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A multi-layer perceptron–Markov chain based LULC change analysis and prediction using remote sensing data in Prayagraj district, India

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Abstract

Land use land cover (LULC) mapping and temporal observations are indispensable drivers for sustainable development. This research showed the growth trends and land use transition for the Prayagraj district in the last three decades. Supervised classification of Landsat images was performed on 5-year temporal intervals using a maximum likelihood classifier. All satellite images were organized into six major LULC feature classes viz agriculture/open land, barren land, built-up, forest, sand, and water. The overall accuracy of LULC classification was achieved by more than 89% in all seven temporal points. Furthermore, the accuracy of the classified maps was estimated through area-based error matrix. The Land Change Modeler tool of TerrSet 2020 software was used to analyze the transition of classes and to incorporate the multi-layer perceptron–Markov chain (MLP-MC) technique. The transition potentials were included in MLP-MC with the help of sensitive explanatory variables and significant transitions of classes. Furthermore, these transition potentials and the Markov chain transition matrix were used to predict the future LULC dynamics and vulnerability. The change analysis revealed that a significant portion of the agriculture/open land gradually decreased and got converted to built-up land. The results depicted that agriculture/open land was reduced by 8.03% in the last three decades while the built-up region was grown by 199.61%. Forest area was continuously decreasing while the sand area increased due to river meandering. Overall, more than 75% of accuracy was achieved in MLP. The prediction model was first validated with observed data, and then the LULC scenario of 2035 and 2050 was simulated. LULC of 2050 showed that the built-up area would likely reach 13.90% of district area whereas the forest area would remain only 0.79%. The prediction model has given the output in the form of future LULC map along with projected potential transition maps. This would be useful for sustainable urban planning to deal with the alarming rate of built-up growth and agriculture/open land shrinkage.

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Availability of data and material

The authors are thankful to the USGS Earth Explorer for freely available Landsat datasets for the study area region.

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  1. GIS Cell, Motilal Nehru National Institute of Technology Allahabad, Uttar Pradesh, Prayagraj, 211004, India

    Vivek Kumar & Sonam Agrawal

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Kumar, V., Agrawal, S. A multi-layer perceptron–Markov chain based LULC change analysis and prediction using remote sensing data in Prayagraj district, India. Environ Monit Assess 195, 619 (2023). https://doi.org/10.1007/s10661-023-11205-w

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