Drones, Volume 3, Issue 1 (March 2019) – 30 articles

This paper presents a novel tool capable of collecting thermal signatures inside a building by using low-cost IR temperature sensors mounted on-board an aerial platform. The proposed system aims to facilitate the detection of heat loss inside buildings, which is a key aspect for improving energy efficiency in large commercial or industrial buildings. Current detection systems usually require manual labor as well as the use of expensive instrumentation. The proposed system on the other hand, relies on the use of a small unmanned aerial vehicle carrying low-cost thermopile IR sensors. Moreover, the system delivers a fast temperature sensing scheme and it provides coverage to inaccessible areas, thus overcoming the limitations of current mobile platforms which use ground robots. Different experiments were carried out in order to assess the behavior of the sensors as well as to validate the full system. Moreover, the hypothesis that thermopile IR sensors can be used to track temperature signature on-the-fly is validated experimentally with the use of the proposed system over different targets. Full article

The availability and precision of unmanned aerial systems (UAS) permit the repeated collection of very-high quality three-dimensional (3D) data to monitor high-interest areas, such as dams, urban areas, or erosion-prone coastlines. However, challenges exist in the temporal analysis of this data, specifically in conducting change-detection analysis on the high-quality point cloud data. These files are very large in size and contain points in varying locations that do not align between scenes. These large file sizes also limit the use of this data for individuals with low computational resources, such as first responders or forward-deployed soldiers. In response, this manuscript presents an approach that aggregates data spatially into voxels to provide the user with a lightweight, web-based exploitation system coupled with a flexible backend database. The system creates a robust set of tools to analyze large temporal stacks of 3D data and reduces data size by 78%, all while being able to query the original point cloud data. This approach offers a solution for organizations analyzing high-resolution, temporal point-clouds, as well as a possible solution for operations in areas with poor computational and connectivity resources requiring high-quality, 3D data for decision support and planning. Full article

With the widespread extirpation of top predators over the past two centuries, mesocarnivores play an increasingly important role in structuring terrestrial trophic webs. However, mesocarnivores are difficult to survey at a population level because their widely spaced territories and nocturnal behavior result in low detection probability. Existing field survey techniques such as track plates and motion-sensitive camera traps are time-consuming and expensive, and yet still yield data prone to systematic errors. Unmanned Aerial Vehicles (UAVs) have recently emerged as a new tool for conducting population surveys on a wide variety of wildlife, eclipsing the efficiency and even accuracy of traditional methods. We used a UAV equipped with a thermal imaging camera to conduct nighttime mesocarnivore surveys in the prairie pothole region of southern Manitoba, Canada. This was part of a much larger ecological study evaluating how lethal removal of mesocarnivores affects duck nest success. Here, our objective was to describe methods and equipment that were successful in detecting mesocarnivores. We used a modified point-count survey from six waypoints that surveyed a spatial extent of 29.5 ha. We conducted a total of 200 flights over 53 survey nights during which we detected 32 mesocarnivores of eight different species. Given the large home ranges of mesocarnivores relative to the spatial and temporal scale of our spot sampling approach, results of these types of point-count surveys should be considered estimates of minimum abundance and not a population census. However, more frequent sampling and advanced statistics could be used to formally estimate population occupancy and abundance. UAV-mounted thermal imaging cameras appear to be an effective tool for conducting nocturnal population surveys on mesocarnivores at a moderate spatial scale. Full article

A multiple sensor payload for a multi-rotor based UAV platform was developed and tested for measuring land surface albedo and spectral measurements at user-defined spatial, temporal, and spectral resolutions. The system includes a Matrice 600 UAV with an RGB camera and a set of four downward pointing radiation sensors including a pyranometer, quantum sensor, and VIS and NIR spectrometers, measuring surface reflected radiation. A companion ground unit consisting of a second set of identical sensors simultaneously measure downwelling radiation. The reflected and downwelling radiation measured by the four sensors are used for calculating albedo for the total shortwave broadband, visible band and any narrowband at a 1.5 nm spectral resolution within the range of 350–1100 nm. The UAV-derived albedo was compared with those derived from Landsat 8 and Sentinel-2 satellite observations. Results show the agreement between total shortwave albedo from UAV pyranometer and Landsat 8 (R² = 0.73) and Sentinel-2 (R² = 0.68). Further, total shortwave albedo was estimated from spectral measurements and compared with the satellite-derived albedo. This UAV-based sensor system promises to provide high-resolution multi-sensors data acquisition. It also provides maximal flexibility for data collection at low cost with minimal atmosphere influence, minimal site disturbance, flexibility in measurement planning, and ease of access to study sites (e.g., wetlands) in contrast with traditional data collection methods. Full article

Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to spatially resolve individual plants or lack temporal resolution to monitor pathogenesis. Here, we used multispectral imaging and unmanned aerial systems (UAS) to explore whether myrtle rust (Austropuccinia psidii) could be detected on a lemon myrtle (Backhousia citriodora) plantation. Multispectral aerial imagery was collected from fungicide treated and untreated tree canopies, the fungicide being used to control myrtle rust. Spectral vegetation indices and single spectral bands were used to train a random forest classifier. Treated and untreated trees could be classified with high accuracy (95%). Important predictors for the classifier were the near-infrared (NIR) and red edge (RE) spectral band. Taking some limitations into account, that are discussedherein, our work suggests potential for mapping myrtle rust-related symptoms from aerial multispectral images. Similar studies could focus on pinpointing disease hotspots to adjust management strategies and to feed epidemiological models. Full article

Condition indices correlating body lipid content with mass and morphometric measurements have been developed for a variety of taxa. However, for many large species, the capture and handling of enough animals to obtain representative population estimates is not logistically feasible. The relatively low cost and reduced disturbance effects of UAVs make them ideal for the rapid acquisition of high volume data for monitoring large species. This study examined the imagery collected from two different UAVs, flown at 25 m altitude, and the subsequent georeferenced orthomosaics as a method for measuring length and axillary girth of Australian fur seals (Arctocephalus pusillus doriferus) to derive an index of body condition. Up to 26% of individuals were orientated correctly (prostrate/sternal recumbent) to allow for body measurements. The UAV-obtained images over-estimated axillary girth diameter due to postural sag on the lateral sides of the thorax while the animals are lying flat in the sternal recumbent position on granite rocks. However, the relationship between axillary girth and standard length was similarly positive for the remotely- and physically-obtained measurements. This indicates that residual values from the remotely-obtained measurements can be used as a relative index of body condition. Full article

Underground peat fires are a major hazard to health and livelihoods in Indonesia, and are a major contributor to carbon emissions globally. Being subterranean, these fires can be difficult to detect and track, especially during periods of thick haze and in areas with limited accessibility. Thermal infrared detectors mounted on drones present a potential solution to detecting and managing underground fires, as they allow large areas to be surveyed quickly from above and can detect the heat transferred to the surface above a fire. We present a pilot study in which we show that underground peat fires can indeed be detected in this way. We also show that a simple temperature thresholding algorithm can be used to automatically detect them. We investigate how different thermal cameras and drone flying strategies may be used to reliably detect underground fires and survey fire-prone areas. We conclude that thermal equipped drones are potentially a very powerful tool for surveying for fires and firefighting. However, more investigation is still needed into their use in real-life fire detection and firefighting scenarios. Full article

This paper presents a vision-based indoor scene recognition method from aerial time-series images obtained using a micro air vehicle (MAV). The proposed method comprises two procedures: a codebook feature description procedure, and a recognition procedure using category maps. For the former procedure, codebooks are created automatically as visual words using self-organizing maps (SOMs) after extracting part-based local features using a part-based descriptor from time-series scene images. For the latter procedure, category maps are created using counter propagation networks (CPNs) with the extraction of category boundaries using a unified distance matrix (U-Matrix). Using category maps, topologies of image features are mapped into a low-dimensional space based on competitive and neighborhood learning. We obtained aerial time-series image datasets of five sets for two flight routes: a round flight route and a zigzag flight route. The experimentally obtained results with leave-one-out cross-validation (LOOCV) revealed respective mean recognition accuracies for the round flight datasets (RFDs) and zigzag flight datasets (ZFDs) of 71.7% and 65.5% for 10 zones. The category maps addressed the complexity of scenes because of segmented categories. Although extraction results of category boundaries using U-Matrix were partially discontinuous, we obtained comprehensive category boundaries that segment scenes into several categories. Full article

