No abstract

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.

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.

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.

Ecosystem services (ES) provided by dryland ecosystems store nearly half and one-third of the Earth's terrestrial biomass and biodiversity, respectively. Pakistan is a typical dryland region with significant land degradation and dramatic changes in ES in recent decades, which has not been sufficiently investigated. This study explored the spatiotemporal variations in ecosystem service values (ESV) over Pakistan (2001Pakistan ( -2018 by combining land use/cover data, economic modeling, and hotspot analysis. From the achieved results, the total ESV indicated an improvement in ES before 2012 and then showed a declining trend. The largest contribution was attributed to the increase in forest/shrubland by 16.17% (2001-2006) and 10.36% (2006-2012). However, the ESV decreased to 7.76% from 2012 to 2018, which was mainly attributed to the decrease in cropland and grassland. Heterogeneous changes were observed in ESV. The hotspot of ESV change (approximately an area of 32,578 km 2 ) was mainly located in the southern and southeastern parts of Khyber Pakhtunkhwa (KPK), the northeastern part of Balochistan and the central and western parts of Sindh Province. The cold spot (approximately an area of 24,491 km 2 ) showed a distribution in the northern part of KPK and Gilgit Baltistan, northeastern Punjab, coastal regions, and southeastern Sindh Province. We also found that although the total ESV and gross domestic product (GDP) indicated growth, the proportion of ESV from the total ESV and GDP (%ESV) exhibited a negative trend.The Provinces of Punjab and Balochistan retained the highest value of %ESV, while Azad Kashmir and Gilgit Baltistan were much lower. These results reflected the spatial imbalance of ecosystem protection and economic development in Pakistan. We recommend that necessary actions for conserving ES are important to strengthen ecological conservation in Pakistan. Additionally, further interdisciplinary research is needed to fully explore synergies in conserving ES and economical conservation.