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Recent advances in computer vision and camera-equipped unmanned aerial systems (UAS) for 3D modeling enable UAS-based photogrammetry surveys with high spatial-temporal resolutions. To generate consistent and high-quality 3D models using UASs, understanding how influence factors (i.e., flight height, image overlap, etc.) affect the 3D modeling accuracy and their levels of significance are important. However, there is little to no quantitative analysis that studies how these influence factors interact with and affect the accuracy when changing the values of the influence factors. Moreover, there is little to no research that assesses more than three influence factors. Therefore, to fill this gap, this paper aims to evaluate and predict the accuracy generated by different flight combinations. This paper presents a study that (1) assessed the significance levels of five influence factors (flight height, average image quality, image overlap, ground control point (GCP) quantity, and camera focal lengths), (2) investigated how they interact and impact 3D modeling accuracy using the multiple regression (MR) method, and (3) used the developed MR models for predicting horizontal and vertical accuracies. To build the MR model, 160 datasets were created from 40 flight missions collected at a site with a facility and open terrain. For validating the prediction model, five testing datasets were collected and used at a larger site with a complex building and open terrain. The results show that the findings of this study can be applied to surveyors' better design flight configurations that result in the highest accuracies, given different site conditions and constraints. The results also provide a reasonable prediction of accuracy given different flight configurations. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems (UAS, UAV, or drones) have become an effective tool for applications in natural resources since the start of the 21st century. With their associated hardware and software technologies, UAS sensor data have provided high resolution and high accuracy results in a range of disciplines. Despite these achievements, only minimal progress has been made in (1) establishing standard operating practices and (2) communicating both the limitations and necessary next steps for future research. In this review of literature published between 2016 and 2022, UAS applications in forestry, freshwater ecosystems, grasslands and shrublands, and agriculture were synthesized to discuss the status and trends in UAS sensor data collection and processing. Two distinct conclusions were summarized from the over 120 UAS applications reviewed for this research. First, while each discipline exhibited similarities among their data collection and processing methods, best practices were not referenced in most instances. Second, there is still a considerable variability in the UAS sensor data methods described in UAS applications in natural resources, with fewer than half of the publications including an incomplete level of detail to replicate the study. If UAS are to increasingly provide data for important or complex challenges, they must be effectively utilized. [ABSTRACT FROM AUTHOR]

Critical national infrastructure sites, such as airports and nuclear facilities, have been subject to a number of airspace breaches from malicious unmanned aerial systems (UASs) in recent years. Not only can these incidents cause major disruptions, but they can have significant financial consequences. This article conducts a short review of security incidents caused by the malicious use of small UASs at airports and nuclear facilities. We conclude that the number of incidents of concern remain high and suggest the increased use of UAS with longer flight times, indicating a move away from commercially available platforms, which have limited flight times. The detection and classification methods presented in this review each have strong benefits but this is not withstanding their own challenges. Hence, this review recommends that more than one sensor type are considered in parallel for live systems. A comprehensive detection and classification system must extend further than typical commercially available platforms if we are to fully realize the threat. We also conclude that while successful countermeasure methods do exist there still remains many policy and legal challenges ahead for their implementation. [ABSTRACT FROM AUTHOR]

Recent advances in computer vision and camera-equipped unmanned aerial systems (UAS) for 3D modeling enable UAS-based photogrammetry surveys with high spatial-temporal resolutions. To generate consistent and high-quality 3D models using UASs, understanding how influence factors (i.e., flight height, image overlap, etc.) affect the 3D modeling accuracy and their levels of significance are important. However, there is little to no quantitative analysis that studies how these influence factors interact with and affect the accuracy when changing the values of the influence factors. Moreover, there is little to no research that assesses more than three influence factors. Therefore, to fill this gap, this paper aims to evaluate and predict the accuracy generated by different flight combinations. This paper presents a study that (1) assessed the significance levels of five influence factors (flight height, average image quality, image overlap, ground control point (GCP) quantity, and camera focal lengths), (2) investigated how they interact and impact 3D modeling accuracy using the multiple regression (MR) method, and (3) used the developed MR models for predicting horizontal and vertical accuracies. To build the MR model, 160 datasets were created from 40 flight missions collected at a site with a facility and open terrain. For validating the prediction model, five testing datasets were collected and used at a larger site with a complex building and open terrain. The results show that the findings of this study can be applied to surveyors’ better design flight configurations that result in the highest accuracies, given different site conditions and constraints. The results also provide a reasonable prediction of accuracy given different flight configurations.

