Articles | Volume 23, issue 1
https://doi.org/10.5194/nhess-23-375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-23-375-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Bare-earth DEM generation from ArcticDEM and its use in flood simulation
School of Geographical Sciences, University of Bristol, Bristol, UK
Paul D. Bates
School of Geographical Sciences, University of Bristol, Bristol, UK
Jeffery C. Neal
School of Geographical Sciences, University of Bristol, Bristol, UK
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Cited articles
Archer, L., Neal, J. C., Bates, P. D., and House, J. I.:
Comparing TanDEM-X data with frequently used DEMs for flood inundation modeling, Water Resour. Res., 54, 10–205, https://doi.org/10.1029/2018WR023688, 2018.
Armston, J., Bunting, P., Flood, N., and Gillingham, S.: Pylidar 0.4.4 documentation [code], http://www.pylidar.org/en/latest/index.html (last access: 26 January 2023), 2015.
Bates, P. D., Horritt, M. S., and Fewtrell, T. J.:
A simple inertial formulation of the shallow water equations for efficient two-dimensional flood inundation modelling, J. Hydrol., 387, 33–45, https://doi.org/10.1016/j.jhydrol.2010.03.027, 2010.
Bates, P. D., Neal, J. C., Alsdorf, D., and Schumann, G. J. P.:
Observing global surface water flood dynamics, in: The Earth's Hydrological Cycle, Springer, 839–852, https://doi.org/10.1007/s10712-013-9269-4, 2013.
Bates, P.D., Quinn, N., Sampson, C., Smith, A., Wing, O., Sosa, J., Savage, J., Olcese, G., Neal, J., Schumann, G., and Giustarini, L.:
Combined modeling of US fluvial, pluvial, and coastal flood hazard under current and future climates, Water Resour. Res., 57, e2020WR028673, https://doi.org/10.1029/2020WR028673, 2021.
Ben-Haim, Z., Anisimov, V., Yonas, A., Gulshan, V., Shafi, Y., Hoyer, S., and Nevo, S.:
Inundation modeling in data scarce regions, arXiv [preprint], https://doi.org/10.48550/arXiv.1910.05006, 11 October 2019.
Chen, Q., Gong, P., Baldocchi, D., and Xie, G.:
Filtering airborne laser scanning data with morphological methods, Photogramm. Eng. Rem. S., 73, 175–185, https://doi.org/10.14358/PERS.73.2.175, 2007.
Chen, Z., Gao, B., and Devereux, B.:
State-of-the-art: DTM generation using airborne LIDAR data, Sensors, 17, 150, https://doi.org/10.3390/s17010150, 2017.
Cui, Z., Zhang, K., Zhang, C., and Chen, S. C.:
A cluster-based morphological filter for geospatial data analysis, in: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data, 4 November 2013, Orlando, Florida, USA, 1–7, https://doi.org/10.1145/2534921.2534922, 2013.
DeWitt, J. D., Warner, T. A., Chirico, P. G., and Bergstresser, S. E.:
Creating high-resolution bare-earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for LIDAR point cloud filtering, GISci. Remote Sens., 54, 552–572, https://doi.org/10.1080/15481603.2017.1295514, 2017.
Faherty, D., Schumann, G. J. P., and Moller, D. K.:
Bare Earth DEM Generation for Large Floodplains Using Image Classification in High-Resolution Single-Pass InSAR, Front. Earth Sci., 8, 27, https://doi.org/10.3389/feart.2020.00027, 2020.
Garbrecht, J. and Martz, L. W.:
Digital elevation model issues in water resources modeling. Hydrologic and hydraulic modeling support with geographic information systems, https://proceedings.esri.com/library/userconf/proc99/proceed/papers/pap866/p866.htm (last assess: 22 July 2022), 1–28, 2000.
Hall, D. K. and Riggs, G. A.:
MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6, NASA National Snow and Ice Data Center Distributed Active Archive Center, Boulder, Colorado USA [data set], https://doi.org/10.5067/MODIS/MOD10A1.006, 2016.
Hawker, L., Bates, P., Neal, J., and Rougier, J.:
Perspectives on digital elevation model (DEM) simulation for flood modeling in the absence of a high-accuracy open access global DEM, Front. Earth Sci., 6, 233, https://doi.org/10.3389/feart.2018.00233, 2018.
