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A. Eltner; D. Schneider; H.-G. Maas (2016)
Publisher: Copernicus Publications
Journal: The International Archives of the Photogrammetry
Languages: English
Types: Article
Subjects: TA1-2040, T, TA1501-1820, Applied optics. Photonics, Engineering (General). Civil engineering (General), Technology
Soil erosion is a decisive earth surface process strongly influencing the fertility of arable land. Several options exist to detect soil erosion at the scale of large field plots (here 600 m²), which comprise different advantages and disadvantages depending on the applied method. In this study, the benefits of unmanned aerial vehicle (UAV) photogrammetry and terrestrial laser scanning (TLS) are exploited to quantify soil surface changes. Beforehand data combination, TLS data is co-registered to the DEMs generated with UAV photogrammetry. TLS data is used to detect global as well as local errors in the DEMs calculated from UAV images. Additionally, TLS data is considered for vegetation filtering. Complimentary, DEMs from UAV photogrammetry are utilised to detect systematic TLS errors and to further filter TLS point clouds in regard to unfavourable scan geometry (i.e. incidence angle and footprint) on gentle hillslopes. In addition, surface roughness is integrated as an important parameter to evaluate TLS point reliability because of the increasing footprints and thus area of signal reflection with increasing distance to the scanning device. The developed fusion tool allows for the estimation of reliable data points from each data source, considering the data acquisition geometry and surface properties, to finally merge both data sets into a single soil surface model. Data fusion is performed for three different field campaigns at a Mediterranean field plot. Successive DEM evaluation reveals continuous decrease of soil surface roughness, reappearance of former wheel tracks and local soil particle relocation patterns.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Abellán, A., Jaboyedoff, M., Oppikofer, T., & Vilaplana, J. M., 2009.
    • Detection of millimetric deformation using a terrestrial laser scanner: experiment and application to a rockfall event. Natural Hazards and Earth System Sciences, 9, pp. 365-372.
    • Barneveld, R. J., Seeger, M., & Maalen-Johansen, I., 2013. Assessment of terrestrial laser scanning technology for obtaining high-resolution DEMs of soils. Earth Surface Processes and Landforms, 38(1), pp. 90- 94.
    • Besl, P., & McKay, N., 1992. A Method for Registration of 3-D Shapes.
    • Bracken, L. J., Turnbull, L., Wainwright, J., & Bogaart, P., 2015.
    • Sediment connectivity: a framework for understanding sediment transfer at multiple scales. Earth Surface Processes and Landforms, 40, pp. 177-188.
    • Brodu, N., & Lague, D., 2012. 3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology. ISPRS Journal of Photogrammetry and Remote Sensing, 68, pp. 121-134.
    • Cammeraat, L., 2004. Scale dependent thresholds in hydrological and erosion response of a semi-arid catchment in southeast Spain.
    • Agriculture, Ecosystems and Environment, 104(2), pp. 317-332.
    • Eltner, A., 2016. Photogrammetric techniques for across-scale soil erosion assessment. PhD thesis. Institute of Photogrammetry and Remote Sensing, TU Dresden. submitted.
    • Eltner, A., Kaiser, A., Carlos, C., Rock, G., Neugirg, F. & Abellan, A., 2015a. Image-based surface reconstruction in geomorphometry - merits, limits and developments of a promising tool for geoscientists. Earth Surface Dynamics Discussions, 3, pp. 1445-1508.
    • Eltner, A., Baumgart, P., Maas, H.-G., & Faust, D., 2015b. Multitemporal UAV data for automatic measurement of rill and interrill erosion on loess soil. Earth Surface Processes and Landforms, 40(6), pp. 741-755.
    • Eltner, A., & Baumgart, P., 2015. Accuracy constraints of terrestrial Lidar data for soil erosion measurement: Application to a Mediterranean field plot. Geomorphology, 245, pp. 243-254.
    • Eltner, A., Mulsow, C., & Maas, H.-G., 2013. Quantitative measurement of soil erosion from TLS and UAV data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL(1/W2), pp. 119-124.