Unoccupied aerial systems (UAS) have become common tools for ecological monitoring and management. However, UAS use has the potential to negatively affect wildlife. Both policy makers and practitioners require data about the potential impacts of UAS on natural biota, but few studies exist and some of the published results conflict. We conducted two experiments to assess the responses of chinstrap penguins (Pygoscelis antarcticus), Antarctic fur seals (Arctocephalus gazella), and leopard seals (Hydrurga leptonyx) to UAS overflights. First, to provide a baseline for assessing disturbance from UAS operations, we compare behavioral responses from UAS flights to those from traditional, ground surveys. Second, to inform users and policy makers about preferred flight methods, we assess behavioral and physiological responses to UAS flown at specific altitudes, during different stages of breeding chronology, and with other site factors. Between January 2017 and March 2018 we conducted 268 UAS flight approaches and 36 ground-based surveys at Cape Shirreff, Antarctic Peninsula. We applied generalized linear mixed effects models and Kruskal-Wallis tests to 10,164 behavioral scores obtained from three independent observers. When directly compared, behavioral responses by all species to UAS overflights at 30 m were not different from control periods, while responses to ground surveys were significantly more intense. Behavioral responses generally increased as UAS flew lower, and for penguins those increases intensified as the breeding season progressed (i.e., guard and molt stages). We argue that results from UAS wildlife response studies need to be assessed relative to the impacts of alternative methods, and within the ecological context of the target species. Finally, we suggest data-driven best practices for both UAS use and for the design of future UAS-wildlife response studies.

Unmanned Aerial Systems (UASs) have rapidly been integrated into the construction industry over the past few years, and their application is continually growing in this domain. The recent development in UAS regulations and technical capabilities have played a significant role in their popularity and wide deployment in various stages of the construction lifecycle. UASs could be used as a platform to enhance the construction practices in general; however, little is known about how construction professionals are adopting this technology in specific construction practices and the barriers they are facing for their successful implementation. The purpose of this study is to explore the current state-of-practice of UAS integration in construction from the industry professionals' viewpoint. A comprehensive survey study was conducted in the United States to identify the practical construction UAS application areas, their adopted technologies, as well as the benefits and barriers encountered during their implementation. Responses (n=56) showed that most common UAS applications include progress monitoring, site planning, and site surveying/mapping. Rotary-wing vehicles and visual and thermal cameras were the most used platforms and onboard sensors, respectively. Saving time, improving accessibility to compromised spaces, and reducing cost while accomplishing construction tasks were highly regarded as UAS implementation benefits in construction. Participants also considered flying in various weather conditions, within confined or congested areas, as well as the advanced technical know-how requirements, and the increased liability and legal challenges as barriers to using UASs in construction-related tasks. By understanding UAS adoption in construction, this study provides a roadmap to better identify the industry needs and guide researchers and professionals in investigating application areas and barriers that might have maximum benefits for the construction industry in the United States. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems, or drones, are very helpful tools for managing open-pit mining operations and developing ecological restoration activities. This article presents a method for identifying water erosion processes in active quarries by means of drone imagery remote sensing, in the absence of pre-existing imagery or mapping for comparison. A Digital Elevation Model (DEM) with a spatial resolution (SR) >10 cm and an orthophoto with an SR >2.5 cm were generated from images captured with a drone and their subsequent photogrammetric processing. By using Geographical Information Systems tools to process the DEM, a detailed drainage network was obtained, the areas of detected water erosion were separated, and the watersheds in the gullies identified. Subsequently, an estimated DEM before the erosive processes was reconstructed by interpolating the gully ridges; this DEM serves as a reference for the relief before the erosion. To calculate the volume of eroded material, the DEM of Differences was calculated, which estimates the volume difference between the previously estimated DEM and the current DEM. Additionally, we calculated the material necessary for the geomorphological adaptation of the quarry and the slope map, which are two valuable factors closely related to the monitoring of erosive processes. The results obtained allowed us to identify the erosion factors quickly and accurately in this type of mining. In the case of water-filled quarries, it would be important to characterize the subsurface relief. Essentially, the presented method can be applied with affordable and non-invasive materials to create digital grid maps at 10 cm resolution, obtaining data ready for 3D metrics, being a very practical landscape modelling tool for characterizing the restoration evolution of open-pit mining spaces.

