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This study assesses the suitability to use RGB and thermal infrared imagery acquired from an UAV to measure surface flow velocities of rivers. The reach of a medium-scale river in Hungary is investigated. Image sequences with a frame rate of 2 Hz were captured with two sensors, a RGB and an uncooled thermal camera, at a flying height that ensures the visibility of both shores. The interior geometry of both cameras were calibrated with an in-house designed target field. The image sequences were automatically co-registered to account for UAV movements during the image acquisition. The TIR data was processed to keep loss-free image information solely in the water area and to enhance the signal to noise ratio. Image velocimetry with PIV applied to the TIR data and PTV applied to the RGB data was utilised to retrieve surface flow velocities. Comparison between RGB and TIR data reveal an average deviation of about 0.01 m/s. Future studies are needed to evaluate the transferability to other non-regulated river reaches.
A. Eltner; D. Mader; N. Szopos; B. Nagy; J. Grundmann; L. Bertalan. USING THERMAL AND RGB UAV IMAGERY TO MEASURE SURFACE FLOW VELOCITIES OF RIVERS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2021, XLIII-B2-2, 717 -722.
AMA StyleA. Eltner, D. Mader, N. Szopos, B. Nagy, J. Grundmann, L. Bertalan. USING THERMAL AND RGB UAV IMAGERY TO MEASURE SURFACE FLOW VELOCITIES OF RIVERS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2021; XLIII-B2-2 ():717-722.
Chicago/Turabian StyleA. Eltner; D. Mader; N. Szopos; B. Nagy; J. Grundmann; L. Bertalan. 2021. "USING THERMAL AND RGB UAV IMAGERY TO MEASURE SURFACE FLOW VELOCITIES OF RIVERS." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2, no. : 717-722.
While the availability and affordability of unmanned aerial systems (UASs) has led to the rapid development of remote sensing applications in hydrology and hydrometry, uncertainties related to such measurements are still to be quantified and mitigated. Physical instability of the UAS platform inevitably induces motion in the acquired videos and can have a significant impact on the accuracy of camera-based measurements such as velocimetry. A common practice in the data preprocessing stages is the compensation of platform-induced motion by means of digital image stabilisation (DIS) methods, which use the visual information from the captured videos – in the form of physically static features – to first estimate and then to compensate such motion. Most existing stabilisation approaches rely either on in-house built tools based on different algorithms, or on general-purpose commercial software. Intercomparison of different stabilisation tools for UAS remote sensing purposes that could serve as a basis for a selection of a particular tool in given conditions has not been found in the available literature. In this paper we have attempted to summarise and describe several freely available DIS tools applicable to UAS velocimetry purposes. A total of seven tools – six aimed specifically at velocimetry and one general purpose software – were investigated in terms of their (1) stabilisation accuracy in various conditions, (2) robustness, (3) computational complexity, and (4) user experience, using three case study videos with different flight and ground conditions. In attempt to adequately quantify the accuracy of the stabilisation using different tools, we have also presented a comparison metric based on root-mean-squared differences (RMSD) of interframe pixel intensities for selected static features. The most apparent differences between the investigated tools have been found with regards to the method for identifying and selecting static features in videos – manual selection of features or automatic. State-of-the-art methods which rely on automatic selection of features require fewer user-provided parameters and are able to select a significantly higher number of potentially static features (by several orders of magnitude) when compared to the methods which require manual identification of such features. This allows the former to achieve a higher stabilisation accuracy, but manual feature selection methods have demonstrated lower computational complexity and better robustness in complex field conditions. While this paper does not intend to identify the optimal stabilisation tool for UAS-based velocimetry purposes, it does aim to shed a light on implementational details which can help engineers and researchers choose the tool suitable for their needs and specific field conditions. Additionally, the RMSD comparison metric presented in this paper can also be used in order to measure the velocity estimation uncertainty induced by UAS motion.
Robert Ljubičić; Dariia Strelnikova; Matthew T. Perks; Anette Eltner; Salvador Peña-Haro; Alonso Pizarro; Silvano Fortunato Dal Sasso; Ulf Scherling; Pietro Vuono; Salvatore Manfreda. A comparison of tools and techniques for stabilising UAS imagery for surface flow observations. 2021, 2021, 1 -42.
AMA StyleRobert Ljubičić, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Peña-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, Salvatore Manfreda. A comparison of tools and techniques for stabilising UAS imagery for surface flow observations. . 2021; 2021 ():1-42.
Chicago/Turabian StyleRobert Ljubičić; Dariia Strelnikova; Matthew T. Perks; Anette Eltner; Salvador Peña-Haro; Alonso Pizarro; Silvano Fortunato Dal Sasso; Ulf Scherling; Pietro Vuono; Salvatore Manfreda. 2021. "A comparison of tools and techniques for stabilising UAS imagery for surface flow observations." 2021, no. : 1-42.
