Unmanned Airborne Vehicles Research Articles

Airborne gamma radiation field simulation method based on finite element analysis

This paper presents a method for simulating an infinite radiation-contaminated field based on finite element analysis, which has been designed to meet the experimental requirements for unmanned airborne vehicle radiation detection in nuclear emergency scenarios. Based on the principle of radiation field superposition, the infinite surface sources are divided into finite elements, and an equivalent model is established with point sources and finite element surface sources. By arranging the finite point sources in accordance with the coordinate distribution calculated by the model, the response of the airborne radiation detector to the finite element surface sources and the corresponding point sources is consistent when the activities of the finite point sources and the corresponding finite element surface sources are identical. Based on the proposed equivalent model, an airborne gamma artificial radioactive field simulation device is designed, and physical validation experiments are carried out using two-dimensional and three-dimensional infinite surface sources. Two experimental results have showed that the maximum relative deviations between the infinite surface and the equivalent model in the full energy peak counts are 1.67 % and 2.62 %, respectively. The equivalent model proposed in this paper can accurately simulate nuclear radiation-contaminated fields with arbitrary activity and terrain distribution.

Individual Tree-Level Monitoring of Pest Infestation Combining Airborne Thermal Imagery and Light Detection and Ranging

The infestation of pine shoot beetles (Tomicus spp.) in the forests of Southwestern China has inflicted serious ecological damages to the environment, causing significant economic losses. Therefore, accurate and practical approaches to detect pest infestation have become an urgent necessity to mitigate these harmful consequences. In this study, we explored the efficiency of thermal infrared (TIR) technology in capturing changes in canopy surface temperature (CST) and monitoring forest health at the scale of individual tree crowns. We combined data collected from TIR imagery and light detection and ranging (LiDAR) using unmanned airborne vehicles (UAVs) to estimate the shoot damage ratio (SDR), which is a representative parameter of the damage degree caused by forest infestation. We compared multiple machine learning methods for data analysis, including random forest (RF), partial least squares regression (PLSR), and support vector machine (SVM), to determine the optimal regression model for assessing SDR at the crown scale. Our findings showed that a combination of LiDAR metrics and CST presents the highest accuracy in estimating SDR using the RF model (R2 = 0.7914, RMSE = 15.5685). Our method enables the accurate remote monitoring of forest health and is expected to provide a novel approach for controlling pest infestation, minimizing the associated damages caused.

Evaluation of SSD Architecture for Small Size Object Detection: A Case Study on UAV Oil Pipeline Monitoring

Oil pipeline monitoring using Unmanned Airborne Vehicles (UAV) can be done by utilizing Deep Learning. Deep Learning can be used to automatically detect harmed or unauthorized objects near the pipeline for further action by the authority. Input video in the pipeline area taken from the UAV has unique characteristics. It has low resolution with dense composition object in the image. The detected object also has a small scale as the objects are far away from the UAV. Thus, the selection of the Deep Learning algorithm is important to get a desirable result with the following conditions. Single Shot Multi-Box (SSD) is one of the popular Deep Learning algorithms with fast calculation compared to others and suitable for real-time object detection. Previous works on this topic using low to medium altitude dataset (20–200 m). This paper provides an evaluation of SSD implementation to detect vehicles on high-altitude dataset (300 m). As much as 2482 dataset is fed into SSD architecture and trained to detect 3 class of vehicles. The result shows the mAP and mAR are 0.026360 and 0.067377, respectively. However, the low lost function value shows that the model is able to classify the object correctly. In conclusion, the SSD cannot process low density information to correctly locate the object.