The success of the wilderness search and rescue missions is highly dependent on the time required to search for the lost person. The use of unmanned aerial systems may enhance search and rescue missions by supplying aerial support of the search process. There are unmanned aerial system–based solutions, which are capable of detecting the lost person using computer vision, infrared sensors, and detection of a mobile phone signal. The most pressing issue is reducing the cost of a search and rescue mission. Thus, to improve the efficiency of the resource utilization in wilderness search scenario, we consider the use of unmanned aerial system for both mobile phone detection and enabling Wi-Fi communication for the ground portion of the search and rescue team. Such an approach does not require specific additional tools (e.g. access point, specific user equipment) for communication, which reduces the cost and improves the scalability and coordination of the search and rescue mission. As a result, the article provides methods of searching the wilderness for a person using beacon signals from a mobile phone for two situations: when the distance to the source of emergency signals is unknown and when the distance is known. In addition, the voice transmission delay and the number of unmanned aircrafts are found to guaranty the quality of a call. [ABSTRACT FROM AUTHOR]

Remotely piloted aircraft systems (RPAS) are increasingly becoming relevant actors that are flying through the airspace and will gain much more importance in the future. In order to allow for their safe integration with manned conventional traffic in non-segregated airspaces, in accordance with the overall air traffic management (ATM) paradigm, specific enabling technologies are needed. As is well known, the detect and avoid (DAA) technology is fundamental among the enabling technologies identified as crucial for RPAS integration into the overall ATM system. In the meantime, to support extended surveillance, the universal introduction of cooperative automatic dependent surveillance-broadcast (ADS-B) on-board aircraft is being increasingly implemented because it has the potential to allow for the coverage of the entire airspaces in remote areas not usually covered by conventional radar surveillance. In this paper, experimental results that were obtained through the real-time validation, with hardware and human in the loop (RTS-HIL) simulations, of an automatic ADS-B based separation assurance and collision avoidance system aimed to support RPAS automatic operations (as well as remote pilot decision making) are presented and discussed. In the paper, after an introductory outline of the concept of operations (ConOps) of the system and its architectural organization, in addition to basic information about the main system functionalities, a description of the tests that were carried out is reported, and the obtained results are described and discussed in order to emphasize the performance and limitations of the proposed system. In particular, the obtained quantitative performances are reported and commented on, and the feedback presented by pilots in order to improve the system, e.g., in terms of preferred typology of conflict resolution maneuver elaborated by the system, is described.

Wetlands provide critical ecosystem services across a range of environmental gradients and are at heightened risk of degradation from anthropogenic pressures and continued development, especially in coastal regions. There is a growing need for high-resolution (spatially and temporally) habitat identification and precise delineation of wetlands across a variety of stakeholder groups, including wetlands loss mitigation programs. Traditional wetland delineations are costly, time-intensive and can physically degrade the systems that are being surveyed, while aerial surveys are relatively fast and relatively unobtrusive. To assess the efficacy and feasibility of using two variable-cost LiDAR sensors mounted on a commercial hexacopter unmanned aerial system (UAS) in deriving high resolution topography, we conducted nearly concomitant flights over a site located in the Atlantic Coastal plain that contains a mix of palustrine forested wetlands, upland coniferous forest, upland grass and bare ground/dirt roads. We compared point clouds and derived topographic metrics acquired using the Quanergy M8 and the Velodyne HDL-32E LiDAR sensors with airborne LiDAR and results showed that the less expensive and lighter payload sensor outperforms the more expensive one in deriving high resolution, high accuracy ground elevation measurements under a range of canopy cover densities and for metrics of point cloud density and digital terrain computed both globally and locally using variable size tessellations. The mean point cloud density was not significantly different between wetland and non-wetland areas, but the two sensors were significantly different by wetland/non-wetland type. Ultra-high-resolution LiDAR-derived topography models can fill evolving wetlands mapping needs and increase accuracy and efficiency of detection and prediction of sensitive wetland ecosystems, especially for heavily forested coastal wetland systems.

