Unmanned Airborne Vehicles Research Articles

Fire Blight Monitoring in Pear Orchards by Unmanned Airborne Vehicles (UAV) Systems Carrying Spectral Sensors

Controlling fire blight in pear production areas depends strongly on regular visual inspections of pome fruit orchards, nurseries and other hosts of Erwinia amylovora. In addition, these inspections play an essential role in delineating fire blight free production areas, which has important implications for fruit export. However, visual monitoring is labor intensive and time consuming. As a potential alternative, the performance of spectral sensors on unmanned airborne vehicles (UAV) or drones was evaluated, since this allows the monitoring of larger areas compared to the current field inspections. Unlike more traditional remote sensing platforms such as manned aircrafts and satellites, UAVs offer a higher flexibility and an extremely high level of detail. In this project, a UAV platform carrying a hyperspectral COSI-cam camera was used to map a heavily infected pear orchard. The hyperspectral data were used to assess which wavebands contain information on fire blight infections. In this study, wavelengths 611 nm and 784 nm were found appropriate to detect symptoms associated with fire blight. Vegetation indices that allow to discriminate between healthy and infected trees were identified, too. This manuscript highlights the potential use of the UAV methodology in fire blight detection and remaining difficulties that still need to be overcome for the technique to become fully operational in practice.

Under-canopy UAV laser scanning for accurate forest field measurements

Surveying and robotic technologies are converging, offering great potential for robotic-assisted data collection and support for labour intensive surveying activities. From a forest monitoring perspective, there are several technological and operational aspects to address concerning under-canopy flying unmanned airborne vehicles (UAV). To demonstrate this emerging technology, we investigated tree detection and stem curve estimation using laser scanning data obtained with an under-canopy flying UAV. To this end, we mounted a Kaarta Stencil-1 laser scanner with an integrated simultaneous localization and mapping (SLAM) system on board an UAV that was manually piloted with the help of video goggles receiving a live video feed from the onboard camera of the UAV. Using the under-canopy flying UAV, we collected SLAM-corrected point cloud data in a boreal forest on two 32 m × 32 m test sites that were characterized as sparse (n = 42 trees) and obstructed (n = 43 trees), respectively. Novel data processing algorithms were applied for the point clouds in order to detect the stems of individual trees and to extract their stem curves and diameters at breast height (DBH). The estimated tree attributes were compared against highly accurate field reference data that was acquired semi-manually with a multi-scan terrestrial laser scanner (TLS). The proposed method succeeded in detecting 93% of the stems in the sparse plot and 84% of the stems in the obstructed plot. In the sparse plot, the DBH and stem curve estimates had a root-mean-squared error (RMSE) of 0.60 cm (2.2%) and 1.2 cm (5.0%), respectively, whereas the corresponding values for the obstructed plot were 0.92 cm (3.1%) and 1.4 cm (5.2%). By combining the stem curves extracted from the under-canopy UAV laser scanning data with tree heights derived from above-canopy UAV laser scanning data, we computed stem volumes for the detected trees with a relative RMSE of 10.1% in both plots. Thus, the combination of under-canopy and above-canopy UAV laser scanning allowed us to extract the stem volumes with an accuracy comparable to the past best studies based on TLS in boreal forest conditions. Since the stems of several spruces located on the test sites suffered from severe occlusion and could not be detected with the stem-based method, we developed a separate work flow capable of detecting trees with occluded stems. The proposed work flow enabled us to detect 98% of trees in the sparse plot and 93% of the trees in the obstructed plot with a 100% correction level in both plots. A key benefit provided by the under-canopy UAV laser scanner is the short period of time required for data collection, currently demonstrated to be much faster than the time required for field measurements and TLS. The quality of the measurements acquired with the under-canopy flying UAV combined with the demonstrated efficiency indicates operational potential for supporting fast and accurate forest resource inventories.

High-Throughput Prediction of Whole Season Green Area Index in Winter Wheat With an Airborne Multispectral Sensor.