Early detection of pine shoot beetle attack using vertical profile of plant traits through UAV-based hyperspectral, thermal, and lidar data fusion

Pine shoot beetle (PSB) is one of the most damaging forest insects of Yunnan pine plantations in southwest China. However, the subtle symptoms of heterogeneous tree crowns make it difficult to accurately detect at early stage of PSB attack. Here, we evaluated the potential of a combination of plant traits (PTs) and vegetation indices (VIs) to distinguish different levels of tree damage by integrating hyperspectral, thermal imagery, and light detection and ranging (lidar) data based on unmanned airborne vehicle (UAV) systems. A voxelization method was used to fuse hyperspectral reflectance, temperature, and lidar point cloud data. Subsequently, PTs such as pigments (i.e., chlorophyll, carotenoid, and anthocyanin contents) were retrieved from a radiative transfer model inversion, and structural, fluorescence, and thermal traits, as well as VIs, were derived from the fusion data. We developed a novel analytical approach using random forest (RF) algorithm with predictors from different spatial distributions (i.e., horizontal directions, vertical layers, and vertical clusters) to compare the performance of tree severity classification. The results showed that the difference of both PTs and VIs in vertical layers between different severity levels are more than that in horizontal directions. The performance of RF model with predictors of the vertical layers (OA = 74 %, kappa = 0.65) was better than that using the predictive variables in horizontal directions (OA = 69 % and kappa = 0.60) of tree crowns. Using the vertical clustering features RF model increased the accuracy (OA = 78 % and kappa = 0.70), especially for slightly and moderately damaged trees, with improvements of 10 % and 12 %, respectively. Among all variables analyzed, chlorophylls were the most important predictor, followed by photochemical reflectance index and structural traits. Our work demonstrates the effectiveness of using fused UAV-based multi-sensor data for early detection of PSB attack, and can be applied other potential forest diseases and insect monitoring.

Quantifying Multi-Scale Performance of Geometric Features for Efficient Extraction of Insulators from Point Clouds

Insulator extraction from images or 3D point clouds is an important part of automatic power inspection by unmanned airborne vehicles (UAVs), which is vital for improving the efficiency of inspection and the stability of power grids. However, for point cloud data, many challenges, such as the diversity of pylon shape and insulator type, complex topology, and similarity of structures, were not tackled with the study of power element extraction. To efficiently identify the small insulators from complex power transmission corridor (PTC) scenarios, this paper proposes a robust extraction method by fusing multi-scale neighborhood and multi-feature entropy weighting. The pylon head is segmented according to the aspect ratio of horizontal slices following the locating of the pylons based on the height difference and continuous vertical distribution firstly. Aiming to quantify the different contributions of features in decision-making and better segment insulators, a feature evaluation system combined with information entropy, eigen entropy-based optimal neighborhood selection, and designed multi-scale features is constructed to identify suspension insulators and tension insulators. In the optimization step, a region erosion and growing method is proposed to segment complete insulator strings by enlarging the perspectives to obtain more object representations. The extraction results of 82 pylons with 654 insulators demonstrate that the proposed method is suitable for different pylon shapes and sizes. The identification accuracy of the whole line achieves 98.23% and the average F1 score is 90.98%. The proposed method can provide technical support for automatic UAV inspection and pylon reconstruction.

BVLOS UAV missions for vegetation mapping in maritime Antarctic

Polar areas are among the regions where climate change occurs faster than on most of the other areas on Earth. To study the effects of climate change on vegetation, there is a need for knowledge on its current status and properties. Both classic field observation methods and remote sensing methods based on manned aircraft or satellite image analysis have limitations. These include high logistic operation costs, limited research areas, high safety risks, direct human impact, and insufficient resolution of satellite images. Fixed-wing unmanned aerial vehicle beyond the visual line of sight (UAV BVLOS) missions can bridge the scale gap between field-based observations and full-scale airborne or satellite surveys. In this study the two operations of the UAV BVLOS, at an altitude of 350 m ASL, have been successfully performed in Antarctic conditions. Maps of the vegetation of the western shore of Admiralty Bay (King George Island, South Shetlands, Western Antarctic) that included the Antarctic Specially Protected Area No. 128 (ASPA 128) were designed. The vegetation in the 7.5 km2 area was mapped in ultra-high resolution (<5 cm and DEM of 0.25 m GSD), and from the Normalized Difference Vegetation Index (NDVI), four broad vegetation units were extracted: “dense moss carpets” (covering 0.14 km2, 0.8% of ASPA 128), “Sanionia uncinata moss bed” (0.31 km2, 1.7% of ASPA 128), “Deschampsia antarctica grass meadow” (0.24 km2, 1.3% of ASPA 128), and “Deschampsia antarctica–Usnea antarctica heath” (1.66 km2, 9.4% of ASPA 128). Our results demonstrate that the presented UAV BVLOS–based surveys are time-effective (single flight lasting 2.5 h on a distance of 300 km) and cost-effective when compared to classical field-based observations and are less invasive for the ecosystem. Moreover, unmanned airborne vehicles significantly improve security, which is of particular interest in polar region research. Therefore, their development is highly recommended for monitoring areas in remote and fragile environments.

