This review is aimed at exploring the use of remote sensing technology with a focus on Unmanned Aerial Vehicles (UAVs) in monitoring and management of palm pests and diseases with a special focus on date palms. It highlights the most common sensor types, ranging from passive sensors such as RGB, multispectral, hyperspectral, and thermal as well as active sensors such as light detection and ranging (LiDAR), expounding on their unique functions and gains as far as the detection of pest infestation and disease symptoms is concerned. Indices derived from UAV multispectral and hyperspectral sensors are used to assess their usefulness in vegetation health monitoring and plant physiological changes. Other UAVs are equipped with thermal sensors to identify water stress and temperature anomalies associated with the presence of pests and diseases. Furthermore, the review discusses how LiDAR technology can be used to capture detailed 3D canopy structures as well as volume changes that may occur during the progressing stages of a date palm infection. Besides, the paper examines how machine learning algorithms have been incorporated into remote sensing technologies to ensure high accuracy levels in detecting diseases or pests. This paper aims to present a comprehensive outline for future research focusing on modern methodologies, technological improvements, and direction for the efficient application of UAV-based remote sensing in managing palm tree pests and diseases.

The main aim of this study was to evaluate the vulnerability of groundwater to contamination in the vicinity of Ramtha wastewater treatment plant using a modified DRASTIC method in a GIS environment. A groundwater pollution potential map was prepared using modified DRASTIC method by adding lineaments and land use/land cover parameters. The values of the modified DRASTIC index were classified into three categories: low, moderate and high. About 36.5 % of the study area is occupied by the high vulnerability class, 56.5 % is occupied by the moderate vulnerability class and 9 % is occupied by the low vulnerability class. Chemical analysis of the water samples collected from wells distributed in the study area and tapping Umm Rijam aquifer indicated that the nitrate concentration ranges from 20 to 193 mg/L with an average 65.5 mg/L. Nitrate exceeded the permissible limits of WHO and Jordanian standards in 69 and 54 % of the NO₃ ⁻ samples, respectively. The modified DRASTIC model was validated using nitrate concentration. Results showed a good match between nitrate concentrations level and the groundwater vulnerability classes.

Water scarcity is the most serious environmental challenge that Jordan faces. Water resources require better protection and management as groundwater accounts for about 50% of the water usage in Wadi Shueib, central Jordan. Intrinsic vulnerability for Wadi Shueib (a valley located south of As-Salt city) watershed was assessed using modified SINTACS model using GIS techniques. According to our model, the investigated area encompassed very high, high and moderate areas of potential pollution with area coverage <1%, 26% and 51%, respectively. The high potential pollution is mainly influenced by combined contributions of parameters such as; unsaturated zone, aquifer media, lineaments density and effective infiltration. Sensitivity analysis indicates that lineament density is significant for the calculation of the groundwater vulnerability index. Nitrate concentrations measured in selected wells and springs were compared with the model to inspect its reliability. The validation rate of our model reaches more than 70%, which is an indication of acceptable modelling approach. Moreover, there was a good match of vulnerability classes with land use land cover categories as indicators of groundwater vulnerability. The SINTACS method proved its versatility even in this complex hydrogeological environment.