Unmanned Aerial Systems Sensors Research Articles

Quantitative and Qualitative Assessments of Geometric Feature Accuracy Using a UAS-Lidar System for Building Surveying Applications

Equipping unmanned aerial systems (UASs) with light detection and ranging (lidar) has been made possible with recent advancements, which has made these sensors compact and gradually more cost-effective. Despite the increased proliferation of UAS-lidar in several fields, the geometric accuracy of lidar-generated point clouds, together with their visual qualities, needs to be explored for building surveying applications. Considering that red−blue−green (RGB) cameras are the most prevalent UAS sensors in building surveying, a lidar- and an RGB camera-equipped UAS was deployed on a mixed infrastructure to simultaneously collect data and generate corresponding point clouds. Different geometric features from both RGB and lidar point clouds were measured and compared quantitatively against benchmark field observations. A qualitative analysis on the point clouds’ visual qualities was also performed and a sensor recommendation matrix was proposed based on desired application accuracy. Lidar has proven to be a viable alternative, providing better geometric accuracy, data quality, and clarity in all three dimensions.

Conflicting spatial representations impairs object tracking performance in an aerospace environment

The purpose of this study is to extend the small number of applied research studies in Unmanned Aerial Systems (UAS) sensor control to a more ecologically valid human performance application set (i.e., airborne object tracking). In this study, United States Naval Aviators and Aircrew (N = 8) assumed the role of a sensor operator for a simulated unmanned aerial system while riding in an airborne Lockheed P-3 Orion aircraft. The P-3 flew in two flight conditions that differed in the level of intermittent ascending, descending, and turning profiles to induce two levels of spatial incongruity with the UAS sensor operator screen. Participants also performed trials on the ground to establish baseline performance measures. Results show that the incongruent visual and vestibular cues experienced by the participants during flight induced spatial incongruity and an overall decrease in operator tracking performance [F(2, 3872) = 14.04, p< 0.001)] compared to ground baseline trials. Exploratory findings suggest that motion sickness could be an important consideration for future in-flight UAS sensor control. Interestingly, results also indicate that performance decrements due to the flight environment alleviate to normal performance levels over time. Therefore, a training regime may suffice to ameliorate the effects of spatially discordant environments on human performance.

Effect of Sensor Sensitivity on Autonomous Aerial Vehicle Target Localization

In recent years, Unmanned Aerial Systems (UASs) development and application have achieved remarkable growth. With the advancement of technology, UASs nowadays feature more advanced autonomous capabilities than ever before. In order to achieve autonomous behavior, intelligent systems are required to be incorporated to support system learning, control and decision-making. With these capabilities, UASs can learn from their past experiences, through interacting with the task environment to adapt their behavior to enhance their future performance. Machine learning is one of the most commonly used techniques for UASs to acquire knowledge from their experience, and research in this area is still developing. In this study, Reinforcement Learning (RL) algorithms were used on autonomous aerial systems to achieve adaptive behavior and decision-making capabilities. The effects of UAS sensor sensitivity, as modeled through Signal Detection Theory (SDT), on the ability of RL algorithms to accomplish a target localization task were investigated. Three levels of sensor sensitivity were simulated and compared to the results of the same system using a perfect sensor, with the consideration of two RL algorithms, namely, Temporal Difference (TD) and Monte Carlo (MC) methods. Target localization and identification task were used as the test bed, and a hierarchical architecture was developed with two distinct agents. Mission performance was analyzed using multiple metrics, including episodic reward and the time taken to locate all targets. Statistical analyses were carried out to detect significant differences in the comparison of steady-state behavior of different factors. Results were discussed, and future research direction was given at the end of the paper.

