How to convert the SRTM DEM orthometric to ellipsoidal heights using ArcGIS or others GIS?

Recently I got the SRTM DEM from Cgiar,which based on WGS84/EGM96.

and I merged the tiles to one tiff file. I want to get the WGS84 ellipsoidal heights.

According the steps from the Arcgis Help 10.2 "Converting from orthometric to ellipsoidal heights",I do the whole steps,but I found the tif file was not changed,the height was the orthometric height based on EGM96 geoid. I Want one tif format DEM with ellipsoidal heights based on WGS84 ellipsoid.

I don't know where the problem is?

The steps I followed :

Create a file geodatabase


  1. Click the Catalog button on the Standard toolbar. This opens the Catalog window. In the Location text box, type C:arcgisArcTutorRaster and press ENTER.
  2. Right-click the Raster folder and click New > Folder. Name the folder Exercises.
  3. Right-click the Exercises folder and click New > File Geodatabase. Rename the new file geodatabase ImageGDB.

Set the default geodatabase

Right-click the ImageGDB geodatabase in the Catalog window and click Make Default Geodatabase.

Create a new mosaic dataset


  1. Right-click ImageGDB in the Catalog window and click New > Mosaic Dataset. Type HMA in the Mosaic Dataset Name text box.
  2. Click the Coordinate System browse button . Expand Projected Coordinate Systems >, choose wgs84 world mercator.prj, then click OK. Click OK on the Create Mosaic Dataset tool dialog box.

Add rasters to the mosaic dataset


  1. Right-click the SRTM mosaic dataset in the Catalog window and click Add Rasters. This opens the Add Rasters To Mosaic Dataset tool.In the Raster Type list, choose Raster Dataset.
  2. Click the drop-down arrow and click Workspace.
  3. Click the Input browse button. Navigate to C:arcgisArcTutorRasterDataSRTM_tif and click Add.
  4. Check Update Overviews.
  5. Click OK to run the tool.

Add the Arithmetic function


  1. Open the Catalog Window and navigate to the mosaic dataset. Right-click the mosaic dataset and click Properties.Click the Functions tab.
  2. Right-click the Mosaic Function and click Insert > Arithmetic Function.

Input Raster 1 is identified as the current contents of the mosaic dataset and will not be changed. Click the Input Raster 2 browse button and navigate to the pedatageoid folder in the ArcGIS install location.Choose the WGS84.img and click Add.

  1. Click the Operation drop-down arrow and click Plus.
  2. Click OK to close the Raster Functions Properties dialog box.
  3. Click OK to close the Mosaic Dataset Properties dialog box.

Based on the steps you tried so far I think you have only created an on-the-fly version of what you want. Mosaic datasets do not change or create any new rasters, they just process them on the fly for fast visualization. If you export the resulting mosaic dataset as a new raster you should get the result you want.

Download EMG96 data which is height of Geoid above the WGS84 ellipsoid. Then use raster calculator to add EMG96 raster to your CIGAR DEM. (You may need upsampling EMG96 raster to get the same resolution as CIGAR DEM)

To orthocorrect a raster dataset, the raster must have RPCs associated with it.

For a more accurate result, use the digital elevation model (DEM) option for elevation. Use a DEM in the orthocorrection process to correct geometric errors caused by relief displacement.

Using a constant elevation value for the Orthorectification Type parameter will not yield accurate results and should only be used when no DEM is available and approximate spatial accuracy is acceptable.

You can save your output to BIL, BIP, BMP, BSQ, DAT, Esri Grid , GIF, IMG, JPEG, JPEG 2000, PNG, TIFF, MRF, CRF, or any geodatabase raster dataset.

Satellite RPCs require a DEM referenced to ellipsoidal heights, but most elevation data (such as USGS NED and ArcGIS Online World Elevation) are referenced to sea level orthometric heights. Check the Geoid parameter ( GEOID in Python) to orthorectify with RPCs unless your DEM is referenced to an ellipsoidal height.

