Un-planned urban growth monitoring from 1991 to 2021 of Aizawl city, north-east India by multi-temporal changes and CA-ANN model

Abstract

Monitoring urban landuse and landcover (LULC) change is a crucial element in developing cities like Aizawl to improve land use planning for future smart cities. The objective of the current study is to analyze the lulc changes of Aizawl city between 1991 and 2021 using multi-date Landsat images and a cellular automata-artificial neural network (CA-ANN) model to predict future scenarios. The present study is highly essential for examining the urban expansion in a vertical hill city and the historical influence of settlement patterns along the edges of hill ranges for proper land use planning. The automatic classification of support vector machines (SVM) in-built at Orfeo tool box (OTB) modules was employed for LULC pattern classification. The land cover change method of the semi-automatic classification plugin (SCP) was used to identify the past LULC using Landsat 4, 5, 7, and 8. The future LULC was stimulated using the machine-learning approaches modules for land use change evaluation (Molusce) plugin in QGIS 2.18. Also, we highlight the factors that influence future LULC changes and the impacts of unplanned hill cities from the results of multi-criteria evaluation (MCE) and analytical hierarchical process (AHP). The study reveals that built-up areas are continuously increasing while open forest, agricultural land, and fallow land are diminishing, even in the projected land use land cover thematic layer in 2031. The built-up area has seen the highest change, from 5.98 to 25.8% in 1991 to 2021; the rate of increase has been 0.636 km²/year-1 during the last 30 years. Similarly, dense forest cover also increased from 12.14 to 18.72% from 1991 to 2021, while other landuse landcover patterns like open forest, fallow land, and agricultural land are declining due to urban expansion. The accuracy level of Kappa coefficients was 97.30% in 1991 and 100% in the years 2001, 2011, and 2021, respectively.