Measures of Canopy Structure from Low-Cost UAS for Monitoring Crop Nutrient Status

Deriving crop information from remotely sensed data is an important strategy for precision agriculture. Small unmanned aerial systems (UAS) have emerged in recent years as a versatile remote sensing tool that can provide precisely-timed, fine-grained data for informing management responses to intra-field crop variability (e.g., nutrient status and pest damage). UAS sensors with high spectral resolution used to compute informative vegetation indices, however, are practically limited by high cost and data dimensionality. This research extends spectral analysis for remote crop monitoring to investigate the relationship between crop health and 3D canopy structure using low-cost UAS equipped with consumer-grade RGB cameras. We used flue-cured tobacco as a case study due to its known sensitivity to fertility variation and nutrient-specific symptomology. Fertilizer treatments were applied to induce plant health variability in a 0.5 ha field of flue-cured tobacco. Multi-view stereo images from three UAS surveys collected during crop development were processed into orthoimages used to compute a visible band spectral index and photogrammetric point clouds using Structure from Motion (SfM). Plant structural metrics were then computed from detailed high resolution canopy surface models (0.05 m resolution) interpolated from the photogrammetric point clouds. The UAS surveys were complimented by nutrient status measurements obtained from plant tissues. The relationships between foliar nitrogen (N), phosphorus (P), potassium (K), and boron (B) concentrations and the UAS-derived metrics were assessed using multiple linear regression. Symptoms of N and K deficiencies were well captured and differentiated by the structural metrics. The strongest relationship observed was between canopy shape and N foliar concentration (adj. r2 = 0.59, increasing to adj. r2 = 0.81 when combined with the spectral index). B foliar concentration was consistently better predicted by canopy structure with a maximum adj. r2 = 0.41 observed at the latest growth stage surveyed. Overall, combining information about canopy structure and spectral reflectance increased model fit for all measured nutrients compared to spectral alone. These results suggest that an important relationship exists between relative canopy shape and crop health that can be leveraged to improve the usefulness of low cost UAS for precision agriculture.

The perspective of unmanned aerial systems in forest management: Do we really need such details?

AbstractForest ecosystems provide countless ecosystem services potentially mitigating the ongoing climate change; those services, however, suffer not only from the change of climate but also from a rising human pressure. Understanding of forest ecosystem complexity and consistent monitoring of ongoing changes can be a key to climate change mitigation. Remote sensing can capture data at various scales and times and can complement and enhance traditional forest field surveys. Low‐altitude aerial surveys conducted with unmanned aerial systems (UAS) may bridge the gap between remote imaging and field surveys, especially at local scales. The current professional UAS sensors offer astounding detail and accuracy but are associated with high costs. For everyday forest practice, information on forest structure and health status is needed. Such information can, however, also be captured by consumer‐grade UAS and casual cameras at reasonable costs with sufficient accuracy, which makes the high costs associated with better detail unjustifiable for the common forest practice.

Upscaling UAS Paradigm to UltraLight Aircrafts: A Low-Cost Multi-Sensors System for Large Scale Aerial Photogrammetry

The use of unmanned aerial systems (UASs) has rapidly grown in many civil applications since the early 2010s. Nowadays, a large variety of reliable low-cost UAS sensors and controllers are available. However, contrary to ultralight aircrafts (ULAs), UASs have a too small operational range to efficiently cover large areas. Flight regulations prevailing in many countries further reduced this operational range; in particular, the “within visual line of sight” rule. This study presents a new system for image acquisition and high-quality photogrammetry of large scale areas (&gt;10 km²). It was developed by upscaling the UAS paradigm, i.e., low-cost sensors and controllers, little (or no) on-board active stabilization, and adequate structure from motion photogrammetry, to an ULA platform. Because the system is low-cost (good quality-price ratio of UAS technologies), multi-sensors (large variety of available UAS sensors) and versatile (high ULA operational flexibility and more lenient regulation than for other platforms), the possible applications are numerous in miscellaneous research domains. The system was described in detail and illustrated from the flight and images acquisition to the photogrammetric routine. The system was successfully used to acquire high resolution and high quality RGB and multispectral images, and produced precisely georeferenced digital elevation model (DEM) and orthophotomosaics for a forested area of 1200 ha. The system can potentially carry any type of sensors. The system compatibility with any sensor can be tested, in terms of image quality and flight plan, with the proposed method. This study also highlighted a major technical limitation of the low-cost thermal infrared cameras: the too high integration time with respect to the flight speed of most UASs and ULAs. By providing the complete information required for reproducing the system, the authors seek to encourage its implementation in different geographical locations and scientific contexts, as well as, its combination with other sensors, in particular, laser imaging detection and ranging (LiDAR) and hyperspectral.

