Displays

CO₂ emissions are the primary cause of man-made climate change. In order to limit this, a reduction of emissions is necessary. For this reason, possibilities must be established to monitor emissions through independent measurements. A large part of the human CO₂ emissions falls on point sources such as coal or gas fired power plants. On a global level, it is planned to explore these remotely by means of satellites. At the regional level, both airborne and UAV-based measurements are suitable, which can also be used for smaller sources and for the validation of the satellite data.

Here we present the development of a UAV for the determination of CO₂ emissions from individual point sources by simultaneously measuring CO₂ concentration, wind speed and other meteorological parameters.

A commercial UAV for industrial tasks is used as platform. CO₂ is measured by a non-dispersive NIR detector with an accuracy of 1-2ppm and an ultrasonic anemometer is used for wind measurements. All relevant data is transmitted to the operator during the flight so that the flight pattern can be spontaneously adapted to the measurement data.

We will introduce the UAV including the installed sensors as well as the measuring principle and present results of the first flights.

How to cite: Reuter, M., Buchwitz, M., Bovensmann, H., and Burrows, J. P.: UAV based measurements of CO2 emissions from anthropogenic point sources, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1641, https://doi.org/10.5194/egusphere-egu2020-1641, 2020.

In recent years, Unmanned aerial vehicle (UAV) tilt photography, InSAR, LiDAR and other technologies have been used in the field of geological disaster surveys and research to varying degrees, with traditional field survey methods being unable to meet the requirements of rapid and subtilized geological surveys nowadays. Thanks to the rapid development of UAV tilt photogrammetry technology, UAVs have played an important role in geological disaster emergency investigations and geological surveys. However, there are still some problems with the application of UAVs: 1. Geological disaster investigators who commonly hold degrees in geology find it difficult to learn how to operate UAVs; 2. professional UAV pilot training involves high costs and long training cycles, and meanwhile, UAV platforms and their loaded multi-lens tilt cameras are of high value, which render UAVs impossible to use as a standard accessory for geological disaster investigation teams; and 3. professional 3D modelling software is expensive and requires highly configured computer hardware, and in field scenarios, it usually has poor timeliness in terms of data processing. A micro-UAV system, mainly consisting of a UAV flight path control app (supporting Android/IOS) and a web-based data processing cloud platform, has been developed to solve the problems emerging in UAV-based geological disaster surveys, such as the difficult data collection, slow data processing, and high human involvement. The system integrates existing consumer-grade micro-UAV hardware and our newly designed UAV path planning and photogrammetry modes applicable in geological disaster surveys to achieve the fast acquisition of images, DOM, DSM, 3D models and point cloud data for geological disaster survey areas, based on high-speed processing and multi-node distributed GPU cluster technology. The main goal of this micro-UAV geological disaster surveying system is to rapidly acquire, transmit, process and distribute large-scale three-dimensional geographic information for small areas. The UAV flight path control app features one-click take-off and automatic landing, and the web data-processing cloud platform can realize one-click automatic data processing. The system has good application prospects due to its low cost and easy operation, and the fact that it can be widely used as a standard accessory by teams in various geological disaster investigations.

Keywords: micro-UAV system, cloud computing, geological disaster survey, rapid and subtilized survey

How to cite: Xing, G., Zhang, M., Ma, J., Huang, Z., and Liu, C.: A Cloud Computing-based Micro-Unmanned Aerial Vehicle System for Geological Disaster Surveys, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6296, https://doi.org/10.5194/egusphere-egu2020-6296, 2020.

Unmanned Vehicles (UV) are used daily for different applications around the world. However, most of the software and technologies on which they are based are closed and proprietary systems. The presentation will start by introducing the history of Unmaned Vehicle technologies, with some unknown historical info, and the different types of Unmaned Vehicles that exist. The aim of the presentation is to demonstrate the integration of free and open source systems starting from the hardware, specially the autopilot component, through some payloads and finishing with the software solutions. Different software solutions for diverse aims will be showed and compared, i.e., first the different software used to configure the UV and manage the mission and later the possible software used to manage the outputs from the mission.

