Abstract
The technological growth and accessibility of Unoccupied Aerial Systems (UAS) have revolutionized the way geographic data are collected. Digital Surface Models (DSMs) are an integral component of geospatial analyses and are now easily produced at a high resolution from UAS images and photogrammetric software. Systematic testing is required to understand the strengths and weaknesses of DSMs produced from various UAS. Thus, in this study, we used photogrammetry to create DSMs using four UAS (DJI Inspire 1, DJI Phantom 4 Pro, DJI Mavic Pro, and DJI Matrice 210) to test the overall accuracy of DSM outputs across a mixed land cover study area. The accuracy and spatial variability of these DSMs were determined by comparing them to (1) 12 high-precision GPS targets (checkpoints) in the field, and (2) a DSM created from Light Detection and Ranging (LiDAR) (Velodyne VLP-16 Puck Lite) on a fifth UAS, a DJI Matrice 600 Pro. Data were collected on July 20, 2018 over a site with mixed land cover near Middleton, NS, Canada. The study site comprised an area of eight hectares (~20 acres) with land cover types including forest, vines, dirt road, bare soil, long grass, and mowed grass. The LiDAR point cloud was used to create a 0.10 m DSM which had an overall Root Mean Square Error (RMSE) accuracy of ±0.04 m compared to 12 checkpoints spread throughout the study area. UAS were flown three times each and DSMs were created with the use of Ground Control Points (GCPs), also at 0.10 m resolution. The overall RMSE values of UAS DSMs ranged from ±0.03 to ±0.06 m compared to 12 checkpoints. Next, DSMs of Difference (DoDs) compared UAS DSMs to the LiDAR DSM, with results ranging from ±1.97 m to ±2.09 m overall. Upon further investigation over respective land covers, high discrepancies occurred over vegetated terrain and in areas outside the extent of GCPs. This indicated LiDAR’s superiority in mapping complex vegetation surfaces and stressed the importance of a complete GCP network spanning the entirety of the study area. While UAS DSMs and LiDAR DSM were of comparable high quality when evaluated based on checkpoints, further examination of the DoDs exposed critical discrepancies across the study site, namely in vegetated areas. Each of the four test UAS performed consistently well, with P4P as the clear front runner in overall ranking.
Highlights
Digital Elevation Models (DEMs) are geometric representations of the topography where elevations are represented as pixels in raster format [1]
DoDs were created by subtracting the Light Detection and Ranging (LiDAR) Digital Surface Models (DSMs) from each Unoccupied Aerial Systems (UAS) DSM to determine where spatial differences occurred across the study area
The LiDAR data collected in this study showed promising results for the construction of DSMs for local-scale surveys
Summary
Digital Elevation Models (DEMs) are geometric representations of the topography where elevations are represented as pixels in raster format [1]. DEMs are categorized into Digital Terrain Models (DTMs), which represent topography void of surface features; and Digital Surface Models (DSMs), which depict the top surfaces of features elevated above the earth, including buildings, trees, and towers (Figure 1). DEMs were arduously produced from surveyed field data, contour lines on topographic maps, and photogrammetry from aerial photography [2,3]. DEM accuracy and production efficiency greatly improved with the onset of Light Detection and Ranging (LiDAR). LiDAR data are costly to obtain for small areas, as they are collected from piloted aircraft (airborne LiDAR) and/or from ground. 2020, 12, x FOR PEER REVIEW level (terrestrial LiDAR). Airborne LiDAR is more efficient for regional scale studies, while terrestrial
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