Photogrammetric models have become a standard tool for the study of surfaces, structures and natural elements. As an alternative to Light Detection and Ranging (LiDAR), photogrammetry allows 3D point clouds to be obtained at a much lower cost. This paper presents an enhanced workflow for image-based 3D reconstruction of high-resolution models designed to work with fixed time-lapse camera systems, based on multi-epoch multi-images (MEMI) to exploit redundancy. This workflow is part of a fully automatic working setup that includes all steps: from capturing the images to obtaining clusters from change detection. The workflow is capable of obtaining photogrammetric models with a higher quality than the classic Structure from Motion (SfM) time-lapse photogrammetry workflow. The MEMI workflow reduced the error up to a factor of 2 when compared to the previous approach, allowing for M3C2 standard deviation of 1.5 cm. In terms of absolute accuracy, using LiDAR data as a reference, our proposed method is 20% more accurate than models obtained with the classic workflow. The automation of the method as well as the improvement of the quality of the 3D reconstructed models enables accurate 4D photogrammetric analysis in near-real time.
Xabier Blanch; Anette Eltner; Marta Guinau; Antonio Abellan. Multi-Epoch and Multi-Imagery (MEMI) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras. Remote Sensing 2021, 13, 1460 .
AMA StyleXabier Blanch, Anette Eltner, Marta Guinau, Antonio Abellan. Multi-Epoch and Multi-Imagery (MEMI) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras. Remote Sensing. 2021; 13 (8):1460.
Chicago/Turabian StyleXabier Blanch; Anette Eltner; Marta Guinau; Antonio Abellan. 2021. "Multi-Epoch and Multi-Imagery (MEMI) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras." Remote Sensing 13, no. 8: 1460.
Image‐based gauging stations can allow for significant densification of monitoring networks of river water stages. However, thus far, most camera gauges do not provide the robustness of accurate measurements due to the varying appearance of water in the stream throughout the year. We introduce an approach that allows for automatic and reliable water stage measurement combining deep learning and photogrammetric techniques. First, a convolutional neural network (CNN), a class of deep learning, is applied to the segmentation (i.e., pixel classification) of water in images. The CNNs SegNet and fully convolutional network (FCN) are associated with a transfer learning strategy to segment water on images acquired by a Raspberry Pi camera. Errors of water segmentation with the two CNNs are lower than 3%. Second, the image information is transformed into metric water stage values by intersecting the extracted water contour, generated using the segmentation results, with a 3D model reconstructed with structure‐from‐motion (SfM) photogrammetry. The highest correlations between a reference gauge and the image‐based approaches reached 0.93, and average deviations were lower than 4 cm. Our approach allows for the densification of river monitoring networks based on camera gauges, providing accurate water stage measurements. This article is protected by copyright. All rights reserved.
Anette Eltner; Patrik Olã Bressan; Thales Akiyama; Wesley Nunes Gonçalves; José Marcato Junior. Using Deep Learning for Automatic Water Stage Measurements. Water Resources Research 2021, 57, 1 .
AMA StyleAnette Eltner, Patrik Olã Bressan, Thales Akiyama, Wesley Nunes Gonçalves, José Marcato Junior. Using Deep Learning for Automatic Water Stage Measurements. Water Resources Research. 2021; 57 (3):1.
Chicago/Turabian StyleAnette Eltner; Patrik Olã Bressan; Thales Akiyama; Wesley Nunes Gonçalves; José Marcato Junior. 2021. "Using Deep Learning for Automatic Water Stage Measurements." Water Resources Research 57, no. 3: 1.
Unmanned Aerial Vehicles (UAV) have become a commonly used measurement tool in geomorphology due to their affordable cost, flexibility, and ease of use. They are regularly used in fluvial geomorphology, among other fields, because the high spatiotemporal resolution of UAV data makes it possible to assess the continuum rather than relying on single samples.
In this study, UAV data are used to hydro-morphologically describe three different river reaches of lengths between 150 and 1000 m. Specifically, the surface flow velocity and bathymetry of the rivers were reconstructed. The flow velocities were calculated using the Particle Tracking Velocimetry (PTV) method applied to UAV video sequences. In addition, UAV-based imagery was acquired to perform 3D reconstruction above and below the water surface using SfM (Structure from Motion) photogrammetry, taking into account refraction effects as well as frame processing to increase the visibility of underwater features. Reference data for flow velocities were generated at selected positions using current meters as well as ADCP (Acoustic Doppler Current Profiler) readings. The image-based calculated bathymetry was compared with RTK-GNSS sampling depth measurements and also ADCP data.
The developed workflow enables rapid and regular measurement of hydrological and morphological data of river channels. This ultimately enables multi-temporal assessment and significantly improves hydro-morphodynamic modelling, in particular their calibration.
Anette Eltner; László Bertalan; Eliisa Lotsari. Hydromorphological monitoring of individual river reaches with UAV-data – image-based measurement of bathymetry and flow velocity. 2021, 1 .
