1991). Radiometric rectification: Toward a common radiometric response among multidate, multisensor images. Remote Sensing of Environment, 35(1), 11–27.

    Article  Google Scholar 

  • Hardin, P. J., Jackson, M. W., & Otterstrom, S. M. (2007). Mapping, measuring, and modeling urban growth. Geo-spatial technologies in urban environments: Policy, practice and pixels, 2nd edn. Springer Berlin Heidelberg.

  • Hester, D. B. (2008). Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery. Faculty of North Carolina State University.

  • Isfahan Department of Environment Portal. www.isfahan-doe.ir.

  • Isfahan Municipality Portal. www.isfahan.ir.

  • Isfahan Provincial Government Portal. www.ostan-es.ir.

  • Jensen, J. R. (1996). Introductory digital image processing: A remote sensing perspective. New Jersey: Prentice Hall.

    Google Scholar 

  • Jensen, J. R. (2004). Digital change detection, introductory digital image processing: A remote sensing perspective. Upper Saddle River: Prentice-Hall.

    Google Scholar 

  • Jensen, J. R. (2005). Introductory digital image processing: A remote sensing perspective. Upper Saddle River: Prentice Hall.

    Google Scholar 

  • Jianya, G., Haigang, S., Guorui, M., & iming, Z. (2008). A review of multi-temporal remote sensing data change detection algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, 36, 757–762.

  • Jihad Agriculture Organization portal: http://www.agri-es.ir/.

  • Khodagholi, M. (2008). Final report on the zoning of droughts in Isfahan Province, Agriculture and Natural Resources Research Center of Isfahan Province. Isfahan: Agriculture and Natural Resources Research Center.

    Google Scholar 

  • Kristensen, L. S., Thenail, C., & Kristensen, S. P. (2004). Landscape changes in agrarian land-scapes in the 1990s: The interaction between farmers and the farmed landscape. A case study from Jutland, Denmark. Journal of Environmental Management, 71, 231–244.

    Article  Google Scholar 

  • Lins, K. S., & Kleckner, R. L. (1996). Land cover mapping: An overview and history of the concepts. In J. M. Scott, T. H. Tear, & F. Davis (Eds.), Gap analysis: A landscape approach to biodiversity planning (pp. 57–65). Bethesda: American Society for Photogrammetry and Remote Sensing.

    Google Scholar 

  • Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2004). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365–2407.

    Article  Google Scholar 

  • National Statistical Yearbook (1976–2010). www.amar.org.ir.

  • National Statistical Yearbook (2011). www.amar.org.ir.

  • Park, S., & Egber, T. S. (2008). Remote sensing-measured impacts of the Conservation Reserve Program on landscape structure in southwestern Kansas. GIS Science and Remote Sensing, 45(1), 83–108.

    Article  Google Scholar 

  • Peterson, U., & Aunap, R. (1998). Changes in agricultural land use in Estonia in the 1990s detected with multitemporal Landsat MSS imagery. Landscape and Urban Planning, 41, 193–201.

    Article  Google Scholar 

  • Prishchepov, A., Radeloff, V. C., Dubinin, M., & Alcantara, C. (2012). The effect of image acquisition dates on detection of agricultural land abandonment in Eastern Europe. Remote Sensing of Environment, 126, 195–209.

    Article  Google Scholar 

  • Sexton, O. J., Urban, L. D., Donohue, J. M., & Song, C. (2013). Long-term land cover dynamics by multi-temporal classification across the Landsat-5 record. Remote Sensing of Environment, 128(1), 246–258.

    Article  Google Scholar 

  • Shafaghi, S. (2002). Geography of Isfahan. Iran: Isfahan University Press.

    Google Scholar 

  • Song, C., Woodcock, C. E., Seto, K. C., Lenney, M. P., & Macomber, S. A. (2001). Classification and change detection using Landsat TM data when and how to correct atmospheric effects? Remote Sensing of Environment, 75(2), 230–244.

    Article  Google Scholar 

  • Strahler, A. H. (1980). The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10(2), 135–163.

    Article  Google Scholar 

  • Tso, B., & Martner, P. M. (2009). Classification methods for remotely sensed data. Taylor and Francis Group. CRC Press: United States of America.

  • Yang, X., & Lo, C. P. (2002). Using a time series of satellite imagery to detect land use and cover changes in the Atlanta, Georgia. International Journal of Remote Sensing, 23(9), 1775–1798.

    Article  Google Scholar 

  • Zheng, M., Cai, Q., & Wang, Z. (2005). Effect of prior probabilities on maximum likelihood classifier. Geoscience and Remote Sensing Symposium. IGARSS 05 Proceedings 2005 I.E. International, 6, 3753–3756.

Download references

Acknowledgments

This article is a part of the multidisciplinary research project “land use planning of Isfahan Province” (code: 2964) funded by Isfahan Provincial Government.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Maliheh Madanian.

Additional information

Alireza Soffianian is an associate professor, Department of Natural Resources, Isfahan University of Technology.

Maliheh Madanian is a Ph.D. student in land use planning, Department of Natural Resources, Isfahan University of Technology.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Soffianian, A., Madanian, M. Monitoring land cover changes in Isfahan Province, Iran using Landsat satellite data. Environ Monit Assess 187, 543 (2015). https://doi.org/10.1007/s10661-015-4442-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-015-4442-5

Keywords

Navigation