The number of unmanned aerial system (UAS) mapping applications that require high-accuracy data sets have grown significantly in recent years. Indirect georeferencing (IG) requires the establishment of multiple, well distributed ground control points (GCPs) throughout a project site. UAS mapping operations can now use precise navigation trajectories with position and/or orientation observations to replace or augment GCPs. Assisted aerial triangulation (AAT), which uses only airborne camera perspective center (CPC) observations to augment GCPs, is the primary method investigated herein. The objective of this study is to rigorously analyze the impact that each additional GCP in UAS structure from motion (SfM) photogrammetric processing has on the spatial accuracy of UAS-derived orthophotos and digital surface models (DSMs) when comparing AAT to IG. Three separate flights with two different cameras were flown over a project site that had a network of nine GCPs and 70 additional checkpoints (CPs). The authors carried out 29 unique processing trials for each flight, varying the georeferencing method and the quantity of GCPs used. American Society of Photogrammetry and Remote Sensing’s (ASPRS’s) Positional Accuracy Standards for Digital Geospatial Data provided reporting metrics to evaluate the derived orthophoto and DSM from each processing trial. UAS SfM processing using AAT with minimal to no augmenting GCPs provided high spatial accuracy products that met or exceeded more traditional workflows reliant on IG. When using AAT, the inclusion of at least one GCP improved vertical spatial accuracy relative to processing trials by using airborne positioning alone. To ensure the focal length was resolved during camera self-calibration, the inclusion of at least two high-accuracy GCPs in the simultaneous bundle block adjustment (SBBA) is recommended.

Data collected from a moving lidar sensor can produce an accurate digital representation of the physical environment that is scanned, provided the time-dependent positions and orientations of the lidar sensor can be determined. The most widely used approach to determining these positions and orientations is to collect data with a GNSS/INS sensor. The use of dual-antenna GNSS/INS sensors within commercial UAS-lidar systems is uncommon due to the higher cost and more complex installation of the GNSS antennas. This study investigates the impacts of using a single-antenna and dual-antenna GNSS/INS MEMS-based sensor on the positional precision of a UAS-lidar generated point cloud, with an emphasis on the different heading determination techniques employed by each type of GNSS/INS sensor. Specifically, the impacts that sensor velocity and acceleration (single-antenna), and a GNSS compass (dual-antenna) have on heading precision are investigated. Results indicate that at the slower flying speeds often used by UAS (≤5 m/s), a dual-antenna GNSS/INS sensor can improve heading precision by up to a factor of five relative to a single-antenna GNSS/INS sensor, and that a point of diminishing returns for the improvement of heading precision exists at a flying speed of approximately 15 m/s for single-antenna GNSS/INS sensors. Additionally, a simple estimator for the expected heading precision of a single-antenna GNSS/INS sensor based on flying speed is presented. Utilizing UAS-lidar mapping systems with dual-antenna GNSS/INS sensors provides reliable, robust, and higher precision heading estimates, resulting in point clouds with higher accuracy and precision.

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

This study mapped and analyzed urban tree canopy change between 2014 and 2019 within the Urban Development Boundary of Miami-Dade County, Florida. The goal was to identify local areas of tree canopy gain or loss over this 5-year period. The comparison is based on land cover maps delineated from WorldView-2 satellite data applying a random forest classification algorithm, in combination with publicly available vector data of infrastructure (roads, railroads) and land use maps (water, cropland). Existing urban tree canopy (EUTC) was computed for census block groups and municipalities to compare tree canopy cover loss or gain to support strategic planning of equitable urban reforestation. For the entire study area, the percentage of EUTC did not change significantly between 2014 (19.9 ± 1.2%) and 2019 (20.1 ± 1.5%). However, some municipalities experienced changes in EUTC by over 10%. Comparison of Landsat-8 Thermal Infrared satellite imagery between both periods identified land cover change patterns that were associated with an increase or decrease in surface temperature. A significantly negative relationship between percentage of African American population and tree canopy in 2014 turned statistically insignificant in 2019, whereas the negative relationship with percentage of Hispanic population further strengthened in 2019 compared to 2014.

United States Geological Survey topographic maps provide a variety of geospatial measurement tools thanks to their built-in georeferencing capabilities. Georeferencing facilitates display of geographic coordinates and measurement of distances, perimeters, and areas on PDFs using the measure tools built into Adobe Reader and Adobe Acrobat. This 7-page fact sheet written by Hartwig H. Hochmair and published by the UF/IFAS School of Forest Resources and Conservation explains and showcases the use of these geospatial readout functions. It also provides background information about and assistance with estimating point coordinates on the Universal Transverse Mercator grid cast on USGS topographic maps as an optional layer.