AMA StyleAnette Eltner, László Bertalan, Eliisa Lotsari. Hydromorphological monitoring of individual river reaches with UAV-data – image-based measurement of bathymetry and flow velocity. . 2021; ():1.
Chicago/Turabian StyleAnette Eltner; László Bertalan; Eliisa Lotsari. 2021. "Hydromorphological monitoring of individual river reaches with UAV-data – image-based measurement of bathymetry and flow velocity." , no. : 1.
Soil erosion as a major environmental challenge, plays a central role in land degradation. Accurate erosion rates assessment and information on erosion, deposition and on occurring processes are important to support soil protection and recovery strategies.
Due to the complexity, variability and discontinuity of erosional processes, model approaches to predict soil erosion are non-transferable to different temporal and spatial scales. Present process-based models are only valid for the particular observation scale which they were parameterized and validated for. In reality processes occur (e.g. spontaneous rill initiation) which are only to some extent reproducible, resulting in an incomplete process description. While model parameterization in the past was limited by the availability and resolution of data, constant development of data assessment technologies help overcome these confines. Time and cost in collecting data decreases, computing power is constantly expended and both the temporal and spatial resolution offer new possibilities on new scales.
Addressing the issue ‘data overhaul models’ we present a unique experimental setup, including flow velocity, erosion and deposition measurements at nested temporal and spatial scales, acquired using high resolution photogrammetric data (RGB and thermal) and structure from motion techniques. At the micro plot scale (3 m2), we perform rainfall simulations, monitored with up to eleven cameras. Using time lapse intervals of 10-20 seconds processes of pool formation and aggregate breakdown are observed. At the hillslope scale (60 m2), we installed a permanent setup – three rigs at three slope positions at four meter height, each equipped with five synchronized RGB cameras, a RGB video-camera and a low cost thermal camera. To capture changes in soil surface during rainfall events, time lapse images are triggered by a low-cost rain gauge. Soil surface changes at the small catchment scale (4 ha) are measured by taking UAV-images before and after rainfall events. These observations are used as parameterization, calibration and validation for modelled soil surface changes and erosion fluxes, using Erosion3D and FullSWOF.
The continuous development and improvement of soil erosion assessment techniques leads to spatially and temporally highly resolved information on different scales. Eventually the adjustment of the erosion models can enable a cross-scale description and validation of scale-dependent processes, offering new perspectives on both interconnectivity of sediment transport and the relationship between event frequency and magnitude.
Lea Epple; Andreas Kaiser; Marcus Schindewolf; Anette Eltner. Data overhaul Models? – Temporal and spatial high resolution assessment techniques for across-scale calibration, parameterization and validation of physically-based soil erosion models. 2021, 1 .
AMA StyleLea Epple, Andreas Kaiser, Marcus Schindewolf, Anette Eltner. Data overhaul Models? – Temporal and spatial high resolution assessment techniques for across-scale calibration, parameterization and validation of physically-based soil erosion models. . 2021; ():1.
Chicago/Turabian StyleLea Epple; Andreas Kaiser; Marcus Schindewolf; Anette Eltner. 2021. "Data overhaul Models? – Temporal and spatial high resolution assessment techniques for across-scale calibration, parameterization and validation of physically-based soil erosion models." , no. : 1.
Soil surface roughness (SSR) is an important factor in controlling sediment and runoff generation influencing directly a wide spectrum of erosion parameters. SSR is highly variable in time and space under natural conditions, and characterizing SSR to improve the parameterization of hydrological and erosion models has proved challenging. Our study uses recent technological and algorithmic developments in capturing and processing Close Aerial Sensing data to evaluate how high‐resolution imagery can assist the temporally and spatially explicit monitoring of SSR. We evaluated the evolution of SSR under natural rainfall and growing vegetation conditions on two arable fields in Denmark. Unmanned aerial vehicle (UAV) photogrammetry was used to monitor small field plots over seven months after seeding of winter wheat following conventional and reduced tillage treatments. Field campaigns were conducted at least once a month from October until April resulting in nine time steps of data acquisition. Structure from Motion photogrammetry was used to derive high‐resolution point clouds with an average ground sampling distance of 2.7 mm and a mean ground control point accuracy of 1.8 mm. A comprehensive workflow was developed to process the point clouds, including the detection of vegetation and the removal of vegetation‐induced point cloud noise. Rasterized and filtered point clouds were then used to determine SSR geostatistically as the standard deviation of height applying different kernel sizes and using semivariograms. The results showed an influence of kernel size on roughness, with a value range of 0.2–1 cm of average height deviation during the monitoring period. Semivariograms showed a measurable decrease in sill variance and an increase in range over time. This research demonstrated multiple challenges to measuring SSR with UAV under natural conditions with increasing vegetation cover. The proposed workflow represents a step forward in tackling those challenges and provides a knowledge base for future research.
