Evaluating Spatial Data Acquisition and Interpolation Strategies for River Bathymetries
- 978 Downloads
The study implements a workflow to evaluate the effects of different data sampling methods and interpolation methods, when measuring and modelling a river bathymetry based on point data. Interpolation and sampling strategies are evaluated against a reference data set. The evaluation of the results includes critically discussing characteristics of the input data, the used methods and the transferability of the results. The results show that the decision for or against a particular sampling method and for a specific setting of the parameters can certainly have a great influence on the quality of the interpolation results. Further, some general guidelines for the acquisition of bathymetries are derived from the study results.
KeywordsSpatial interpolation Riverbed modelling Spatial sampling Water frame work directive Bathymetry
This work was supported by the European Social Fund [grant number 100270097, project “Extruso”] and by the Federal Ministry of Education and Research of Germany [grant number 033W039A, project “Boot monitoring”].
- Cressie NAC (1993) Statistics for spatial data (revised edition). New YorkGoogle Scholar
- ESRI (2001) Using ArcGIS geostatistical analyst. ESRI, RedlandsGoogle Scholar
- European Union (2000) Directive 2000/60/EC of the European parliament and of the council. Off J Eur Communities 43:1–73Google Scholar
- Hofstra N, Haylock M, New M, et al (2008) Comparison of six methods for the interpolation of daily, European climate data. J Geophys Res Atmos 113. https://doi.org/10.1029/2008jd010100
- Krige DG (1966) Two-dimensional weighted moving average trend surfaces for ore-evaluation. J South African Inst Min Metallurgy 66:13–38Google Scholar
- Merwade VM, Maidment DR, Hodges BR (2005) Geospatial representation of river channels. J Hydrol Eng 10:243–251. https://doi.org/10.1061/(ASCE)1084-0699(2005)10:3(243) CrossRefGoogle Scholar
- Rase W-D (2016) Kartographische Oberflächen. Books on DemandGoogle Scholar
- Santillan JR, Serviano JL, Makinano-Santillan M, Marqueso JT (2016) Influence of river bed elevation survey configurations and interpolation methods on the accuracy of lidar Dtm-based river flow simulations. ISPRS Int Arch Photogram Remote Sens Spat Inf Sci XLII-4/W1:225–235. https://doi.org/10.5194/isprs-archives-xlii-4-w1-225-2016
- Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. In: Proceedings of the 23rd ACM National Conference, pp. 517–524. https://doi.org/10.1145/800186.810616
- US Army Corps of Engineers: Hydrographic survey data. http://www.lrp.usace.army.mil/Missions/Navigation/Navigation-Charts/HydrographicSurveyData/. Accessed 18 Sep 2016
- Vande Wiele, T.: Mapping with multibeam data : are there ideal model settings ? In: International Cartographic Conference, Beijing, China (2001)Google Scholar