Object structure


Szorstkość pokrycia terenu jako źródło błędu metody SfM zastosowanej do rekonstrukcji zasięgu pokrywy śnieżnej = Terrain roughness as a source of error with the SfM method applied to the reconstruction of snow cover extent


Przegląd Geograficzny T. 92 z. 3 (2020)


Szafert, Damian ORCID ; Miziński, Bartłomiej ORCID ; Niedzielski, Tomasz ORCID


Niedzielski, Tomasz : Autor ; Miziński, Bartłomiej : Autor ; Szafert, Damian : Autor



Place of publishing:


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24 cm

Subject and Keywords:

extent of snow cover ; Izerskie Mountains ; Structure-from-Motion ; roughness of land cover ; unmanned aerial vehicle ; geoinformatics


A comparison between errors associated with snow-cover reconstruction performed by processing aerial imagery acquired by a visible-light camera mounted on board unmanned aerial vehicles, one the one hand; and average terrain roughness, on the other, revealed a dependent relationship between these variables. A stronger correlation is noted for two of the studied test areas (Polana Izerska and Krobica, both located in SW Poland), as opposed to the remaining site (Drożyna, SW Poland). In particular, correlations are noticeable where the analysis is performed in moving windows. It is typical for terrain where depth of snow cover is reconstructed with severe errors to reveal a high degree of roughness caused by single trees, clumps of trees or buildings. Ambiguous results are obtained for the Drożyna research field. While the character of the dependent relationship there seems consistent with results for the remaining sites, the strength is low. The lower values for the correlation coefficient were driven by observations for which errors were found to be high while values for the Topographic Ruggedness Index were at the same time low. This effect can be explained by reference to the specific nature of the area reconstructed, which is much transformed by human activity. It proves difficult to reconstruct the depth of snow cover on roads properly, as these are either partially cleared or snow or characterised by its loss in the course of melting. Low thickness of snow cover is thus found to be a constrained when it comes to the generation of accurate reconstructions of the depth of snow cover. This is in fact a finding in agreement with what has been reported by other authors.


