Abstract
Land use and land cover (LULC) both define the earth’s surface both anthropogenically and naturally. It helps maintain global balance but changes in land use create inequality. The LULC modification adversely affects physical parameters such as infiltration, groundwater recharge, surface runoff, ground temperature, and air quality. It is high time to monitor land use changes globally. Remote sensing and GIS techniques help to monitor these changes with a low budget and time. Various types of LULC classifiers have been invented to classify the LULC types. Maximum likelihood is a popular LULC classifier, but nowadays, support vector machine (SVM) classifier is gaining popularity because it provides a more accurate LULC than the maximum likelihood classifier. Therefore, in this study, the SVM classification technique has been applied to produce good accuracy LULC maps. Using the SVM classifier, six LULC maps are produced from 1995 to 2020 for the Shali reservoir area in India which is a medium irrigation project irrigating ~ 3211 hectares of land per year. It plays an important role in the agricultural production of the region by providing irrigation water in monsoon and post-monsoon seasons. The impact of LULC change on the environment is also studied. The LULC forecast maps are also created using the cellular automata (CA) model and MOLUSCE plugin. Kappa coefficient and validation methods are used to validate the LULC and simulated maps. Both maps produce high accuracy with a kappa coefficient of 0.9. Secondary data, collected from the governmental gazette, such as population, crop production, and water level is also used to justify the results. The simulated map shows that 4% of agricultural land and built-up area may increase from 2020 to 2030. Overall, it has been proven that the SVM and CA models can produce accurate classified results.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability statement
All data generated or analysed during this study are included in this published article.
References
Abatzoglou, T. J., Dobrowski, S. Z., Parks, S. A., & Hegewisch, K. C. (2018). TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Scientific Data, 5, 170191. https://doi.org/10.1038/sdata.2017.191
Acker, J., Soebiyanto, R., Kiang, R., & Kempler, S. (2014). Use of the NASA Giovanni data system for geospatial public health research: Example of weather-influenza connection. International Journal of Geo-Information, 3(4), 1372–1386. https://doi.org/10.3390/ijgi3041372
Alam, N., Saha, S., Gupta, S., & Chakraborty, S. (2021). Prediction modelling of riverine landscape dynamics in the context of sustainable management of floodplain: A geospatial approach. Annals of GIS, 27(3), 299–314. https://doi.org/10.1080/19475683.2020.1870558
Aspinall, R. (2004). Modeling land use change with generalized linear models—A multi-model analysis of change between 1860 and 2000 in Gallatin Valley. Montana. Journal of Environmental Management, 72(1–2), 91–103. https://doi.org/10.1016/j.jenvman.2004.02.009
Bhatta, B. (2017). Remote sensing and GIS. Third Edition. Oxford University Press.
CPCB. (2022). Central Pollution Control Board, Ministry of Environment, Forest and Climate Change, Government of India. https://cpcb.nic.in/. (Accessed last on January 02, 2022).
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, XX(1), 37–46. https://doi.org/10.1177/001316446002000104
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297. https://doi.org/10.1007/BF00994018
Dale, V. (1997). The relationship between land-use change and climate change. Ecological Applications, 7(3), 753–769. https://doi.org/10.1890/1051-61(1997)0070753
Dale, V. H., Efroymson, R. A., & Kline, K. L. (2011). The land use–climate change–Energy nexus. Landscape Ecology, 26, 755–773. https://doi.org/10.1007/s10980-011-9606-2
District Statistical Handbooks. (2010–2014). Bankura 2010–11, Bankura 2013, Bankura 2014. Department of Planning and Statistics, Government of West Bengal. http://wbpspm.gov.in/publications/District%20Statistical%20Handbook. (Accessed last on December 27, 2021).
Dixon, B., & Candade, N. (2008). Multispectral landuse classification using neural networks and support vector machines: One or the other, or both? International Journal of Remote Sensing, 29(4), 1185–1206. https://doi.org/10.1080/01431160701294661
Dosdogru, F., Kalin, L., Wang, R., & Yen, H. (2020). Potential impacts of land use/cover and climate changes on ecologically relevant flows. Journal of Hydrology, 584, 124654. https://doi.org/10.1016/j.jhydrol.2020.124654
Feng, Y. W., Wang, R., Tong, X., & Shafizadeh-Moghadam, H. (2019). How much can temporally stationary factors explain cellular automata-based simulations of past and future urban growth? Computers, Environment and Urban Systems, 76, 150–162. https://doi.org/10.1016/j.compenvurbsys.2019.04.010
Gopinath, G., Sasidharan, N., & Surendran, U. (2019). Landuse classification of hyperspectral data by spectral angle mapper and support vector machine in humid tropical region of India. Earth Science Informatics, 13, 633–640. https://doi.org/10.1007/s12145-019-00438-4
Halder, S., Das, S., & Basu, S. (2020). A review on the decadal irrigation system of Shali water reservoir. Earth and Environmental Science, 505, 012023. https://doi.org/10.1088/1755-1315/505/1/012023
Huang, C., Davis, L. S., & Townshed, J. R. G. (2002). An assessment of support vector machines for land cover classification. International Journal of Remote Sensing, 23(4), 725–749. https://doi.org/10.1080/01431160110040323
https://earthexplorer.usgs.gov/. (Accessed last on November 17, 2021).
https://wbiwd.gov.in/. (Accessed last on November 22, 2021).
https://www.imdpune.gov.in/Clim_Pred_LRF_New/Grided_Data_Download.html. (Accessed last on November 11, 2021).
https://www.openstreetmap.org/. (Accessed last on November 11, 2021).