IntroductionIn recent decades, the interest has grown to quantify the green area index as one of the key characteristics of crop canopies (e.g. for modelling transpiration, light interception, growth). The approach of estimating green area index based on multispectral reflection data from unmanned airborne vehicles with lightweight sensors might have the potential to deliver data with sufficient accuracy and high throughput during the whole season.Materials and MethodsWe therefore examined the applicability of a recently launched drone-based multispectral system (Sequoia, Parrot) for the prediction of whole season green area index in winter wheat, with data from field trials in Northern Germany (2017, 2018 and 2019). The explanatory power of different modeling approaches to predict green area index based on multispectral data was tested: linear and non-linear regression models, multivariate techniques, and machine learning algorithms. Further, different predictors were implemented in these models: multispectral data as raw bands and as ratios. Additionally, a new approach for the evaluation of green area index predictions during senescence is introduced. It is shown that a robust calibration during growth phase is applicable during senescence as well.Results and DiscussionA linear model which includes all four wavebands provided by the sensor in three ratios (VIQUO) and a Support Vector Machine (SVM) algorithm allow a reliable and sufficiently accurate whole season prediction. The VIQUO-model is recommended as the best model, as it is precise but still relatively simple, thus easier to communicate and to apply than the SVM. The integrated values of predicted green area indices in an independent trial are highly correlated with their final biomass (R2: VIQUO = 0.84, SVM = 0.85) which represents the process of radiation interception, one of the determining factors of growths. This is an indicator for both, a robust model calibration and a high potential of the tested multispectral system for agricultural research and crop management.

Modeling and Simulation of a Robotic Bridge Inspection System

Inspection and preservation of the aging bridges to extend their service life has been recognized as one of the important tasks of the State Departments of Transportation. Yet manual inspection procedure is not efficient to determine the safety status of the bridges in order to facilitate the implementation of appropriate maintenance. In this paper, a complex model involving a remotely controlled robotic platform is proposed to inspect the safety status of the bridges which will eliminate labor-intensive inspection. Mobile cameras from unmanned airborne vehicles (UAV) are used to collect bridge inspection data in order to record the periodic changes of bridge components. All the UAVs are controlled via a control station and continuously feed image data to a deep learning-based detection algorithm to analyze the data to detect critical structural components. A cellular automata-based pattern recognition algorithm is used to find the pattern of structural damage. A simulation model is developed to validate the proposed method by knowing the frequency and time required for each task involved in bridge inspection and maintenance. The effectiveness of the model is demonstrated by simulating the bridge inspection and maintenance with the proposed model for five years in AnyLogic. The simulated result shows around 80% of man-hour can be saved with the proposed approach.

Thermal data analysis for urban climate research: A case study of Olomouc, Czechia

Due to the recent advancements in thermal camera technology, it is easier than ever to acquire information about surface temperature in very-high or extreme spatial resolution from manned or unmanned airborne vehicles. The main objective of the paper is to investigate the possibilities of extreme resolution thermal data for urban climate research. The main dataset used for the work is thermal mosaic acquired by an aeroplane above the city of Olomouc in the morning and afternoon on 10th July 2016. The spatial resolution of the dataset fluctuates between 90 and 100 cm per pixel edge. Auxiliary data used for the analysis included open data from European programmes Urban Atlas and CORINE Land Cover. For further analysis, building height information, material type and Local Climate Zones classification were used. The results show a possible comparison in various neighbourhoods and material types while maintaining reference units – Urban Atlas/CORINE Land Cover classes or Local Climate Zones. Verticality analysis shows a significant difference between ground and rooftop levels that need to be taken into account when investigating heat for quality of life. All the analyses confirmed knowledge about the difference between natural and artificial surfaces that most artificial surfaces tend to heat up much more during daytime and fail to cool down completely during the night-time. Moreover, this research lays the foundation to future urban climate research based on extreme resolution thermal images over multiple areas or during long-lasting campaigns.

