Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory

Schumann, Guy; Muhlhausen, Joseph; Andreadis, Konstantinos M.

doi:10.3390/rs11080982

Cited by 39 publications

(41 citation statements)

References 24 publications

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“…The three main factors identified were related to catchment size, flood source type, and phase of a flood event (Table 1). Catchment size influences the amount of data gathered [23] and the type of UAS that is required to provide the spatial coverage [24].…”

Section: Development Of An Uas Deployment Analysis Matrixmentioning

confidence: 99%

Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

Salmoral

Casado

Muthusamy

et al. 2020

Water

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There is increasing interest in using Unmanned Aircraft Systems (UAS) in flood risk management activities including in response to flood events. However, there is little evidence that they are used in a structured and strategic manner to best effect. An effective response to flooding is essential if lives are to be saved and suffering alleviated. This study evaluates how UAS can be used in the preparation for and response to flood emergencies and develops guidelines for their deployment before, during and after a flood event. A comprehensive literature review and interviews, with people with practical experience of flood risk management, compared the current organizational and operational structures for flood emergency response in both England and India, and developed a deployment analysis matrix of existing UAS applications. An online survey was carried out in England to assess how the technology could be further developed to meet flood emergency response needs. The deployment analysis matrix has the potential to be translated into an Indian context and other countries. Those organizations responsible for overseeing flood risk management activities including the response to flooding events will have to keep abreast of the rapid technological advances in UAS if they are to be used to best effect.

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Section: Development Of An Uas Deployment Analysis Matrixmentioning

confidence: 99%

Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

Salmoral

Casado

Muthusamy

et al. 2020

Water

View full text Add to dashboard Cite

show abstract

“…SfM is increasingly becoming more popular amongst scientists due to its affordability and the existence of easy-to-use commercial and open source software applications that allow even nonscientists to build three-dimensional models of surface features which are subjects of scientific research (Wróżyński, Pyszny, Sojka, Przybla, & Murat-Blażejewska, 2017). SfM has been used in a vast array of ecohydrological and river management applications such as for the observation of different river stages (Duró, Crosato, Kleinhans, & Uijttewaal, 2018;Niedzielski, Witek, & Spallek, 2016), analysis of feedbacks between fluvial geomorphology and riparian vegetation (Hortobágyi et al, 2017); floodplain inundation mapping (Schumann, Muhlhausen, & Andreadis, 2019) and river management and restoration (Kubota, Kawai, & Kadotani, 2017;Marteau, Vericat, Gibbins, Batalla, & Green, 2017).…”

Section: Advancing the Use Of Uav And Sfm Photogrammetry To Improvementioning

confidence: 99%

“…Researchers working with such measurements (such as calculating canopy height) should therefore look to improve error-assessment approaches, making them easier to apply to these large spatiotemporal datasets. Another key concern regarding the application of UAVs in Mediterranean IRES is one of strict legislation regarding UAV flights over populated areas (Schumann et al, 2019). Because Mediterranean IRES are often highly modified environments lying close to human settlement, the use of UAV approaches may be limited either by pragmatic health and safety concerns or by legal impediments to flight over population centres.…”

Section: Advancing the Use Of Uav And Sfm Photogrammetry To Improvementioning

confidence: 99%

Mediterranean intermittent rivers and ephemeral streams: Challenges in monitoring complexity

et al. 2019

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In most cases, intermittent rivers and ephemeral streams (IRES) form the principal surface water bodies in Mediterranean regions and people are heavily dependent on them. Strong reliance on such streams results in numerous pressures, and complex relationships are formed between their various natural and anthropogenic components, which creates numerous challenges to decision-makers who require a better understanding of IRES to establish effective measures to restore ecosystem functionality and implement changes, while maintaining human well-being. In this paper, we present a conceptual framework that identifies process cascades and links to identify the variables governing Mediterranean IRES and map their complex interrelations.Climate is the main external driver, and it affects IRES through precipitation, temperature, wind action, and geomorphic factors, which in turn influence the physical and biological attributes of IRES. Monitoring challenges and knowledge gaps are presented from the conceptual framework, highlighting the need to identify solutions for the monitoring and mapping the spatio-temporal variability of flow regimes and their influence on bio-physical variables and mapping and quantifying ecosystem services for integrated environmental management. Finally, we argue that remote sensing techniques, specifically, the combined use of unmanned aerial vehicles and structure from motion photogrammetry, are key to bridging gaps in understanding and meeting the challenges of managing Mediterranean IRES.

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“…Regarding flood risk modeling and mapping, derived UAVs are recognized as reliable data sources to produce DEMs and orthophoto mosaics [14][15][16][17]. In this field, several approaches have been tested to extract DEMs from the UAV point clouds, as the examples below show.…”

Section: Data Sources To Obtain Digital Elevation Models (Dems) For Fmentioning

confidence: 99%

“…Along the same lines, Govedarica et al [17] presented the possibility of using UAV DEMs in flood risk assessment in low-lying areas, obtaining a root mean square error (RMSE) of 60 cm compared with LiDAR data. Schumann et al [16] claim that the SfM DEM should yield nearly identical performances when used for flood mapping and prediction as those typically obtained from LiDAR with a trivial bias of 1.6 cm and root mean standard deviation (RMSD) of 39 cm between bare-earth terrain models.…”

Section: Data Sources To Obtain Digital Elevation Models (Dems) For Fmentioning

confidence: 99%

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

et al. 2019

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Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and -0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

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Rapid Mapping of Small-Scale River-Floodplain Environments Using UAV SfM Supports Classical Theory

Cited by 39 publications

References 24 publications

Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

Guidelines for the Use of Unmanned Aerial Systems in Flood Emergency Response

Mediterranean intermittent rivers and ephemeral streams: Challenges in monitoring complexity

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

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