Hawker, L., Uhe, P., Paulo, L., Sosa, J., Savage, J., Sampson, C., and Neal, J.:
A 30 m global map of elevation with forests and buildings removed, Environ. Res. Lett., 17, 024016, https://doi.org/10.1088/1748-9326/ac4d4f, 2022.
Helsingin seudun ympäristöpalvelut HSY: Building information grid of the Helsinki metropolitan area, HSY [data set], https://hri.fi/data/en_GB/dataset/rakennustietoruudukko (last access: 26 January 2023), 2022.
Hu, F., Gao, X. M., Li, G. Y., and Li, M.:
DEM EXTRACTION FROM WORLDVIEW-3 STEREO-IMAGES AND ACCURACY EVALUATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 327-332, https://doi.org/10.5194/isprs-archives-XLI-B1-327-2016, 2016.
Hui, Z., Hu, Y., Yevenyo, Y. Z., and Yu, X.:
An improved morphological algorithm for filtering airborne LiDAR point cloud based on multi-level kriging interpolation, Remote Sens.-Basel, 8, 35, https://doi.org/10.3390/rs8010035, 2016.
Jensen, J. L. and Mathews, A. J.: Assessment of image-based point
cloud products to generate a bare earth surface and estimate canopy heights in a woodland ecosystem, Remote Sens., 8, 50, https://doi.org/10.3390/rs8010050, 2016.
Kilian, J., Haala, N., and Englich, M.:
Capture and evaluation of airborne laser scanner data, Int. Arch. Photogramm., 31, 383–388, 1996.
Lakshmi, S. E. and Yarrakula, K.:
Review and critical analysis on digital elevation models, Geofizika, 35, 129–157, https://doi.org/10.15233/gfz.2018.35.7, 2018.
Liu, Y., Bates, P. D., Neal, J. C., and Yamazaki, D.:
Bare-Earth DEM Generation in Urban Areas for Flood Inundation Simulation Using Global Digital Elevation Models, Water Resour. Res., 57, e2020WR028516, https://doi.org/10.1029/2020WR028516, 2021.
LISFLOOD developers: LISFLOOD-FP 8.0 hydrodynamic model (8.0), Zenodo [code], https://doi.org/10.5281/zenodo.4073011, 2020.
Liu, Y., Bates, P., and Neal, J.: Bare-earth ArcticDEM, University of Bristol [data set], https://doi.org/10.5523/bris.3c1l2q7u1x14a262m6z7hh0c4r, 2022.
Majasalmi, T. and Rautiainen, M.:
Representation of tree cover in global land cover products: Finland as a case study area, Environ. Monit. Assess., 193, 1–19, https://doi.org/10.1007/s10661-021-08898-2, 2021.
Marconcini, M., Marmanis, D., Esch, T., and Felbier, A.: A novel method for building height estimation using TanDEM-X data, in: 2014 IEEE Geoscience and Remote Sensing Symposium, 13–18 July 2014, Quebec City, Quebec, Canada, IEEE, 4804–4807, https://doi.org/10.1109/IGARSS.2014.6947569, 2014.
Mason, D. C., Horritt, M. S., Hunter, N. M., and Bates, P. D.:
Use of fused airborne scanning laser altimetry and digital map data for urban flood modelling, Hydrol. Process., 21, 1436–1447, https://doi.org/10.1002/hyp.6343, 2007.
Meng, X., Wang, L., Silván-Cárdenas, J. L., and Currit, N.:
A multi-directional ground filtering algorithm for airborne LIDAR, ISPRS J. Photogramm., 64, 117–124, https://doi.org/10.1016/j.isprsjprs.2008.09.001, 2009.
Moudrý, V., Lecours, V., Gdulová, K., Gábor, L., Moudrá, L., Kropáček, J., and Wild, J.:
On the use of global DEMs in ecological modelling and the accuracy of new bare-earth DEMs, Ecol. Model., 383, 3–9, https://doi.org/10.1016/j.ecolmodel.2018.05.006, 2018.
National Land Survey of Finland: Coordinate transformations, National Land Survey of Finland [data set], https://www.maanmittauslaitos.fi/kartat-ja-paikkatieto/asiantuntevalle-kayttajalle/koordinaattimuunnokset (last access: 26 January 2023), 2005.
