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.

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Reliable remote sensing platforms and methods for monitoring phytoplankton are needed for mitigating the detrimental impacts of cyanobacterial harmful algal blooms on small inland waterbodies. Commercial unoccupied aerial systems (UASs) present an affordable high‐resolution solution for the rapid assessment of cyanobacterial abundance in small aquatic systems by recording the reflectance of photosynthetic pigments found in all phytoplankton (i.e., chlorophyll a [Chl a]) and those related to cyanobacteria (i.e., phycocyanin). This study evaluates the performance of four sensors, including visible light spectra (red, green, blue ‐ [RGB]) sensors on the Phantom 4 and Phantom 4 Professional platforms, the MAPIR Survey3W modified multispectral (i.e., near‐infrared, green, blue) sensor, and the Parrott Sequoia multispectral (i.e., green, red, near‐infrared, red‐edge) sensor for estimating cyanobacterial abundance. The performance of each sensor was determined by comparing 26 vegetation indices to Chl a and phycocyanin measurements of 54 ponds that varied in size and productivity. Vegetation indices that included the red and near‐infrared wavelengths generated from Parrot Sequoia aerial images provided the best Chl a (i.e., Normalized Difference Vegetation Index, r² = 0.79, p < 0.0001) and phycocyanin (i.e., Green Normalized Difference Vegetation Index, r² = 0.64, p < 0.0001) estimates. The RGB sensors were moderately effective for estimating Chl a, whereas the MAPIR Survey3W generated poor estimates of both pigments due to differences in recorded wavelengths. Results suggest commercial multiband multispectral UAS sensors provide a low‐cost, plug‐and‐play alternative for managers and researchers interested in integrating remote sensing tools for quantitatively estimating phytoplankton abundance in small inland systems.

Drone remote sensing research has surged over the last few decades as the technology has become increasingly accessible. Relatively easy-to-operate drones put data collection directly in the hands of the remote sensing community. While an abundance of remote sensing studies using drones in myriad areas of application (e.g., agriculture, forestry, and geomorphology) have been published, little consensus has emerged regarding best practices for drone usage and incorporation into research. Therefore, this paper synthesizes relevant literature, supported by the collective experiences of the authors, to propose ten fundamental practices for drone remote sensing research, including (1) focus on your question, not just the tool, (2) know the law and abide by it, (3) respect privacy and be ethical, (4) be mindful consumers of technology, (5) develop or adopt a data collection protocol, (6) treat Structure from Motion (SfM) as a new form of photogrammetry, (7) consider new approaches to analyze hyperspatial data, (8) think beyond imagery, (9) be transparent and report error, and (10) work collaboratively. These fundamental practices, meant for all remote sensing researchers using drones regardless of area of interest or disciplinary background, are elaborated upon and situated within the context of broader remote sensing research.