In ecological research, a key interest is to explore movement patterns of individual organisms across different spatial scales as one driver of biotic interactions. While various methods exist to detect and record the presence and movements of individuals in combination with UAS, addressing these for smaller animals, such as insects, is challenging and often fails to reveal information on potential interactions. Here, we address this gap by combining the UAS-based detection of small tracers of fluorescent dyes by means of a simple experiment under field conditions for the first time. We (1) excited fluorescent tracers utilizing an UV radiation source and recorded images with an UAS, (2) conducted a semi-automated selection of training and test samples to (3) train a simple SVM classifier, allowing (4) the classification of the recorded images and (5) the automated identification of individual traces. The tracer detection success significantly decreased with increasing altitude, increasing distance from the UV radiation signal center, and decreasing size of the fluorescent traces, including significant interactions amongst these factors. As a first proof-of-principle, our approach has the potential to be broadly applicable in ecological research, particularly in insect monitoring. Full article

In this paper, we present an alternate method for the generation and implementation of the sensor measurement variance used in an Extended Kalman Filter (EKF). Furthermore, it demonstrates the limitations of a conventional EKF implementation and postulates an alternate form for representing the sensor measurement variance by extending and improving the characterisation methodology presented in the previous work. As presented in earlier work, the use of surveying grade optical measurement instruments allows for a more effective characterisation of Ultra-Wide Band (UWB) localisation sensors; however, in cluttered environments, the sensor measurement variance will change, making this method not robust. To compensate for the noisier readings, an EKF using a model based sensor measurement variance was developed. This approach allows for a more accurate representation of the sensor measurement variance and leads to a more robust state estimation system. Simulations were run using synthetic data in order to test the effectiveness of the EKF against the originally developed EKF; next, the new EKF was compared to the original EKF using real world data. The new EKF was shown to function much more stably and consistently in less ideal environments for UWB deployment than the previous version. Full article

Topography derived using human-portable unmanned aerial vehicles (UAVs) and structure from motion photogrammetry offers an order of magnitude improvement in spatial resolution and uncertainty over small survey extents, compared to global digital elevation model (DEM) products, which are often the only available choice of DEMs in the high-mountain Himalaya. Access to fine-resolution topography in the high mountain Himalaya is essential to assess where flood and landslide events present a risk to populations and infrastructure. In this study, we compare the topography of UAV-derived DEMs, three open-access global DEM products, and the 8 m High Mountain Asia (HMA) DEMs (released in December 2017) and assess their suitability for landslide- and flood-related hazard assessments. We observed close similarity between UAV and HMA DEMs when comparing terrain elevation, river channel delineation, landside volume, and landslide-dammed lake area and volume. We demonstrate the use of fine-resolution topography in a flood-modelling scenario relating to landslide-dammed lakes that formed on the Marsyangdi River following the 2015 Gorkha earthquake. We outline a workflow for using UAVs in hazard assessments and disaster situations to generate fine-resolution topography and facilitate real-time decision-making capabilities, such as assessing landslide-dammed lakes, mass movement volumes, and flood risk. Full article

This paper examines the potential use of fire extinguishing balls as part of a proposed system, where drone and remote-sensing technologies are utilized cooperatively as a supplement to traditional firefighting methods. The proposed system consists of (1) scouting unmanned aircraft system (UAS) to detect spot fires and monitor the risk of wildfire approaching a building, fence, and/or firefighting crew via remote sensing, (2) communication UAS to establish and extend the communication channel between scouting UAS and fire-fighting UAS, and (3) a fire-fighting UAS autonomously traveling to the waypoints to drop fire extinguishing balls (environmental friendly, heat activated suppressants). This concept is under development through a transdisciplinary multi-institutional project. The scope of this paper encloses general illustration of this design, and the experiments conducted so far to evaluate fire extinguishing balls. The results of the experiments show that smaller size fire extinguishing balls available in the global marketplace attached to drones might not be effective in aiding in building fires (unless there are open windows in the buildings already). On the contrary, results show that even the smaller size fire extinguishing balls might be effective in extinguishing short grass fires (around 0.5 kg size ball extinguished a circle of 1-meter of short grass). This finding guided the authors towards wildfire fighting rather than building fires. The paper also demonstrates building of heavy payload drones (around 15 kg payload), and the progress of development of an apparatus carrying fire-extinguishing balls attachable to drones. Full article

