Advertisement

Earth Science Informatics

, Volume 11, Issue 3, pp 423–431 | Cite as

A modified HASM algorithm and its application in DEM construction

  • Ling Jiang
  • Mingwei Zhao
  • Tianxiang Yue
  • Na Zhao
  • Chun Wang
  • Jinglu Sun
Research Article
  • 37 Downloads

Abstract

In many spatial interpolation fields, high accuracy surface modeling (HASM) has yielded better accuracy than classical interpolation methods. The Gaussian equation is the core of the HASM algorithm; The current version of the HASM method builds the Gaussian equation in Cartesian coordinates and, computes the two partial derivatives of the surface in the horizontal and vertical directions for each grid. In this paper, a modified HASM method is proposed that integrates flow paths to improve the original HASM methodology. The modified HASM approach involves two steps. The first step generates an initial DEM, which is used to compute the flow path. Then, the second step is conducted based on scatter points and the flow direction. The output from this step is better than the initial DEM. First, we used a theoretical mathematical surface to validate the correctness of the modified model. Then, we chose a small study area where the topography is affected by hydrological erosion for analysis. The test results showed that the modified HASM method constructed a DEM with low MAE and RMSE values compared to those of traditional methods, and it more accurately characterized topographic features. Finally, a relatively gently sloping area was selected to validate that the applicability of the new method in other areas.

Keywords

HASM Flow direction DEM Accuracy 

Notes

Acknowledgements

We are thankful for all of the helpful comments provided by the reviewers. This study was supported by the Natural Science Foundation of China (41701450, 41571398, 41501445), Key Project of Natural Science Research of Anhui Provincial Department of Education (KJ2016A536), Research project on the application of public welfare Technology in Anhui Province (1704f0704064), and Anhui Provincial Natural Science Foundation of China (1608085QD77).