National Land Survey of Finland: Elevation model 2 m data download, National Land Survey of Finland [data set], https://tiedostopalvelu.maanmittauslaitos.fi/tp/kartta?lang=en (last access: 26 January 2023), 2017a.
National Land Survey of Finland: Elevation model 2 m description, National Land Survey of Finland [data set], https://www.maanmittauslaitos.fi/en/maps-and-spatial-data/expert-users/product-descriptions/elevation-model-2-m (last access: 26 January 2023), 2017b.
Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., and Horritt, M. S.:
Distributed whole city water level measurements from the Carlisle 2005 urban flood event and comparison with hydraulic model simulations, J. Hydrol., 368, 42–55, https://doi.org/10.1016/j.jhydrol.2009.01.026, 2009.
Neuenschwander, A., Guenther, E., White, J. C., Duncanson, L., and Montesano, P.:
Validation of ICESat-2 terrain and canopy heights in boreal forests, Remote Sens. Environ., 251, 112110, https://doi.org/10.1016/j.rse.2020.112110, 2020.
Noh, M. J. and Howat, I. M.:
Automated stereo-photogrammetric DEM generation at high latitudes: Surface Extraction with TIN-based Search-space Minimization (SETSM) validation and demonstration over glaciated regions, GISci. Remote Sens., 52, 198–217, https://doi.org/10.1080/15481603.2015.1008621, 2015.
O'Loughlin, F. E., Paiva, R. C., Durand, M., Alsdorf, D. E., and Bates, P. D.:
A multi-sensor approach towards a global vegetation corrected SRTM DEM product, Remote Sens. Environ., 182, 49–59, https://doi.org/10.1016/j.rse.2016.04.018, 2016.
OpenStreetMap: Building footprint, OpenStreetMap [code], https://overpass-turbo.eu/, last access: 26 January 2023.
Pingel, T. J.: SMRF code, GitHub [code], https://github.com/thomaspingel/smrf-matlab (last access: 26 January 2023), 2016.
Pingel, T. J., Clarke, K. C., and McBride, W. A.:
An improved simple morphological filter for the terrain classification of airborne LIDAR data, ISPRS J. Photogramm., 77, 21–30, https://doi.org/10.1016/j.isprsjprs.2012.12.002, 2013.
Porter, C., Morin, P., Howat, I., Noh, M. J., Bates, B., Peterman, K., Keesey, S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier, M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, M. J.; Williamson, C., Bauer, G., Enos, J., Arnold, G., Kramer, W., Becker, P., Doshi, A., D'Souza, C., Cummens, Pat., Laurier, F., Bojesen, M., and Bojesen, M.: ArcticDEM, Harvard Dataverse, V1, https://doi.org/10.7910/DVN/OHHUKH, 2018.
Rodriguez, E., Morris, C. S., and Belz, J. E.:
A global assessment of the SRTM performance, Photogramm. Eng. Rem. S., 72, 249–260, https://doi.org/10.14358/PERS.72.3.249, 2006.
Rokhmana, C. A. and Sastra, A. R.: Filtering DSM extraction from
Worldview-3 images to DTM using open source software, in: IOP Conference Series: Earth and Environmental Science, The Fifth International Conferences of Indonesian Society for Remote Sensing, 17–20 September 2019, West Java, Indonesia, https://doi.org/10.1088/1755-1315/500/1/012054, 2020.
Schubert, J. E. and Sanders, B. F.:
Building treatments for urban flood inundation models and implications for predictive skill and modeling efficiency, Adv. Water Resour., 41, 49–64, https://doi.org/10.1016/j.advwatres.2012.02.012, 2012.
Schumann, G. J. and Bates, P. D.:
The need for a high-accuracy, open-access global DEM, Front. Earth Sci., 6, 225, https://doi.org/10.3389/feart.2018.00225, 2018.
Schwanghart, W. and Scherler, D.:
Bumps in river profiles: uncertainty assessment and smoothing using quantile regression techniques, Earth Surf. Dynam., 5, 821–839, https://doi.org/10.5194/esurf-5-821-2017, 2017.
Shean, D. E., Alexandrov, O., Moratto, Z. M., Smith, B. E., Joughin, I. R., Porter, C., and Morin, P.:
An automated, open-source pipeline for mass production of digital elevation models (DEMs) from very-high-resolution commercial stereo satellite imagery, ISPRS J. Photogramm., 116, 101–117, https://doi.org/10.1016/j.isprsjprs.2016.03.012, 2016.