Detection of Pine Shoot Beetle (PSB) Stress on Pine Forests at Individual Tree Level using UAV-Based Hyperspectral Imagery and Lidar

In recent years, the outbreak of the pine shoot beetle (PSB), Tomicus spp., has caused serious shoots damage and the death of millions of trees in Yunnan pine forests in southwestern China. It is urgent to develop a convincing approach to accurately assess the shoot damage ratio (SDR) for monitoring the PSB insects at an early stage. Unmanned airborne vehicles (UAV)-based sensors, including hyperspectral imaging (HI) and lidar, have very high spatial and spectral resolutions, which are very useful to detect forest health. However, very few studies have utilized HI and lidar data to estimate SDRs and compare the predictive power for mapping PSB damage at the individual tree level. Additionally, the data fusion of HI and lidar may improve the detection accuracy, but it has not been well studied. In this study, UAV-based HI and lidar data were fused to detect PSB. We systematically evaluated the potential of a hyperspectral approach (only-HI data), a lidar approach (only-lidar data), and a combined approach (HI plus lidar data) to characterize PSB damage of individual trees using the Random Forest (RF) algorithm, separately. The most innovative point is the proposed new method to extract the three dimensional (3D) shadow distribution of each tree crown based on a lidar point cloud and the 3D radiative transfer model RAPID. The results show that: (1) for the accuracy of estimating the SDR of individual trees, the lidar approach (R2 = 0.69, RMSE = 12.28%) performed better than hyperspectral approach (R2 = 0.67, RMSE = 15.87%), and in addition, it was useful to detect dead trees with an accuracy of 70%; (2) the combined approach has the highest accuracy (R2 = 0.83, RMSE = 9.93%) for mapping PSB damage degrees; and (3) when combining HI and lidar data to predict SDRs, two variables have the most contributions, which are the leaf chlorophyll content (Cab) derived from hyperspectral data and the return intensity of the top of shaded crown (Int_Shd_top) from lidar metrics. This study confirms the high possibility to accurately predict SDRs at individual tree level if combining HI and lidar data. The 3D radiative transfer model can determine the 3D crown shadows from lidar, which is a key information to combine HI and lidar. Therefore, our study provided a guidance to combine the advantages of hyperspectral and lidar data to accurately measure the health of individual trees, enabling us to prioritize areas for forest health promotion. This method may also be used for other 3D land surfaces, like urban areas.

Sub-Daily Temperature Heterogeneity in a Side Channel and the Influence on Habitat Suitability of Freshwater Fish

Rising surface water temperatures in fluvial systems increasingly affect biodiversity negatively in riverine ecosystems, and a more frequent exceedance of thermal tolerance levels of species is expected to impoverish local species assemblages. Reliable prediction of the effect of increasing water temperature on habitat suitability requires detailed temperature measurements over time. We assessed (1) the accuracy of high-resolution images of water temperature of a side channel in a river floodplain acquired using a consumer-grade thermal camera mounted on an unmanned airborne vehicle (UAV), and (2) the associated habitat suitability for native and alien fish assemblages. Water surface temperatures were mapped four times throughout a hot summer day and calibrated with 24 in-situ temperature loggers in the water at 0.1 m below the surface using linear regression. The calibrated thermal imagery was used to calculate the potentially occurring fraction (POF) of freshwater fish using species sensitivity distributions. We found high temperatures (25–30 °C) in the side channel during mid-day resulting in reduced habitat suitability. The accuracy of water temperature estimates based on the RMSE was 0.53 °C over all flights (R2 = 0.94). Average daily POF was 0.51 and 0.64 for native and alien fish species in the side channel. The error of the POF estimates is 76% lower when water temperature is estimated with thermal UAV imagery compared to temperatures measured at an upstream gauging station. Accurately quantifying water temperature and the heterogeneity thereof is a critical step in adaptation of riverine ecosystems to climate change. Our results show that measurements of surface water temperature can be made accurately and easily using thermal imagery from UAVs allowing for an improved habitat management, but coincident collection of long wave radiation is needed for a more physically-based prediction of water temperature. Because of climate change, management of riverine ecosystems should consider thermal pollution control and facilitate cold water refugia and connectivity between waterbodies in floodplains and the cooler main channel for fish migration during extremely hot summer periods.

