Article
Engineering, Environmental
Junfeng Liu, Rensheng Chen, Yongjian Ding, Chuntan Han, Shaoxiu Ma
Summary: The novel O-T-SfM photogrammetry system can accurately monitor snow-surface processes in high-mountain snow-covered regions, especially performing best in snow-free conditions. However, it struggles to capture the snow melting processes from July to September and smooth fresh snowfalls from April to June.
COLD REGIONS SCIENCE AND TECHNOLOGY
(2021)
Article
Environmental Sciences
Muhammad Waqas Khan, Stuart Dunning, Rupert Bainbridge, James Martin, Alejandro Diaz-Moreno, Hamdi Torun, Nanlin Jin, John Woodward, Michael Lim
Summary: The study introduces a novel, low-cost flow visualization technique using time-lapsed imagery for real-time analysis of slope movement, aiming to identify precursor events of landslides. The approach, applied to the Rest and Be Thankful slope in Scotland, successfully detected and reported precursor slope movement, providing early warning, effective management, and landslide impact mitigation.
Article
Environmental Sciences
Daniele Ventura, Francesca Napoleone, Silvia Cannucci, Samuel Alleaume, Emiliana Valentini, Edoardo Casoli, Sabina Burrascano
Summary: This study used a low-cost unmanned aerial vehicle (UAV) and Structure from Motion (SfM) photogrammetry to capture high-resolution RGB imagery of semi-natural grasslands. Image classification through Object-Based Image Analysis (OBIA) allowed for accurate identification of three grassland types. The use of orthomosaics, digital elevation models (DEMs), and canopy height models (CHMs) achieved high classification accuracies and provided valuable information on vegetation cover and terrain characteristics.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Environmental Sciences
Xabier Blanch, Anette Eltner, Marta Guinau, Antonio Abellan
Summary: This paper presents an enhanced workflow for image-based 3D reconstruction of high-resolution models using fixed time-lapse camera systems, based on multi-epoch multi-images (MEMI) to exploit redundancy. The workflow is capable of obtaining photogrammetric models with a higher quality than the classic Structure from Motion (SfM) time-lapse photogrammetry workflow, reducing the error up to a factor of 2.
Article
Environmental Sciences
Sebrian Mirdeklis Beselly, Mick van der Wegen, Uwe Grueters, Johan Reyns, Jasper Dijkstra, Dano Roelvink
Summary: This study presents a novel approach to exploring mangrove dynamics on a prograding delta by integrating UAV and satellite imagery, resulting in high-spatiotemporal-resolution mangrove extent maps and vegetation coverage dynamics. The analysis is essential for ecologists, coastal managers, and policymakers.
Article
Environmental Sciences
Anette Eltner, Patrik Ola Bressan, Thales Akiyama, Wesley Nunes Goncalves, Jose Marcato Junior
Summary: This study introduces an automatic water stage measurement method combining deep learning and photogrammetric techniques, providing accurate water stage measurements. The method first segments water in images using convolutional neural networks and then transforms image information into metric water stage values by intersecting with a 3D model reconstructed with structure-from-motion photogrammetry. Experimental results show that the method achieves a correlation of 0.93 with reference gauges and average deviations lower than 4 cm.
WATER RESOURCES RESEARCH
(2021)
Article
Geosciences, Multidisciplinary
Seth N. N. Goldstein, Jonathan C. C. Ryan, Penelope R. R. How, Sarah E. E. Esenther, Lincoln H. H. Pitcher, Adam L. L. LeWinter, Brandon T. T. Overstreet, Ethan D. D. Kyzivat, Jessica V. V. Fayne, Laurence C. C. Smith
Summary: Georectified time-lapse camera images accurately retrieve stage fluctuations of the proglacial Minturn River, enabling effective monitoring of meltwater runoff from the Greenland Ice Sheet. This non-contact approach provides a promising method for studying proglacial hydrological processes in harsh polar environments.
