Visual and statistical comparison of ASTER, SRTM, and Cartosat digital elevation models for watershed

  • Vikas Kumar RanaEmail author
  • T. M. V. Suryanarayana


Accurate delineation of watershed and drainage networks is crucial for hydrological and geomorphological models, water resource management, change of floodplains, flood risk management, and surface water mapping. Since high-resolution digital elevation models (DEMs) are often not available, it is necessary to evaluate open source products. Various statistical measures were used to estimate the vertical accuracy of these freely available DEMs. Moreover, DEM products from Shuttle Radar Topography Mission (SRTM), Advanced Thermal Emission and Reflection Radiometer (ASTER), and Cartosat data were also compared. The study areas are located in the Vadodara district of Gujarat State of India. A comparison of SRTM-, ASTER-, and Cartosat-derived DEMs allowed a qualitative assessment of the vertical component of the error, whereas statistical measurements were used to estimate their vertical accuracy. In order to compare the frequency histograms of the elevation distributions in the DEMs in the study area, skewness and kurtosis were determined. Further, to obtain the degree of relationship between the DEMs, scatterplots, as well as correlation coefficients, were used. The results showed that all DEMs have imperfections in the delineation of a small river like Vishwamitri, and the comparison showed that SRTM 30 m and ASTER 30 m failed to delineate proper main drainage for the river. Cartosat 30 m DEM exhibited better results. The root mean square error (RMSE) was calculated as 7.21 m for ASTER and 3.24 m for SRTM. The correlation value of 0.94 indicates the existence of a strong positive linear correlation between SRTM and Cartosat. The study shows that for the study area ASTER elevation data were highly underestimated, whereas SRTM elevation data were slightly overestimated.