In recent years, FANET-related research and development has doubled, due to the increased demands of unmanned aerial vehicles (UAVs) in both military and civilian operations. Equipped with more capabilities and unique characteristics, FANET is able to play a vital role in mission-critical applications. However, these distinctive features enforce a series of guidelines to be considered for its efficient deployment. Particularly, the use of FANET for on-time data communication services presents demanding challenges in terms of energy efficiency and quality of service (QoS). Proper use of communication architecture and wireless technology will assist to solve these challenges. Therefore, in this paper, we review different communication architectures, including the existing wireless technologies, in order to provide seamless wireless connectivity. Based on the discussions, we conclude that a multi-layer UAV ad-hoc network is the most suitable architecture for networking a group of heterogeneous UAVs, while Bluetooth 5 (802.15.1) is the most favored option because of its low-cost, low power consumption, and longer transmission range for FANET. However, 802.15.1 has the limitation of a lower data rate as compared to Wi-Fi (802.11). Therefore, we propose a hybrid wireless communication scheme so as to utilize the features of the high data transmission rate of 802.11 and the low-power consumption of 802.15.1. The proposed scheme significantly reduces communication cost and improves the network performance in terms of throughput and delay. Further, simulation results using the Optimized Network Engineering Tool (OPNET) further support the effectiveness of our proposed scheme. Full article

Small unmanned aerial systems (UASs) equipped with an optical camera are a cost-effective strategy for topographic surveys. These low-cost UASs can provide useful information for three-dimensional (3D) reconstruction even if they are equipped with a low-quality navigation system. To ensure the production of high-quality topographic models, careful consideration of the flight mode and proper distribution of ground control points are required. To this end, a commercial UAS was adopted to monitor a small earthen dam using different combinations of flight configurations and by adopting a variable number of ground control points (GCPs). The results highlight that optimization of both the choice and combination of flight plans can reduce the relative error of the 3D model to within two meters without the need to include GCPs. However, the use of GCPs greatly improved the quality of the topographic survey, reducing error to the order of a few centimeters. The combined use of images extracted from two flights, one with a camera mounted at nadir and the second with a 20° angle, was found to be beneficial for increasing the overall accuracy of the 3D model and especially the vertical precision. Full article

Stream velocity and flow are very important parameters that must be measured accurately to develop effective water resource management plans. There are various methods and tools to measure the velocity but, nowadays, image-based methods are a promising alternative that does not require physical contact with the water body. The current study describes the application of a low cost unmanned aerial vehicle that was selected in order to capture a video over a specific reach of Aggitis River in Greece. The captured frames were analyzed by three different software (PIVlab, PTVlab, and KU-STIV) in order to estimate accurately the surface water velocity. These three software also represent three different image-based methodologies. Although there are differences among these three methods, the analysis produced similar trends for all. The velocity ranged between 0.02 and 3.98 m/s for PIVlab, 0.12 and 3.44 m/s for PTVlab, and 0.04 and 3.99 m/s for KU-STIV software. There were parts, especially in the existing vegetation, where differences were observed. Further applications will be examined in the same or different reaches, to study the parameters affecting the analysis. Finally, the image-based methods will be coupled with verification measurements by a current meter to produce more rigorous results. Full article

This paper studies the network performance of collaboration between the Internet of public safety things (IoPST) and drones. Drones play a vital role in delivering timely and essential wireless communication services for the recovery of services right after a disaster by increasing surge capacity for the purposes of public safety, exploring areas that are difficult to reach, and providing an area of rapid coverage and connectivity. To provide such critical facilities in the case of disasters and for the purposes of public safety, collaboration between drones and IoPST is able to support public safety requirements such as real-time analytics, real-time monitoring, and enhanced decision-making to help smart cities meet their public safety requirements. Therefore, the deployment of drone-based wireless communication can save people and ecosystems by helping public safety organizations face threats and manage crises in an efficient manner. The contribution of this work lies in improving the level of public safety in smart cities through collaborating between smart wearable devices and drone technology. Thus, the collaboration between drones and IoPST devices establishes a public safety network that shows satisfying results in terms of enhancing efficiency and information accuracy. Full article

