UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment

Trepekli, Katerina; Balstrøm, Thomas; Friborg, Thomas; Fog, Bjarne; Allotey, Albert N.; Kofie, Richard Y.; Møller-Jensen, Lasse

doi:10.1007/s11069-022-05308-9

Cited by 40 publications

(18 citation statements)

References 67 publications

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“…Other, higher-resolution DEMs are available, such as Environment Agency lidar, which typically have considerable advantages for flood modelling (Sanders, 2007;Muhadi et al, 2020;Md Ali et al, 2015;Trepekli et al, 2022). However, complete lidar coverage of the study area was not available.…”

Section: Digital Elevation Modelmentioning

confidence: 99%

Intercomparison of global reanalysis precipitation for flood risk modelling

McClean

Dawson

Kilsby

2023

Hydrol. Earth Syst. Sci.

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Abstract. Reanalysis datasets are increasingly used to drive flood models, especially for continental and global analysis and in areas of data scarcity. However, the consequence of this for risk estimation has not been fully explored. We investigate the implications of four reanalysis products (ERA-5, CFSR, MERRA-2 and JRA-55) on simulations of historic flood events in five basins in England. These results are compared to a benchmark national gauge-based product (CEH-GEAR1hr). The benchmark demonstrated better accuracy than reanalysis products when compared with observations of water depth and flood extent. All reanalysis products predicted fewer buildings would be inundated by the events than the national dataset. JRA-55 was the worst by a significant margin, underestimating by 40 % compared with 14 %–18 % for the other reanalysis products. CFSR estimated building inundation the most accurately, while ERA-5 demonstrated the lowest error in terms of river stage (29.4 %) and floodplain depth (28.6 %). Accuracy varied geographically, and no product performed best across all basins. Global reanalysis products provide a useful resource for flood modelling where no other data are available, but they should be used with caution due to the underestimation of impacts shown here. Until a more systematic international strategy for the collection of rainfall and flood impact data ensures more complete global coverage for validation, multiple reanalysis products should be used concurrently to capture the range of uncertainties.

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Section: Digital Elevation Modelmentioning

confidence: 99%

Intercomparison of global reanalysis precipitation for flood risk modelling

McClean

Dawson

Kilsby

2023

Hydrol. Earth Syst. Sci.

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“…Five hours and two days were required to complete the work, respectively. Similarly, Trepekli et al [193] demonstrated that a fine digital terrain model (DTM) obtained using airborne LiDAR technology can significantly improve the accuracy of urban waterlogging simulation. The authors acquired high-resolution (0.3 m) DTMs for three urban areas in the city of Accra, Ghana, Africa.…”

Section: B Urban Disaster Emergency Responsementioning

confidence: 99%

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

Xu,

Wang,

Han

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Smart management of urban space is an important way to achieve the goal of sustainable urban development. Unmanned aerial vehicles (UAVs) have been widely used in urban space management work and have greatly improved the level of it. UAV technology assisting urban space management involves many technical details and has generated a number of problems. Therefore, a systematic review of the applications of UAVs in urban spatial management work is very necessary and can provide a comprehensive reference for relevant researchers. This is conducive to the generation of more new insights, methods, and applications. However, according to our research, there is a lack of relevant systematic investigation. We screened and researched a large number of relevant literatures (about 230) with the help of search tools such as Web of Science and IEEE Explore, and combined with our own working experience to review and summarize the field in an all-round way. Taking the definition, needs, and challenges of urban spatial management as an entry point, this paper systematically summarizes the task flow, working paradigm, technical system, and application direction of UAV-assisted urban spatial management. It also takes our recently developed UAV urban management system as an example to provide an introduction on how to integrate advanced technologies such as UAV and artificial intelligence. Finally, this paper summarizes several concluding findings and proposes several future research directions. The research in this paper shows that UAV technology has already played a great role in urban spatial management work, but it is still far from realizing automation and intelligence, and it needs to continue to make efforts in terms of methods, systems, applications, and policies. In order to achieve these goals, UAV technology can be further integrated with advanced technologies such as deep learning, more types of UAV industry applications can be carried out, more functional UAS can be developed, and better management policies can be formulated.

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“…Some research has been conducted to assess the performance of different satellite‐based DEMs in landslide susceptibility, river morphology and watershed analysis, where different DEMs are shown to produce different results (Arash & Yasi, 2022; Chanu & Bakimchandra, 2022; Karabulut & Özdemir, 2019; Pryde et al, 2007; Rabby et al, 2020; Saleem et al, 2019). Also, studies are available on the use of UAV‐based DTM in flood modeling in Indonesia (Derka et al, 2022), South Korea (Lee et al, 2022), China (Li et al, 2021), Italy (Annis et al, 2020), Philippines (Gagula et al, 2023), Ghana (Trepekli et al, 2022), Colombia (Escobar Villanueva et al, 2019), Turkey (Yalcin, 2020), and Poland (Karamuz et al, 2020). However, comparative studies on effectiveness of satellite‐based DEMs and UAV‐based DTM in local‐level flood modeling are scarce (Adesina et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of UAV‐based DTM and satellite‐based DEMs for local‐level flood modeling in Jamuna floodplain

Iqbal

Mondal

Veerbeek

et al. 2023

J Flood Risk Management

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Open‐source, satellite‐based digital elevation models (DEMs) are widely used for flood modeling. However, studies on effectiveness of these DEMs in depicting local‐level flood processes are limited. This study generated a high‐resolution digital terrain model (DTM) based on unmanned aerial vehicle (UAV) photogrammetry and used in a two‐dimensional (2D) hydrodynamic model (HEC‐RAS) to simulate the flood processes in a floodplain environment of the Jamuna River in northern Bangladesh. The effectiveness of a few satellite‐based DEMs was also compared with this DTM by using the DEMs in the same hydrodynamic model. Field data for two flood seasons were collected to develop the model. The results indicate that the 2D model with UAV‐based DTM provides the flood parameters, such as flood arrival time, depth, duration and extent, better than those from the satellite‐based DEMs. Of the open‐source DEMs, the FABDEM and the WorldDEM™ have the least errors and provide better results compared to the SRTM30, ALOS PALSAR, and ASTER DEMs. The UAV technique with ground control points and field measurements for the tree‐canopy and water areas is very useful in generating a fit‐for‐purpose DTM. The findings of this study would be useful for terrain generation and DEM selection for local‐level flood modeling elsewhere.

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UAV-borne, LiDAR-based elevation modelling: a method for improving local-scale urban flood risk assessment

Cited by 40 publications

References 67 publications

Intercomparison of global reanalysis precipitation for flood risk modelling

Intercomparison of global reanalysis precipitation for flood risk modelling

A Survey on UAV Applications in Smart City Management: Challenges, Advances, and Opportunities

Effectiveness of UAV‐based DTM and satellite‐based DEMs for local‐level flood modeling in Jamuna floodplain

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