Remote sensing, GIS, the geospatial technologies, and Unmanned Airborne Vehicles at Aberdeen University

ABSTRACTAberdeen University has a long history of using aerial photography, photogrammetry, cartography, remote sensing and surveying in Geography teaching and research. The development of an MSc in Environmental Remote Sensing (ERS) in 1987 offered students a new opportunity to specialise further in this rapidly evolving area of technology. The programme has continued to thrive and the MSc in GIS celebrated its 30th Anniversary in 2017. Recently the degree has included courses on current applications of GIS and web-GIS. The introduction of UAV (Unmanned Aerial Vehicle) remote sensing broadened the appeal and competitiveness of the degree, ensuring students have access to cutting edge remote sensing technology. Alongside PhD studies, applied academic research in the Aberdeen Institute for Coastal Science and Management (AICSM), and the Unmanned Airborne Vehicle (UAV)/Unmanned Airborne System (UAS) Centre for Environmental Monitoring and Mapping (UCEMM), make widespread use of GIS and remote sensing. As the geospatial technologies continue to evolve and are integrated with drones the role of education, training and research utilising these technologies will become integral to the Geography degree. Geospatial technologies have and will continue to provide valuable tools and techniques for graduates.

Coastal and marine studies in the department of geography and environment at the University of Aberdeen

ABSTRACTThe Department of Geography and Environment at the University of Aberdeen has a long history of coastal and marine teaching and research from the 1960s to the present day. Beginning with the work of Walton and latterly Ritchie, the paper traces the coastal and marine studies in the Geography degree programme, the development of the Marine and Coastal Resource Management degree, and the introduction of more recent courses such as Managing Our Coasts and Seas, Hydrography, and Land and Marine Conservation. Establishment of the Centre for Marine and Coastal Zone Management (CMCZM), and later the Aberdeen Institute for Coastal Science and Management (AICSM), provided a clear focus for continuing academic research into integrated coastal management and marine spatial planning. This complemented long-term involvement with Shell, SOTEAG (Shetland Oil Terminal Environmental Advisory Group), the Association for Geographic Information (AGI), OceanLab, the EUCC, and several Scottish coastal fora. The creation of the Unmanned Airborne Vehicle (UAV)/Unmanned Airborne System (UAS) Centre for Environmental Monitoring and Mapping (UCEMM) in 2014 brought together the role of remote sensing, GIS and photogrammetry in monitoring, mapping and modelling the coastal environment.

Image Tracking of Rotating Scene Based on Fusion of MEMS-IMU Data and Vision Data

Image tracking is a research field in constant evolution due to the new technological advances. The limited-space and low-cost features of missiles restrict the application and development of image tracking. Missile-borne image guidance system is still facing a series of difficult issues of anti-interference. This paper constructs a practical image tracking model using Kalman Filter based on fusion of MEMS-IMU Data and Vision Data after deriving the transformation matrix between the image sequences and MEMS-IMU data. The tracking model is tested by experiments. The result shows tracking based on fusion of MEMS-IMU Data and Vision Data is an effective solution of rotating scene and tracking accuracy is about 4 times better than vision only. The achievements can be not only used in military field such as Precision Guided Weapons and Photoelectric Reconnaissance Device, but also broadly applied in commercial products such as Virtual Reality, Intelligent Robot, Unmanned Airborne Vehicle and so on.

Unmanned Vehicles and Safety in the Context of EU and Slovak Legislation

The study which follows on findings from the scientific-research task named “Analysis of the use of unmanned airborne vehicles in selected Police Force services” conducted by the Academy of the Police Force in Bratislava in cooperation with the Police Force Headquarters focuses on theoretical and application contexts of selected issues in regulation of unmanned vehicles within EU. The author closely examines the newly adopted drone legislation which affects the Slovak regulation. The author points out to EU regulation, drawing attention to her own findings determined by her own scientific and pedagogical activities, and presents her explanation of elements which may be considered the core elements of unmanned vehicles legislation.