Data sources

The image and raster data can come from a variety of sources, such as aerial or satellite sensors, scanned maps, the output from analysis, and even lidar data. It can be panchromatic, multispectral, thermal, elevation, or thematic. It can be stored as files on disk or in a file storage system (such as NAS or SAN), within a geodatabase, or accessed through a service (such as an image service or web coverage service (WCS)).

Image and raster data is added to a mosaic dataset according to its raster type. The raster type simplifies the processing of adding complex image data to a mosaic dataset. It is designed to understand the file format and specific information about a product such as metadata, as georeferencing, acquisition date, and sensor type, processing, and wavelengths, along with a raster format, whereas a raster format only defines how pixels are stored, such as number of rows and columns, number of bands, actual pixel values, and other raster format-specific parameters. In ArcGIS for Desktop there are several different raster types, some for specific image products and others for specific image sensors, such as Landsat 7, WorldView-2, or IKONOS.

By adding raster data according to a raster type, the metadata is read and used to define any processing that needs to be applied. For example, when adding a QuickBird Standard scene the raster type knows the metadata is stored in an .imd file and the bands are organized into one or more .tif files. It also knows that this imagery could be pan-sharpened and orthorectified, so depending on the options you choose, it will add the appropriate functions so the image can be processed accordingly. If you added this data as a regular raster dataset, then only the .tif files will be recognized and added, and any metadata information that would affect the functions needed or the orthorectification would be missing.

It is important that you use the correct raster type to add your imagery to a mosaic dataset. You may need to examine the files and their metadata sources to identify the file format or image product that is identified using the raster type.

Functions that define processing can also be added after imagery has been added to a mosaic dataset. This is often done to convert the output to a particular image product or to apply corrections to individual images. The functions can be applied to individual images or to the entire mosaic dataset.

No matter what mosaic dataset configuration you are implementing, you must make sure that the imagery is readable otherwise, the mosaic dataset will not be able to display the imagery. The location of the imagery is identified in a hard-coded path therefore, if you move the imagery, you must update the mosaic dataset and vice versa.

  1. Open the Catalog Window and navigate to the mosaic dataset.
  2. Right-click the mosaic dataset and click Properties .
  3. Click the Functions tab.

You will see a function chain with the single Mosaic function listed. The Mosaic function is listed by default, because this is a mosaic dataset, and all rasters are being mosaicked. It is after the mosaicking that you will be applying your algorithm.

When you insert a function, it is inserted above the function you've clicked.

This opens the Raster Functions Properties dialog box, which allows you to set the options for this function.

This represents H in the equation above.

This represents N in the equation above.

These steps allow you to convert orthometric height to ellipsoidal. If you need to convert ellipsoidal height to orthometric, you can use the Arithmetic function to apply the H = h - N equation.

Once completed, the mosaic dataset can be used as the DEM input to orthorectify the imagery in another mosaic dataset.


Select the raster dataset that you want to orthorectify. The raster must have rational polynomial coefficients (RPCs) in its metadata.

Specify a name, location and format for the dataset you are creating.

When storing the raster dataset in a file format, you need to specify the file extension:

  • .bil —Esri BIL
  • .bip —Esri BIP
  • .bmp —BMP
  • .bsq —Esri BSQ
  • .dat —ENVI DAT
  • .gif —GIF
  • .jpg —JPEG
  • .jp2 —JPEG 2000
  • .png —PNG
  • .tif —TIFF
  • no extension for Esri Grid

When storing a raster dataset in a geodatabase, do not add a file extension to the name of the raster dataset.

When storing your raster dataset to a JPEG file, a JPEG 2000 file, or a geodatabase, you can specify a Compression Type type and Compression Quality within the Environment Settings.

Use a Digital Elevation Model (DEM) or specify a value that represents the average elevation across your image.

  • CONSTANT_ELEVATION —Uses a specified elevation value.
  • DEM —Uses a specified digital elevation model raster.

The constant elevation value to be used when the ortho_type parameter is CONSTANT_ELEVATION .

If a DEM is used in the orthocorrection process, this value is not used.

The digital elevation model raster to be used for orthorectification when the ortho_type parameter is DEM .