Development of Attentional Skills Training for Operators of Unmanned Aerial Systems

The operators of unmanned aerial systems (UAS) have multiple screens of information they need to monitor, and numerous interruptions that challenge them in their work. Research on attentional training has revealed that it is possible to train operators to improve their visual scanning, prioritization, and interruption management performance. This paper describes the analyses that were conducted to identify the information processing requirements associated with UAS tasks and the integration of findings into a prototype attentional skills training tool. This tool uses adaptive scenario-based training to improve the attentional management skills of UAS sensor operators.

Optimizing Spectral and Spatial Resolutions of Unmanned Aerial System Imaging Sensors for Monitoring Antarctic Vegetation

Antarctic moss communities, found in the spatially fragmented and fragile moss beds, can serve as indicators of the regional impacts of climate change. Unmanned aerial systems (UAS) carrying visible and near infrared (VNIR) sensors are a suitable nonintrusive mapping platform. UAS deployments in Antarctica are, due to weather and logistical restrictions, infrequent and short, thus it is essential that field time is optimized. This article identified the optimal spectral and spatial resolution of the UAS-based sensors to facilitate efficient data acquisition without jeopardizing the accuracy of remotely sensed moss health indicators. A hyperspectral line scanner was used to collect imagery of two moss study sites near the Casey Australian Antarctic base. The spectral and spatial data degradation simulated two lightweight sensors that could be used for more efficient spectral image acquisition in the future. These simulations revealed that the spectral quality deteriorated more definitively at the spatial resolution where moss spectra started to mix with spectra of surrounding rocks. Subsequently, random forest models (RFMs) were trained with lab measurements for predicting chlorophyll content and effective leaf density. The RFMs were applied to the UAS imagery of the reduced spectral and spatial resolutions to quantify decline in accuracy of both indicators. We identified the optimal UAS sensor capable of mapping a relatively large moss bed (~5 ha) with the prediction accuracy similar to the hyperspectral system. This sensor would be a frame camera acquiring 25 VNIR spectral bands at a spatial resolution of 8 cm. This developed methodology has the potential to be adopted for other similar vegetation biophysical/chemical plant traits.

Unmanned Aerial System multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition

Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes.

Radiometric calibration assessments for UAS-borne multispectral cameras: Laboratory and field protocols

The main objective of this study was to develop and test a framework that can be used by Unmanned Aerial Systems (UAS) operators with varying technical backgrounds to estimate the accuracy and reliability of multispectral (visible and Near-Infrared or NIR) sensor measurements. We evaluated the performance of two multispectral sensors – the MicaSense RedEdge and the Airinov MultiSpec 4C – in both a laboratory and field setting. In the laboratory, we measured the reflectance of a number of reference target materials using each UAS sensor, and compared the values to those measured using a calibrated spectrometer. We found a strong linear relationship between the measurements made by the MicaSense RedEdge and the spectrometer, while the relationship was much weaker for the Airinov MultiSpec 4C, particularly in the longer wavelength bands (red-edge and NIR). A sub-set of the target materials were selected as ground reference targets for three field calibration exercises. In field calibration assessment No. 1, imagery was collected using each UAS sensor and reflectance values were extracted from pixels covering the ground reference targets. The extracted values were compared to the reflectance values acquired in the laboratory, and both UAS sensors were found to over-estimate reflectance, with lower accuracy in red-edge and NIR bands. Field calibration assessment No. 2 involved a calculation of Normalized Difference Vegetation Index (NDVI) values at field control points using both UAS sensors, and we found a strong linear relationship between the NDVI values and measurements made by a hand-held NDVI sensor, suggesting that the calculation of a normalized band ratio (i.e., NDVI) effectively reduces the reflectance measurement inaccuracy that we observed previously. Field calibration assessment No. 3 included image acquisition of ground reference targets using the MicaSense RedEdge sensor over seventeen sequential field surveys. Results revealed measurement variability over time, suggesting that daily differences in solar illumination and atmospheric conditions may influence derived reflectance values. In light of these results, we propose simplified procedures that can be adopted by UAS operators to periodically assess the radiometric fidelity of their multispectral sensors.

Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery

Open-pit mine is still an unavoidable activity but can become unsustainable without the restoration of degraded sites. Monitoring the restoration after extractive activities is a legal requirement for mine companies and public administrations in many countries, involving financial provisions for environmental liabilities. The objective of this contribution is to present a rigorous, low-cost and easy-to-use application of Unmanned Aerial Systems (UAS) for supporting opencast mining and restoration monitoring, complementing the inspections with very high (<10 cm) spatial resolution multispectral imagery, and improving any restoration documentation with detailed land cover maps. The potential of UAS as a tool to control restoration works is presented in a calcareous quarry that has undergone different post-mining restoration actions in the last 20 years, representing 4 reclaimed stages. We used a small (<2 kg) drone equipped with a multispectral sensor, along with field spectroradiometer measurements that were used to radiometrically correct the UAS sensor data. Imagery was processed with photogrammetric and Remote Sensing and Geographical Information Systems software, resulting in spectral information, vegetation and soil indices, structural information and land cover maps. Spectral data and land cover classification, which were validated through ground-truth plots, aided in the detection and quantification of mine waste dumping, bare soil and other land cover extension. Moreover, plant formations and vegetation development were evaluated, allowing a quantitative, but at the same time visual and intuitive comparison with the surrounding reference systems. The protocol resulting from this research constitutes a pipeline solution intended for the implementation by public administrations and privates companies for precisely evaluating restoration dynamics in an expedient manner at a very affordable budget. Furthermore, the proposed solution prevents subjective interpretations by providing objective data, which integrate new technologies at the service of scientists, environmental managers and decision makers.

Unmanned aerial systems for agriculture and natural resources

Feature Unmanned aerial systems for agriculture and natural resources Sean D. Hogan, Drone Service Coordinator, Informatics and GIS Statewide Program, UC Agriculture and Natural Resources Maggi Kelly, Director, Informatics and GIS Statewide Program, UC Agriculture and Natural Resources; Faculty Director, Geospatial Innovation Facility, College of Natural Resources, UC Berkeley; Professor and UC Cooperative Extension Specialist, Department of Environmental Sciences, Policy and Management, UC Berkeley Brandon Stark, Director of the Center of Excellence on Unmanned Aircraft System Safety, UC Merced and UC Office of the President YangQuan Chen, Director, Mechatronics, Embedded Systems and Automation (MESA) Lab, UC Merced S mall unmanned aerial systems (UAS), also known as drones or unmanned aerial vehicles, have a rapidly growing role in research and practice in agriculture and natural resources. Here, we present the parameters and key limitations of the technology, summarize current regulations and cover examples of University of California research enabled by UAS technology. Motorized UAS were introduced as a potential remote sensing tool for scientific research in the late 1970s. However, due to a variety of limitations (the weight and limited functionality of available sensors and cameras, the lack of GPS-guided autopi- lots and so on) these platforms had few practical applica- tions (Przybilla and Wester-Ebbinghaus 1979; Wester- Ebbinghaus 1980; cited by Colomina and Molina 2014). For years, UAS technology was led by military needs and ap- plications. The relatively few applications in research and agriculture included deployments in Japan for crop dusting and in Australia for meteorological studies (Colomina and Molina 2014). In the past decade, several factors have greatly increased the utility and ease of use of UAS, while prices have fallen. Consumer demand drove the hobby craft industry to make major improvements in UAS vehicles. Integrating improved battery tech- nology, miniature inertia measurement units (IMU, initially developed for smartphones), GPS and cus- tomizable apps for smartphones and tablets has deliv- ered improved flight longevity, reliability, ease of use and the ability to better utilize cameras and other sen- sors needed for applications in agriculture and natu- ral resources (see below, Types of UAS). Innovations in sensor technology now include dozens of models of lightweight visible-spectrum and multispectrum cameras capable of capturing reliable, scientifically valid data from UAS platforms (see UAS sensors) (Whitehead and Hugenholtz 2014). Meanwhile, the Federal Aviation Administration (FAA) has helped fa- cilitate increased UAS use, with rule changes adopted in August 2016 that lowered what have previously been significant regulatory obstacles to the legal use of UAS for research and commercial purposes (see Regulations sidebar). UC faculty throughout California are using UAS in a wide range of agricultural and environmental research projects — from grazed rangelands to field crops and orchards, forests, lakes and even the ice sheets of Greenland http://calag.ucanr.edu • JANUARY–MARCH 2017 5 Will Suckow The Inspire 1 drone, made by DJI, flies with an RGB camera over the UC Berkeley Blue Oak Ranch Reserve in Santa Clara County.