Groundwater for Sustainable Development 14 (2021) 100587 Available online 18 April 2021 2352-801X/© 2021 Elsevier B.V. All rights reserved. Research paper Impact of land use/land cover changes on groundwater resources in Al Ain region of the United Arab Emirates using remote sensing and GIS techniques Muhammad Usman Liaqat a , Mohamed Mostafa Mohamed, PhD b, c, * , Rezaul Chowdhury d , Samy Ismail Elmahdy e , Qasim Khan b , Rubina Ansari f a Department of Civil, Environmental and Architectural Engineering, University of Brescia, Italy b Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, PO Box 15551, United Arab Emirates c National Water Center, United Arab Emirates University, Al Ain, PO Box 15551, United Arab Emirates d School of Civil Engineering and Surveying, University of Southern Queensland, Toowoomba, QLD, 4350, Australia e Department of Civil and Environmental Engineering, American Unversity of Sharjah, Sharjah, United Arab Emirates f Department of Irrigation and Drainage, University of Agriculture, Faisalabad, Pakistan A R T I C L E INFO Keywords: Al Ain Land use land cover change Remote sensing Groundwater mapping Urbanisation causes land degradation problems, including an increased pressure on natural resources and management of water resources. This study aims to investigate the impact of the spatio-temporal dynamics of land use land cover (LULC) changes on groundwater table in the region of Al Ain, United Arab Emirates (UAE), from 2006 to 2016. The Landsat images, Landsat ETM for 2006 and Landsat 8 for 2016, were acquired from the earth explorer site. A semi-supervised hybrid classification method was used for image classification and post- classification techniques for LULC change detection. The study area was categorised into six major LULC clas- ses. These are agriculture/farms/oasis, gardens/playgrounds, urban areas, sandy areas, lake and mixed urban/ sandy areas. Accuracy assessment of LULC were evaluated using confusion matrix and ground truthing. The obtained land use and land cover maps were also correlated with spatial groundwater table maps prepared with groundwater data. It was found that agriculture/farms/oasis and urban areas expanded from 42,560 ha to 45,950 ha (7.38%) and from 8150 ha to 9105 ha (10.49%) from 2006 to 2016, respectively. The corresponding water demand was increased by 9.56% and 22.22%, respectively. Natural sandy area was found to decrease by 8.10%. As groundwater is major source of water for agriculture in this region, the spatial maps also revealead average declining rate of groundwater depth 40.44% with expansion of urban and agricultural areas over the last 10 years. The outcomes of the study would help concerning authorities for a sustainable management of its land and groundwater resources. 1. Introduction Change detection is an integral step in monitoring urban growth. It facilitate quantitative analysis of the spatial distribution of the area of interest (Mohamed and Elmahdy 2018). Land use change also alter hydrological process at spatial and temporal scales especially in arid areas (Chao et al., 2011). It has been observed that the combined effect of both anthropogenic and natural activities have considerable in- fluences on land use and land cover, which consequently affects phenology of local ecosystem to regulate fresh surface and groundwater resources (Chu et al., 2013; Veldkamp et al., 2016). The enormous rise in imperviousness may cause declining in infiltration which consequently affects the groundwater recharge and storage. Hence, it is mandatory to investigate impact of land use and land cover change on groundwater resources using traditional as well as latest remote sensing and GIS techniques. Monitoring the Earth’s surface from space is now decisive to un- derstand the impact of human activities on the natural environment. Remote Sensing (RS) data also assists in strengthening the existing institutional capacity for LULC change detection (Lo and Choi 2004; Veldkamp et al., 2016). The traditional practices of monitoring LULC changes are timely and economically consuming. (Campbell, 2002) stated that supervised and unsupervised are two primary methods of Image classification. In the * Corresponding author. Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain, PO Box 15551, United Arab Emirates. E-mail address: m.mohamed@uaeu.ac.ae (M.M. Mohamed). Contents lists available at ScienceDirect Groundwater for Sustainable Development journal homepage: http://www.elsevier.com/locate/gsd https://doi.org/10.1016/j.gsd.2021.100587 Received 4 August 2019; Received in revised form 6 March 2021; Accepted 13 April 2021