    • Eltner, A., & Schneider, D., 2015. Analysis of Different Methods for 3D Reconstruction of Natural Surfaces from Parallel-Axes UAV Images.
    • The Photogrammetric Record, 30(151), pp. 279-299.
    • Faust, D., & Schmidt, M., 2009. Soil erosion processes and sediment fluxes in a Mediterranean marl landscape, Campiña de Cádiz, SW Spain. Zeitschrift für Geomorphologie, 53, pp. 247-265.
    • García-Ruiz, J., Nadal-Romero, E., Lana-Renault, N., & Beguería, S., 2013. Erosion in Mediterranean landscapes: Changes and future challenges. Geomorphology, 15, pp. 20-36.
    • Girardeau-Montaut, D., 2016. CloudCompare (version 2.6.2; GPL software), EDF RandD, Telecom ParisTech, available at: http://www.cloudcompare.org/, last access: 1 March 2016.
    • Haubrock, S.-N., Kuhnert, M., Chabrillat, S., Güntner, A., & Kaufmann, H., 2009. Spatiotemporal variations of soil surface roughness from insitu laser scanning. Catena, 79, pp. 128-139.
    • doi:10.1016/j.catena.2009.06.005 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, pp. 1413-1420.
    • Kraus, K., 2007. Photogrammetry: Geometry from Images and Laser Scans. 2nd edition, De Gruyter, Berlin, pp. 459.
    • Küng, O., Strecha, C., Beyeler, A., Zufferey, J.-C., Floreano, D., Fua, P., & Gervaix, F., 2012. the Accuracy of Automatic Photogrammetric Techniques on Ultra-Light Uav Imagery. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVIII(1/C22), pp. 125-130.
    • Lichti, D. D., 2007. Error modelling, calibration and analysis of an AM-CW terrestrial laser scanner system. ISPRS Journal of Photogrammetry and Remote Sensing, 61(5), pp. 307-324.
    • A., Lague, D., Sangireddy, H., Schaffrath, K., Tarboton, D. G., Wasklewicz, T. & Wheaton, J. M., 2015. Analyzing high resolution topography for advancing the understanding of mass and energy transfer through landscapes: A review. Earth-Science Reviews, 148, pp.174-193.
    • Pesci, A., & Teza, G., 2008. Terrestrial laser scanner and retroreflective targets: an experiment for anomalous effects investigation.
    • International Journal of Remote Sensing, 29, pp. 5749-5765.
    • Poesen, J., & Hooke, J. (1997). Erosion, flooding and channel management in Mediterranean environments of southern Europe.
    • Progress in Physical Geography, 21(2), pp. 157-199.
    • Prosdocimi, M., Calligaro, S., Sofia, G., Dalla Fontana, G., & Tarolli, P., 2015. Bank erosion in agricultural drainage networks: new challenges from Structure-from-Motion photogrammetry for post-event analysis. Earth Surface Processes and Landforms, 40(14), pp. 1891- 1906.
    • Rusu, R. & Cousins, S., 2011. 3D is here: point cloud library (PCL).
    • W., 2011. Detection of surface change in complex topography using terrestrial laser scanning: application to the Illgraben debris-flow channel. Earth Surface Processes and Landforms, 36, pp. 1847-1859.
    • Soudarissanane, S., Lindenbergh, R., Menenti, M., & Teunissen, P., 2011. Scanning geometry: Influencing factor on the quality of terrestrial laser scanning points. ISPRS Journal of Photogrammetry and Remote Sensing, 66(4), pp. 389-399.
    • Stöcker, C., Eltner, A. & Karrasch, P., 2015. Measuring gullies by synergetic application of UAV and close range photogrammetry - A case study from Andalusia, Spain. Catena, 132, pp. 1-11.
    • Vosselman, G. & Maas, H.-G., 2010. Airborne and Terrestrial Laser Scanning. Whittles Publishing, pp. 336.
    • Wheaton, J. M., Brasington, J., Darby, S. E. & Sear, D. A., 2010.
    • Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surface Processes and Landforms, 35, pp. 136-156.
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