Unmanned Aerial Vehicles (UAVs) as a surveying tool are mainly characterized by a large amount of data and high computational cost. This research investigates the use of a small amount of data with less computational cost for more accurate three-dimensional (3D) photogrammetric products by manipulating UAV surveying parameters such as flight lines pattern and image overlap percentages. Sixteen photogrammetric projects with perpendicular flight plans and a variation of 55% to 85% side and forward overlap were processed in Pix4DMapper. For UAV data georeferencing and accuracy assessment, 10 Ground Control Points (GCPs) and 18 Check Points (CPs) were used. Comparative analysis was done by incorporating the median of tie points, the number of 3D point cloud, horizontal/vertical Root Mean Square Error (RMSE), and large-scale topographic variations. The results show that an increased forward overlap also increases the median of the tie points, and an increase in both side and forward overlap results in the increased number of point clouds. The horizontal accuracy of 16 projects varies from ±0.13m to ±0.17m whereas the vertical accuracy varies from ± 0.09 m to ± 0.32 m. However, the lowest vertical RMSE value was not for highest overlap percentage. The tradeoff among UAV surveying parameters can result in high accuracy products with less computational cost.

Tularemia is an endemic zoonotic disease in many parts of the world including Asia. A cross-sectional study was conducted to determine genome-based prevalence of Francisella tularensis ( Ft ) in soil, assess an association between its occurrence in soil and likely predictors i.e., macro and micro-nutrients and several categorical variables, and determine seroconversion in small and large ruminants. The study included a total of 2,280 soil samples representing 456 villages in eight districts of the Punjab Province of Pakistan followed by an analysis of serum antibodies in 707 ruminants. The genome of Ft was detected in 3.25% ( n = 74, 95% CI: 2.60–4.06) of soil samples. Soluble salts (OR: 1.276, 95% CI: 1.043–1.562, p = 0.015), Ni (OR: 2.910, 95%CI: 0.795–10.644, p = 0.106), Mn (OR:0.733, 95% CI:0.565–0.951, p = 0.019), Zn (OR: 4.922, 95% CI:0.929–26.064, p = 0.061) and nutrients clustered together as PC-1 (OR: 4.76, 95% CI: 2.37–9.54, p = 0.000) and PC-3 (OR: 0.357, 95% CI: 0.640, p = 0.001) were found to have a positive association for the presence of Ft in soil. The odds of occurrence of Ft DNA in soil were higher at locations close to a water source, including canals, streams or drains, [χ 2 = 6.7, OR = 1.19, 95% CI:1.05–3.09, p = 0.004] as well as places where animals were present [χ 2 = 4.09, OR = 2.06, 95% CI: 1.05–4.05, p = 0.02]. The seroconversion was detected in 6.22% ( n = 44, 95% CI: 4.67–8.25) of domestic animals. An occurrence of Ft over a wide geographical region indicates its expansion to enzootic range, and demonstrates the need for further investigation among potential disease reservoirs and at-risk populations, such as farmers and veterinarians.

Unmanned Aerial Vehicle (UAV) has emerged as latest and widely used surveying equipment worldwide for topographic mapping. There has been a growing increased in number and type of UAV platform for image capturing. This research aims at analysing the effects of UAV platform on image quality and further on its output products like orthomosaic, Digital Surface Model (DSM), Digital Terrain Model (DTM) and contours. For this study, part of the main campus area of Universiti Teknologi Malaysia has been selected, covering around 0.52 sq. km. Two different types of UAV surveying platforms used are eBee classic fixed wing (eBee) and DJI Phantom 4 Advanced (P4) multirotor quadcopter. Surveying through both the platforms conducted by flying both UAV’s on 300 meters height, with 75%, front and 75% side overlap. Both have almost identical camera parameters so as ground sampling distance (GSD). 8 ground control points (GCPs) for image processing and 15 checkpoints (CP) were used for accuracy assessment. Final processing is done by using Pix4D software. The results show that all the UAV output were successfully produced for both type of UAVs. In general, the output of P4 UAV is superior to eBee UAV. As conclusion, the P4 UAV is more practical to be employed for topographic map of small area coverage as shown in this study due to capability of producing accurate result, portable, low cost and offer other advantages.

Unmanned Aerial Vehicle (UAV) is one of the latest technologies for high spatial resolution 3D modeling of the Earth. The objectives of this study are to assess low-cost UAV data using image radiometric transformation techniques and investigate its effects on global and local accuracy of the Digital Surface Model (DSM). This research uses UAV Light Detection and Ranging (LIDAR) data from 80 meters and UAV Drone data from 300 and 500 meters flying height. RAW UAV images acquired from 500 meters flying height are radiometrically transformed in Matrix Laboratory (MATLAB). UAV images from 300 meters flying height are processed for the generation of 3D point cloud and DSM in Pix4D Mapper. UAV LIDAR data are used for the acquisition of Ground Control Points (GCP) and accuracy assessment of UAV Image data products. Accuracy of enhanced DSM with DSM generated from 300 meters flight height were analyzed for point cloud number, density and distribution. Root Mean Square Error (RMSE) value of Z is enhanced from ±2.15 meters to 0.11 meters. For local accuracy assessment of DSM, four different types of land covers are statistically compared with UAV LIDAR resulting in compatibility of enhancement technique with UAV LIDAR accuracy.