Adams M.S., Bühler Y., Fromm R., 2018, Multitemporal Accuracy and Precision Assessment of Unmanned Aerial System Photogrammetry for Slope-Scale Snow Depth Maps in Alpine Terrain, Pure and Applied Geophysics, 175, 9, s. 3303-3324. https://doi.org/10.1007/s00024-017-1748-y
Baltsavias E.P., Favey E., Bauder A., Bosch H., Pateraki M., 2001, Digital surface modelling by airborne laser scanning and digital photogrammetry for glacier monitoring, The Photogrammetric Record, 17, 98, s. 243-273. https://doi.org/10.1111/0031-868X.00182
Bolstad P., Lillesand T.M., 1991, Rapid maximum likehood classification, Photogrammetric Engineering and Remote Sensing, 57, 1, s. 67-74.
Bühler Y., Adams M.S., Bösch R., Stoffel A., 2016, Mapping snow depth in alpine terrain with unmanned aerial systems (UASs): potential and limitations, The Cryosphere, 10, 3, s. 1075-1088. https://doi.org/10.5194/tc-10-1075-2016
De Michele C., Avanzi F., Passoni D., Barzaghi R., Pinto L., Dosso P., Ghezzi A., Gianatti R., Della Vedova G., 2015, Microscale variability of snow depth using UAS technology, Cryosphere, 9, 1, s. 1047-1075. https://doi.org/10.5194/tcd-9-1047-2015
ESRI, 2012, ArcGIS: Desktop. Release 10.0, Enviromental System Research Institute, Redlands.
Harder P., Schirmer M., Pomeroy J., Helgason W., 2016, Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle, The Cryosphere, 10, 6, s. 2559-2571. https://doi.org/10.5194/tc-10-2559-2016
James M.R., Robson S., 2014, Mitigating systematic error in topographic models derived from UAV and ground-based image networks, Earth Surface Processes and Landforms, 39, 10, s. 1413-1420. https://doi.org/10.1002/esp.3609
Keutterling A., Thomas A., 2006, Monitoring glacier elevation and volume changes with digital photogrammetry and GIS at Gepatschferner glacier, Austria, International Journal of Remote Sensing, 27, 19, s. 4371-4380. https://doi.org/10.1080/01431160600851819
Kondracki J., 2000, Geografia regionalna Polski, Wydawnictwo Naukowe PWN, Warszawa.
Langhammer J., Bernsteinová J., Miřijovský J., 2017, Building a High-Precision 2D Hydrodynamic Flood Model Using UAV Photogrammetry and Sensor Network Monitoring, Water, 9, 11, 861. https://doi.org/10.3390/w9110861
Langhammer J., Hartvich F., Kliment Z., Jeníček M., Bernsteinová (Kaiglová) J., Vlček L., Su Y., Štych P., Miřijovský J., 2015, The impact of disturbance on the dynamics of fluvial processes in mountain landscapes, Silva Gabreta, 21, s. 105-116.
Lucieer A., de Jong S.M., Turner D., 2014, Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography, Progress in Physical Geography, 38, 1, s. 97-116. https://doi.org/10.1177/0309133313515293
Maguire C., 2014, Using Unmanned Aerial Vehicles and "Structure from Motion" Software to Monitor Coastal Erosion in Southeast Florida, University of Miami, Miami.
Memarasadeghi N., Mount D.M., Netanyahu N.S., Le Moigne J., 2005, A fast implementation of the ISODATA clustering algorithm, International Journal of Computational Geometry & Applications, 17, 1, s. 71-103. https://doi.org/10.1142/S0218195907002252
Miřijovský J., Šulc Michalková M., Petyniak O., Máčka Z., Trizna M., 2015, Spatiotemporal evolution of a unique preserved meandering system in Central Europe - The Morava River near Litovel, Catena, 127, s. 300-311. https://doi.org/10.1016/j.catena.2014.12.006
Miziński B., Niedzielski T., 2017, Fully-automated estimation of snow depth in near real time with the use of unmanned aerial vehicles without utilizing ground control points, Cold Regions Science and Technology, 138, s. 63-72. https://doi.org/10.1016/j.coldregions.2017.03.006
Niedzielski T., Spallek W., Witek-Kasprzak M., 2018, Automated Snow Extent Mapping Based on Orthophoto Images from Unmanned Aerial Vehicles, Pure and Applied Geophysics, 175, s. 3285-3302. https://doi.org/10.1007/s00024-018-1843-8
Niedzielski T., Szymanowski M., Miziński B., Spallek W., Witek-Kasprzak M., Ślopek J., Kasprzak M., Błaś M., Sobik M., Jancewicz K., Borowicz D., Remisz J., Modzel P., Męcina K., Leszczyński L., 2019, Estimating snow water equivalent using unmanned aerial vehicles for determining snow-melt runoff, Journal of Hydrology, 578,124046. https://doi.org/10.1016/j.jhydrol.2019.124046
Niedzielski T., Witek M., Spallek W., 2016, Observing river stages using unmanned aerial vehicles, Hydrology and Earth System Sciences, 20, s. 3193-3205. https://doi.org/10.5194/hess-20-3193-2016
Nolan M., Larsen C.F., Sturm M., 2015, Mapping snow-depth from manned-aircraft on landscape scales at centimeter resolution using Structure-from-Motion photogrammetry, Cryosphere Discussions, 9, s. 1445-1463. https://doi.org/10.5194/tc-9-1445-2015
Riley S.J., DeGloria S.D., Elliot R., 1999, Index that quantifies topographic heterogeneity, Intermountain Journal of Sciences, 5, 1-4, s. 23-27.
Vander Jagt B., Lucieer A., Wallace L., Turner D., Durand M., 2015, Snow depth retrieval with UAS using photogrammetric techniques, Geosciences, 5, 3, s. 264-285. https://doi.org/10.3390/geosciences5030264
Warrick J.A., Ritchie A.C., Adelman G., Adelman K., Limber P.W., 2016, New techniques to measure cliff change from historical oblique aerial photographs and structure-from-motion photogrammetry, Journal of Coastal Research, 33, 1, s. 39-55. https://doi.org/10.2112/JCOASTRES-D-16-00095.1
Westoby M.J., Brasington J., Glasser N.F., Hambrey M.J., Reynolds J.M., 2012, 'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications, Geomorphology, 179, s. 300-314. https://doi.org/10.1016/j.geomorph.2012.08.021
Woodget A.S., Carbonneau P.E., Visser F., Maddock I.P., 2015, Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry, Earth Surface Processes and Landforms, 40, s. 47-64. https://doi.org/10.1002/esp.3613


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0033-2143 (print) ; 2300-8466 (on-line) ; 10.7163/PrzG.2020.3.4


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Copyright-protected material. [CC BY 4.0] May be used within the scope specified in Creative Commons Attribution BY 4.0 license, full text available at: ; -

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Institute of Geography and Spatial Organization of the Polish Academy of Sciences

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Central Library of Geography and Environmental Protection. Institute of Geography and Spatial Organization PAS

Projects co-financed by:

Operational Program Digital Poland, 2014-2020, Measure 2.3: Digital accessibility and usefulness of public sector information; funds from the European Regional Development Fund and national co-financing from the state budget.





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