References

  1. Aguilar FJ, Agüera F, Aguilar MA, Carvajal F (2005) Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogramm Eng Remote Sens 71(7):805–816CrossRefGoogle Scholar
  2. Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Oxford University Press, New YorkGoogle Scholar
  3. Chaplot V, Darboux F, Bourennane H, Leguédois S, Silvera N, Phachomphon K (2006) Accuracy of interpolation techniques for the derivation of digital elevationmodels in relation to landform types and data density. Geomorphology 77(1–2):126–141CrossRefGoogle Scholar
  4. Chen CF, Li YY, Yue TX (2013) Surface modeling of DEMs based on a sequential adjustment method. Int J Geogr Inf Sci 27(7):1272–1291CrossRefGoogle Scholar
  5. Claessens et al (2005) DEM resolution effects on shallow landslide hazard and soil redistribution modelling. Earth Surf Process Landf 30(4):461–477CrossRefGoogle Scholar
  6. Fisher P (1991) First experiments in viewshed uncertainty: the accuracy of the viewshed area. Photogramm Eng Remote Sens 57(10):1321–1327Google Scholar
  7. Golub GH, Van Loan CF (2009) Matrix computations. Posts & Telecom Press, BeijingGoogle Scholar
  8. Goovaerts P (2000) Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. J Hydrol 228(1–2):113–129CrossRefGoogle Scholar
  9. Henderson DW (1998) Differential geometry. Prentice-Hall, LondonGoogle Scholar
  10. Hunter GJ, Goodchild MF (1997) Modeling the uncertainty of slope and aspect estimates derived from spatial databases. Geogr Anal 29:35–49CrossRefGoogle Scholar
  11. Hutchinson MF, Gallant JC (2000) Digital elevation models and representation of terrain shape. In: Wilson JP, Gallant JC (eds) Terrain analysis: principles and applications. Wiley, New York, pp 29–50Google Scholar
  12. Kawabata D et al (2010) Landslide susceptibility mapping using geological data, a DEM from ASTER images and an artificial neural network (ANN). Geomorphology 113(1–2):97–109Google Scholar
  13. Li JH, Chen WJ (2005) A rule-based method for mapping Canada's wetlands using optical, radar and DEM data. Int J Remote Sens 26(22):5051–5069CrossRefGoogle Scholar
  14. Liu XJ et al (2004) A study of accuracy and algorithms for calculating slope and aspect based on grid digital elevation model (DEM). Acta Geodaetica Et Cartographic Sinica 33:258–263Google Scholar
  15. Lu YG, Wong DW (2008) An adaptive inverse-distance weighting spatial interpolation technique. Comput Geosci 34(9):1044–1055CrossRefGoogle Scholar
  16. Moore ID, Grayson RB, Ladson AR (1991) Digital terrain modelling: a review of hydrological, geomorphological, and biological applications. Hydrol Process 5(1):3–30CrossRefGoogle Scholar
  17. Murphy PNC, Ogilvie J, Meng FR, Arp P (2008) Stream network modelling using lidar and photogrammetric digital elevation models: a comparison and field verification. Hydrol Process 22(12):1747–1754CrossRefGoogle Scholar
  18. O’Callaghan JF, Mark DM (1984) The extraction of drainage networks from digital elevation data. Computer and Image Processing 28:323–344CrossRefGoogle Scholar
  19. Pike RJ (2000) Geomorphometry—diversity in quantitative surface analysis. Prog Phys Geogr 24:1–20Google Scholar
  20. Somasundaram D (2005) Differential geometry. Alpha Science International Ltd., HarrowGoogle Scholar
  21. Toponogov VA (2006) Differential geometry of curves and surfaces. Birkhaeuser Boston, New YorkGoogle Scholar
  22. Xiong LY, Tang GA, Yuan BY, Lu ZC, Li FY, Zhang L (2014) Geomorphological inheritance for loess landform evolution in a severe soil erosion region of loess plateau of China based on digital elevation models. Sci China Earth Sci 57(8):1944–1952CrossRefGoogle Scholar
  23. Yue TX (2011) Surface modeling: high accuracy and high speed methods. CRC Press, New YorkCrossRefGoogle Scholar
  24. Yue TX, Wang SH (2010) Adjustment computation of HASM: a high-accuracy and high-speed method. Int J Geogr Inf Sci 24(11):1725–1743CrossRefGoogle Scholar
  25. Yue TX, Du ZP, Song DJ (2007) A new method of surface modelling and its application to DEM construction. Geomorphology 91(1–2):161–172CrossRefGoogle Scholar
  26. Yue TX, Chen CF, Li BL (2010) An adaptive method of high accuracy surface modeling and its application to simulating elevation surfaces. Trans GIS 14(5):615–630CrossRefGoogle Scholar
  27. Yue TX, Chen CF, Li BL (2012) A high accuracy method for filling SRTM voids and its verification. Int J Remote Sens 33(9):2815–2830CrossRefGoogle Scholar
  28. Zhao N, Yue TX, Zhao MW, du ZP, Fan ZM, Chen CF (2014) Sensitivity studies of a high accuracy surface modeling method. Sci China Earth Sci 57(10):2386–2396CrossRefGoogle Scholar
  29. Zhao MW et al (2015) Parallel algorithm of a modified HASM and its application in DEM construction. Environ Earth Sci 74:6551–6561CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Ling Jiang
    • 1
  • Mingwei Zhao
    • 1
  • Tianxiang Yue
    • 2
  • Na Zhao
    • 2
  • Chun Wang
    • 1
  • Jinglu Sun
    • 3
  1. 1.Anhui Center for Collaborative Innovation in Geographical Information Integration and ApplicationChuzhou UniversityChuzhouChina
  2. 2.State Key Laboratory of Resources and Environment Information SystemInstitute of Geographical Science and Natural Resources Research, Chinese Academic ScienceBeijingChina
  3. 3.Anhui Institute of EconomicsHefeiChina

Personalised recommendations