Geospatial Assessment And Prediction Of Land Use Changes In Bikaner Urbanizable Area: A QGIS-Based Approach Using MOLUSCE Plugin

Abstract

The current research explores the Spatio-temporal dynamics of land use/land cover (LULC) in Bikaner district, Rajasthan—a semi-arid district with high urbanization and changing land use trends. Multi-temporal satellite data for the years 2000, 2014, and 2024 were analyzed under supervised classification using the Maximum Likelihood Classification (MLC) method in QGIS. Five prominent LULC classes—Built-up, Agricultural Land, Forest, Grazing/Wasteland, and Water Bodies—were identified and analyzed. The classification accuracy of the outputs provided an overall accuracy of [insert]% with a Kappa value of [insert] reflecting high agreement between the classified results and the reference data.
Transition matrix was produced to measure LULC dynamics over time.

The result showed a high growth in urban areas, along with high degradation of agricultural and grazing lands, due to the effects of urbanization and land degradation. To forecast future land cover configurations, the MOLUSCE plugin was utilized under Cellular Automata and Markov Chain modeling, following land cover changes witnessed between 2000 and 2014, to project the LULC scenario in 2044.
The results highlight the extent and orientation of land change in the area and provide useful insights for sustainable urban design, resource administration, and policy formulation in arid and semi-arid environments.