Abstract: Contemporary techniques predicting habitat suitability under climate change projections often underestimate availability of thermal refuges. Habitat structure contributes to thermal heterogeneity at a variety of spatial scales, but quantifying microclimates at organism‐relevant resolutions remains a challenge. Landscapes that appear homogeneous at large scales may offer patchily distributed thermal refuges at finer scales. We quantified the relationship between vegetation structure and the thermal environment at a scale relevant to small, terrestrial animals using a new approach for mapping fine‐scale thermal heterogeneity. We expected that vegetation would create attenuated microclimates and that the influence of vegetation structure would vary seasonally. We measured shrub volume, horizontal cover and operative temperature (Te) in a sagebrush‐steppe habitat in Idaho, USA, at 534 microsites across two study sites of c. 1 km2 each. We modelled relationships between habitat structure and both mean daily maximum temperature ( T ¯ max) and mean diurnal temperature range ( DTR ¯) for each study site during summer and winter. Aerial imagery from unmanned aerial systems was used to estimate shrub volume and canopy cover at 1‐m resolution, and we applied the best fit model to map thermal heterogeneity across broader extents. Increasing shrub volume and cover was associated with lower T ¯ max and DTR ¯, but strengths of the relationships differed between study sites. There was considerable heterogeneity in availability of thermal refuges across sagebrush‐steppe rangelands that have traditionally been considered relatively homogeneous. This technique can help ecologists and land managers identify critical thermal refuges that large‐scale climate modelling can overlook and thus contribute to an understanding of animal–habitat relationships under changing climates and land uses. [ABSTRACT FROM AUTHOR]

Small unmanned aerial systems (sUAS) are a promising technology for atmospheric monitoring of trace atmospheric gases. While sUAS can be navigated to provide information with higher spatiotemporal resolution than tethered balloons, they can also bridge the gap between the regions of the atmospheric boundary layer (ABL) sampled by ground stations and manned aircraft. Additionally, sUAS can be effectively employed in the petroleum industry, e.g., to constrain leaking regions of hydrocarbons from long gasoducts. Herein, sUAS are demonstrated to be a valuable technology for studying the concentration of important trace tropospheric gases in the ABL. The successful detection and quantification of gases is performed with lightweight sensor packages of low-power consumption that possess limits of detection on the ppm scale or below with reasonably fast response times. The datasets reported include timestamps with position, temperature, relative humidity, pressure, and variable mixing ratio values of ~400 ppm CO2, ~1900 ppb CH4, and ~5.5 ppb NH3. The sensor packages were deployed aboard two different sUAS operating simultaneously during the second CLOUDMAP flight campaign in Oklahoma, held during 26−29 June 2017. A Skywalker X8 fixed wing aircraft was used to fly horizontally at a constant altitude, while vertical profiles were provided by a DJI Phantom 3 (DJI P3) quadcopter flying upward and downward at fixed latitude-longitude coordinates. The results presented have been gathered during 8 experiments consisting of 32 simultaneous flights with both sUAS, which have been authorized by the United States Federal Aviation Authority (FAA) under the current regulations (Part 107). In conclusion, this work serves as proof of concept showing the atmospheric value of information provided by the developed sensor systems aboard sUAS.

The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.

Structural inspection supported by new inspection techniques - First results The road infrastructure has reached the edge of its service capability by increased external impacts and increasing average age and maintenance measure will be implemented in future to a considerable extent. To carry out the maintenance measures in a technically and economically optimal may, it's necessary to be informed at an early stage as fully as possible about the current state of the structure. For this it's necessary to find and evaluate new innovative techniques to support bridge inspection in accordance with German Standard DIN 1076. Unmanned Aerial Systems (UAS) provide a range of possibilities of photo recording and may thus be used as an auxiliary in bridge inspections. As part of this research project, flights were operated to three bridges and the use of UAS allowed identifying damage to the bridges in question. Given the possibility to operate flights to areas which are not easily accessible as well as time savings during testing offered by the use of UAS, this technology seems attractive for inspections of bridges. The targeted use of UAS will result in reduced service failures and an increase in the quality of results. [ABSTRACT FROM AUTHOR]

The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and "Structure from Motion" approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Systems, or drones, are very helpful tools for managing open-pit mining operations and developing ecological restoration activities. This article presents a method for identifying water erosion processes in active quarries by means of drone imagery remote sensing, in the absence of pre-existing imagery or mapping for comparison. A Digital Elevation Model (DEM) with a spatial resolution (SR) >10 cm and an orthophoto with an SR >2.5 cm were generated from images captured with a drone and their subsequent photogrammetric processing. By using Geographical Information Systems tools to process the DEM, a detailed drainage network was obtained, the areas of detected water erosion were separated, and the watersheds in the gullies identified. Subsequently, an estimated DEM before the erosive processes was reconstructed by interpolating the gully ridges; this DEM serves as a reference for the relief before the erosion. To calculate the volume of eroded material, the DEM of Differences was calculated, which estimates the volume difference between the previously estimated DEM and the current DEM. Additionally, we calculated the material necessary for the geomorphological adaptation of the quarry and the slope map, which are two valuable factors closely related to the monitoring of erosive processes. The results obtained allowed us to identify the erosion factors quickly and accurately in this type of mining. In the case of water-filled quarries, it would be important to characterize the subsurface relief. Essentially, the presented method can be applied with affordable and non-invasive materials to create digital grid maps at 10 cm resolution, obtaining data ready for 3D metrics, being a very practical landscape modelling tool for characterizing the restoration evolution of open-pit mining spaces. [ABSTRACT FROM AUTHOR]