Hyperspectral remote sensing provides a wealth of data essential for vegetation studies encompassing a wide range of applications (e.g., species diversity, ecosystem monitoring, etc.). The development and implementation of UAV-based hyperspectral systems have gained popularity over the last few years with novel efforts to demonstrate their operability. Here we describe the design, implementation, testing, and early results of the UAV-μCASI system, which showcases a relatively new hyperspectral sensor suitable for ecological studies. The μCASI (288 spectral bands) was integrated with a custom IMU-GNSS data recorder built in-house and mounted on a commercially available hexacopter platform with a gimbal to maximize system stability and minimize image distortion. We deployed the UAV-μCASI at three sites with different ecological characteristics across Canada: The Mer Bleue peatland, an abandoned agricultural field on Ile Grosbois, and the Cowichan Garry Oak Preserve meadow. We examined the attitude data from the flight controller to better understand airframe motion and the effectiveness of the integrated Differential Real Time Kinematic (RTK) GNSS. We describe important aspects of mission planning and show the effectiveness of a bundling adjustment to reduce boresight errors as well as the integration of a digital surface model for image geocorrection to account for parallax effects at the Mer Bleue test site. Finally, we assessed the quality of the radiometrically and atmospherically corrected imagery from the UAV-μCASI and found a close agreement (<2%) between the image derived reflectance and in-situ measurements. Overall, we found that a flight speed of 2.7 m/s, careful mission planning, and the integration of the bundling adjustment were important system characteristics for optimizing the image quality at an ultra-high spatial resolution (3–5 cm). Furthermore, environmental considerations such as wind speed (<5 m/s) and solar illumination also play a critical role in determining image quality. With the growing popularity of “turnkey” UAV-hyperspectral systems on the market, we demonstrate the basic requirements and technical challenges for these systems to be fully operational. Full article

A recently proposed navigation methodology for aerial platforms based on the vehicle dynamic model (VDM) has shown promising results in terms of navigation autonomy. Its practical realization requires that control inputs are related to the same absolute time frame as inertial measurement unit (IMU) data and all other observations when available (e.g., global navigation satellite system (GNSS) position, barometric altitude, etc.). This study analyzes the (non-) tolerances of possible delays in control-input command with respect to navigation performance on a fixed-wing unmanned aerial vehicle (UAV). Multiple simulations using two emulated trajectories based on real flights reveal the vital importance of correct time-tagging of servo data while that of motor data turned out to be tolerable to a considerably large extent. Full article

Park managers call for cost-effective and innovative solutions to handle a wide variety of environmental problems that threaten biodiversity in protected areas. Recently, drones have been called upon to revolutionize conservation and hold great potential to evolve and raise better-informed decisions to assist management. Despite great expectations, the benefits that drones could bring to foster effectiveness remain fundamentally unexplored. To address this gap, we performed a literature review about the use of drones in conservation. We selected a total of 256 studies, of which 99 were carried out in protected areas. We classified the studies in five distinct areas of applications: “wildlife monitoring and management”; “ecosystem monitoring”; “law enforcement”; “ecotourism”; and “environmental management and disaster response”. We also identified specific gaps and challenges that would allow for the expansion of critical research or monitoring. Our results support the evidence that drones hold merits to serve conservation actions and reinforce effective management, but multidisciplinary research must resolve the operational and analytical shortcomings that undermine the prospects for drones integration in protected areas. Full article

Understorey vegetation plays an important role in many ecosystems, yet identifying and monitoring understorey vegetation through remote sensing has proved a challenge for researchers and land managers because understorey plants tend to be small, spatially and spectrally similar, and are often blocked by the overstorey. The emergence of Unmanned Aerial Systems (UAS) is revolutionising how vegetation is measured, and may allow us to measure understorey species where traditional remote sensing previously could not. The goal of this paper was to review current literature and assess the current capability of UAS to identify and monitor understorey vegetation. From the literature, we focused on the technical attributes that limit the ability to monitor understorey vegetation—specifically (1) spatial resolution, (2) spectral sensitivity, (3) spatial extent, and (4) temporal frequency at which a sensor acquires data. We found that UAS have provided improved levels of spatial resolution, with authors reporting successful classifications of understorey vegetation at resolutions of between 3 mm and 200 mm. Species discrimination can be achieved by targeting flights to correspond with phenological events to allow the detection of species-specific differences. We provide recommendations as to how UAS attributes can be tailored to help identify and monitor understorey species. Full article