High-resolution mapping of paddy rice fields from unmanned airborne vehicle images using enhanced-TransUnet

Through remote sensing to obtain accurate information on the area of rice fields is of great significance for precision agriculture. Currently, rice extraction is primarily based on multi-temporal but low spatial resolution remote sensing images, which are unsuitable for a wide range of applications in efficient agricultural management and production. Exploring new methods for acquiring very-high processing resolution (VHR) images from Unmanned Aerial Vehicles (UAV) is a viable research avenue. Given that emerging deep learning networks have shown potential in image processing and object detection, this research proposed a deep learning network named Enhanced-TransUnet (ETUnet) for identifying paddy rice fields from VHR images. The developed network utilizes a dilated convolution approach and introduces the Convolutional Block Attention Module (CBAM) to the feature extraction layer in the convolutional neural networks to reduce unnecessary feature extractions by combining the self-attention mechanism in the Transformer. We applied the developed deep-learning network to extract rice fields from UAV images at three different growth stages, including transplanting, tilling, and maturing in Guangzhou city in China. The results demonstrate that ETUnet can accurately extract paddy fields during the phases of transplanting, tillering, and maturing, where the attained F1 scores are 94.87 %, 95.05 %, and 92.95 %, respectively. The attained IoUs are 90.24 %, 90.55 %, and 87.84 %, respectively, and the Kappa coefficients obtained are 93.13 %, 93.07 %, and 90.15 %, respectively. We identified that training samples had a substantial impact on the performance of the deep neural networks. The study revealed that both the timing of image acquisition and the model architecture affected paddy rice mapping using deep learning networks based on UAV data. It provides reference and help for studying the changes of crop phenology.

Cotton Blight Identification with Ground Framed Canopy Photo-Assisted Multispectral UAV Images

Cotton plays an essential role in global human life and economic development. However, diseases such as leaf blight pose a serious threat to cotton production. This study aims to advance the existing approach by identifying cotton blight infection and classifying its severity at a higher accuracy. We selected a cotton field in Shihezi, Xinjiang in China to acquire multispectral images with an unmanned airborne vehicle (UAV); then, fifty-three 50 cm by 50 cm ground framed plots were set with defined coordinates, and a photo of its cotton canopy was taken of each and converted to the L*a*b* color space as either a training or a validation sample; finally, these two kinds of images were processed and combined to establish a cotton blight infection inversion model. Results show that the Red, Rededge, and NIR bands of multispectral UAV images were found to be most sensitive to changes in cotton leaf color caused by blight infection; NDVI and GNDVI were verified to be able to infer cotton blight infection information from the UAV images, of which the model calibration accuracy was 84%. Then, the cotton blight infection status was spatially identified with four severity levels. Finally, a cotton blight inversion model was constructed and validated with ground framed photos to be able to explain about 86% of the total variance. Evidently, multispectral UAV images coupled with ground framed cotton canopy photos can improve cotton blight infection identification accuracy and severity classification, and therefore provide a more reliable approach to effectively monitoring such cotton disease damage.

Mapping Crop Leaf Area Index and Canopy Chlorophyll Content Using UAV Multispectral Imagery: Impacts of Illuminations and Distribution of Input Variables

