Surface roughness measurements using UAV LiDAR and analysis of uncertainty factors

Abstract

Surface root mean square height (SRMSH) is a key parameter characterizing surface roughness; it reflects soil hydrological properties and influences related physical processes. LiDAR offers an effective means for measuring SRMSH over large areas, yet the method involves several uncertainty factors that require further investigation. In this study, we proposed a method based on UAV LiDAR data, utilizing linear sampling in target areas. Three potential sources of uncertainty are examined: the spatial interpolation method, the interpolation interval, and the linear sampling length. By selecting and combining representative values for these factors, SRMSH is computed for multiple sample areas. The effects of each factor are then evaluated by comparing results of different parameter configurations. The main findings are as follows: (1) The linear sampling method is capable of estimating SRMSH but introduces systematic errors that correlate primarily with the sampling length; (2) Sensitivity analysis reveals that the sampling length dominates measurement outcomes (contributing >50% of variation), followed by the interpolation method (<10%), while the interpolation interval has minimal influence (<2%). The quantification of parameter impacts provides valuable methodological references for optimizing measurement protocols and developing robust alternatives.

Keywords

interpolation interval / LiDAR data / sampling length / spatial interpolation / surface root mean square height / surface roughness

References

[1] Ackermann, F. (1999). Airborne laser scanning-present status and future expectations. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 64-67. https://doi.org/10.1016/S0924-2716(99)00009-X

[2] Bei, Y. X., Chen, C. F., Wang, X., Sun, Y. N., He, Q. X., & Li, K. Y. (2023). Effects of airborne LiDAR point cloud density and interpolation methods on the accuracy of DEM and surface roughness. Journal of Geo-Information Science, 25(2), 265-276.

[3] Berti, M., Corsini, A., & Daehne, A. (2013). Comparative analysis of surface roughness algorithms for the identification of active landslides. Geomorphology, 182(427), 1-18. https://doi.org/10.1016/j.geomorph.2012.10.022

[4] Brovelli, M. A., Cannata, M., & Longoni, U. M. (2002). Managing and processing LIDAR data within GRASS. in Proceedings of the GRASS users conference, 29, 1-29.

[5] Brubaker, K. M., Myers, W. L., Drohan, P. J., Miller, D. A., & Boyer, E. W. (2013). The use of LiDAR terrain data in characterizing surface roughness and microtopography. Applied and Environmental Soil Science, 2013, 1-13. https://doi.org/10.1155/2013/891534

[6] Candela, A., Noto, L. V., & Aronica, G. (2005). Influence of surface roughness in hydrological response of semiarid catchments. Journal of Hydrology, 313(3-4), 119-131. https://doi.org/10.1016/j.jhydrol.2005.01.023

[7] Casas, A., Lane, S. N., Yu, D., & Benito, G. (2010). A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data. Hydrology and Earth System Sciences, 14(8), 1567-1579. https://doi.org/10.5194/hess-14-1567-2010

[8] Colliander, A., Chan, S., Kim, S., Das, N., Yueh, S., Cosh, M., Bindlish, R., Jackson, T., & Njoku, E. (2012). Long term analysis of PALS soil moisture campaign measurements for global soil moisture algorithm development. Remote Sensing of Environment, 121, 309-322. https://doi.org/10.1016/j.rse.2012.02.002

[9] Cook, A., & Merwade, V. (2009). Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. Journal of Hydrology, 377(1-2), 131-142. https://doi.org/10.1016/j.jhydrol.2009.08.015

[10] Elmqvist, M., Jungert, E., Lantz, F., Perssona, A., & Söderman, U. (2001). Terrain modelling and analysis using laser scanner data. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/W4), 219-226.

[11] Gharechelou, S., Tateishi, R., & Johnson, B. A. (2018). A simple method for the parameterization of surface roughness from microwave remote sensing. Remote Sensing, 10(11), 1711. https://doi.org/10.3390/rs10111711

[12] Gherboudj, I., Magagi, R., Berg, A. A., & Brenda, T. (2011). Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data. Remote Sensing of Environment, 115(1), 33-43. https://doi.org/10.1016/j.rse.2010.07.011

[13] Guha, A., Jacobs, J. M., Jackson, T. J., Cosh, M. H., Hsu, E., & Judge, J. (2003). Soil moisture mapping using ESTAR under dry conditions from the Southern Great Plains Experiment (SGP99). IEEE Transactions on Geoscience and Remote Sensing, 41(10), 2392-2397. https://doi.org/10.1109/TGRS.2003.817187

[14] Haubrock, S. N., Kuhnert, M., Chabrillat, S., Güntner, A., & Kaufmann, H. (2009). Spatiotemporal variations of soil surface roughness from in-situ laser scanning. Catena, 79(2), 128-139. https://doi.org/10.1016/j.catena.2009.06.005

