Unmanned Airborne Research Articles

Surface Water Extent Mapping in Denmark: Comparing Airborne Thermal Imagery and Satellite Earth Observation

Mapping and prediction of inundated areas are increasingly important for climate change adaptation and emergency preparedness. Flood forecasting tools and flood risk models have to be compared to observe flooding patterns for training, calibration, validation, and benchmarking. At the regional to continental scales, satellite earth observation (EO) is the established method for surface water extent (SWE) mapping, and several operational global-scale data products are available. However, the spatial resolution of satellite-derived SWE maps remains a limiting factor, especially in low-lying areas with complex hydrography, such as Denmark. We collected thermal imagery using an unmanned airborne system (UAS) for three areas in Denmark shortly after major flooding events. We combined the thermal imagery with an airborne lidar-derived high-resolution digital surface model of the country to retrieve high-resolution (40 cm) SWE maps. The resulting SWE maps were compared with low-resolution SWE maps derived from satellite earth observation and with potential flooded areas derived from the high-resolution digital elevation model. We conclude that UASs have significant potential for SWE mapping at intermediate scales (up to a few square kilometers), can bridge the scale gap between ground observations and satellite EO, and can be used to benchmark and validate SWE mapping products derived from satellite EO as well as models predicting inundation.

Evaluation of Neural Network Verification Methods for Air-to-Air Collision Avoidance

Neural network approximations have become attractive to compress data for automation and autonomy algorithms for use on storage-limited and processing-limited aerospace hardware. However, unless these neural network approximations can be exhaustively verified to be safe, they cannot be certified for use on aircraft. An example of such systems is the unmanned Airborne Collision Avoidance System (ACAS) Xu, which is a very popular benchmark for open-loop neural network control system verification tools. This paper proposes a new closed-loop extension of this benchmark, which consists of a set of 10 closed-loop properties selected to evaluate the safety of an ownship aircraft in the presence of a co-altitude intruder aircraft. These closed-loop safety properties are used to evaluate five of the 45 neural networks that comprise the ACAS Xu benchmark (corresponding to co-altitude cases) as well as the switching logic between the five neural networks. The combination of nonlinear dynamics and switching between five neural networks is a challenging verification task accomplished with star-set reachability methods in two verification tools. The safety of the ownship aircraft under initial position uncertainty is guaranteed in every scenario proposed.

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.

Retrieving Water Quality Parameters from Noisy-Label Data Based on Instance Selection

As an important part of the "air–ground" integrated water quality monitoring system, the inversion of water quality from unmanned airborne hyperspectral image has attracted more and more attention. Meanwhile, unmanned aerial vehicles (UAVs) have the characteristics of small size, flexibility and quick response, and can complete the task of water environment detection in a large area, thus avoiding the difficulty in obtaining satellite data and the limitation of single-point monitoring by ground stations. Most researchers use UAV for water quality monitoring, they take water samples back to library or directly use portable sensors for measurement while flying drones at the same time. Due to the UAV speed and route planning, the actual sampling time and the UAV passing time cannot be guaranteed to be completely synchronized, and there will be a difference of a few minutes. For water quality parameters such as chromaticity (chroma), chlorophyll-a (chl-a), chemical oxygen demand (COD), etc., the changes in a few minutes are small and negligible. However, for the turbidity, especially in flowing water body, this value of it will change within a certain range. This phenomenon will lead to noise error in the measured suspended matter or turbidity, which will affect the performance of regression model and retrieval accuracy. In this study, to solve the quality problem of label data in a flowing water body, an unmanned airborne hyperspectral water quality retrieval experiment was carried out in the Xiao River in Xi’an, China, which verified the rationality and effectiveness of label denoising analysis of different water quality parameters. To identify noisy label instances efficiently, we proposed an instance selection scheme. Furthermore, considering the limitation of the dataset samples and the characteristic of regression task, we build a 1DCNN model combining a self attention mechanism (SAM) and the network achieves the best retrieving performance on turbidity and chroma data. The experiment results show that, for flowing water body, the noisy-label instance selection method can improve retrieval performance slightly on the COD parameter, but improve greatly on turbidity and chroma data.

Unmanned airborne miniaturized pulsed CO2 laser with wavelength automatic tuning

This paper presents a miniaturized mechanically Q-switched pulsed CO2 laser with wavelength automatic tuning, which could be used in unmanned airborne lidar system. A three-mirror cavity structure with real focus in the cavity is designed to provide a stable resonator, which has short beam transit time for mechanically Q-switched laser. The matching relationship between the beam in the resonant cavity and the aperture of the optical chopper is experimentally studied, the CO2 laser pulse output with good waveform is obtained, and the wavelength automatic tuning is realized. The CO2 laser produces a maximum output peak power of 3.7 kW at 10.59 μm for pulse width of 350 ns, corresponding beam quality M2 of 1.75, and the laser weight is about 18 kg.