Leaf area index (LAI) and canopy chlorophyll content (CCC) are important indicators that describe the growth status and nitrogen deficiencies of crops. Several studies have been performed to estimate LAI and CCC using multispectral cameras onboard an unmanned airborne vehicle (UAV) system. However, the impacts of illuminations during UAV flight and problems of how to invert still need more investigation. UAV flights with a multispectral camera were performed under clear (diffuse ratio 0) and cloudy illumination conditions (diffuse ratio 1) over rapeseed, wheat and sunflower (only clear) fields. One-dimension radiative transfer model PROSAIL was run twice to generate a clear-sky model and a cloudy-sky model, respectively. The LAI and CCC of flights under a clear sky were inverted from the clear-sky model, and the flights under cloudy conditions were inverted from both clear-sky and cloudy-sky models to compare the results. Moreover, three Look-Up-Tables (LUT) were built with same input variables but different distributions of LAI. Results showed that LAI from uniform dense LUT had better correspondence with ground measurements for all crops (R2 = 0.51~0.69). The illumination condition had little impact on small to medium LAI (LAI < 5) and CCC. However, the inversion of imageries during cloudy sky conditions from the clear-sky model led to an overestimation of high LAI values.

Recent progress and perspective on batteries made from nuclear waste

Sustainable energy sources are an immediate need to cope with the imminent issue of climate change the world is facing today. In particular, the long-lasting miniatured power sources that can supply energy continually to power handheld gadgets, sensors, electronic devices, unmanned airborne vehicles in space and extreme mining are some of the examples where this is an acute need. It is known from basic physics that radioactive materials decay over few years and some nuclear materials have their half-life until thousands of years. The past five decades of research have been spent harnessing the decay energy of the radioactive materials to develop batteries that can last until the radioactive reaction continues. Thus, an emergent opportunity of industrial symbiosis to make use of nuclear waste by using radioactive waste as raw material to develop batteries with long shelf life presents a great opportunity for sustainable energy resource development. However, the current canon of research on this topic is scarce. This perspective draws fresh discussions on the topic while highlighting future directions in this wealthy arena of research.Graphical abstractA long-lasting miniaturised nuclear battery utilising 14C radioactive isotope as fuel

Identification of tree species based on the fusion of UAV hyperspectral image and LiDAR data in a coniferous and broad-leaved mixed forest in Northeast China.

Rapid and accurate identification of tree species via remote sensing technology has become one of the important means for forest inventory. This paper is to develop an accurate tree species identification framework that integrates unmanned airborne vehicle (UAV)-based hyperspectral image and light detection and ranging (LiDAR) data under the complex condition of natural coniferous and broad-leaved mixed forests. First, the UAV-based hyperspectral image and LiDAR data were obtained from a natural coniferous and broad-leaved mixed forest in the Maoer Mountain area of Northeast China. The preprocessed LiDAR data was segmented using a distance-based point cloud clustering algorithm to obtain the point cloud of individual trees; the hyperspectral image was segmented using the projection outlines of individual tree point clouds to obtain the hyperspectral data of individual trees. Then, different hyperspectral and LiDAR features were extracted, respectively, and the importance of the features was analyzed by a random forest (RF) algorithm in order to select appropriate features for the single-source and multi-source data. Finally, tree species identification in the study area were conducted by using a support vector machine (SVM) algorithm together with hyperspectral features, LiDAR features and fused features, respectively. Results showed that the total accuracy for individual tree segmentation was 84.62%, and the fused features achieved the best accuracy for identification of the tree species (total accuracy = 89.20%), followed by the hyperspectral features (total accuracy = 86.08%) and LiDAR features (total accuracy = 76.42%). The optimal features for tree species identification based on fusion of the hyperspectral and LiDAR data included the vegetation indices that were sensitive to the chlorophyll, anthocyanin and carotene contents in the leaves, the partial components of the transformed independent component analysis (ICA), minimum noise fraction (MNF) and principal component analysis (PCA), and the intensity features of the LiDAR echo, respectively. It was concluded that the framework developed in this study was effective in tree species identification under the complex conditions of natural coniferous and broad-leaved mixed forest and the fusion of UAV-based hyperspectral image and LiDAR data can achieve enhanced accuracy compared the single-source UAV-based remote sensing data.

Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR

Infestations of Tomicus spp. have caused the deaths of millions of Yunnan pine forests in Southwest China; consequently, accurate monitoring methods are required to assess the damage caused by these pest insects at an early stage. Considering the limited sensitivity of optical reflectance on the early stage of beetle stress, the potential of thermal infrared (TIR) can be exploited for monitoring forest health on the basis of the change of canopy surface temperature (CST). However, few studies have investigated the impact of the leaf area index (LAI) on the accuracy of TIR data-based SDR assessments. Therefore, the current study used unmanned airborne vehicle (UAV)-based TIR and light detection and ranging (LiDAR) data to assess the capacity of determining the potential for using TIR data for determining SDR under different LAI conditions. The feasibility of using TIR for monitoring SDRs at the tree level and plot scales were analyzed using the relationship between SDR and canopy temperature. Results revealed that: (1) prediction accuracy of SDR from CST is promising at high LAI values and decreases quickly with LAI, and is higher at the single tree scale (R2 = 0.7890) than at the plot scale (R2 = 0.5532); (2) at either single tree or plot scale, a significant negative correlation can be found between CST and LAI (−0.9121 at tree scale and −0.5902 at plot scale); (3) LAI affects the transmission paths of sunlight and sensor, which mainly disturbs the relationship between CST and SDR. This article evaluated the high possibility of using TIR data to monitor SDRs at both tree and plot levels and assessed the negative impact of a low LAI (<1) on the relationship between temperature and SDR. Accordingly, when measuring forest health using TIR data, additional data sources are required to eliminate the negative impact of low LAIs and to improve the monitoring accuracy.

Adaptive Fault-Tolerant Distributed Formation Control of Clustered Vertical Takeoff and Landing UAVs

This article investigates the formation control issue of clustered vertical takeoff and landing (VTOL) unmanned airborne vehicles (UAVs) subject to undesired perturbations composed by actuator faults, parametric uncertainties, and external disturbances. In particular, a cluster of VTOL UAVs fly together in a given velocity with a well-specified formation configuration. For this purpose, an adaptive fault-tolerant distributed control algorithm via local information interaction is proposed under a hierarchical framework. Specifically, a distributed command force resorting to projection-based adaptation is first synthesized in the outer position loop. Such a command force is of a saturation attribute in order that a boundeddesired thrust and a nonsingular command attitude can be extracted in terms of appropriate control parameters. Next, in the inner attitude loop, a desired torque also with the help of projection-based adaptation is synthesized. Given that the underlying undirected topology is connected, it is shown based on the Lyapunov theory that the proposed adaptive fault-tolerant distributed control algorithm ensures the accomplishment of the concerned formation control objective of the clustered VTOL UAVs in spite of the undesired perturbations. Simulations validate and further highlight the effectiveness of the proposed distributed control algorithm.

Early Detection of Dendroctonus valens Infestation with Machine Learning Algorithms Based on Hyperspectral Reflectance

The red turpentine beetle (Dendroctonus valens LeConte) has caused severe ecological and economic losses since its invasion into China. It gradually spreads northeast, resulting in many Chinese pine (Pinus tabuliformis Carr.) deaths. Early detection of D. valens infestation (i.e., at the green attack stage) is the basis of control measures to prevent its outbreak and spread. This study examined the changes in spectral reflectance after initial attacking of D. valens. We also explored the possibility of detecting early D. valens infestation based on spectral vegetation indices and machine learning algorithms. The spectral reflectance of infested trees was significantly different from healthy trees (p < 0.05), and there was an obvious decrease in the near-infrared region (760–1386 nm; p < 0.01). Spectral vegetation indices were input into three machine learning classifiers; the classification accuracy was 72.5–80%, while the sensitivity was 65–85%. Several spectral vegetation indices (DID, CUR, TBSI, DDn2, D735, SR1, NSMI, RNIR•CRI550 and RVSI) were sensitive indicators for the early detection of D. valens damage. Our results demonstrate that remote sensing technology could be successfully applied to early detect D. valens infestation and clarify the sensitive spectral regions and vegetation indices, which has important implications for early detection based on unmanned airborne vehicle and satellite data.