Assessing the distribution and drivers of mangrove dieback in Kakadu National Park, northern Australia

Satellite observations of Australia's Gulf of Carpentaria between 1987 and 2015 highlighted that many mangroves on the coastline bounding low-lying plains were progressively extending inland and to a lesser extent, in a seaward direction. However, in 2015/16, a significant and widely publicised mangrove dieback event occurred, this was attributed to a combination of climate (temperature, precipitation anomalies) and a ~20–30 cm decline in sea level. A similar but unreported event also occurred in Kakadu National Park (NP) in the Northern Territory. This study aimed to a) quantify the extent of this dieback in the NP, b) establish the characteristics of mangroves (floristics, structure) and the substrate elevation prior to and following event and c) establish links with climate and sea level. Using time-series of high resolution airborne and Unmanned Airborne Vehicle (UAV) data, the majority of mangroves experiencing full or partial dieback were found to occur on the landward margins. Reference to sea-level data indicated that mangroves had colonised and retreated in unison with sea level fluctuations over previous decades but increased in overall extent and cover as sea level rise dominated. Mangroves experiencing full mortality were located on higher elevation substrates where the sea level/tidal influence was least. The study concluded that whilst short-term ENSO-related sea level may result in dieback in northern Australia, the long-term projection of an increase in sea level is anticipated to lead to extension of mangroves in the landward direction.

Digital Heritage as a Scholarly Field—Topics, Researchers, and Perspectives from a Bibliometric Point of View

Digital heritage comprises a broad variety of approaches and topics and involves researchers from multiple disciplines. Against this background, this article presents a four-stage investigation on standards, publications, disciplinary cultures, as well as scholars in the field of digital heritage and particularly tangible objects as monuments and sites, carried out in 2016 and 2017. It includes results of (1) the inquiry of nearly 4,000 publications from major conferences, (2) a workshop-based survey involving 44 researchers, (3) 15 qualitative interviews, as well as (4) two online surveys with 1,000 and 700 participants, respectively. As an overall finding, the community is driven by researchers from European countries, especially Italy, with a background in humanities. Cross-national co-authorships are promoted by cultural and spatial closeness and—probably due to funding policy—EU membership. A discourse is primarily driven by technologies, and the most common keywords refer to the technologies used. Most prominent research areas are data acquisition and management, visualization, and analysis. Recent topics are, for instance, unmanned airborne vehicle (UAV)-based 3D surveying technologies, augmented and virtual reality visualization, metadata and paradata standards for documentation, and virtual museums. Since a lack of money is named as the biggest obstacle nowadays, competency and human resources are most frequently named as demand. An epistemic culture in the scholarly field of digital heritage is closer to engineering than to humanities. Moreover, conference series are most relevant for a scientific discourse, and especially EU projects set pace as most important research endeavors.

Ship Detection from Optical Remote Sensing Images Using Multi-Scale Analysis and Fourier HOG Descriptor

Automatic ship detection by Unmanned Airborne Vehicles (UAVs) and satellites is one of the fundamental challenges in maritime research due to the variable appearances of ships and complex sea backgrounds. To address this issue, in this paper, a novel multi-level ship detection algorithm is proposed to detect various types of offshore ships more precisely and quickly under all possible imaging variations. Our object detection system consists of two phases. First, in the category-independent region proposal phase, the steerable pyramid for multi-scale analysis is performed to generate a set of saliency maps in which the candidate region pixels are assigned to high salient values. Then, the set of saliency maps is used for constructing the graph-based segmentation, which can produce more accurate candidate regions compared with the threshold segmentation. More importantly, the proposed algorithm can produce a rather smaller set of candidates in comparison with the classical sliding window object detection paradigm or the other region proposal algorithms. Second, in the target identification phase, a rotation-invariant descriptor, which combines the histogram of oriented gradients (HOG) cells and the Fourier basis together, is investigated to distinguish between ships and non-ships. Meanwhile, the main direction of the ship can also be estimated in this phase. The overall algorithm can account for large variations in scale and rotation. Experiments on optical remote sensing (ORS) images demonstrate the effectiveness and robustness of our detection system.

