Skip to main content

Advertisement

SpringerLink
Log in

Assessment of Spatio-Temporal Changes in Land Use/Land Cover Over a Decade (2000–2014) Using Earth Observation Datasets: A Case Study of Varanasi District, India

  • Research paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

Land use/land cover (LULC) changes have emerged as a major concern on global as well as on the local stage because of its considerable impact on climate and environment, especially in rapidly developing areas. Therefore, accurate mapping of LULC and ongoing changes over a time period have drawn a lot of attention in recent years. Remote sensing images from Landsat series satellites are a major information source for LULC change analysis. The present study mainly focuses on the evaluation of three classification techniques, namely maximum likelihood classifier (MLC), artificial neural network (ANN) and support vector machine (SVM) using multi-temporal Landsat images in order to choose the best method among them. The overall analysis based on accuracy measures indicates that the SVM is superior to ANN and MLC. The classification results achieved by the best recognized technique (SVM) were applied to assess the spatio-temporal changes in LULC that occurred in a fast growing Varanasi district of India over a period of 14 years (2001–2014). A paired samples t test was also carried out to determine the statistical significance of changes in LULC between different studied time periods. The results reveal the rapid expansion in built-up area resulted in substantial decrease in agricultural land and other LULC classes. This study also highlights the importance of Landsat images to provide accurate and timely LULC maps that can be used as inputs in a number of land management and planning activities.

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

References

  • Atkinson PM, Tatnall ARL (1997) Introduction neural networks in remote sensing. Int J Remote Sens 18:699–709

    Article  Google Scholar 

  • Bagan H, Wang QX, Watanabe M, Yang YH, Ma JW (2005) Land cover classification from MODIS EVI times-series data using SOM neural network. Int J Remote Sens 26:4999–5012

    Article  Google Scholar 

  • Bartholome E, Belward AS (2005) GLC 2000: a new approach to global land cover mapping from earth observation data. Int J Remote Sens 26:1959–1977

    Article  Google Scholar 

  • Borak JS, Strahler AH (1999) Feature selection and land cover classification of a MODIS-like data set for a semiarid environment. Int J Remote Sens 20(5):919–938

    Article  Google Scholar 

  • Congalton RG, Green K (1999) Assessing the accuracy of remotely sensed data: principles and practices. CRC/Lewis Press, Boca Raton

    Google Scholar 

  • Cortes C, Vapnik VN (1995) Support-vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  • Dickinson RE (1995) Land processes in climate models. Remote Sens Environ 51:27–38

    Article  Google Scholar 

  • Dimyati M, Mizuno K, Kitamura T (1996) An analysis of land use/cover change using the combination of MSS Landsat and land use map: a case study in Yogyakarta, Indonesia. Int J Remote Sens 17:931–944

    Article  Google Scholar 

  • Dixon B, Candade N (2008) Multispectral land use classification using neural networks and support vector machines: One or the other, or both? Int J Remote Sens 29(4):1185–1206

    Article  Google Scholar 

  • Ellis E (2007) Land–use and land–cover change. In: Cleveland CJ (ed) Encyclopedia of earth. Environmental Information Coalition, National Council for Science and the Environment, Washington

    Google Scholar 

  • ENVI User’s Guide (2009) ENVI on-line software user’s manual. ITT Visual Information Solutions, Boulder

    Google Scholar 

  • Foody G (2003) Remote sensing of tropical forest environments: towards the monitoring of environmental resources for sustainable development. Int J Remote Sens 24:4035–4046

    Article  Google Scholar 

  • Foody GM, Arora MK (1997) An evaluation of some factors affecting the accuracy of classification by an artificial neural network. Int J Remote Sens 18:799–810

    Article  Google Scholar 

  • Gong P, Wang J, Yu L, Zhao YC, Zhao YY, Liang L, Niu ZG, Huang XM, Fu HH, Liu S et al (2013) Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM + data. Int J Remote Sens 34:2607–2654