Nils Onnen; Anette Eltner; Goswin Heckrath; Kristof Van Oost. Monitoring soil surface roughness under growing winter wheat with low‐altitude UAV sensing: Potential and limitations. Earth Surface Processes and Landforms 2020, 45, 3747 -3759.
AMA StyleNils Onnen, Anette Eltner, Goswin Heckrath, Kristof Van Oost. Monitoring soil surface roughness under growing winter wheat with low‐altitude UAV sensing: Potential and limitations. Earth Surface Processes and Landforms. 2020; 45 (14):3747-3759.
Chicago/Turabian StyleNils Onnen; Anette Eltner; Goswin Heckrath; Kristof Van Oost. 2020. "Monitoring soil surface roughness under growing winter wheat with low‐altitude UAV sensing: Potential and limitations." Earth Surface Processes and Landforms 45, no. 14: 3747-3759.
The use of deep learning (DL) with convolutional neural networks (CNN) to monitor surface water can be a valuable supplement to costly and labour-intense standard gauging stations. This paper presents the application of a recent CNN semantic segmentation method (SegNet) to automatically segment river water in imagery acquired by RGB sensors. This approach can be used as a new supporting tool because there are only a few studies using DL techniques to monitor water resources. The study area is a medium-scale river (Wesenitz) located in the East of Germany. The captured images reflect different periods of the day over a period of approximately 50 days, allowing for the analysis of the river in different environmental conditions and situations. In the experiments, we evaluated the input image resolutions of 256 × 256 and 512 × 512 pixels to assess their influence on the performance of river segmentation. The performance of the CNN was measured with the pixel accuracy and IoU metrics revealing an accuracy of 98% and 97%, respectively, for both resolutions, indicating that our approach is efficient to segment water in RGB imagery.
T. S. Akiyama; J. Marcato Junior; W. N. Gonçalves; P. O. Bressan; A. Eltner; F. Binder; T. Singer. DEEP LEARNING APPLIED TO WATER SEGMENTATION. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, XLIII-B2-2, 1189 -1193.
AMA StyleT. S. Akiyama, J. Marcato Junior, W. N. Gonçalves, P. O. Bressan, A. Eltner, F. Binder, T. Singer. DEEP LEARNING APPLIED TO WATER SEGMENTATION. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; XLIII-B2-2 ():1189-1193.
Chicago/Turabian StyleT. S. Akiyama; J. Marcato Junior; W. N. Gonçalves; P. O. Bressan; A. Eltner; F. Binder; T. Singer. 2020. "DEEP LEARNING APPLIED TO WATER SEGMENTATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2, no. : 1189-1193.
As key-components of the urban-drainage system, storm-drains and manholes are essential to the hydrological modeling of urban basins. Accurately mapping of these objects can help to improve the storm-drain systems for the prevention and mitigation of urban floods. Novel Deep Learning (DL) methods have been proposed to aid the mapping of these urban features. The main aim of this paper is to evaluate the state-of-the-art object detection method RetinaNet to identify storm-drain and manhole in urban areas in street-level RGB images. The experimental assessment was performed using 297 mobile mapping images captured in 2019 in the streets in six regions in Campo Grande city, located in Mato Grosso do Sul state, Brazil. Two configurations of training, validation, and test images were considered. ResNet-50 and ResNet-101 were adopted in the experimental assessment as the two distinct feature extractor networks (i.e., backbones) for the RetinaNet method. The results were compared with the Faster R-CNN method. The results showed a higher detection accuracy when using RetinaNet with ResNet-50. In conclusion, the assessed DL method is adequate to detect storm-drain and manhole from mobile mapping RGB images, outperforming the Faster R-CNN method. The labeled dataset used in this study is available for future research.
Anderson Santos; José Marcato Junior; Jonathan De Andrade Silva; Rodrigo Pereira; Daniel Matos; Geazy Menezes; Leandro Higa; Anette Eltner; Ana Paula Ramos; Lucas Osco; Wesley Gonçalves. Storm-Drain and Manhole Detection Using the RetinaNet Method. Sensors 2020, 20, 4450 .
AMA StyleAnderson Santos, José Marcato Junior, Jonathan De Andrade Silva, Rodrigo Pereira, Daniel Matos, Geazy Menezes, Leandro Higa, Anette Eltner, Ana Paula Ramos, Lucas Osco, Wesley Gonçalves. Storm-Drain and Manhole Detection Using the RetinaNet Method. Sensors. 2020; 20 (16):4450.
Chicago/Turabian StyleAnderson Santos; José Marcato Junior; Jonathan De Andrade Silva; Rodrigo Pereira; Daniel Matos; Geazy Menezes; Leandro Higa; Anette Eltner; Ana Paula Ramos; Lucas Osco; Wesley Gonçalves. 2020. "Storm-Drain and Manhole Detection Using the RetinaNet Method." Sensors 20, no. 16: 4450.