The scaling factor used to convert the elevation values in the DEM.

If your vertical units are in meters, the Z Factor should be set to 1. If your vertical units are in feet, the Z Factor should be set to 0.3048. If any other vertical units are used, use the Z Factor to scale the units to meters.

The base value to be added to the elevation value in the DEM. This could be used to offset elevation values that do not start at sea level.

The geoid correction is required by RPCs that reference ellipsoidal heights. Most elevation datasets are referenced to sea level orthometric heights, so this correction would be required in these cases to convert to ellipsoidal heights.

  • NONE —No geoid correction is made. Use NONE only if your DEM is already expressed in ellipsoidal heights.
  • GEOID —A geoid correction will be made to convert orthometric heights to ellipsoidal heights (based on EGM96 geoid).


Landform, one of the key components of geographical classification and regionalization, is the most important factor for identifying regional differences 1 . The types of landform are generally classified into three major categories of mountains, hills, and plains. Elevation and relative elevation differences as well as surface incision variations between these categories are obvious regional relief is therefore the result of interactions between exogenic and endogenic forces that not only profoundly affect basic patterns and changes in other environmental factors but also directly influence regional land use as well as agriculture and industrial production.

Automatic classification of geomorphological types is currently based mainly on the use of regular statistical windows 2 , applying digital elevation models (DEMs) to extract a variety of surface morphological factors for classification, including elevation, slope, relief amplitude (RA), surface incision (SI), surface roughness (SR), profile curvature (PC), and the elevation variance coefficient (VC) 3,4,5,6,7,8,9,10,11 . As topographic variables can, to some extent, reflect aspects of regional geomorphology, this approach forms the basis of geomorphological regionalization. This approach has been widely used in the field since Hammond 12 first proposed that geomorphology can be classified on the basis of slope, relief, and profile types using statistical windows 1,13,14,15 . It remains the case, however, that topographic factors will not be exactly the same in all applications Dragut 16 used a suite of four parameters, elevation, profile and plan curvature, as well as slope gradient, while Liu utilized six parameters: relief amplitude, surface incision, surface roughness, and the elevation variance coefficient, as well as the mean slope and elevation 7 . In contrast, Hu 7 selected just three parameters: relief amplitude, surface incision, and the terrain position parameter (TPI). Large-scale geomorphological patterns can mostly be extracted from SRTM DEM data 4,17,18 indeed, a great deal of attention has been afforded to these data, as this application boasts near-global coverage (i.e., between 56°S and 60°N), has relatively high spatial resolution and is free of charge. SRTM DEM data outputs have therefore been applied widely 9,19 in terms of regional-scale digital geomorphology, SRTM data have commonly been upscaled to obtain macroscopic geomorphological information 20 . The accuracy of DEM data is mainly controlled by the measurement accuracy, resolution of DEM data, the slope of a pixel, and modelling error of DEM model 20 , which depends to a large extent on sample data reliability 21 and therefore means that simple resampling methods can significantly increase errors 19 . The traditional DEM models in this area suffers from a series of inherent shortcomings, including subjective parameter selection for depicting macrogeographical patterns as well as a greater level of error and the loss of detailed information from DEM data subsequent to upscaling processing. Therefore, on the same condition of the measurement accuracy, terrain complex level and resolution of DEM data, how to develop a suitable upscaling method to improve the DEM modelling accuracy is important in the geomorphological Regionalization.

The Beijing-Tianjin-Hebei (BTH) region is located between 113°04′-119°53′E and 36°01′-42°37′N within China (Fig. 1). Administrative zoning in this area includes the municipalities of Beijing and Tianjin as well as Hebei Province, encompasses a total area of 21.60 × 10 4 km 2 22 and is the political, cultural, and economic centre of China. This region also includes the Taihang and Yanshan Mountains as well as the North China Plain, is located within the transitional zone between the second and third steps and thus contains both complex and diverse geological structures and types. The BTH region, therefore, has important political and economic status and occupies a very important physical geographical region within China. There has also been no research to date that specifically addresses the macroscopic geomorphological features of this area. The main objective of this paper is therefore to propose a new upscaling method and to reveal the basic characteristics and spatial patterns of geomorphology within the BTH region. These results not only reveal the macroscopic landform features of this region but also provide a scientific basis for the sustainable utilization of regional resources, urban and rural development, and industrial and agricultural layout.


