Ahsan Ali

Infected COVID-19 patients, especially after March 11, 2020, grew drastically in Oman. Hence, a variety of measures were issued to restrict all social gatherings, commercial activities, and mandating preventative health practices. This study aimed to i) understand distribution patterns and impact of decisions and responses at the spread of confirmed cases; ii) highlight and verify most concentrated regions with infections; and iii) overview spatial changes of cases overtime. The analysis was carried out using inverse–distance-weighted interpolation and hotspot (Getis–Ord GI*) techniques. Results showed a substantial relationship between spatial structure of COVID–19 and population distribution and density. COVID–19 has increased by 11.5% weekly in the capital, which were locked down since April 2020. However, after health quarantine was lifted on May 29, 2020, weekly cases surged in the capital. Al-Batinah-North and Dhofar recorded an increase of 32.1% and 30.5%, respectively, after restrictions had eased. The analysis illustrated that spread of COVID–19 was shifting from Northeast to Southeast and later shifted back to the Northeast of the country at the end of year 2022. This study is beneficial for pertinent organizations to perform detailed studies for developing and monitoring disease systems and dominating relevant factors.

Many insects attack date palm trees but date palm trees in the Sultanate are particularly under threat due to the spread of pests and the Dubas bug (Db). Date palm productivity in Oman has been reduced by 28% due to Db infestation. The manual field detection of these pests requires huge efforts and costs, making field surveys time consuming and difficult. In this context, remote sensing integrated with deep learning techniques can help in the early detection of Db infestation. A total of 240 date palms with corrected geospatial locations and coordinates and their health status were systematically recorded throughout the 66-square-kilometer study area. We used advanced remote sensing tools and deep learning techniques to detect individual palm trees and their health levels in terms of Db infestation. Very-high-resolution (50 cm) satellite images rendered in visible and NIR bands were used as datasets to delineate and identify individual tree positions and determine their health condition. Our proposed method resulted in an overall accuracy of 87% for the detection of date palm trees and 85% for the detection of health levels of the plants. The overall detection accuracy of high and low infestation levels was observed with high precision at 95% and 93%, respectively. Hence, we can conclude with confidence that our technique performed well by accurately detecting individual date palm trees and determining their level of Db infestation. The approach used in this study can also provide farmers with useful knowledge regarding the Db risk and damage control for better management of Db. Moreover, the model used in this study may also lay the foundations for other models to detect infested plants and trees other than date palms.

The impact of urbanization on vegetation is poorly studied in sparsely vegetated desert steppe-type vegetation. This study investigates the spatial and temporal impacts of urbanization over 40 years on plant species in Dhofar’s mountains, plains, and bay areas. Satellite images covering 1978 to 2018 were used as Remote Sensing (RS) tools, along with Geographic Information System (GIS) techniques, to detect changes in vegetation patterns. The results showed an increase in urban development in Dhofar over the 40 years, reaching 173.3%, which contributed to the decline in vegetation cover of 36.4%. However, both percentages indicate that other factors, including climate change, have also impacted the vegetation. It was found that air temperature in the study area increased from 1978 to 2018 by 0.8°C, whereas precipitation decreased by 104 mm. The results also indicate a 23.2% and 18.5% reduction in plant species in coastal areas and mountains, respectively, between 1978 and 2018 due to the increase in urbanization. This warns of the impact of urban expansion on rare plant species in the governorate, as 48 plant species only found in Dhofar are threatened with extinction due to construction and urbanization. We recommend making the biodiversity of Dhofar a priority in developing any urban projects, especially at sites that house rare species.

Actual evapotranspiration (ETa) plays an important role in irrigation planning and supervision. Traditionally, the estimation of ETa was approximated using different in situ techniques, having high initial and maintenance costs with low spatial resolution. In this context, satellite imagery models play an effective role in water management practices by estimating ETa in small and large-scale areas. All existing models have been widely used for the estimation of ETa around the globe, but there is no definite conclusion on which approach is best for the hot and hyper-arid region of Oman. Our study introduces an innovative approach that uses in situ, meteorological, and satellite imagery (Landsat-OLI/TIRS) datasets to estimate ETa. The satellite-based water and energy balance model for the arid region to determine evapotranspiration (SMARET) was developed under the hot and hyper-arid region conditions of Oman by incorporating soil temperature in the sensible heat flux. The performance of SMARET ran through accuracy assessment against in situ measurements via sap flow sensors and lysimeters. The SMARET was also evaluated against three existing models, including the surface energy balance algorithm for land (SEBAL), mapping evapotranspiration at high-resolution with internalized calibration (METRIC), and the Penman–Monteith (PM) model. The study resulted in a significant correlation between SMARET (R² = 0.73), as well as the PM model (R² = 0.72), and the ETa values calculated from Lysimeter. The SMARET model also showed a significant correlation (R² = 0.66) with the ETa values recorded using the sap flow meter. The strong relationship between SMARET, sap flow measurement, and lysimeter observation suggests that SMARET has application capability in hot and hyper-arid regions.