Land Use/Land Cover Changes Impact on Groundwater Level and Quality in the Northern Part of the United Arab Emirates

Abstract

This study aims to develop an integrated approach for mapping and monitoring land use/land cover (LULC) changes and to investigate the impacts of LULC changes and population growth on groundwater level and quality using Landsat images and hydrological information in a Geographic information system (GIS) environment. All Landsat images (1990, 2000, 2010, and 2018) were classified using a support vector machine (SVM) and spectral analysis mapper (SAM) classifiers. The result of validation metrics, including precision, recall, and F1, indicated that the SVM classier has a better performance than SAM. The obtained LULC maps have an overall accuracy of more than 90%. Each pair of enhanced LULC maps (1990–2000, 2000–2010, 2010–2018, and 1990–2018) were used as input data for an image difference algorithm to monitor LULC changes. Maps of change detection were then imported into a GIS environment and spatially correlated against the spatiotemporal maps of groundwater level and groundwater quality. The results also show that the approximate built-up area increased from 227.26 km2 (1.39%) to 869.77 km2 (7.41%), while vegetated areas (farmlands, parks and gardens) increased from about 76.70 km2 (0.65%) to 290.70 km2 (2.47%). The observed changes in LULC are highly linked to the depletion in groundwater level and quality across the study area from the Oman Mountains to the coastal areas.

Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

“…9 and 19 in Figure 2). Lithologically, the upper streams (mountainous areas) are dominated by the igneous and metamorphic rocks in the east and carbonate rocks in the north and alluvial deposits at the foot of the mountainous areas [13]. The area has weather varying from hot and humid during the summer and being warm during the winter (Figure 3a).…”

“…Each metric includes accuracy, precision, recall and F1 score. The F1 score was found to the best technique and used widely in literature [13,14,80]. The F1 score was calculated based on four parameters, namely true positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) using the following equations from 7-11:…”

“…Susceptibility can be defined as a prediction of where the future hazardous event is likely to occur [9,10]. The wide availability of free of charge remote sensing data and machine learning algorithms allowed researchers to susceptibility map and predict flash floods over a regional scale efficiently and economically [11][12][13][14].…”

Quantitative analysis of the driving factors for groundwater resource changes in arid irrigated areas

“…For arid irrigated areas, most of the irrigation water in arid areas comes from groundwater, and human activities have become the main factor affecting groundwater in irrigated areas (Xia et al, 2019). Human activities include changes in land use, for most regions, the impact of land use changes on groundwater is very significant (Elmahdy, Mohamed, & Ali, 2020). However, in our research, due to the small area of land use change and because we did not further subdivide the type of farmland into specific agricultural crops, the impact of land use change on groundwater is very weak.…”

Spatiotemporal Mapping and Monitoring of Mangrove Forests Changes From 1990 to 2019 in the Northern Emirates, UAE Using Random Forest, Kernel Logistic Regression and Naive Bayes Tree Models

“…Thus, integration of RF and KLR and ID was adopted to extract spatiotemporal information about the NUAE mangrove forests. The use of machine learning algorithms decreases the overfitting and variance in the classified maps (Belgiu and Dȃguţ, 2016;Feng et al, 2018;Mondal et al, 2019;Elmahdy et al, 2020b;Ha et al, 2020). Thus, the main goals of this study were to present a novel ensemble machine learning approach which integrates RF with KLR and NBID algorithms and uses Landsat images for spatiotemporal mapping of the NAEU mangroves, comparing the performance of these algorithms, and implementing a novel image to image change detection technique for monitoring mangrove changes over multiple scales.…”