This study explores drones’ applications and proposes a cost-effective drone monitoring system for both palm trees and street lighting networks. The planned drone technical system has two monitoring sections. First, a model is developed to examine the health of date palm trees, in which drone photos are used to determine whether palm trees are suffering from diseases such as black scorch and sudden decline syndrome. These images are transferred into a central computer to stimulate normalized difference vegetation index (NDVI) models using AgiSoft software. The simulated NDVI models indicated that there are no health issues with date palm trees, which has resulted in the positive feedback in terms of the economic growth. Second, drone technology is utilized to detect the technical faults in the lighting network to ensure proper maintenance and social security. Twelve images of street lights are captured to demonstrate the working condition and the operational status of the street lights. These images are processed in MATLAB software, and a stimulated image processing model is implemented to enhance the monitoring of the street lighting network. The simulation findings indicate that the light in one of the images is not functioning, and ArcGIS Pro is utilized to locate it.
UAE's average temperature has risen in recent years and is expected to rise more in the next 40 years, creating a massive heat island agglomeration. Therefore, the demand for energy saving and diversified personal thermal management requires innovative solutions combining advanced building materials and structural designs to provide personal thermal comfort during indoor and outdoor activities. However, due to the complexities of structural designs and their associated materials, analytical and numerical strategies are for revealing real-world scenarios are limited. Therefore, full-scale experiments are required for exploring and demonstrating dynamic scenarios under thermal stress. This study aimed to explore the feasibility of using drone along with various thermal image analysis software that enables thermal photogrammetric mapping for monitoring and classification of heat rates based on building components surveyed across the UAEU campus. Thermal aerial images were collected in March 2022 and analyzed using SPSS, Agisoft Metashape Professional, DJI Thermal Tool, and FLIR for two buildings, A and B, and pedestrian spaces across UAEU's main campus in shaded, unshaded, and green zones. Noramilty and Kruskal-Wallis H tests were applied to examine if there was a statistically significant variation in surface temperatures. The pedestrian space thermal analysis showed that the natural shaded grass surface has the most tolerable heat environment (mean rank = 7.6), while the unshaded sand surface has the most unfriendly thermal environment (mean rank = 52.0), with an 18°C difference in mean surface temperature. The study also revealed the temperature evolution process and its dependence on building materials and structural designs, providing first-hand research data based on building components for the UAE climate, setting the path for future research in the era of sustainability and urban development.
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