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.

Remote Sensing Assessment of the Expansion of Ips typographus Attacks in the Chuprene Reserve, Western Balkan Range

The Chuprene Reserve was created in 1973 to preserve the natural coniferous forests in the Western Balkan Range in Bulgaria. The first infestations by European spruce bark beetle (Ips typographus) were registered in Norway spruce (Picea abies) stands in the mid-1980s. The aim of this study is to assess the damages caused by I. typographus in the Chuprene Reserve using remote sensing techniques – unmanned aerial vehicle (UAV) images, airborne images, and satellite images of European Space Imaging (EUSI), combined with terrestrial verification. High-resolution images in four bands of the electromagnetic spectrum and in a standard RGB channel were taken in 2017 via a multispectral camera ‘Parrot Sequoia’, integrated with a specialized professional UAV system eBee ‘Flying Wing’. The health status of Norway spruce stands in the reserve was assessed with the normalized difference vegetation index, based on the digital mixing of imagery captured in the red and near infrared range. The dynamic of bark beetle attacks was studied in GIS on the basis of maps generated from photographic surveys, airborne images taken in 2011 and 2015, and satellite images from 2020. In the UAV-captured area (314.0 ha), the size of Norway spruce stands attacked by I. typographus increased from 7.6 ha (2.4%) in 2011 to 44.9 ha (14.3%) in 2020. The satellite images showed that on the entire territory of the Chuprene Reserve (1451.9 ha), I. typographus killed spruce trees on 137.4 ha, which is 9.6% of the total area.

Bidirectional Reflectance Distribution Function Modeling Considerations in Small Unmanned Multispectral Systems

With the rapid advancement of miniaturized sensors and unmanned airborne system (UAS) platforms, scientific measurements that previously were carried out by large government or academic institution programs can now be successfully accomplished by smaller collection activities. In the case of vegetation assessment (e.g., forestry and agriculture), spatial, temporal, radiometric, and spectral resolution can be rigorously controlled. Although each of these is important, accurate temporal assessment of chemistry and morphological plant attributes is heavily dependent on spectral resolution and correction. A particular phenomenology of interest is the extraction and modeling of bidirectional reflectance distribution function (BRDF) data. In this effort, an approach is discussed to reflectance calibrate and derive BRDF signatures from soybeans through a newly available multispectral sensor integrated to an industry common UAS platform. Methods were developed to extract the reflectance data across the azimuth and elevation observations and fit these field data to previously derived models from the literature. A modular processing pipeline was developed to allow for the implementation of additional algorithms and efficient numerical analysis. Results show that the coefficients derived to fit the modeled BRDF data are consistent across spatial resolution and within cover type. Additionally, the modeled-fit root-mean-square error was inversely proportional to the spatial resolution of the image data used for signature extraction. In conjunction with more traditional spectral and ratio-based analytical indices, this approach provides important dimensionality in both classification and land-cover assessment applications critical to more accurate temporal vegetation assessment.

Analysis of designs used in monitoring crop growth based on remote sensing methods

Choosing appropriate designs and methods for monitoring crop growth is a challenging process of major importance. Remote sensing from space and manned or unmanned airborne operations are used to measure crop reflectance and a wide variety of other agricultural parameters. While some experiments use only a few, specific methods and designs and organize the results in lists of evidence, other experiments use a wider range of techniques to create a more credible and comprehensive assessment of crop yield. Particular situations related to the available resources in terms of data collection and expertise in addition to the intended use of the results may require specific designs or a combination of methods and design. This review intended to explore the challenges and document a range of possible approaches for remote evaluation of crop growth. The scope of the analysis was designed to provide information on methods that work under different circumstances and why and how effective a particular method is an approach to monitoring crop growth. Considering the agricultural ecosystems as complex systems and the working methodology as having a high degree of complexity, we propose an approach suitable for complex systems. New sets of models and methods need to be developed for approaching complex systems, which are characterized by self-organization, nonlinearity, ongoing adaptation, and networking.

Calculation of the power required to operate a tethered unmanned airborne platform in gust conditions

This work aims to assess the necessary power for a tethered high-altitude unmanned airborne platform, in which the propulsion system and payload are powered from a ground-based cable power source. A summary and solution to a system of differential equations are given to describe the spatial position of the cable and its action on the high-altitude unmanned platform in gust conditions. The mass of the airborne platform including payload, the effect of the cable on the airborne platform, and its wind resistance are taken into account when calculating the power. The paper describes the system architecture, consisting of a ground station, a cable, and an airborne platform, and presents the results of field tests, which showed good agreement with the results of theoretical studies.

