Comparative Evaluation of SVM and MLC for Land Use and Land Cover Change Mapping Using Landsat Data: A Case Study of Bhaktapur District, Nepal
DOI:
https://doi.org/10.64862/ajeg.2025.202.02.265Keywords:
Bhaktapur, LULC, Support Vector MachineAbstract
The availability of reliable land cover maps is vital for effective planning, as their absence can compromise project validity. This study investigates the land use and land cover (LULC) changes in Bhaktapur district, Nepal, from 2015 to 2025 using Landsat imagery employing Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC). The primary objective is to compare the performance of SVM and MLC in LULC mapping and to calculate changes between two time periods. The results show increase in Built-up area and a reduction in barren land between the two time periods. Built-up area is increased by 12.36% (SVM) and 3.59% (MLC), between 2015 and 2025. Vegetation areas showed substantial gains, while forest area remained relatively stable. SVM achieved an overall accuracy of 79% (kappa = 0.69) whereas MLC achieved 73% (kappa = 0.60) indicating slightly higher classification accuracy for SVM. This study provides a comparative evaluation of two commonly used classifiers for LULC mapping and change analysis in Bhaktapur district. It also provides baseline spatial information for future planning studies.
References
Alharthi, A., El-Sheikh, M.A., Elhag, M., Alatar, A.A., Abbadi, G.A., Abdel-Salam, E.M., Arif, I.A., Baeshen, A.A. and Eid, E.M., (2020). Remote sensing of 10 years changes in the vegetation cover of the northwestern coastal land of Red Sea, Saudi Arabia. Saudi Journal of Biological Sciences, 27(11), 3169–3179. https://doi.org/10.1016/j.sjbs.2020.07.021
Batty, M. and Xie, Y., (1997). Possible urban automata. Environment and Planning B: Planning and Design, 24(2), 175–192. https://doi.org/10.1068/b240175
Chhetri, D.B.T. and Moriwaki, R., (2017). Monitoring urban growth, land use and land cover using remote sensing and GIS techniques: A case study of Bhaktapur District, Nepal. Engineering Science and Technology International Journal (ESTIJ), 7(1).
Congleton, R.G., (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1),35–46. https://doi.org/10.1016/0034-4257(91)90048-B
Dimyati, M., Mizuno, K., Kobayashi, S. and Kitamura, T., (1996). An analysis of land use/cover change in Indonesia. International Journal of Remote Sensing, 17(5),931–944. https://doi.org/10.1080/01431169608949056
DHM (2023). Department of Hydrology and Meteorology, Annual climate report. Government of Nepal, Ministry of Energy, Water Resources and Irrigation. https://www.dhm.gov.np
Gibril, M.B.A., Bakar, S.A., Yao, K., Idrees, M.O. and Pradhan, B., (2017). Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto International, 32(7), 735–748. https://doi.org/10.1080/10106049.2016.1170893
Huang, C., Davis, L.S. and Townshend, 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
Ishtiaque, A., Shrestha, M. and Chhetri, N., (2017). Rapid urban growth in the Kathmandu Valley, Nepal: Monitoring land use land cover dynamics of a Himalayan city with Landsat imageries. Environments, 4(4), 72. https://doi.org/10.3390/environments4040072
Johnson, B.A. and Iizuka, K., (2016). Integrating OpenStreetMap crowdsourced data and Landsat time-series imagery for rapid land use/land cover (LULC) mapping: Case study of the Laguna de Bay area of the Philippines. Applied Geography, 67, 140–149. https://doi.org/10.1016/j.apgeog.2015.12.006
Kanellopoulos, I., Varfis, A., Wilkinson, G.G. and Mégier, J., (1992). Land-cover discrimination in SPOT HRV imagery using an artificial neural network—a 20-class experiment. International Journal of Remote Sensing, 13(5), 917–924. https://doi.org/10.1080/01431169208904164
Kavzoglu, T. and Colkesen, I., (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352–359. https://doi.org/10.1016/j.jag.2009.06.002
Landis, J.R. and Koch, G.G., (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.
Maleki, M., Van Genderen, J.L., Tavakkoli-Sabour, S.M., Saleh, S.S. and Babaee, E., (2020). Land use/cover change in Dinevar rural area of west Iran during 2000–2018 and its prediction for 2024 and 2030. Geographia Technica, 15(2), 93–105. https://doi.org/10.21163/GT_2020.152.10
MohanRajan, S.N., Loganathan, A. and Manoharan, P., (2020). Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and challenges. Environmental Science and Pollution Research, 27(24), 29900–29926. https://doi.org/10.1007/s11356-020-09091-7
Naghadehi, S.Z., Asadi, M., Maleki, M., Tavakkoli-Sabour, S.M., Van Genderen, J.L. and Saleh, S.S., (2021). Prediction of urban area expansion with implementation of MLC, SAM and SVM classifiers incorporating artificial neural network using Landsat data. ISPRS International Journal of Geo-Information, 10(8). https://doi.org/10.3390/ijgi10080513
Owojori, A. and Xie, H., (2005). Landsat image-based LULC changes of San Antonio, Texas using advanced atmospheric correction and object-oriented image analysis approaches. In: Proceedings of the 5th International Symposium on Remote Sensing of Urban Areas, Tempe, Arizona, USA, 14–16 March 2005. Available at: https://www.researchgate.net/publication/253839686
Prajapati, R.N., (2024). Land Use Land Cover and Population Changes in Bhaktapur District. PhD Centre Nepal. Available at: www.phdcentre.edu.np
Simwanda, M. and Murayama, Y. (2018). Spatiotemporal patterns of urban land use change in the rapidly growing city of Lusaka, Zambia: Implications for sustainable urban development. Sustainable Cities and Society, 39, 262–274. https://doi.org/10.1016/j.scs.2018.01.039
Subasinghe, S., Estoque, R. and Murayama, Y., (2016). Spatiotemporal analysis of urban growth using GIS and remote sensing: A case study of the Colombo Metropolitan Area, Sri Lanka. ISPRS International Journal of Geo-Information, 5(11),197. https://doi.org/10.3390/ijgi5110197
Timsina, N.P., (2020). Trend of urban growth in Nepal with a focus in Kathmandu Valley: A review of processes and drivers of change. Tomorrow’s Cities Working Paper 001. Available at: www.tomorrowscities.org
Wellmann, T., Haase, D., Knapp, S., Salbach, C., Selsam, P. and Lausch, A., (2018). Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing. Ecological Indicators, 85, 190–203. https://doi.org/10.1016/j.ecolind.2017.10.029
Xian, G. and Crane, M., (2005). Assessments of urban growth in the Tampa Bay watershed using remote sensing data. Remote Sensing of Environment, 97(2), 203–215. https://doi.org/10.1016/j.rse.2005.04.017
Yadav, R.K., (2013). Ageing population in Nepal: Challenges and management. Academic Voices: A Multidisciplinary Journal, 2, 48–53. https://doi.org/10.3126/av.v2i1.8287
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