Sheet erosion is common on agricultural lands, and understanding the dynamics of the erosive process as well as the quantification of soil loss is important for both soil scientists and managers. However, measuring rates of soil loss from sheet erosion has proved difficult due to requiring the detection of relatively small surface changes over extended areas. Consequently, such measurements have relied on the use of erosion plots, which have limited spatial coverage and have high operating costs. For measuring the larger erosion rates characteristic of rill and gully erosion, structure-from-motion (SfM) photogrammetry has been demonstrated to be a valuable tool. Here, we demonstrate the first direct validation of UAV-SfM measurements of sheet erosion using sediment collection data collected from erosion plots. Three erosion plots (12 m × 4 m) located at Lavras, Brazil, with bare soil exposed to natural rainfall from which event sediment and runoff was monitored, were mapped during two hydrological years (2016 and 2017), using a UAV equipped with a RGB camera. DEMs of difference (DoD) were calculated to detect spatial changes in the soil surface topography over time and to quantify the volumes of sediments lost or gained. Precision maps were generated to enable precision estimates for both DEMs to be propagated into the DoD as spatially variable vertical uncertainties. The point clouds generated from SfM gave mean errors of ~2.4 mm horizontally (xy) and ~1.9 mm vertically (z) on control and independent check points, and the level of detection (LoD) along the plots ranged from 1.4 mm to 7.4 mm. The soil loss values obtained by SfM were significantly (p < 0.001) correlated (r2 = 95.55%) with those derived from the sediment collection. These results open up the possibility to use SfM for erosion studies where channelized erosion is not the principal mechanism, offering a cost-effective method for gaining new insights into sheet, and interrill, erosion processes.
Bernardo M. Cândido; John N. Quinton; Mike R. James; Marx L.N. Silva; Teotônio S. de Carvalho; Wellington de Lima; Adnane Beniaich; Anette Eltner. High-resolution monitoring of diffuse (sheet or interrill) erosion using structure-from-motion. Geoderma 2020, 375, 114477 .
AMA StyleBernardo M. Cândido, John N. Quinton, Mike R. James, Marx L.N. Silva, Teotônio S. de Carvalho, Wellington de Lima, Adnane Beniaich, Anette Eltner. High-resolution monitoring of diffuse (sheet or interrill) erosion using structure-from-motion. Geoderma. 2020; 375 ():114477.
Chicago/Turabian StyleBernardo M. Cândido; John N. Quinton; Mike R. James; Marx L.N. Silva; Teotônio S. de Carvalho; Wellington de Lima; Adnane Beniaich; Anette Eltner. 2020. "High-resolution monitoring of diffuse (sheet or interrill) erosion using structure-from-motion." Geoderma 375, no. : 114477.
An automatic workflow to measure surface flow velocities in rivers is introduced, including a Python tool. The method is based on particle-tracking velocimetry (PTV) and comprises an automatic definition of the search area for particles to track. Tracking is performed in the original images. Only the final tracks are geo-referenced, intersecting the image observations with water surface in object space. Detected particles and corresponding feature tracks are filtered considering particle and flow characteristics to mitigate the impact of sun glare and outliers. The method can be applied to different perspectives, including terrestrial and aerial (i.e. unmanned-aerial-vehicle; UAV) imagery. To account for camera movements images can be co-registered in an automatic approach. In addition to velocity estimates, discharge is calculated using the surface velocities and wetted cross section derived from surface models computed with structure-from-motion (SfM) and multi-media photogrammetry. The workflow is tested at two river reaches (paved and natural) in Germany. Reference data are provided by acoustic Doppler current profiler (ADCP) measurements. At the paved river reach, the highest deviations of flow velocity and discharge reach 4 % and 5 %, respectively. At the natural river highest deviations are larger (up to 31 %) due to the irregular cross-section shapes hindering the accurate contrasting of ADCP- and image-based results. The provided tool enables the measurement of surface flow velocities independently of the perspective from which images are acquired. With the contactless measurement, spatially distributed velocity fields can be estimated and river discharge in previously ungauged and unmeasured regions can be calculated, solely requiring some scaling information.
Anette Eltner; Hannes Sardemann; Jens Grundmann. Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery. Hydrology and Earth System Sciences 2020, 24, 1429 -1445.
AMA StyleAnette Eltner, Hannes Sardemann, Jens Grundmann. Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery. Hydrology and Earth System Sciences. 2020; 24 (3):1429-1445.
Chicago/Turabian StyleAnette Eltner; Hannes Sardemann; Jens Grundmann. 2020. "Technical Note: Flow velocity and discharge measurement in rivers using terrestrial and unmanned-aerial-vehicle imagery." Hydrology and Earth System Sciences 24, no. 3: 1429-1445.
The Sajó River in Hungary is a medium-sized sand-bed river along which intensive meander development and bank erosion occur. The process threatens agricultural lands and populated areas extensively. Therefore, preventive river management is needed.