Although many climate research experiments are providing valuable data, long-term measurements are not always affordable. In the last decades, several facilities have secured long-term experiments, but few studies have incorporated spatial and scale effects. Most of them have been implemented in experimental agricultural fields but none for ecological studies. Scale effects can be assessed using remote sensing images from space or airborne platforms. Unmanned aerial vehicles (UAVs) are contributing to an increased spatial resolution, as well as becoming the intermediate scale between ground measurements and satellite/airborne image data. In this paper we assess the applicability of UAV-borne multispectral images to provide complementary experimental data collected at point scale (field sampling) in a long-term rain manipulation experiment located at the Kiskun Long-Term Socio-Ecological Research (LTSER) site named ExDRain to assess the effects on grassland vegetation. Two multispectral sensors were compared at different scales, the Parrot Sequoia camera on board a UAV and the portable Cropscan spectroradiometer. The NDVI values were used to assess the effect of plastic roofs and a proportional reduction effect was found for Sequoia-derived NDVI values. Acceptable and significant positive relationships were found between both sensors at different scales, being stronger at Cropscan measurement scale. Differences found at plot scale might be due to heterogeneous responses to treatments. Spatial variability analysis pointed out a more homogeneous response for plots submitted to severe and moderate drought. More investigation is needed to address the possible effect of species abundance on NDVI at plot scale contributing to a more consistent representation of ground measurements. The feasibility of carrying out systematic UAV flights coincident or close to ground campaigns will certainly reveal the consistency of the observed spatial patterns in the long run. Full article

During recent years unmanned aerial vehicles (UAVs) have been increasingly used for research and application in both agriculture and forestry. Nevertheless, most of this work has been devoted to improving accuracy and explanatory power, often at the cost of usability and affordability. We tested a low-cost UAV and a simple workflow to apply four different greenness indices to the monitoring of pine (Pinus sylvestris and P. nigra) post-fire regeneration in a Mediterranean forest. We selected two sites and measured all pines within a pre-selected plot. Winter flights were carried out at each of the sites, at two flight heights (50 and 120 m). Automatically normalized images entered an structure from motion (SfM) based photogrammetric software for restitution, and the obtained point cloud and orthomosaic processed to get a canopy height model and four different greenness indices. The sum of pine diameter at breast height (DBH) was regressed on summary statistics of greenness indices and the canopy height model. Excess green index (ExGI) and green chromatic coordinate (GCC) index outperformed the visible atmospherically resistant index (VARI) and green red vegetation index (GRVI) in estimating pine DBH, while canopy height slightly improved the models. Flight height did not severely affect model performance. Our results show that low cost UAVs may improve forest monitoring after disturbance, even in those habitats and situations where resource limitation is an issue. Full article

This paper presents the results of a study undertaken to classify lowland native grassland communities in the Tasmanian Midlands region. Data was collected using the 20 band hyperspectral snapshot PhotonFocus sensor mounted on an unmanned aerial vehicle. The spectral range of the sensor is 600 to 875 nm. Four vegetation classes were identified for analysis including Themeda triandra grassland, Wilsonia rotundifolia, Danthonia/Poa grassland, and Acacia dealbata. In addition to the hyperspectral UAS dataset, a Digital Surface Model (DSM) was derived using a structure-from-motion (SfM). Classification was undertaken using an object-based Random Forest (RF) classification model. Variable importance measures from the training model indicated that the DSM was the most significant variable. Key spectral variables included bands two (620.9 nm), four (651.1 nm), and 11 (763.2 nm) from the hyperspectral UAS imagery. Classification validation was performed using both the reference segments and the two transects. For the reference object validation, mean accuracies were between 70% and 72%. Classification accuracies based on the validation transects achieved a maximum overall classification accuracy of 93. Full article