FRONTIERS IN EARTH SCIENCE
(2023)
Article
Geography, Physical
Shashank Bhushan, David Shean, Oleg Alexandrov, Scott Henderson
Summary: The text discusses the potential of the Planet SkySat-C SmallSat constellation in acquiring high-resolution imagery for 3D mapping, but highlights limitations in processing software and geolocation accuracy. The authors propose an open-source workflow to refine camera models and improve image geolocation, resulting in accurate DEMs with high vertical accuracy. The study demonstrates the scalability of the workflow for batch processing of SkySat stereo imagery and its potential for other frame camera systems with limited initial geolocation accuracy.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2021)
Article
Environmental Sciences
Daniele Ventura, Luca Grosso, Davide Pensa, Edoardo Casoli, Gianluca Mancini, Tommaso Valente, Michele Scardi, Arnold Rakaj
Summary: This study evaluated an integrated approach using low-cost unmanned aerial and surface vehicles to collect detailed remote sensing data and accurately map shallow benthic communities. Photogrammetric outputs from UAV and USV were classified using OBIA approach and achieved overall classification accuracies over 70%. The results demonstrated the practicality and feasibility of using aerial and underwater ultra-high spatial resolution imagery for detailed analysis.
FRONTIERS IN MARINE SCIENCE
(2023)
Article
Environmental Sciences
Francesco Mugnai, Grazia Tucci
Summary: The main aim of this study is to compare the photogrammetric performances of four user-grade unmanned aircraft systems (UAS) including Phantom 4 Adv, Mavic 2 Pro, Mavic Air 2, and Mavic Mini 2. The results show that Phantom 4 has the best performance, while Mavic Mini 2 has lower costs among ultralight drones.
Article
Remote Sensing
Martin Stroner, Rudolf Urban, Tomas Kremen, Jaroslav Braun
Summary: In this study, the accuracy and coverage of LiDAR-UAV system DJI Zenmuse L1 and Digital Aerial Photogrammetric system (DAP-UAV) DJI Zenmuse P1 were evaluated in a forested area under leaf-off conditions on three sites with varying terrain ruggedness and tree type combinations. The results showed that branches did not affect the accuracy of the LiDAR-UAV and DAP-UAV derived terrain clouds. The photogrammetric data had even better elevation accuracy than LiDAR data, reaching as low as 0.015 m on all sites. However, the LiDAR system provided better coverage, with almost full coverage at all sites, while the DAP-UAV coverage declined with increasing density of branches.
EUROPEAN JOURNAL OF REMOTE SENSING
(2023)
Article
Remote Sensing
Artur Gafurov
Summary: This paper presents the results of surveying the Sarycum area of the Dagestan Nature Reserve of Russia with a DJI Phantom 4 UAV and methodological recommendations for conducting work on such a large territory. The obtained DEM with 0.5 m resolution and ultrahigh resolution orthophotoplane allow for assessing aeolian processes dynamics at a qualitatively different level.
Article
Remote Sensing
Massimo Fabris, Pietro Fontana Granotto, Michele Monego
Summary: The study focuses on using SfM photogrammetry and laser scanning techniques to create a 3D model of a degraded historical building for structural analysis. A low-cost drone, SLR camera, and smartphone were used for the survey. The accuracy and performance of the SfM approach were evaluated by comparing the data with high-precision TLS acquisitions. The integration of the best SfM model and TLS model was used for finite element analysis to assess the safety of the structure.
Article
Geography, Physical
Tyler Wong, Sami Khanal, Kaiguang Zhao, Steve W. Lyon
Summary: Grain size assessments are important for understanding geomorphological, hydrological, and ecological processes within rivers. Recent research has shown that utilizing Structure-from-Motion (SfM) photogrammetry with imagery from unmanned aerial vehicles (UAVs) provides a promising method for rapidly characterizing grain sizes along rivers compared to traditional field-based methods. This study evaluated different methods for estimating grain sizes in gravel bars along the Olentangy River in Columbus, Ohio. Findings revealed that statistical models calibrated on image texture were more accurate than those based on topographic roughness, possibly due to site-specific patterns of grain size, shape, and imbrication. The research demonstrates the potential of UAV-SfM approaches as an accessible method for characterizing surface grain sizes along rivers at higher resolutions than traditional methods provide.