Main geomorphological features, processes and in-channel flow conditions have to be studied in detail in order to reveal main driving factors. Datasets with high spatio-temporal resolution are necessary to identify relevant parameters. However, so far data density at this river is sparse and gauging stations are distributed poorly.
The aim of this study is the improvement of data availability to measure and model hydromorphodynamics of single reaches of the Sajó River. Therefore, multi-temporal field campaigns along selected sub-reaches are conducted with Unmanned Aerial Vehicles (UAV) and Unmanned Water Vehicles (UWV) to survey the topography, the river bed and flow conditions. The channel bathymetry is measured by a single-beam echo sounder mounted on a self-designed remotely controlled boat. The boat also integrates a Mobile Laser Scanner (MLS) to measure the river banks. Furthermore, a panorama camera system is installed to improve the pose estimation of the UWV functioning as a calibrated multi-sensor platform. UAV surveys were performed, using RGB and Thermal Infrared image sequences, to apply image velocimetry algorithms to characterize the river flow at selected cross-sections. ADCP measurements and Terrestrial Laser Scans (TLS) are used for accuracy assessment of the novel datasets.
Eventually, data captured over a 2-years period will be implemented into hydrodynamic modeling of the studied sub-reaches to better understand seasonal variations in channel morphodynamics.
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The project has been founded by the DAAD (57448822) and (Tempus Public Foundation & DAAD 307670). The research is also influenced by the HARMONIOUS COST Action (CA16219).
László Bertalan; Hannes Sardemann; David Mader; Noémi Mária Szopos; Bálint Nagy; Anette Eltner. Geomorphological and hydrological characterization of a meandering river by UAV and UWV applications. 2020, 1 .
AMA StyleLászló Bertalan, Hannes Sardemann, David Mader, Noémi Mária Szopos, Bálint Nagy, Anette Eltner. Geomorphological and hydrological characterization of a meandering river by UAV and UWV applications. . 2020; ():1.
Chicago/Turabian StyleLászló Bertalan; Hannes Sardemann; David Mader; Noémi Mária Szopos; Bálint Nagy; Anette Eltner. 2020. "Geomorphological and hydrological characterization of a meandering river by UAV and UWV applications." , no. : 1.
Soil erosion is one of the most prominent environmental problems of major interest to a vast field of research. Due to the complexity, variability and discontinuity of erosional processes, erosion model approaches are non-transferable to different spatial and temporal scales.
The objective of our project is the across-scale modelling of soil erosion, using photogrammetric measurements and optimization methods as well as physical based model approaches. Present process-based models are only valid for the observation scale they are parametrized and validated for. In the observed reality phenomena therefore occur, which are not or only to some extent reproducible by complex model concepts (e.g. development of rills or concentrated runoff within driving lanes). We present the synergetic combination of a physically described model with highly redundant observations from photogrammetric data processing. This enables both the validation of the erosion model EROSION-3D as well as the optimization of its parameters and potentially advancement of the mathematical process description. The photogrammetric observations (RGB and thermal) offer the opportunity of a temporal and spatial differentiated process assessment (splash, sheet and rill erosion, as well as deposition and transport). To this purpose, the acquisition of the respective operating processes and contributing factors, will be nested defined at three different scales (micro plot, single slope and catchment scale) on two sites (loess soil and residual soil).
Flexible cross-scale applicable photogrammetric methods, considering 3D reconstruction and flow measurement, combined with physical-based methods of soil erosion modelling shall enable a better and reliable understanding of soil erosion processes on various spatial and temporal observation scales. Consequently, the implementation of the adjusted model is aimed for to enable a cross-scale description and validation of scale-dependent processes (e.g. discrete consideration of thin sheet flow and rill genesis) to offer new perspectives on both interconnectivity of sediment transport and relationship between event frequency and magnitude.
Lea Epple; Andreas Kaiser; Marcus Schindewolf; Anette Eltner. High-resolution photogrammetric methods for nested parameterization and validation of a physical-based soil erosion model. 2020, 1 .
AMA StyleLea Epple, Andreas Kaiser, Marcus Schindewolf, Anette Eltner. High-resolution photogrammetric methods for nested parameterization and validation of a physical-based soil erosion model. . 2020; ():1.
Chicago/Turabian StyleLea Epple; Andreas Kaiser; Marcus Schindewolf; Anette Eltner. 2020. "High-resolution photogrammetric methods for nested parameterization and validation of a physical-based soil erosion model." , no. : 1.
We introduce a Python based software tool to measure surface flow velocities and to estimate discharge eventually. Minimum needed input are image sequences, some camera parameters and object space information to scale the image measurements. Reference information can be provided either indirectly via ground control point measurements or directly providing camera pose parameters. To improve the reliability and density of velocity measurements the area of interest has to be masked for image velocimetry. This can either be performed with a binary mask file or considering a 3D point cloud, for instance retrieved with Structure from Motion (SfM) photogrammetry, describing the region of interest. The tracking task can be done with particle image velocimetry (PIV) considering small interrogation regions or using particle tracking velocimetry (PTV) and thus detecting and tracking features at the water surface. To improve the robustness of the tracking results, filtering can be applied that implements statistical information about the flow direction, flow steadiness and average velocities.