THE SUPERSPECTRAL/HYPERSPATIAL WORLDVIEW-3 AS THE LINK BETWEEN SPACEBORNE HYPERSPECTRAL AND AIRBORNE HYPERSPATIAL SENSORS: THE CASE STUDY OF THE COMPLEX TROPICAL COAST

Abstract. Earth observation of complex scenes, such as coastal fringes, is based on a plethora of optical sensors constrained by trade-offs between spatial, spectral, temporal and radiometric resolution. The spaceborne hyperspectral EO-1 Hyperion sensor (decommissioned in 2017) was able to acquire imagery with 10 nm spectral (220 bands) at 30 m spatial resolutions over 1424.5 km2 scenes. Conversely, the widespread unmanned airborne vehicle (UAV) hyperspatial DJI Mavic Pro camera can collect only natural-coloured imagery of 100 nm spectral (3 bands) but at 0.1 m spatial resolution over ∼10 km2 scenes (with a single battery and calm meteo-marine conditions). The spaceborne WorldView-3 (WV3), featured by 60 nm spectral (16 bands) at 0.3 m spatial resolution (when pansharpened) over 1489.6 km2 scenes, has the capacity to bridge both sensors. This study aims at testing the spectral and spatial performances of the WV3 to discriminate 10 complex coastal classes, ranging from ocean, reefs and terrestrial vegetation in Moorea Island (French Polynesia). Our findings show that geometrically- and radiometrically-corrected 0.3-m 16-band WV3 bands competed with (30-m) 167-band Hyperion performance for classifying 10 coastal classes with 2-neuron artificial neural network modelling, while being able to segment objects seized by 0.1-m (3-band) UAV. Unifying superspectral and hyperspatial specificities, the WV3 also leverages hypertemporal resolution, that is to say 1-day temporal resolution, rivalling UAV’s one.

Identification of Ramularia Leaf Blight Cotton Disease Infection Levels by Multispectral, Multiscale UAV Imagery

The reduction of the production cost and negative environmental impacts by pesticide application to control cotton diseases depends on the infection patterns spatialized in the farm scale. Here, we evaluate the potential of three-band multispectral imagery from a multi-rotor unmanned airborne vehicle (UAV) platform for the detection of ramularia leaf blight from different flight heights in an experimental field. Increasing infection levels indicate the progressive degradation of the spectral vegetation signal, however, they were not sufficient to differentiate disease severity levels. At resolutions of ~5 cm (100 m) and ~15 cm (300 m) up to a ground spatial resolution of ~25 cm (500 m flight height), two-scaled infection levels can be detected for the best performing algorithm of four classifiers tested, with an overall accuracy of ~79% and a kappa index of ~0.51. Despite limited classification performance, the results show the potential interest of low-cost multispectral systems to monitor ramularia blight in cotton.

DESIGN AND IMPLEMENTATION OF NONLINEAR PID CONTROLLER FOR A QUADROTOR CONTROL

The following article represents the control of an unmanned airborne vehicle (Quadcopter), using nonlinear PID controllers in order to control the altitude, as well as the attitudes (pitch, roll and yaw) of the Quadcopter. Two approaches have been proposed for adjusting the parameters of a PID controller. Foremost, the PID controller gains are settled in an ideal way by using a reference model strategy. In the moment approach, these parameters are adjusted Nonlinear PID. MATLAB/Simulink has been utilized to test and compare the execution of the controllers gotten. The obtained results using the nonlinear PID controllers show the efficiency of the proposed controllers it present a response with less error and minimal over shoot compared to the results of classical PID controller.

