Analysis of the distribution and density of measurement points in terms of terrain modelling

• Autorzy: Gradka R.

Identyfikatory

DOI

10.15576/GLL/2017.3.35

• Języki publikacji: EN

Abstrakty

EN

For the implementation of direct measurements, proper understanding of the existing relationships and spatial variability, and at later stages, for obtaining reliable results of geostatistical analysis, adequate planning network measurement and correct placement of, and/or the evaluation of the number of measurement points in the measurement network are not the only necessary conditions. Another key prerequisite is choosing the right model for creating a DTM, which depends on the shape of the terrain. Correct spatial sampling should provide much information on the spatial distribution of the studied variable in an area, at minimal cost and with minimal effort. Faithful reproduction of the land surface that reflects any of the characteristics of the environment is not possible through DTM, due to a number of restrictions, manifesting themselves in the form and size of the data set; due to time and economic constraints; and also because the full complexity of the terrain’s surface cannot be measured or expressed. The present work undertakes to analyse the density and distribution of measuring points on four areas that have specific characteristics in common, yet they remain different in terms of surfaces, height differences, as well as their complexity. After selecting the research areas, these were designed and laid out in a grid with the shape of rectangles that were similar in structure to the GRID model. The data were analysed using geostatistical interpolation by ordinary kriging, in order to conduct a proper analysis of the distribution and density of the measuring points, to calculate the surface properties of a particular point, and in order to attempt to reduce the workload and cost factor.

Słowa kluczowe

EN

DTM; distribution; density; GRID; ordinary kriging

PL

DTM; dystrybucja; gęstość

• Wydawca: Wydawnictwo Uniwersytetu Rolniczego w Krakowie
• Czasopismo: Geomatics, Landmanagement and Landscape
• Rocznik: 2017
• Tom: no. 3
• Strony: 35--45
• Opis fizyczny: Bibliogr. 22 poz., rys., tab.

Twórcy

autor

Gradka R.

• Politechnika Wrocławska Wydział Geoinżynierii, Górnictwa i Geologii 50-421 Wrocław, ul. Na Grobli 15

Bibliografia

• Aguilar F.J., Agüera F., Aguilar Manuel A., Carvajal F. 2005. Effects of Terrain Morphology, Sampling Density, and Interpolation Methods on Grid DEM Accuracy. Photogram. Engin. Remote Sens., 71(7), 805–816.
• Childs C. (ed.). 2012. Interpolating surfaces in ArcGIS Spatial Analyst, ESRI Education Services.
• Flotron A., Koelbl O. 2000. Precision terrain models for civil engineering. OEEPE Offical Publication, 38, 37–134.
• Franke R. 1982. Scattered Data Interpolation: Test of Some Methods. Mathemat. Comput., 33(157), 181–200.
• Gościewski D. (ed.). 2005. Influence of measurement points location on selection of interpolation algorithm, The 6th International Conference Environmental Engineering, Gediminas Technical University Press, Vilnius-Lithunia.
• Gościewski D. 2007. Analiza dokładności interpolacyjnych modeli powierzchni typu GRID. Materiały 20. Jesiennej Szkoły Geodezji, Wrocław.
• Gotlib D., Olszewski R. 2006. Co z trzecim wymiarem? O modelowaniu rzeźby terenu w referencyjnych bazach danych, Geodeta, 4, 31–34.
• Hansen M.H., Hurwitz W.N., Madow G. 1953. Sample Survey Methods and Theory, vol. 1: Methods and Applications, John Wiley & Sons Inc., New York.
• Hejmanowska B. 2007. Analiza NTM w postaci GRID i TIN na przykładzie danych z OKI. Arch. Fotogram. Kartogr. Teledet., 17a, 281–289.
• Krige Danie G. 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. J. Chem., Metal. Mining Soc. South Africa, 52 (6), 119–139.
• Lam N.S. 1983. Spatial interpolation methods: A review, Am. Cartogr., 10(2), 129–149.
• Li J., Heap A.D. 2008. A review of Spatial Interpolation Methods for Environmental Scientists, no. 23.
• Matheron G. 1962. Traité de géostatistique appliquée. Technip, Paris.
• Mulugeta G. 1999. The elusive nature of expertise in spatial interpolation. Cart. Geog. Inf. Sys., 25(1), 33–41.
• Mund J.-P. 2013. Geospatial statistics and spatial data interpolation methods, GIS’EM 2013 at Eberswalde, Eberswalde.
• Oliver M.A. 1990. Kriging: A Method of Interpolation for Geographical Information Systems. Int. J. Geogr. Inform. Syst., 4, 313–332.
• Royle A.G., Clausen F.L., Frederiksen P. 1981. Practical Universal Kriging and Automatic Contouring. Geoprocessing, 1, 377–394.
• Sauer T. 2006. Numerical Analysis. Pearson Education Inc., Boston.
• Suchocki Cz., Damięcka-Suchocka M., Błoch P., Stec M. 2013. An evaluation of digital terrain model accuracy using direct survey data. Acta Sci. Pol., Geod. Descr. Terr., 12(3), 17–26.
• Wackernagel H. 2003. Multivariate geostatistics. An introduction with applications. Third completely revised edition, Springer-Verlag, Heidelberg.
• Watson D.F. 1992. Contouring: a guide to the analysis and display of spatial data. Oxford, Pergamon.
• Zawadzki J. 2011. Metody geostatystyczne dla kierunków przyrodniczych i technicznych. Oficyna Wyd. Politechniki Warszawskiej, Warszawa.