The FlowVeloTool has been tested with two different datasets; one at a gauging station and one at a natural river reach. Thereby, UAV and terrestrial data were acquired and processed. Velocities can be estimated with an accuracy of 0.01 m/s. If information about the river topography and bathymetry are available, as in our demonstration, discharge can be estimated with an error ranging from 5 to 31 % (Eltner et al. 2019). Besides these results we demonstrate further developments of the FlowVeloTool regarding filtering of tracking results, discharge estimation, and processing of time series. Furthermore, we illustrate that thermal data can be used, as well, with our tool to retrieve river surface velocities.
Eltner, A., Sardemann, H., and Grundmann, J.: Flow velocity and discharge measurement in rivers using terrestrial and UAV imagery, Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-289, 2019.
Anette Eltner; Jens Grundmann. FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV. 2020, 1 .
AMA StyleAnette Eltner, Jens Grundmann. FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV. . 2020; ():1.
Chicago/Turabian StyleAnette Eltner; Jens Grundmann. 2020. "FlowVeloTool: Measuring flow velocities in terrestrial and UAV image sequences automatically with PIV and PTV." , no. : 1.
Knowledge about the interior and exterior camera orientation parameters is required to establish the relationship between 2D image content and 3D object data. Camera calibration is used to determine the interior orientation parameters, which are valid as long as the camera remains stable. However, information about the temporal stability of low-cost cameras due to the physical impact of temperature changes, such as those in smartphones, is still missing. This study investigates on the one hand the influence of heat dissipating smartphone components at the geometric integrity of implemented cameras and on the other hand the impact of ambient temperature changes at the geometry of uncoupled low-cost cameras considering a Raspberry Pi camera module that is exposed to controlled thermal radiation changes. If these impacts are neglected, transferring image measurements into object space will lead to wrong measurements due to high correlations between temperature and camera’s geometric stability. Monte-Carlo simulation is used to simulate temperature-related variations of the interior orientation parameters to assess the extent of potential errors in the 3D data ranging from a few millimetres up to five centimetres on a target in X- and Y-direction. The target is positioned at a distance of 10 m to the camera and the Z-axis is aligned with camera’s depth direction.
Melanie Elias; Anette Eltner; Frank Liebold; Hans-Gerd Maas. Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras. Sensors 2020, 20, 643 .
AMA StyleMelanie Elias, Anette Eltner, Frank Liebold, Hans-Gerd Maas. Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras. Sensors. 2020; 20 (3):643.
Chicago/Turabian StyleMelanie Elias; Anette Eltner; Frank Liebold; Hans-Gerd Maas. 2020. "Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras." Sensors 20, no. 3: 643.
Quantifying plant biomass in ecosystems is an essential basis for many ecological questions. A direct estimation of macrophyte biomass proves to be difficult for the large number of kettle holes in Pleistocene landscapes, due to their strong heterogeneities. This study compared a classical non-destructive method for biomass estimation based on allometric relationships built from a larger selection of plant trait variables with regressions only based on plant height and cover of four macrophyte species typical for kettle holes in northeast Germany (i.e. Carex riparia, Phalaris arundinacea, Persicaria amphibia, Rorippa amphibia). Their predictive power and potential applicability for remotely sensed biomass estimation using unmanned aerial systems (UAS) was evaluated. The usage of several in-situ measured plant traits of individual plants revealed best macrophyte biomass predictions (R² = 0.84 to 0.95). Yet, using only plant height and cover to predict biomass still showed a moderate to good correlation (R² = 0.52 to 0.81). Using P. arundinacea as an example, we demonstrated for one kettle hole the potential of calculating plant patch height from digital surface models (DSM) derived from UAS RGB images processed with structure-from-motion (SfM) photogrammetry. After applying a site-specific correction factor for discrepancies between reference field measurements of plant heights and DSM derived plant heights, we were able to calculate P. arundinacea biomass of the entire kettle hole based on allometric relationships using plant height and cover. Finally, we briefly discuss how further methodological development can improve UAS-derived plant height as predictor variable for biomass estimation.
Marlene Pätzig; Frenze Geiger; Daniel Rasche; Philipp Rauneker; Anette Eltner. Allometric relationships for selected macrophytes of kettle holes in northeast Germany as a basis for efficient biomass estimation using unmanned aerial systems (UAS). Aquatic Botany 2020, 162, 103202 .
AMA StyleMarlene Pätzig, Frenze Geiger, Daniel Rasche, Philipp Rauneker, Anette Eltner. Allometric relationships for selected macrophytes of kettle holes in northeast Germany as a basis for efficient biomass estimation using unmanned aerial systems (UAS). Aquatic Botany. 2020; 162 ():103202.