PREDICTING THE INFRARED UAV IMAGERY OVER THE COAST

Abstract. The infrared (IR) imagery provides additional information to the visible (red-green-blue, RGB) about vegetation, soil, water, mineral, or temperature, and has become essential for various disciplines, such as geology, hydrology, ecology, archeology, meteorology or geography. The integration of the IR sensors, ranging from near-IR (NIR) to thermal-IR through mid-IR, constitutes a baseline for Earth Observation satellites but not for unmanned airborne vehicles (UAV). Given the hyperspatial and hypertemporal characteristics associated with the UAV survey, it is relevant to benefit from the IR waveband in addition to the visible imagery for mapping purposes. This paper proposes to predict the NIR reflectance from RGB digital number predictors collected with a consumer-grade UAV over a structurally and compositionally complex coastal area. An array of 15 000 data, distributed into calibration, validation and test datasets across 15 representative coastal habitats, was used to build and compare the performance of the standard least squares, decision tree, boosted tree, bootstrap forest and fully connected neural network (NN) models. The NN family surpassed the four other ones, and the best NN model (R2 = 0.67) integrated two hidden layers provided, each, with five nodes of hyperbolic tangent and five nodes of Gaussian activation functions. This perceptron enabled to produce a NIR reflectance spatially-explicit model deprived of original artifacts due to the flight constraints. At the habitat scale, sedimentary and dry vegetation environments were satisfactorily predicted (R2 > 0.6), contrary to the healthy vegetation (R2 < 0.2). Those innovative findings will be useful for scientists and managers tasked with hyperspatial and hypertemporal mapping.

Visually smooth multi-UAV formation transformation

Unmanned airborne vehicles (UAVs) are useful in both military and civilian operations. In this paper, we consider a recreational scenario, i.e., multi-UAV formation transformation show. A visually smooth transformation needs to enforce the following three requirements at the same time: (1) visually pleasing contour morphing - for any intermediate frame, the agents form a meaningful shape and align with the contour, (2) uniform placement - for any intermediate frame, the agents are (isotropically) evenly spaced, and (3) smooth trajectories - the trajectory of each agent is as rigid/smooth as possible and completely collision free. First, we use the technique of 2-Wasserstein distance based interpolation to generate a sequence of intermediate shape contours. Second, we consider the spatio-temporal motion of all the agents altogether, and integrate the uniformity requirement and the spatial coherence into one objective function. Finally, the optimal formation transformation plan can be inferred by collaborative optimization.Extensive experimental results show that our algorithm outperforms the existing algorithms in terms of visual smoothness of transformation, boundary alignment, uniformity of agents, and rigidity of trajectories. Furthermore, our algorithm is able to cope with some challenging scenarios including (1) source/target shapes with multiple connected components, (2) source/target shapes with different typology structures, and (3) existence of obstacles. Therefore, it has a great potential in the real multi-UAV light show. We created an animation to demonstrate how our algorithm works; See the demo at https://1drv.ms/v/s!AheMg5fKdtdugVL0aNFfEt_deTbT?e=le5poN .

Early detection of pine wilt disease in Pinus tabuliformis in North China using a field portable spectrometer and UAV-based hyperspectral imagery

BackgroundPine wilt disease (PWD) is a major ecological concern in China that has caused severe damage to millions of Chinese pines (Pinus tabulaeformis). To control the spread of PWD, it is necessary to develop an effective approach to detect its presence in the early stage of infection. One potential solution is the use of Unmanned Airborne Vehicle (UAV) based hyperspectral images (HIs). UAV-based HIs have high spatial and spectral resolution and can gather data rapidly, potentially enabling the effective monitoring of large forests. Despite this, few studies examine the feasibility of HI data use in assessing the stage and severity of PWD infection in Chinese pine.MethodTo fill this gap, we used a Random Forest (RF) algorithm to estimate the stage of PWD infection of trees sampled using UAV-based HI data and ground-based data (data directly collected from trees in the field). We compared relative accuracy of each of these data collection methods. We built our RF model using vegetation indices (VIs), red edge parameters (REPs), moisture indices (MIs), and their combination.ResultsWe report several key results. For ground data, the model that combined all parameters (OA: 80.17%, Kappa: 0.73) performed better than VIs (OA: 75.21%, Kappa: 0.66), REPs (OA: 79.34%, Kappa: 0.67), and MIs (OA: 74.38%, Kappa: 0.65) in predicting the PWD stage of individual pine tree infection. REPs had the highest accuracy (OA: 80.33%, Kappa: 0.58) in distinguishing trees at the early stage of PWD from healthy trees. UAV-based HI data yielded similar results: the model combined VIs, REPs and MIs (OA: 74.38%, Kappa: 0.66) exhibited the highest accuracy in estimating the PWD stage of sampled trees, and REPs performed best in distinguishing healthy trees from trees at early stage of PWD (OA: 71.67%, Kappa: 0.40).ConclusionOverall, our results confirm the validity of using HI data to identify pine trees infected with PWD in its early stage, although its accuracy must be improved before widespread use is practical. We also show UAV-based data PWD classifications are less accurate but comparable to those of ground-based data. We believe that these results can be used to improve preventative measures in the control of PWD.