    Article  Google Scholar 

  • Hilbert DW, Ostendorf B (2001) The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecol Model 146:311–327

    Article  Google Scholar 

  • Hord RM (1982) Digital image processing of remotely sensed data. Academic Press, New York

    Google Scholar 

  • Huang C, Davis LS, Townshend JRG (2002) An assessment of support vector machines for land cover classification. Int J Remote Sens 23:725–749

    Article  Google Scholar 

  • Jensen JR (2005) Introductory digital image processing: a remote sensing perspective, 3rd edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  • Kavzoglu T (2009) Increasing the accuracy of neural network classification using refined training data. Environ Model Softw 4:850–858

    Article  Google Scholar 

  • Kavzoglu T, Mather PM (2003) The use of back propagating artificial neural networks in land cover classification. Int J Remote Sens 24:4907–4938

    Article  Google Scholar 

  • Kumar P, Gupta DK, Mishra VN, Prasad R (2015a) Comparison of support vector machine, artificial neural network and spectral angle mapper algorithms for crop classification using LISS IV data. Int J Remote Sens 36(6):1604–1617

    Article  Google Scholar 

  • Kumar P, Prasad R, Gupta DK, Mishra VN, Choudhary A (2015b) Support vector machine for classification of various crop using high resolution LISS-IV imagery. Bull Environ Sci Res 4:1–5

    Google Scholar 

  • Kumar P, Prasad R, Choudhary A, Mishra VN, Gupta DK, Srivastava PK (2017) A statistical significance of differences in classification accuracy of crop types using different classification algorithms. Geocarto Int 32:206–224

    Google Scholar 

  • Lambin EF, Geist HJ, Lepers E (2003) Dynamics of land-use and land cover change in tropical regions. Annu Rev Environ Resour 28:205–241

    Article  Google Scholar 

  • Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823–870

    Article  Google Scholar 

  • Mas JF, Flores JJ (2008) The application of artificial neural networks to the analysis of remotely sensed data. Int J Remote Sens 29:617–663

    Article  Google Scholar 

  • Mishra VN, Rai PK (2016) A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India. Arab J Geosci 9:1–18

    Article  Google Scholar 

  • Mishra VN, Kumar P, Gupta DK, Prasad R (2014a) Classification of various land features using RISAT-1 dual polarimetric data. Int Arch Photogramm Remote Sens Spat Inf Sci XL-8:833–837

    Article  Google Scholar 

  • Mishra VN, Rai PK, Mohan K (2014b) Prediction of land use changes based on land change modeler (LCM) using remote sensing: a case study of Muzaffarpur (Bihar), India. J Geogr Inst Jovan Cvijic 64:111–127

    Article  Google Scholar 

  • Mishra VN, Rai PK, Kumar P, Prasad R (2016) Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images. Forum Geogr 15(1):45–53

    Article  Google Scholar 

  • Misra A, ManiMurli R, Vethamony P (2015) Assessment of the land use/land cover (LU/LC) and mangrove changes along the Mandovi–Zuari estuarine complex of Goa, India. Arab J Geosci 8(1):267–279

    Article  Google Scholar 

  • Mouat DA, Mahin GG, Lancaster J (1993) Remote sensing techniques in the analysis of change detection. Geocarto Int 8:39–50

    Article  Google Scholar 

  • Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens 66:247–259

    Article  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 Observ Geoinf 12:S27–S31

    Article  Google Scholar 

  • Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48:2297–2307

    Article  Google Scholar 

  • Puissant A, Rougiera S, André S (2014) Object-oriented mapping of urban trees using random forest classifiers. Int J Appl Earth Observ Geoinf 26:235–245

    Article  Google Scholar 

  • Puyravaud JP (2003) Standardizing the calculation of the annual rate of deforestation. For Ecol Manag 177:593–596