Coverage path planning consists of finding the route which covers every point of a certain area of interest. In recent times, Unmanned Aerial Vehicles (UAVs) have been employed in several application domains involving terrain coverage, such as surveillance, smart farming, photogrammetry, disaster management, civil security, and wildfire tracking, among others. This paper aims to explore and analyze the existing studies in the literature related to the different approaches employed in coverage path planning problems, especially those using UAVs. We address simple geometric flight patterns and more complex grid-based solutions considering full and partial information about the area of interest. The surveyed coverage approaches are classified according to a classical taxonomy, such as no decomposition, exact cellular decomposition, and approximate cellular decomposition. This review also contemplates different shapes of the area of interest, such as rectangular, concave and convex polygons. The performance metrics usually applied to evaluate the success of the coverage missions are also presented. Full article

Long-term ecological research (LTER) sites need a periodic assessment of the state of their ecosystems and services in order to monitor trends and prevent irreversible changes. The ecological integrity (EI) framework opens the door to evaluate any ecosystem in a comparable way, by measuring indicators on ecosystem structure and processes. Such an approach also allows to gauge the sustainability of conservation management actions in the case of protected areas. Remote sensing (RS), provided by satellite, airborne, or drone-borne sensors becomes a very synoptic and valuable tool to quickly map isolated and inaccessible areas such as wetlands. However, few RS practical indicators have been proposed to relate to EI indicators for wetlands. In this work, we suggest several RS wetlands indicators to be used for EI assessment in wetlands and specially to be applied with unmanned aerial vehicles (UAVs). We also assess the applicability of multispectral images captured by UAVs over two long-term socio-ecological research (LTSER) wetland sites to provide detailed mapping of inundation levels, water turbidity and depth as well as aquatic plant cover. We followed an empirical approach to find linear relationships between UAVs spectral reflectance and the RS indicators over the Doñana LTSER platform in SW Spain. The method assessment was carried out using ground-truth data collected in transects. The resulting empirical models were implemented for Doñana marshes and can be applied for the Braila LTSER platform in Romania. The resulting maps are a very valuable input to assess habitat diversity, wetlands dynamics, and ecosystem productivity as frequently as desired by managers or scientists. Finally, we also examined the feasibility to upscale the information obtained from the collected ground-truth data to satellite images from Sentinel-2 MSI using segments from the UAV multispectral orthomosaic. We found a close multispectral relationship between Parrot Sequoia and Sentinel-2 bands which made it possible to extend ground-truth to map inundation in satellite images. Full article

Owing to the combination of technological progress in Unmanned Aerial Vehicles (UAVs) and recent advances in photogrammetry processing with the development of the Structure-from-Motion (SfM) approach, UAV photogrammetry enables the rapid acquisition of high resolution topographic data at low cost. This method is particularly widely used for geomorphological surveys of linear coastal landforms. However, linear surveys are generally pointed out as problematic cases because of geometric distortions creating a “bowl effect” in the computed Digital Elevation Model (DEM). Secondly, the survey of linear coastal landforms is associated with peculiar constraints for Ground Control Points (GCPs) measurements and for the spatial distribution of the tie points. This article aims to assess the extent of the bowl effects affecting the DEM generated above a linear beach with a restricted distribution of GCPs, using different acquisition scenarios and different processing procedures, both with PhotoScan^® software tool and MicMac^® software tool. It appears that, with a poor distribution of the GCPs, a flight scenario that favors viewing angles diversity can limit DEM’s bowl effect. Moreover, the quality of the resulting DEM also depends on the good match between the flight plan strategy and the software tool via the choice of a relevant camera distortion model. Full article

This paper presents a framework for simulating visually realistic motion of underwater Remotely Operated Vehicles (ROVs) in highly complex models of aquatic environments. The models include a wide range of objects such as rocks, fish and marine plankton in addition to an ROV tether. A modified cable simulation for the underwater physical conditions has been developed for a tethered ROV. The simulation framework also incorporates models for low visibility conditions and intrinsic camera effects unique to the underwater environment. The visual models were implemented using the Unreal Engine 4 realistic game engine to be part of the presented framework. We developed a generalized method for implementing an ROV dynamics model and this method serves as a highly configurable component inside our framework. In this paper, we explore the unique characteristics of underwater simulation and the specialized models we developed for that environment. We use computer vision algorithms for feature extraction and feature tracking as a probe for comparing experiments done in our simulated environment against real underwater experiments. The experimental results presented in this paper successfully demonstrate the contribution of this realistic simulation framework to the understanding, analysis and development of computer vision and control algorithms to be used in today’s ROVs. Full article