Multilayer perceptron and Markov Chain analysis based hybrid-approach for predicting land use land cover change dynamics with Sentinel-2 imagery

As urbanization accelerates, the degree of human impact on land use is increasing. land use land cover change (LULC) is acknowledged as crucial factor in environmental change. The best way to understand historical land use patterns, changes, drivers, and developments is through a rigorous assessment of LULC changes. In this study, we aim to identify LULC changes from 2015 to 2022, and predict changes for 2030. Sentinel-2 images were employed to analyze LULC change patterns and predict future trends. The Random Forest algorithm was used to classify the various LULC classes with high accuracy and reliability. Multilayer Perceptron and Markov Chain Analysis (MLP-MCA) based Hybrid-Approach was employed to predict the future dynamics of LULC change for 2030. The study revealed that built-up area expanded 90.64 km2 from 2015 to 2022 due to natural resource substitution. Predictions indicate that 58.84% of the study area will be transform into built-up by 2030.

Automated summary

As urbanization accelerates, human impact on land use is increasing, making land use and land cover change (LULC) a crucial factor in environmental change. This study aims to identify and predict LULC changes from 2015 to 2022 and predict changes for 2030 using Sentinel-2 images and the Random Forest algorithm for classification and Multilayer Perceptron and Markov Chain Analysis (MLP-MCA) for prediction. The study revealed an expansion of 90.64 km2 in the built-up area from 2015 to 2022 and predicts that 58.84% of the study area will be transformed into built-up by 2030.