A Comparison of Stateless Position-based Packet Routing Algorithms for FANETs

Scalable routing for wireless communication systems is a compelling and challenging task. To this aim, routing algorithms exploiting geographic information have been proposed. These algorithms refer to nodes by their location, rather than their address, and use those coordinates to route greedily towards a destination. With the advent of unmanned airborne vehicle (UAV) technology, a lot of research effort has been devoted to extend position-based packet routing proposals to three dimensional environments. In this context, Flying Ad-hoc Networks (FANETs), comprised of autonomous flying vehicles, pose several issues. This work focuses on the state-of-the-art, stateless geographic packet routing protocols conceived or adapted for three-dimensional network scenarios. Proposals are evaluated over a common scenario through a comprehensive comparative analysis.

Monitoring river morphology & bank erosion using UAV imagery – A case study of the river Buëch, Hautes-Alpes, France

River morphology dynamics and river bank erosion were mapped using multi-temporal images acquired in June 2014 and June 2015 by an Unmanned Airborne Vehicle (UAV). The selected study sites are located in two dynamic parts of the floodplain of the river Buëch in the Hautes-Alpes province in south-eastern France. The images were processed using the Structure from Motion algorithm into high spatial resolution OrthoMosaics of 5 cm pixel size and DEMs (Digital Elevation Model) of 10 cm pixel size. The positional and vertical accuracy of the UAV products were evaluated using Real Time Kinematic GPS (RTK-GPS) points measurements of markers laid out in the floodplain during image acquisition. Obtained accuracies are centimeters to decimeters. River morphology such as channel displacements, gravel bank displacement and avulsions were evaluated using the OrthoMosaics. The Buëch river shows mainly braided river properties but at locations some meandering river properties were observed. Bank erosion volume calculations were made by comparing the high spatial resolution DEMs of 2014 and 2015. Accuracy of bank erosion assessment was evaluated using RTK-GPS transects of known sites of bank erosion. Bank retreat could be mapped at centimeter to decimeter detail but sometimes hampered by overhanging vegetation, water glitter and shadows. Our conclusions are that time-series of high spatial resolution UAV images can be acquired in a flexible and easy way, the individual images can nowadays be processed in a straightforward way into suitable products i.e. OrthoMosaics and DEMs which are valuable products for land administrators having a responsibility to survey and monitor rivers and control them. Accuracy of the UAV products is high in XYZ direction and sufficient for river monitoring purposes and for designing management measures.

Addressing uncertainties in interpreting soil surface changes by multitemporal high‐resolution topography data across scales

AbstractExtensive amounts of water erosion combined with the magnitude of on‐site and off‐site impacts have led to considerable reduction in soil fertility and increased production costs in agriculture. Digital elevation models are a useful and established tool to better understand surface processes and to quantify damages. Ongoing developments in camera‐based 3D reconstruction allow non‐expert users to produce high‐quality elevation models. Multitemporal surveys allow geomorphic change detection serving as a valuable tool for geomorphologists and erosion researchers. Despite various benefits, these recent developments are accompanied by novel challenges in the form of small‐scale nonerosive surface processes (e.g., swelling/shrinking, consolidation/compaction, and aggregate breakdown). If neglected, these alterations can sum up to distinct errors in soil loss quantification.However, as yet, there have been no studies discussing their influence on surface data sets in erosion research. We present an across‐scales approach of visualising and quantifying detailed soil physical changes and their influence on surface morphology at submillimetre resolution.The data sets were acquired at field scale from an unmanned airborne vehicle, at plot scale during an artificial rainfall simulation on an agricultural site, and at microplot scale in a laboratory set‐up. Resulting high‐precision data sets enable the desired insights in soil movements and surface alterations without preceding particle transport and soil loss. Surface changes sum up to 260 m3/ha on an Andalusian test site and could be falsely interpreted as soil loss (of 286 t/ha). These results recommend cautious interpretation of high‐resolution topography in soil erosion research.