EARTH SURFACE PROCESSES AND LANDFORMS
(2023)
Article
Environmental Sciences
Zoe Bessin, Marion Jaud, Pauline Letortu, Emmanuel Vassilakis, Niki Evelpidou, Stephane Costa, Christophe Delacourt
Summary: Many issues arise from sea cliff recession, including threats to coastal communities and infrastructure. This study compares three remote sensing methods based on structure-from-motion (SfM) photogrammetry or stereorestitution: boat-based SfM photogrammetry with smartphones, unmanned aerial system (UAS) or unmanned aerial vehicle (UAV) photogrammetry with centimetric positioning, and Pleiades tri-stereo imagery. Results show that the satellite approach is better for long-term monitoring, while the boat-based approach provides better reconstruction of cliff foot. The UAS with centimetric positioning offers a compromise between the two, although flight autonomy limits its coverage area.
Article
Geography, Physical
Nils Onnen, Anette Eltner, Goswin Heckrath, Kristof Van Oost
EARTH SURFACE PROCESSES AND LANDFORMS
(2020)
Article
Soil Science
Bernardo M. Candido, John N. Quinton, Mike R. James, Marx L. N. Silva, Teotonio S. de Carvalho, Wellington de Lima, Adnane Beniaich, Anette Eltner
Article
Environmental Sciences
Anette Eltner, Patrik Ola Bressan, Thales Akiyama, Wesley Nunes Goncalves, Jose Marcato Junior
Summary: This study introduces an automatic water stage measurement method combining deep learning and photogrammetric techniques, providing accurate water stage measurements. The method first segments water in images using convolutional neural networks and then transforms image information into metric water stage values by intersecting with a 3D model reconstructed with structure-from-motion photogrammetry. Experimental results show that the method achieves a correlation of 0.93 with reference gauges and average deviations lower than 4 cm.
WATER RESOURCES RESEARCH
(2021)
Article
Environmental Sciences
Xabier Blanch, Anette Eltner, Marta Guinau, Antonio Abellan
Summary: This paper presents an enhanced workflow for image-based 3D reconstruction of high-resolution models using fixed time-lapse camera systems, based on multi-epoch multi-images (MEMI) to exploit redundancy. The workflow is capable of obtaining photogrammetric models with a higher quality than the classic Structure from Motion (SfM) time-lapse photogrammetry workflow, reducing the error up to a factor of 2.
Article
Geography, Physical
Anette Eltner, Laszlo Bertalan, Jens Grundmann, Matthew Thomas Perks, Eliisa Lotsari
Summary: Unmanned aerial systems (UASs) are commonly utilized in fluvial geomorphology for their ability to capture continuous data. This study introduces a (semi-)automated workflow for measuring river bathymetry and surface flow velocities using UAS videos and imagery. By combining video frame filtering, structure from motion (SfM) photogrammetry, and image velocimetry analysis, the workflow accurately estimates flow patterns and discharge, providing valuable input for hydro-morphological models.
EARTH SURFACE PROCESSES AND LANDFORMS
(2021)
Review
Environmental Sciences
Lea Epple, Andreas Kaiser, Marcus Schindewolf, Anne Bienert, Jonas Lenz, Anette Eltner
Summary: Soil erosion modelling tools and assessment techniques play a crucial role in investigating soil erosion processes and predicting their impacts. However, current models still have limitations and require further development and improvement.
Article
Ecology
Jose Augusto Correa Martins, Jose Marcato Junior, Marlene Patzig, Diego Andre Sant'Ana, Hemerson Pistori, Veraldo Liesenberg, Anette Eltner
Summary: The use of unmanned aerial vehicles and deep learning techniques enables accurate vegetation segmentation and classification in wetland environments, which is crucial for assessing the health of ecosystems.