Indian Census. (2011). Population data. India Ministry of Home Affairs, Government of India. https://censusindia.gov.in/. (Accessed last on November 11, 2021).
Islam, K., Rahman, M. F., & Jashimuddin, M. (2018). Modeling land use change using cellular automata and artificial neural network: The case of Chunati Wildlife Sanctuary, Bangladesh. Ecological Indicators, 88, 439–453. https://doi.org/10.1016/j.ecolind.2018.01.047
James, G. W., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-7138-7
Kadavi, P. R., & Lee, C. (2018). Land cover classification analysis of volcanic island in Aleutian Arc using an artificial neural network (ANN) and a support vector machine (SVM) from Landsat imagery. Geosciences Journal, 22, 653–665. https://doi.org/10.1007/s12303-018-0023-2
Kafy, A., Naim, M. N. H., Subramanyam, G., Faisal, A., Ahmed, N. U., Rakib, A. A., Kona, M. K., & Sattar, G. S. (2021). Cellular automata approach in dynamic modelling of land cover changes using Rapideye images in Dhaka, Bangladesh. Environmental Challenges, 4.
Karan, S. K., & Samadder, S. R. (2016). Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas. Environmental Monitoring and Assessment, 188, 486. https://doi.org/10.1007/s10661-016-5494-x
Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823–870. https://doi.org/10.1080/01431160600746456
Lu, Y., Wu, P., Ma, X., & Li, X. (2019). Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model. Environmental Monitoring and Assessment, 191, 68. https://doi.org/10.1007/s10661-019-7200-2
Mohamed, A., & Worku, H. (2020). Simulating urban land use and cover dynamics using cellular automata and Markov chain approach in Addis Ababa and the surrounding. Urban Climate, 31, 100545. https://doi.org/10.1016/j.uclim.2019.100545
Mondal, A., Kundu, S., Chandniha, S., Shukla, R., & Mishra, P. (2012). Comparison of support vector machine and maximum likelihood classification technique using satellite imagery. International Journal of Remote Sensing and GIS, 1(2), 116–123.
Mountrakis, G., Im, J., & Ogole, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66(3), 247–259. https://doi.org/10.1016/j.isprsjprs.2010.11.001
Muller, M. R., & Middleton, J. (1994). A Markov model of landuse change dynamics in the Niagara region, Ontario. Canada. Landscape Ecology, 9(2), 151–157. https://doi.org/10.1007/BF00124382
Neumann, J. V., & Burks, A. W. (1966). Theory of self-reproducing automata. Edited and completed by Burks, A.W. University of Illinois Press, Urbana and London. https://cba.mit.edu/events/03.11.ASE/docs/VonNeumann.pdf. (Accessed last on January 02, 2022).
Ramachandra, T. S. (2016). Stimulus of developmental projects to landscape dynamics in Uttara Kannada, Central Western Ghats. The Egyptian Journal of Remote Sensing and Space Sciences, 19(2), 175–193. https://doi.org/10.1016/j.ejrs.2016.09.001
Rawat, J. S., & Kumar, M. (2015). Monitoring land use/cover change using remote sensing and GIS techniques: A case study of Hawalbagh block, district Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Sciences, 18(1), 77–84. https://doi.org/10.1016/j.ejrs.2015.02.002
Rounsevell, M. D. A., & Recay, D. S. (2009). Land use and climate change in the UK. Land Use Policy, 26(1), S160–S169. https://doi.org/10.1016/j.landusepol.2009.09.007
Satya, A. B., Sashi, M., & Deva, P. (2020). Future land use land cover scenario simulation using open source GIS for the city of Warangal, Telangana, India. Applied Geomatics, 12, 281–290. https://doi.org/10.1007/s12518-020-00298-4
Sinha, S., Sharma, L. K., & Nathawat, M. S. (2015). Improved land-use/land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Sciences, 18(2), 217–233. https://doi.org/10.1016/j.ejrs.2015.09.005
TerraClimate. (2020). National Center for Atmospheric Research (NCAR) – Climate Data Guide. Boulder, United States. https://climatedataguide.ucar.edu/. (Accessed last on December 19, 2021).
Tehrany, M. S., Pradhan, B., & Jebur, M. N. (2014). Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. Journal of Hydrology, 512, 332–343. https://doi.org/10.1016/j.jhydrol.2014.03.008
Tong, X., & Feng, Y. (2019). A review of assessment methods for cellular automata models of land-use change and urban growth. International Journal of Geographical Information Science, 34(5), 866–898. https://doi.org/10.1080/13658816.2019.1684499
Xing, W., Qian, Y., Guan, X., Yang, T., & Wu, H. (2020). A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation. Computers and Geosciences, 137, 104430. https://doi.org/10.1016/j.cageo.2020.104430
Ye, B., & Bai, Z. (2007). Simulating land use cover changes of Nenjiang County based on CA-Markov model. Simulating Land Use/Cover Changes of Nenjiang County Based on CA-Markov Model. In: Li, D. (eds) Computer And Computing Technologies In Agriculture, Volume I. CCTA 2007. The International Federation for Information Processing, vol 258. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-77251-6_35
Acknowledgements
The first author would also like to thank the University Grants Commission of India for funding a research fellowship for this work. The full cooperation from the Deputy Secretary to the Government of West Bengal, Irrigation & Waterways Department, and his entire team are also acknowledged for providing relevant field data related to Shali reservoir.
Author information
Authors and Affiliations
Corresponding author
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.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Halder, S., Das, S. & Basu, S. Use of support vector machine and cellular automata methods to evaluate impact of irrigation project on LULC. Environ Monit Assess 195, 3 (2023). https://doi.org/10.1007/s10661-022-10588-6
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10661-022-10588-6