Conference Preview: 2021 Summit on Drone Geophysics set for 2 to 5 November

Postconvention workshops focused on drones, unmanned airborne systems (UASs), and unmanned aerial vehicles (UAVs) have occurred previously at the SEG Annual Meeting. In 2017, the SEG Near-Surface Geophysics Technical Section conducted the first such event called Drones Applied to Geophysical Mapping. This first event ushered in the topic for future Annual Meetings. In 2018, the postconvention event was called Advances in Unmanned Airborne Systems Geophysics. Both events were hugely successful and helped set the stage for the standalone reoccurring virtual Summit on Drone Geophysics.

Simultaneous line shift and source parameter inversion applied to a scalar magnetic survey for small unexploded ordnance

ABSTRACTContinuing advances within unmanned aerial vehicles and scalar magnetic sensor technology are revolutionizing geophysical remote sensing. For perspective, examples of high resolution, low noise data presented in this study enabled detection and accurate positioning of human‐made ferrous objects with a total mass of 0.5 kg, from altitudes around and above 2 m. However, with these new possibilities come a requirement of higher fidelity, where good sensor position estimates, in particular, are key to achieving a data quality sufficient for reliable data interpretation and geophysical inversion. Apart from inherent instrument properties, typical factors that hamper sensor position estimation are lag and parallax errors. Lag errors arise from time‐stamping delays between the positioning system and sensor reading, while parallax errors stem from the imperfect recovery of sensor positions due to a decoupling and/or distancing of the positioning system and sensor head. The impact from each of these error sources is typically similar and partially or even completely inseparable. A number of correctional techniques exist, typically based on large‐scale data correlations, or collection of specific additional data. We present a survey data adjustment method based on the simultaneous inversion of source parameters and a number of independent survey shifts or lags in pre‐specified sections of a survey. The method is particularly suited for surveys where the sensor path through space can be assumed well known, but the sensor position itself may suffer lags or shifts relative to the position measurement. The approach is based on modelling an existing source signature within the data and requires no data transformations, such as continuation to a two‐dimensional plane, or enforcing of smoothness. The approach is an amendment to independent source inversion and is ideal for scenarios where the source behaviour is thought to be well known, such as processing efforts in which the objective is source–parameter inversion. In this study, we validate the approach using a simple point‐dipole model to adjust synthetic data and subsequently demonstrate it on an unmanned airborne scalar magnetic survey for small unexploded ordnance targets, where a hardware malfunction resulted in unknown time‐shifts between magnetic sensor readings and positioning unit output. The adjustment procedure was carried out on line data, based on a single anomaly from a 155 mm shell, and enabled the recovery of signatures from several other ferrous unexploded ordnance targets as small as 0.5 kg. Subsequent inversion on the corrected signatures provided unexploded ordnance positions with an accuracy suitable for recovery purposes.

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.

Impact of Tropical Cyclones on Inhabited Areas of the SWIO Basin at Present and Future Horizons. Part 1: Overview and Observing Component of the Research Project RENOVRISK-CYCLONE

The international research program “ReNovRisk-CYCLONE” (RNR-CYC, 2017–2021) directly involves 20 partners from 5 countries of the south-west Indian-Ocean. It aims at improving the observation and modelling of tropical cyclones in the south-west Indian Ocean, as well as to foster regional cooperation and improve public policies adapted to present and future tropical cyclones risk in this cyclonic basin. This paper describes the structure and main objectives of this ambitious research project, with emphasis on its observing components, which allowed integrating numbers of innovative atmospheric and oceanic observations (sea-turtle borne and seismic data, unmanned airborne system, ocean gliders), as well as combining standard and original methods (radiosoundings and global navigation satellite system (GNSS) atmospheric soundings, seismic and in-situ swell sampling, drone and satellite imaging) to support research on tropical cyclones from the local to the basin-scale.

Fusion of crown and trunk detections from airborne UAS based laser scanning for small area forest inventories

High density point clouds from unmanned airborne laser scanning (UALS) systems have great potential for small area forest inventories. We propose an UALS-based tree level inventory method that takes advantage of both the segmented crowns and segmented trunks: hybrid tree detection (HTD). The method is tested at twenty 30 m × 30 m validation plots of varying maturity, tree species distribution and stocking density. With the traditional individual tree crown delineation (ITC) approach, tree attributes are only predicted for crown segmented trees. Here, we assume that a segmented crown can contain more than one tree. Our idea is to identify segmented crowns that contain more than one segmented trunk. One of the trunks is linked to a segmented crown (upper most tree) and the remainders are treated as understory trunks. Heights of the crown segmented trees and the diameters of the understory trunks are used as predictor variables in nonlinear mixed-effects models of tree volume. The %RMSE and %MD values of volume predictions at the 30 m × 30 m validation plot level were 22.2%, −13.3% and 18.8%, −8.3% for ITC and HTD, respectively. We conclude that the proposed HTD approach improves the accuracy of ITC in managed boreal forests when using UALS data.

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.