Chicago/Turabian StyleMarlene Pätzig; Frenze Geiger; Daniel Rasche; Philipp Rauneker; Anette Eltner. 2020. "Allometric relationships for selected macrophytes of kettle holes in northeast Germany as a basis for efficient biomass estimation using unmanned aerial systems (UAS)." Aquatic Botany 162, no. : 103202.
Anette Eltner. comment to review 2. 2019, 1 .
AMA StyleAnette Eltner. comment to review 2. . 2019; ():1.
Chicago/Turabian StyleAnette Eltner. 2019. "comment to review 2." , no. : 1.
Anette Eltner. comment to review by Manfreda. 2019, 1 .
AMA StyleAnette Eltner. comment to review by Manfreda. . 2019; ():1.
Chicago/Turabian StyleAnette Eltner. 2019. "comment to review by Manfreda." , no. : 1.
Digital Terrain analysis (DTA) and modeling has been a flourishing interdisciplinary field for decades, with applications in hydrology, geomorphology, soil science, engineering projects and computer sciences. Currently, DTA is characterized by a proliferation of multispectral data from new sensors and platforms, driven by regional and national governments, commercial businesses, and scientific groups, with a general trend towards data with higher spatial, spectral or temporal resolutions. Deriving meaningful and interpretable products from such a large pool of data sources sets new challenges. The articles included in this special issue (SI) focuses on terrain analysis applications that advance the fields of hydrology, geomorphology, soil science, geographic information software (GIS), and computer science. They showcase challenging examples of DTA tackling different subjects or different point of views on the same subject.
Giulia Sofia; Anette Eltner; Efthymios Nikolopoulos; Christopher Crosby. Leading Progress in Digital Terrain Analysis and Modeling. ISPRS International Journal of Geo-Information 2019, 8, 372 .
AMA StyleGiulia Sofia, Anette Eltner, Efthymios Nikolopoulos, Christopher Crosby. Leading Progress in Digital Terrain Analysis and Modeling. ISPRS International Journal of Geo-Information. 2019; 8 (9):372.
Chicago/Turabian StyleGiulia Sofia; Anette Eltner; Efthymios Nikolopoulos; Christopher Crosby. 2019. "Leading Progress in Digital Terrain Analysis and Modeling." ISPRS International Journal of Geo-Information 8, no. 9: 372.
Detection and classification of tree species from remote sensing data were performed using mainly multispectral and hyperspectral images and Light Detection And Ranging (LiDAR) data. Despite the comparatively lower cost and higher spatial resolution, few studies focused on images captured by Red-Green-Blue (RGB) sensors. Besides, the recent years have witnessed an impressive progress of deep learning methods for object detection. Motivated by this scenario, we proposed and evaluated the usage of Convolutional Neural Network (CNN)-based methods combined with Unmanned Aerial Vehicle (UAV) high spatial resolution RGB imagery for the detection of law protected tree species. Three state-of-the-art object detection methods were evaluated: Faster Region-based Convolutional Neural Network (Faster R-CNN), YOLOv3 and RetinaNet. A dataset was built to assess the selected methods, comprising 392 RBG images captured from August 2018 to February 2019, over a forested urban area in midwest Brazil. The target object is an important tree species threatened by extinction known as Dipteryx alata Vogel (Fabaceae). The experimental analysis delivered average precision around 92% with an associated processing times below 30 miliseconds.
Anderson Aparecido Dos Santos; José Marcato Junior; Márcio Santos Araújo; David Robledo Di Martini; Everton Castelão Tetila; Henrique Lopes Siqueira; Camila Aoki; Anette Eltner; Edson Takashi Matsubara; Hemerson Pistori; Raul Queiroz Feitosa; Veraldo Liesenberg; Wesley Nunes Gonçalves. Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs. Sensors 2019, 19, 3595 .
AMA StyleAnderson Aparecido Dos Santos, José Marcato Junior, Márcio Santos Araújo, David Robledo Di Martini, Everton Castelão Tetila, Henrique Lopes Siqueira, Camila Aoki, Anette Eltner, Edson Takashi Matsubara, Hemerson Pistori, Raul Queiroz Feitosa, Veraldo Liesenberg, Wesley Nunes Gonçalves. Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs. Sensors. 2019; 19 (16):3595.
Chicago/Turabian StyleAnderson Aparecido Dos Santos; José Marcato Junior; Márcio Santos Araújo; David Robledo Di Martini; Everton Castelão Tetila; Henrique Lopes Siqueira; Camila Aoki; Anette Eltner; Edson Takashi Matsubara; Hemerson Pistori; Raul Queiroz Feitosa; Veraldo Liesenberg; Wesley Nunes Gonçalves. 2019. "Assessment of CNN-Based Methods for Individual Tree Detection on Images Captured by RGB Cameras Attached to UAVs." Sensors 19, no. 16: 3595.