How to cite: Delucchi, L. and Bezzi, L.: Free and Open Source unmanned vehicles, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7142, https://doi.org/10.5194/egusphere-egu2020-7142, 2020.

Both the sensor viewing angle and the solar angle influence the remote sensing signal of terrestrial ecosystems. This influence is characterized by the bidirectional reflectance distribution function (BRDF). Knowledge of this BRDF is needed to correctly interpret the signal, but can also provide information on vegetation characteristics and structure. Obtaining the BRDF is far from straightforward: at leaf scale, laboratory goniometers can measure reflected radiation over a range of sensor-solar angle; for very homogeneous ecosystems, such as grassland or agricultural cropland, unmanned aerial vehicles (UAVs) can be programmed as giant goniometer, scanning the BRDF of an area of up to a few m². For heterogeneous ecosystems such as forests, this is not feasible. In this case, BRDF could so far only be derived from theoretical radiation transfer models or semi-empirical models; yet these models do not always agree.

We here propose a new method for measuring BRDF of forest ecosystems with UAVs, by measuring a star-shaped area of the ecosystem, covering in total about 3600m² and capturing 6 different sensor-solar azimuth angle and three different zenith angles. This approach was applied over two sites of tropical rainforests in Queensland, Australia, with measurements with a RGB camera and a spectrometer. By repeating the flights several times during the day, we were able to test the Helmholtz reciprocity principle – that states the BRDF function of ecosystems remains the same, regardless of the solar angle – and are able to increase the range of sensor-solar angles. Our results present the first strictly empirical BRDF of tropical rainforests and confirm the importance of accurate BRDF correction of remote sensing products from forest ecosystems. 

How to cite: Maes, W., Bovend'aerde, L., Lauwers, M., Steppe, K., and Huete, A.: Empirical acquisition of bidirectional reflectance of tropical forest ecosystems using unmanned aerial vehicles, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8300, https://doi.org/10.5194/egusphere-egu2020-8300, 2020.

River networks are dynamic entities, periodically subject to expansion and contraction processes due to natural hydrological and climatic fluctuations. The ERC project "DyNET: Dynamical River Networks" aims at providing a systematic and quantitative description of such processes. The experimental activities are focused on the mapping at the basin scale of the active (i.e., characterized by flowing water) portion of the river network with the aid of drones, satellite images and field surveys, for the collection of data useful to the modelling of evolutionary processes and the development of theories to be extended on a regional scale. The use of UAVs (Unmanned Air Vehicles) specifically concerns the observation of the space-time evolution of processes, allowing to monitor wide areas and identify the presence/absence of flowing water in the river network with the help of infrared (IR) thermal imaging cameras.

The contribution discusses the effectiveness of UAVs for river networks dynamics monitoring in the Turbolo creek network (Calabria, southern Italy). Specifically, an experimental method is described that identifies and extrapolates from thermal images the pixels representing the active river network. The method is defined based on multiple acquisitions of thermal IR images on some channelized sites in different periods of the year, weather conditions, daytimes and flight altitudes. Several surveys were carried out in autumn, winter and spring seasons, with variable cloud conditions, always repeating the same flight plan, at three different altitudes and at three different times for each day of analysis. During the experiments, air temperature data were recorded by a weather station near the test area, as well as the water temperature values ​​in a small control area in the river bed, with the ascertained presence of water, monitored by the UAV. The thermal images were analyzed on GIS software, extrapolating the pixels falling within a range of values defined from the control area. The "water pixels" thus obtained allowed, through appropriate post-processing, to reconstruct the active river network even in areas not accessible by land. The methodology developed allows defining, for different periods of the year and weather conditions, optimal altitudes and flight times to accurately identify the expansion/contraction dynamics of river networks.

How to cite: Micieli, M., Botter, G., Mendicino, G., and Senatore, A.: UAV thermal images to support the study of the expansion and contraction dynamics of river networks: a preliminary methodological approach, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13166, https://doi.org/10.5194/egusphere-egu2020-13166, 2020.