    Article  Google Scholar 

  • Raju K, Kumar RA (2006) Land use changes in Udumbanchola taluk, Idukki district-Kerala: an analysis with the application of remote sensing data. J Indian Soc Remote Sens 34(2):161–169

    Article  Google Scholar 

  • Rawat JS, Kumar M (2015) Monitoring land use/cover change using remote sensing and GIS techniques: a case study of Hawalbagh block, district Almora, Uttarakhand, India. Egypt J Remote Sens Space Sci 18:77–84

    Google Scholar 

  • Richards JA, Jia X (2006) Remote sensing digital image analysis, 4th edn. Springer, Heidelberg

    Google Scholar 

  • Ridd MK, Liu J (1998) A comparison of four algorithms for change detection in an urban environment. Remote Sens Environ 63:95–100

    Article  Google Scholar 

  • Singh SK, Srivastava PK, Gupta M, Thakur JK, Mukherjee S (2014) Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine. Environ Earth Sci 71(5):2245–2255

    Article  Google Scholar 

  • Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T (2012) Selection of classification techniques for land use/land cover change investigation. Adv Space Res 50:1250–1265

    Article  Google Scholar 

  • Swain PH, Davis SM (1978) Remote sensing: the quantitative approach. McGraw-Hill, New York

    Google Scholar 

  • Thakur JK, Singh SK, Ramanathan A, Prasad MBK, Gossel W (2012) Geospatial techniques for managing environmental resources, 1st edn. Springer, New York

    Book  Google Scholar 

  • Vapnik VN (2000) The nature of statistical learning theory. Springer, New York

    Book  MATH  Google Scholar 

  • Vapnik VN, Chervonenkis AY (1971) On the uniform convergence of relative frequencies of events to their probabilities. Theory Probab Appl 16:264

    Article  MATH  Google Scholar 

  • Vitousek PM (1994) Beyond global warming: ecology and global change. Ecology 75(7):1861–1876

    Article  Google Scholar 

  • Yuan D, Elvidge CD, Lunetta RS (1998) Survey of multispectral methods for land cover change analysis. Remote sensing change detection: environmental monitoring methods and applications. Michigan’ Ann Arbor Press, Ann Arbor, pp 21–39

    Google Scholar 

  • Yuan F, Sawaya KE, Loeffelholz BC, Bauer ME (2005) Land cover classification and change analysis of the Twin Cities (Minnesota) metropolitan area by multitemporal Landsat remote sensing. Remote Sens Environ 98:317–328

    Article  Google Scholar 

  • Zhu Z, Woodcock CE (2014) Continuous change detection and classification of land cover using all available Landsat data. Remote Sens Environ 144:152–171

    Article  Google Scholar 

Download references

Acknowledgements

Authors wish to gratefully acknowledge the United States Geological Survey (USGS) for providing Landsat data at no cost. Finally, authors are grateful to the anonymous reviewers and editor for their valuable comments which assisted in improving the manuscript.

Author information

Authors and Affiliations

  1. Department of Physics, Indian Institute of Technology (BHU), Varanasi, India

    Varun Narayan Mishra, Rajendra Prasad, Pradeep Kumar & Dileep Kumar Gupta

  2. Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Sarang Agarwal

  3. Department of Electrical Engineering, Indian Institute of Technology (BHU), Varanasi, India

    Anil Gangwal

Authors
  1. Varun Narayan Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rajendra Prasad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pradeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dileep Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sarang Agarwal
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Anil Gangwal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rajendra Prasad.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mishra, V.N., Prasad, R., Kumar, P. et al. Assessment of Spatio-Temporal Changes in Land Use/Land Cover Over a Decade (2000–2014) Using Earth Observation Datasets: A Case Study of Varanasi District, India. Iran J Sci Technol Trans Civ Eng 43 (Suppl 1), 383–401 (2019). https://doi.org/10.1007/s40996-018-0172-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40996-018-0172-6

Keywords

Search

Navigation