Impacts of Urbanization on Agricultural Land in Multan District: Past and Future Trend Analysis Using Remote Sensing Approach

Abstract

Agriculture plays a consequential role in the economy of Pakistan. Cotton being one of the major agricultural production supports the textile sector of the country, and is the main contributor to the Gross Domestic Product (GDP). The planned as well as unplanned land use changes in the urban areas are consuming the precious agricultural land, which in turn can severely affect food security and the industry, especially the textile sector. Thus, there is a need to timely monitor such changes for better-informed decisions making. Remote sensing is an advanced technology that has proved to be advantageous in the temporal Land use and Land cover (LULC) change analysis. The current study aims to explore the relationship between LULCs using multi-temporal, multi-sensor data to analyze how urban growth and other land use changes affected the agricultural land (especially the traditional cotton-cropped areas) in the Multan district. The LULC temporal analysis will help highlight the upcoming challenges for this highly agriculture-productive area and sustainability. This study used Landsat-7 data and Sentinel-2 (L1C) data for the years 2001, 2017 and 2021. LULC classification accuracy for the respective years was 80, 88, and 84%. The change analysis revealed that built-up area increased from 2% to 9% from 2001 to 2021, bare land increased from 2% to 5%, cotton decreased from 39% to 24%, and other agricultural production increased from 55% to 60%. Cellular Automata-Artificial Neural Network was used for the future LULC prediction for the year 2031. The model prediction accuracy was about 83%. The future LULC prediction results depict that the urban areas and bare land would increase at the expense of agricultural areas including the cotton-cropped areas. These results highlight the importance of sustainable land use planning, its effective implementation, and regular monitoring. The study results can help local stakeholders and development authorities in decision-making for sustainable.