Abstract: High spatial resolution three-dimensional (3D) measurements of vegetation by remote sensing are advancing ecological research and environmental management. However, substantial economic and logistical costs limit this application, especially for observing phenological dynamics in ecosystem structure and spectral traits. Here we demonstrate a new aerial remote sensing system enabling routine and inexpensive aerial 3D measurements of canopy structure and spectral attributes, with properties similar to those of LIDAR, but with RGB (red-green-blue) spectral attributes for each point, enabling high frequency observations within a single growing season. This “Ecosynth” methodology applies photogrammetric “Structure from Motion” computer vision algorithms to large sets of highly overlapping low altitude (<130m) aerial photographs acquired using off-the-shelf digital cameras mounted on an inexpensive (

Wetlands provide critical ecosystem services across a range of environmental gradients and are at heightened risk of degradation from anthropogenic pressures and continued development, especially in coastal regions. There is a growing need for high-resolution (spatially and temporally) habitat identification and precise delineation of wetlands across a variety of stakeholder groups, including wetlands loss mitigation programs. Traditional wetland delineations are costly, time-intensive and can physically degrade the systems that are being surveyed, while aerial surveys are relatively fast and relatively unobtrusive. To assess the efficacy and feasibility of using two variable-cost LiDAR sensors mounted on a commercial hexacopter unmanned aerial system (UAS) in deriving high resolution topography, we conducted nearly concomitant flights over a site located in the Atlantic Coastal plain that contains a mix of palustrine forested wetlands, upland coniferous forest, upland grass and bare ground/dirt roads. We compared point clouds and derived topographic metrics acquired using the Quanergy M8 and the Velodyne HDL-32E LiDAR sensors with airborne LiDAR and results showed that the less expensive and lighter payload sensor outperforms the more expensive one in deriving high resolution, high accuracy ground elevation measurements under a range of canopy cover densities and for metrics of point cloud density and digital terrain computed both globally and locally using variable size tessellations. The mean point cloud density was not significantly different between wetland and non-wetland areas, but the two sensors were significantly different by wetland/non-wetland type. Ultra-high-resolution LiDAR-derived topography models can fill evolving wetlands mapping needs and increase accuracy and efficiency of detection and prediction of sensitive wetland ecosystems, especially for heavily forested coastal wetland systems. [ABSTRACT FROM AUTHOR]

Small unmanned aerial systems (sUAS) are a promising technology for atmospheric monitoring of trace atmospheric gases. While sUAS can be navigated to provide information with higher spatiotemporal resolution than tethered balloons, they can also bridge the gap between the regions of the atmospheric boundary layer (ABL) sampled by ground stations and manned aircraft. Additionally, sUAS can be effectively employed in the petroleum industry, e.g., to constrain leaking regions of hydrocarbons from long gasoducts. Herein, sUAS are demonstrated to be a valuable technology for studying the concentration of important trace tropospheric gases in the ABL. The successful detection and quantification of gases is performed with lightweight sensor packages of low-power consumption that possess limits of detection on the ppm scale or below with reasonably fast response times. The datasets reported include timestamps with position, temperature, relative humidity, pressure, and variable mixing ratio values of ~400 ppm CO2, ~1900 ppb CH4, and ~5.5 ppb NH3. The sensor packages were deployed aboard two different sUAS operating simultaneously during the second CLOUDMAP flight campaign in Oklahoma, held during 26–29 June 2017. A Skywalker X8 fixed wing aircraft was used to fly horizontally at a constant altitude, while vertical profiles were provided by a DJI Phantom 3 (DJI P3) quadcopter flying upward and downward at fixed latitude-longitude coordinates. The results presented have been gathered during 8 experiments consisting of 32 simultaneous flights with both sUAS, which have been authorized by the United States Federal Aviation Authority (FAA) under the current regulations (Part 107). In conclusion, this work serves as proof of concept showing the atmospheric value of information provided by the developed sensor systems aboard sUAS. [ABSTRACT FROM AUTHOR]