[15] Hengl, T. (2006). Finding the right pixel size. Computers & Geosciences, 32(9), 1283-1298. https://doi.org/10.1016/j.cageo.2005.11.008

[16] Hodgson, M. E., & Bresnahan, P. (2004). Accuracy of airborne lidar-derived elevation. Photogrammetric Engineering & Remote Sensing, 70(3), 331-339. https://doi.org/10.14358/PERS.70.3.331

[17] Huang, C. H., & Bradford, J. M. (1992). Applications of a laser scanner to quantify soil microtopography. Soil Science Society of America Journal, 56(1), 14-21. https://doi.org/10.2136/sssaj1992.03615995005600010002 x

[18] Jiang, L. H. (2020). Research on Soil Moisture Retrieval Base on Sentinel Data. University of Electronic Science and Technology of China.

[19] Kornelsen, K. C., & Coulibaly, P. (2013). Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications. Journal of Hydrology, 476, 460-489. https://doi.org/10.1016/j.jhydrol.2012.10.044

[20] Liu, X. (2008). Airborne LiDAR for DEM generation: Some critical issues. Progress in Physical Geography: Earth and Environment, 32(1), 31-49. https://doi.org/10.1177/0309133308089496

[21] Mattia, F., Davidson, M. W. J., Le Toan, T., Haese, C. M., Verhoest, N. E., Gatti, A. M., & Borgeaud, M. (2003). A comparison between soil roughness statistics used in surface scattering models derived from mechanical and laser profilers. IEEE Transactions on Geoscience and Remote Sensing, 41(7), 1659-1671. 813359. https://doi.org/10.1109/TGRS.2003.

[22] Narayan, U., Lakshmi, V., Jackson, T. J. (2006). High-resolution change estimation of soil moisture using L-band radiometer and radar observations made during the SMEX02 experiments. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1545-1554. https://doi.org/10.1109/TGRS.2006.871199

[23] Narvekar, P. S., Entekhabi, D., Kim, S. B., Njoku, E. G. (2015). Soil moisture retrieval using L-band radar observations. IEEE Transactions on Geoscience and Remote Sensing, 53(6), 3492-3506. https://doi.org/10.1109/TGRS.2014.2377714

[24] Roggero, M. (2001). Airborne laser scanning-clustering in raw data. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, 34(3/W4), 227-232.

[25] Römkens, M. J. M., Helming, K., & Prasad, S. N. (2002). Soil erosion under different rainfall intensities, surface roughness, and soil water regimes. Catena, 46(2-3), 103-123. https://doi.org/10.1016/S0341-8162(01)0016-8

[26] Shepard, M. K., Campbell, B. A., Bulmer, M. H., Farr, T. G., Gaddis, L. R., & Plaut, J. J. (2001). The roughness of natural terrain: A planetary and remote sensing perspective. Journal of Geophysical Research: Planets, 106(E12), 32777-32795. https://doi.org/10.1029/2000JE001429

[27] Stenberg, L., Tuukkanen, T., Finér, L., Marttila, H., Piirainen, S., Kløve, B., Koivusalo, H. (2016). Evaluation of erosion and surface roughness in peatland forest ditches using pin meter measurements and terrestrial laser scanning. Earth Surface Processes and Landforms, 41(10), 1299-1311.

[28] Shepard, M. K., Campbell, B. A., Bulmer, M. H., Farr, T. G., Gaddis, L. R., Plaut, J. J. (2016). Evaluation of erosion and surface roughness in peatland forest ditches using pin meter measurements and terrestrial laser scanning. Earth Surface Processes and Landforms, 41(10), 1299-1311. https://doi.org/10.1002/esp.3897

[29] Sithole, G., & Vosselman, G. (2004). Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59(1-2), 85-101. https://doi.org/10.1016/j.isprsjprs.2004.05.004

[30] Sohn, G., & Dowman, I. J. (2002). Terrain surface reconstruction by the use of tetrahedron model with the MDL criterion. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences. Natural Resources Canada, 34(3/A), 336-344.

[31] Wehr, A., & Lohr, U. (1999). Airborne laser scanning—An introduction and overview. ISPRS Journal of Photogrammetry and Remote Sensing, 54(2-3), 68-82. https://doi.org/10.1016/S0924-2716(99)00011-8

[32] Yuan, C., Ren, C., & Ng, E. (2014). GIS-based surface roughness evaluation in the urban planning system to improve the wind environment—A study in Wuhan, China. Urban Climate, 10, 585-593. https://doi.org/10.1016/j.uclim.2014.06.005

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2026 The Author(s). River published by Wiley-VCH GmbH on behalf of China Institute of Water Resources and Hydropower Research (IWHR).