REMOTE SENSING IN ECOLOGY AND CONSERVATION
(2023)
Article
Geography, Physical
Radek Malinowski, Goswin Heckrath, Marcin Rybicki, Anette Eltner
Summary: Soil erosion by water is a significant global issue, and accurate monitoring and mapping of erosion is crucial for calibration and evaluation of erosion models. This study developed automated remote sensing techniques for identification and mapping of rills, and tested the methods in different agricultural fields. The results showed high accuracy in rill recognition, although sensitivity to small rills and similar geometry with other features was observed.
EARTH SURFACE PROCESSES AND LANDFORMS
(2023)
Article
Environmental Sciences
Farhad Bahmanpouri, Anette Eltner, Silvia Barbetta, Laszlo Bertalan, Tommaso Moramarco
Summary: The current research aims to predict the velocity distribution and discharge rates in rivers based on the entropy concept using only one surface velocity measurement. The uncrewed aerial vehicle (UAV)-based image acquisition technique was applied to collect the surface velocity distribution along two European rivers. The entropy approach accurately predicts the velocity distribution and discharge rates with a percentage error lower than 13%.
WATER RESOURCES RESEARCH
(2022)
Article
Remote Sensing
Hannes Brassel, Thomas Zeh, Hartmut Fricke, Anette Eltner
Summary: With unmanned aerial vehicles, quick responses to urgent needs can be realized, but geographical zones restrict their usage. This study combines facility location problem and routing problem to optimize hangar locations and UAV mission trajectories, considering geographical zones, battery constraints, and wind impact. Water rescue missions are used as an example, and the solution decreases the average service time from 570.4s to 351.1s for one hangar and 287.2s for two hangars.
Article
Remote Sensing
Franz Wagner, Anette Eltner, Hans-Gerd Maas
Summary: A comparison of 32 convolutional neural networks was conducted to obtain reliable water segmentations for real-time monitoring of river water levels. The networks were trained on a new river water segmentation dataset and two augmentation methods were developed to prevent overfitting. U-Net with a ResNeXt 50 encoding network achieved the best performance on the water segmentation dataset, with IoU scores of 0.91, 0.98, and 0.93 for no augmentation, offline augmentation, and online augmentation respectively. The applied augmentation strategies on Cityscapes also showed improvements in IoU scores.
INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
(2023)
Article
Geography, Physical
Ferdinand Maiwald, Denis Feurer, Anette Eltner
Summary: With the ongoing digitization in archives, historical aerial images and their detailed information are becoming increasingly available for research. However, conventional workflows often fail in registering these images due to their radiometric characteristics and vast temporal changes. In this study, the authors propose using two synergetic neural network methods to improve feature matching for historical aerial images, enabling the generation of high-quality multi-temporal DSMs.
ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
(2023)
Article
Environmental Sciences
Frank Liebold, David Mader, Hannes Sardemann, Anette Eltner, Hans-Gerd Maas
Summary: Newly developed low-cost cameras exhibit lens distortion patterns that cannot be well handled with existing models. This study presents an approach that divides the image sensor and distortion modeling into two zones for the application of an extended radial lens distortion model. Practical tests show that the new model reduces the distortion better than the standard model.
Article
Geosciences, Multidisciplinary
Wouter J. M. Knoben, Diana Spieler
Summary: This paper introduces a computational exercise to help students understand model structure uncertainty. Through this exercise, students can gain insights on the impact of uncertainties from different sources on modeling results and the usability of acquired model simulations.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2022)
Article
Geosciences, Multidisciplinary
Robert Ljubicic, Dariia Strelnikova, Matthew T. Perks, Anette Eltner, Salvador Pena-Haro, Alonso Pizarro, Silvano Fortunato Dal Sasso, Ulf Scherling, Pietro Vuono, Salvatore Manfreda
Summary: This study summarizes several freely available DIS tools applicable to UAS velocimetry and evaluates them in terms of stabilization accuracy, robustness, computational complexity, and user experience. Differences were found in the method for identifying static features in videos, with state-of-the-art methods relying on automatic selection showing higher stabilization accuracy but requiring fewer user-provided parameters.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