The growing demand for innovative and sustainable exploration technologies is boosting opportunities for non-invasive geophysical surveys using unmanned aerial systems (UASs). During the last few years lightweight magnetometers have been increasingly developed for their use on UASs. Aeromagnetic surveys can provide a rapid and cost-effective technology to improve the detection of shallow targets and to delineate magnetite-pyrrhotite-rich mineralizations. With low altitude flights and tight flight lines, magnetometers lifted by rotary wing UAS systems can deliver high resolution maps in small-to-medium scale areas (<100 sq.km). We propose an adaptive workflow for aeromagnetic survey acquisitions by using multi-copters that in combination with a programmed processing tool can efficiently achieve valid observations and reliable maps. Results suggest that minimizing and compensating for the magnetometers attitude changes during flight as well as the removal of temporal variations plays an important role to avoid small anomalies to go undetected. For this study we present a comprehensive data set where UAS aeromagnetic surveys aids to overcome the scale gap between ground and airborne magnetics in potentially hazardous environments where UAS have operational advantage over traditional techniques.

How to cite: Madriz, Y., Zimmermann, R., Shaik Fareedh, J., Lorenz, S., and Gloaguen, R.: UAS magnetics as a non-invasive exploration technology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16008, https://doi.org/10.5194/egusphere-egu2020-16008, 2020.

Since some decades, cultural and traditional alpine farming have been changed enormously, mainly due to anthropogenic activities and economic factors: some pastures were abandoned, some others changed from farming to touristic areas (eg. ski resorts) and some others have been dramatically intensified by changing to monoculture. In consequence, these activities allow practices of deforestation, the massive use of fertilizers and pesticides, the excessive use of machinery, grading, drainage among others. Additionally, human activity have impacted on weather by resulting on low rainfall or drought extended periods. The combination of these factors result that vegetation and species are more vulnerable to the infestation of pests and diseases.

On this feasibility study, we propose the identification, mapping and classification of individual trees affected by fungal species (alternaria) in apple orchards located in South Tyrol, Italy, based on hyperspectral and thermal imagery. We have conducted terrestrial and UAV-based surveys to identify (un)healthy indivuals (trees). High spatial resolution scale consisted on terrestrial monitoring approaches based on the identification of trees and leaves, the collection of leave spectral signature based on a dedicated spectroradiometer (300 to 2000 nm) and spectral imagery of individuals. Medium spatial resolution consisted on UAV-based spectral data collection and interpretation. 30 hyperspectral bands (400 to 900 nm) in the VIR range and thermal imagery (14 µm) in combination with leave-spectral bands allowed the identification and mapping of un-healthy individuals for further treatment.

How to cite: Mejia-Aguilar, A., Prechsl, U., and Monsorno, R.: Monitoring diseases by using Hyperspectral and Thermal techniques at two different spatial scales: A feasibility study in alpine regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16101, https://doi.org/10.5194/egusphere-egu2020-16101, 2020.

Unmanned aerial vehicle (UAV) sampling was used to derive high-precision methane mole fraction measurements downwind of the United Kingdom’s first onshore exploratory operation to horizontally hydraulically fracture shale rock. Sampling took place using two UAVs on five intermittent sampling days between October 2018 and February 2019. One UAV carried an on-board prototype sensor while the other was connected to a sensor on the ground, using a tethered air inlet. Both instruments used near infrared spectroscopy. Methane emissions were observed on one sampling day (14^th January 2019) over a 1.4-hour sampling window, due to cold venting of methane following a nitrogen lift. The nitrogen lift procedure was used to induce gas flow during liquid unloading. The near-field Gaussian plume inversion flux quantification method was used to derive four instantaneous flux ranges (within uncertainty) from the four UAV flight surveys conducted during the emission window.

How to cite: Shah, A., Ricketts, H., Pitt, J., Shaw, J., Kabbabe, K., Leen, B., and Allen, G.: Methane emission detection and flux quantification from exploratory hydraulic fracturing in the United Kingdom, using unmanned aerial vehicle sampling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-16797, https://doi.org/10.5194/egusphere-egu2020-16797, 2020.