Sithole, G. and Vosselman, G.:
Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds, ISPRS J. Photogramm., 59, 85–101, https://doi.org/10.1016/j.isprsjprs.2004.05.004, 2004.
Tian, X. and Shan, J.:
Comprehensive evaluation of the ICESat-2 ATL08 terrain product, IEEE T. Geosci. Remote, 59, 8195–8209, https://doi.org/10.1109/TGRS.2021.3051086, 2021.
Takaku, J., Tadono, T., Tsutsui, K., and Ichikawa, M.:
VALIDATION OF “AW3D” GLOBAL DSM GENERATED FROM ALOS PRISM, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., III-4, 25–31, https://doi.org/10.5194/isprs-annals-III-4-25-2016, 2016.
Tan, Y., Wang, S., Xu, B., and Zhang, J.:
An improved progressive morphological filter for UAV-based photogrammetric point clouds in river bank monitoring, ISPRS J. Photogramm., 146, 421–429, https://doi.org/10.1016/j.isprsjprs.2018.10.013, 2018.
Trigg, M. A., Wilson, M. D., Bates, P. D., Horritt, M. S., Alsdorf, D. E., Forsberg, B. R., and Vega, M. C.:
Amazon flood wave hydraulics, J. Hydrol., 374, 92–105, https://doi.org/10.1016/j.jhydrol.2009.06.004, 2009.
Wessel, B., Huber, M., Wohlfart, C., Marschalk, U., Kosmann, D., and Roth, A.:
Accuracy assessment of the global TanDEM-X Digital Elevation Model with GPS data, ISPRS J. Photogramm., 139, 171–182, https://doi.org/10.1016/j.isprsjprs.2018.02.017, 2018.
Wing, O. E., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A., and Erickson, T. A.:
Validation of a 30 m resolution flood hazard model of the conterminous United States, Water Resour. Res., 53, 7968–7986, https://doi.org/10.1002/2017WR020917, 2017.
Wing, O. E., Bates, P. D., Neal, J. C., Sampson, C. C., Smith, A. M., Quinn, N., Shustikova, I., Domeneghetti, A., Gilles, D. W., Goska, R., and Krajewski, W. F.: A new automated method for improved flood defense representation in large-scale hydraulic models, Water Res. Res., 55, 11007–11034, https://doi.org/10.1029/2019WR025957, 2019.
Yamazaki, D., Sato, T., Kanae, S., Hirabayashi, Y., and Bates, P. D.:
Regional flood dynamics in a bifurcating mega delta simulated in a global river model, Geophys. Res. Lett., 41, 3127–3135, https://doi.org/10.1002/2014GL059744, 2014.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F., Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.:
A high-accuracy map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853, https://doi.org/10.1002/2017GL072874, 2017.
Zaidi, S. M., Akbari, A., Gisen, J. I., Kazmi, J. H., Gul, A., and Fhong, N. Z.: Utilization of Satellite-based Digital Elevation Model (DEM) for Hydrologic Applications: A Review, J. Geol. Soc. India, 92, 329–336, https://doi.org/10.1007/s12594-018-1016-5, 2018.
Zhang, K., Chen, S. C., Whitman, D., Shyu, M. L., Yan, J., and Zhang, C.:
A progressive morphological filter for removing nonground measurements from airborne LIDAR data, IEEE T. Geosci. Remote, 41, 872–882, https://doi.org/10.1109/TGRS.2003.810682, 2003.
Zhang, W., Qi, J., Wan, P., Wang, H., Xie, D., Wang, X., and Yan, G.:
An easy-to-use airborne LIDAR data filtering method based on cloth simulation, Remote Sens.-Basel, 8, 501, https://doi.org/10.3390/rs8060501, 2016.
Short summary
In this paper, we test two approaches for removing buildings and other above-ground objects from a state-of-the-art satellite photogrammetry topography product, ArcticDEM. Our best technique gives a 70 % reduction in vertical error, with an average difference of 1.02 m from a benchmark lidar for the city of Helsinki, Finland. When used in a simulation of rainfall-driven flooding, the bare-earth version of ArcticDEM yields a significant improvement in predicted inundation extent and water depth.
In this paper, we test two approaches for removing buildings and other above-ground objects from...
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