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Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach

  • Research on Sustainable Developments for Environment Management
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Abstract

Land use and land cover (LULC) change has become a critical issue for decision planners and conservationists due to inappropriate growth and its effect on natural ecosystems. As a result, the goal of this study is to identify the LULC for the Vembanad Lake system (VLS), Kerala, in the short term, i.e., within a decade, utilizing three standard machine learning approaches, random forest (RF), classification and regression trees (CART), and support vector machines (SVM), on the Google Earth Engine (GEE) platform. When comparing the three techniques, SVM performed poor at an average accuracy of around 82.5%, CART being the next at accuracy of 87.5%, and the RF model being good at the average of 89.5%. The RF outperformed the SVM and CART in almost identical spectral classes such as barren land and built-up areas. As a result, RF-classified LULC is considered to predict the spatio-temporal distribution of LULC transition analysis for 2035 and 2050. The study was conducted in Idrisi TerrSet software using the cellular automata (CA)–Markov chain analysis. The model’s efficiency is evaluated by comparing the projected 2019 image to the actual 2019 classified image. The efficiency was good with more than 94.5% accuracy for the classes except for barren land, which might have resulted from the recent natural calamities and the accelerated anthropogenic activity in the area.

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The data generated or analyzed during this study are included in this published article and its supplementary information files.

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Acknowledgements

The authors would like to acknowledge Dr. Subrahmanya Kundapura for his valuable suggestions and timely help, and would also like to acknowledge the Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka Surathkal, India, for providing the infrastructural support.

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  1. Department of Water Resources and Ocean Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore, India

    Kulithalai Shiyam Sundar Parthasarathy & Deka Paresh Chandra

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  1. Kulithalai Shiyam Sundar Parthasarathy
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Participation of P.K.S.S. includes data collection, analyzing the results, writing the article, and revising the reviewers’ comments. Participation of PCD includes supervision, conceptualization, and reviewing and editing the article.

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Correspondence to Kulithalai Shiyam Sundar Parthasarathy.

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Kulithalai Shiyam Sundar, P., Deka, P.C. Spatio-temporal classification and prediction of land use and land cover change for the Vembanad Lake system, Kerala: a machine learning approach. Environ Sci Pollut Res 29, 86220–86236 (2022). https://doi.org/10.1007/s11356-021-17257-0

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