Soil compaction due to heavy machinery is one of the major soil degradation threats in modern agriculture. Field traffic under unfavorable weather conditions can increase bulk density and penetration resistance, reduce soil hydraulic conductivity and plant growth. An increased surface runoff caused by a decreasing infiltration further grows the effects of nutrient leaching and the eutrophication of adjacent water bodies. For soil compaction prevention, it is necessary to know where soil compaction occurs. The detection of this issue is, however, cost, time and labor intensive.

The aim of this study is to evaluate the use of UAV-based multispectral imagery analyses to detect soil compaction pattern at field scale. Therefore, UAV imagery of two sugar beet (Beta vulgaris L.) fields were captured in April, June, July and November of 2019 to analyze different crop signals. The crop surface model and the NDVI were used as a predictor to reflect plant and biomass status. The k-means clustering algorithm was used to combine plant height and NDVI to detect spatial-temporal patterns of low crop performance. Sites with lower crop performance were assumed as potential sites of soil compaction; here, further measurements (penetration resistance, infiltration rate) were conducted and soil and yield samples were taken.

First results show that (1) spatio-temporal patterns of crop performance can be found; (2) sites with low crop performance have a lower infiltration rate and lower crop yield; (3) the measurements of penetration resistance are inconclusive.

As soil compaction reduces infiltration rate and yield, this study shows first indications that it is possible to detect soil compaction via plant signals using UAV. This has a big potential for practical use as costs for drones are declining and they are gaining popularity under farmers. Thus, the use of UAV may enable farmers to monitor their fields, identify areas of soil compaction and in the following implement measures against soil compaction.

How to cite: Lindenstruth, F.: Spatio-temporal patterns of crop signals: is UAV-based multispectral imagery a suitable tool to detect soil compaction at field scale?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19619, https://doi.org/10.5194/egusphere-egu2020-19619, 2020.

Surface waters can contain pathogenic microorganisms that may ​be detrimental to individuals consuming produce grown with irrigation. Fecal indicator organisms, primarily Escherichia coli, are commonly used to estimate the potential presence of pathogens in irrigation waters.  Concentrations of E. coli in the water of irrigation ponds are often highly variable in space and time. Water sampling that is frequent in time and dense in space, is impractical. Unmanned aerial systems (drones, or UAVs) have shown the potential to provide informative imagery. We hypothesized that the UAV-based imagery can facilitate the microbial water quality monitoring in ponds by reflecting the differences in bacteria habitats. ​Six times over the summer, we coupled monitoring of 17 water quality parameters ​of 23 locations across an irrigation pond in Maryland with 14 images ​captured by a MicaSense RedEdge M and modified GoPro cameras. The modified GoPro Images were demosaiced into red, green, and blue bands for each of the cameras. The random forest methodology was used to evaluate the accuracy and reliability of relationships between several combinations of measured ​explanatory variables, and the logarithm of the E. coli concentration as the variable to predict. Random forest models with only imagery data as the ​explanatory variables, and ​models with all measured data as explanatory variables had coefficients of determination between 0.5 ​to 0.6, and 0.6 ​to 0.7, respectively. The most important explanatory variables for the model with only imagery input were digital numbers ​obtained from the blue band of the “visible only” filter image, and from the red bands of the “infrared only” and “visible only” filter images.  When all measurements were used, the most important explanatory variables were concentrations of chlorophyll a and fluorescent dissolved organic matter, as well as and digital number​s from the red band ​of the “infrared only” filter image. There appears to be a potential for the UAV-based imagery to provide dense spatial coverage of ponds with subsequent delineation of a small number of relatively uniform zones for informed water sampling. 

How to cite: Pachepsky, Y., Morgan, B., Stocker, M., and Kim, M.: UAV-based imagery analysis with machine learning to facilitate microbial water quality monitoring of irrigation ponds, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22365, https://doi.org/10.5194/egusphere-egu2020-22365, 2020.