DEMs ASTER SRTM Cartosat Watershed 


Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. Cain MK, Zhang Z, Yuan KH (2017) Univariate and multivariate skewness and kurtosis for measuring nonnormality: prevalence, influence and estimation. Behav Res Methods 49(5):1716–1735. CrossRefGoogle Scholar
  2. Chaplot V (2014) Impact of spatial input data resolution on hydrological and erosion modeling: recommendations from a global assessment. Phys Chem Earth 67–69:23–35. CrossRefGoogle Scholar
  3. Chaubey I, Cotter AS, Costello TA, Soerens TS (2005) Effect of DEM data resolution on SWAT output uncertainty. Hydrol Process 19(3):621–628. CrossRefGoogle Scholar
  4. Crespi M, De Vendictis L, Poli D, Wolff K, Colosimo G, Gruen A, Volpe F (2008) Radiometric quality and DSM generation analysis of CartoSat-1 stereo imagery. Int Arch Photogramm Remote Sens Spat Inf Sci 37(3):1349–1355Google Scholar
  5. Ficklin DL, Tan ML, Chaplot V, Dixon B, Ibrahim AL, Yusop Z (2015) Impacts of DEM resolution, source, and resampling technique on SWAT-simulated streamflow. Appl Geogr 63:357–368. CrossRefGoogle Scholar
  6. Garcia MJL, Camarasa A (1999) Use of geomorphological units to improve network extraction from a DEM drainage Comparison between automated extraction and photointerpretation methods in the Carraixet catchment (Valencia, Spain). Int J Appl Earth Obs Geoinf 1(3):187–195CrossRefGoogle Scholar
  7. Guth PL (2010). Geomorphometric comparison of ASTER GDEM and SRTM. In A sspecial joint symposium of ISPRS Technical Commission IV & AutoCarto in conjunction with ASPRS/CaGIS.Google Scholar
  8. Hidayatullah P, Syakrani N, Suhartini I, Muhlis W (2012) Optical character recognition improvement for license plate recognition in Indonesia. In 2012 Sixth UKSim/AMSS European Symposium on Computer Modeling and Simulation. IEEE:249–254Google Scholar
  9. Hopkins K, Weeks D (1990) Tests for normality and measures of skewness and kurtosis: their place in research reporting. Educ Psychol Meas 50(4):717–729. CrossRefGoogle Scholar
  10. Hubalek Z (1982) Coefficients of association and similarity, based on binary (presence-absence) data: an evaluation. Biol Rev 57(4):669–689CrossRefGoogle Scholar
  11. Jaccard P (1912) The distribution of the flora in the alpine zone. New Phytologist, 11(2):37–50. Retrieved from
  12. Jain AO, Thaker T, Chaurasia A, Patel P, Singh AK (2018) Vertical accuracy evaluation of SRTM-GL1, GDEM-V2, AW3D30 and CartoDEM-V3. 1 of 30-m resolution with dual frequency GNSS for lower Tapi Basin India. Geocarto Int 33(11):1237–1256CrossRefGoogle Scholar
  13. Jarihani AA, Callow JN, Mcvicar TR, Van Niel TG, Larsen JR (2015) Satellite-derived digital elevation model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments. J Hydrol 524:489–506. CrossRefGoogle Scholar
  14. Jenson SK, Domingue JO (1988) Extracting topographic structure from digital elevation data for geographic information system analysis. Photogramm Eng Remote Sens 54(11):1593–1600Google Scholar
  15. Li S, MacMillan RA, Lobb DA, McConkey BG, Moulin A, Fraser WR (2011) Lidar DEM error analyses and topographic depression identification in a hummocky landscape in the prairie region of Canada. Geomorphology 129(3–4):263–275. CrossRefGoogle Scholar
  16. Li J, Wong DWS (2010) Computers, environment and urban systems effects of DEM sources on hydrologic applications. Comput Environ Urban Syst 34(3):251–261. CrossRefGoogle Scholar
  17. Lindsay JB (2016) Efficient hybrid breaching-filling sink removal methods for flow path enforcement in digital elevation models. Hydrol Process 30(6):846–857. CrossRefGoogle Scholar
  18. Lindsay JB, Creed IF (2006) Distinguishing actual and artefact depressions in digital elevation data. Comput Geosci 32(8):1192–1204. CrossRefGoogle Scholar
  19. Martz LW, Garbrecht J (1998) The treatment of flat areas and depressions in automated drainage analysis of raster digital elevation models. Hydrol Process 12(6):843–855.<843::AID-HYP658>3.0.CO;2-R CrossRefGoogle Scholar
  20. Martz LW, Garbrecht J (1999) An outlet breaching algorithm for the treatment of closed depressions in a raster DEM. Comput Geosci 25(7):835–844. CrossRefGoogle Scholar
  21. McCormack JE, Gahegan MN, Roberts SA, Hogg J, Hoyle BS (1993) Feature-based derivation of drainage networks. Int J Geogr Inf Syst 7(3):263–279. CrossRefGoogle Scholar
  22. Medeiros G. de O. R., Giarolla A., Sampaio G., & Marinho M. de A. (2016). Estimates of annual soil loss rates in the state of São Paulo, Brazil. Revista Brasileira de Ciencia Do Solo, 40(November).
  23. Morais JD, Faria TS, Elmiro MAT, Nero MA, Silva AA, Nobrega RAA (2017) Altimetry assessment of aster GDEM v2 and SRTM v3 digital elevation models: A case study in urban area of Belo Horizonte, MG, Brazil. Boletim de Ciencias Geodesicas 23(4):654–668. CrossRefGoogle Scholar
  24. Mukherjee S, Joshi PK, Mukherjee S, Ghosh A, Garg RD, Mukhopadhyay A (2013) Evaluation of vertical accuracy of open source digital elevation model (DEM). Int J Appl Earth Obs Geoinf 21:205–217CrossRefGoogle Scholar
  25. Nikolakopoulos KG, Kamaratakis EK, Chrysoulakis N (2006) SRTM vs ASTER elevation products. Comparison for two regions in Crete, Greece. Int J Remote Sens 27(21):4819–4838. CrossRefGoogle Scholar
  26. Persendt FC, Gomez C (2016) Assessment of drainage network extractions in a low-relief area of the Cuvelai Basin (Namibia) from multiple sources: LiDAR, topographic maps, and digital aerial orthophotographs. Geomorphology 260:32–50. CrossRefGoogle Scholar
  27. Pham HT, Marshall L, Johnson F, Sharma A (2018) A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data. Remote Sens Environ 210:229–241CrossRefGoogle Scholar
  28. Pryde JK, Osorio J, Wolfe ML, Heatwole C, Benham B, & Cardenas, . (2007). Comparison of watershed boundaries derived from SRTM and ASTER digital elevation datasets and from a digitized topographic mapGoogle Scholar
  29. Qian J, Ehrich RW, Campbell JB (1990) DNESYS-an expert system for automatic extraction of drainage networks from digital elevation data. IEEE Trans Geosci Remote Sens 28(1):29–45. CrossRefGoogle Scholar
  30. Sharma A, Tiwari KN (2014) A comparative appraisal of hydrological behavior of SRTM DEM at catchment level. J Hydrol 519:1394–1404. CrossRefGoogle Scholar
  31. Wang W, Yang X, Yao T (2012) Evaluation of ASTER GDEM and SRTM and their suitability in hydraulic modelling of a glacial lake outburst flood in Southeast Tibet. Hydrol Process 26(2):213–225. CrossRefGoogle Scholar
  32. Winter TC, LaBaugh JW (2006) Hydrologic considerations in defining isolated wetlands. Wetlands 23(3):532–540.[0532:hcidiw];2 CrossRefGoogle Scholar
  33. Wolock DM, Price CV (1994) Effects of digital elevation model map scale and data resolution on a topography-based watershed model. Water Resour Res 30(11):3041–3052CrossRefGoogle Scholar
  34. Zhu D, Ren Q, Xuan Y, Chen Y, Cluckie ID (2013) An effective depression filling algorithm for DEM-based 2-D surface flow modelling. Hydrol Earth Syst Sci 17(2):495–505. CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Water Resources Engineering and Management Institute, Faculty of Technology& EngineeringThe Maharaja Sayajirao University of BarodaVadodaraIndia

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