Multifield acquisition technology for a narrow beacon laser on the non-orbit determination platform.

The application of free space optical communications technology between unmanned airborne vehicles (UAVs) can significantly improve its communications rate and capacity. However, it will produce strong disturbance due to the change of external conditions when the UAV is flying, which will affect the acquisition probability of a light beacon. The wide beacon can generally increase the acquisition range and acquisition probability, but there are disadvantages of increased weight and energy consumption. We report on an advanced method to improve the performance of narrow beacon spatial acquisition under the condition of UAV drift. The acquisition probability model has been established to study narrow beacon spatial acquisition considering UAV drift. A multiscanning method is proposed to improve the acquisition probability instead of simply increasing the scanning area to overcome severe UAV drift, which is also proven by experimental validation. Moreover, through the control method of active disturbance rejection control (ADRC), the pointing accuracy of multifield fast scanning is improved under the condition of external interference. This technique is extremely useful to develop a smaller, lighter terminal for UAV optical communications in the future.

First Evidence of Peat Domes in the Congo Basin using LiDAR from a Fixed-Wing Drone

The world’s most extensive tropical peatlands occur in the Cuvette Centrale depression in the Congo Basin, which stores 30.6 petagrams of carbon (95% CI, 6.3–46.8). Improving our understanding of the genesis, development and functioning of these under-studied peatlands requires knowledge of their topography and, in particular, whether the peat surface is domed, as this implies a rain-fed system. Here we use a laser altimeter mounted on an unmanned airborne vehicle (UAV) to measure peat surface elevation along two transects at the edges of a peatland, in the northern Republic of Congo, to centimetre accuracy and compare the results with an analysis of nearby satellite LiDAR data (ICESat and ICESat-2). The LiDAR elevations on both transects show an upward slope from the peatland edge, suggesting a surface elevation peak of around 1.8 m over ~20 km. While modest, this domed shape is consistent with the peatland being rainfed. In-situ peat depth measurements and our LiDAR results indicate that this peatland likely formed at least 10,000 years BP in a large shallow basin ~40 km wide and ~3 m deep.

Towards Better Mapping of Seagrass Meadows using UAV Multispectral and Topographic Data

James, D.; Collin, A.; Houet, T.; Mury, A.; Gloria, H., and Le Poulain, N., 2020. Towards better mapping of seagrass meadows using UAV multispectral and topographic data. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1117-1121. Coconut Creek (Florida), ISSN 0749-0208Seagrasses are exposed to global changing such as ocean warming and acidification, sedimentation, eutrophication and direct degradation. Seagrass meadows are traditionally monitored with visible imagery in red-green-blue (RGB). To date, the acquisition methodology dedicated to coastal habitats ranges from satellite to SoNAR through LiDAR sensors. This research proposes to evaluate the contribution of unmanned airborne vehicle (UAV) multispectral bands (red edge - RE -, and near-infrared - NIR) and digital surface model (DSM) to the habitat classification based on the traditional red-green-blue (RGB) dataset. Surveyed by a fixed-wing UAV (eBee+ provided with the Sequoia and S.O.D.A. sensors), five classes were studied: water, Zostera marina L. eelgrass, boat, sediment, and algae. Supported by the maximum likelihood algorithm, the classifications showed a very satisfactory performance for the RGB benchmark (93.64%), and gains when the spectral bands and DSM were added separately (RE+NIR contributions: +4.46%, DSM contribution: +4.78%) and jointly (RE+NIR+DSM contributions: +5.84%). The contributions of the spectral and topographic data were further discussed at class level, with an emphasis on the boat-driven seagrass fragmentation (15% of the meadow).