Digital Elevation Model is imperative to many earth surface process analyses. In this study, the quality of DEMs acquired by SRTM ver.3 and ASTER ver.2 is evaluated. The reference levels produced from GPS elevations, and the topographic map is used to assess the vertical accuracy of SRTM and ASTAR DEMs in Najran city, Saudi Arabia. The GPS reference elevations gave us the values of ±5.94 m and ±5.07 m for used SRTM and ASTER DEMs. Also, by using elevation from the topographic map as a reference elevations the obtained accuracy was ±6.87 m and ±7.97 m for SRTM and ASTER DEMs. For our study area, the 30 m SRTM elevations data featured a much greater absolute vertical accuracy than the absolute vertical accuracy value of ±16 m, which published in the SRTM data specification.

How to convert the SRTM DEM orthometric to ellipsoidal heights using ArcGIS or others GIS? - Geographic Information Systems

Paper Information

Journal Information

American Journal of Geographic Information System

p-ISSN: 2163-1131 e-ISSN: 2163-114X

Delineation and Characterization of Sub-catchments of Owerri, South East Nigeria, Using GIS

Akajiaku C. Chukwuocha 1 , Joel I. Igbokwe 2

1 Department of Surveying and Geoinformatics, Federal University of Technology, Owerri, Nigeria

2 Department of Surveying and Geoinformatics, Nnamdi Azikiwe University Awka, Nigeria

Correspondence to: Akajiaku C. Chukwuocha, Department of Surveying and Geoinformatics, Federal University of Technology, Owerri, Nigeria.


Copyright © 2012 Scientific & Academic Publishing. All Rights Reserved.

Catchment delineation and characterization are gaining increasing global attention as scientists seek better understanding of how runoff interacts with the landscape in the face of increasing flood devastations across the globe. All surface water flow systems occur in units of sub-catchments, the basic unit of landscape that drains its runoff through the same outlet to contribute to the main stream of the overall catchment. The delineation and characterization of sub-catchments would provide some basic data required for flood prediction, drainage design, water quality studies, erosion data, and sediment transport among others. In this study, Geographic Information Systems (GIS) were used to create a Digital Elevation Model (DEM) of Owerri, South East Nigeria. The DEM was validated using Global Navigational Satellite Systems (GNSS) surveys. The DEM was processed through a number of steps in GIS to determine drainage routes with a minimum accumulation threshold. All cells that contribute into each stream were dissolved into a single unit of sub-catchment polygon and delineated. Characteristics of the sub-catchments including the average slope, the longest flow distance, the area and the centroid coordinates required for input in the Storm Water Management Model of the Environmental Protection Agency of U.S.A. were determined.

Keywords: Digital Elevation Model (DEM), Sub-catchments, Drainage routes, Delineation, Characterization


Several post-processing methods have been developed over the last years in order to take into consideration topography within satellite-based solar radiation maps using digital elevation models (DEM). If the main part of these procedures is to estimate the obstructed horizon around each DEM point of a given region so as to consider terrain-based shading effects, the size of the area can also limit this implementation. That is why we have developed a new efficient horizon model based on the DEM retrieved during the Shuttle Radar Topography Mission (SRTM). In order to be usable at any world location with the same expected accuracy, this model is only derived from mathematical statements without any kind of empirical approximation. Validation against in situ horizons and comparison with some other models have finally shown this one presents both better accuracy (RMSE of 1.555 ° against 1.712 ° or more) and lower computation time (at least 4 times faster). Furthermore, in the case of very large areas, we propose an optimization procedure allowing the user to knowingly alter the modeling error in order to reduce processing time. Finally, using in situ data, we have also developed a method for predicting the repercussion of the original SRTM DEM error on the final horizon precision.

The analyses presented in this work were done with the freely available software R 25 and the transformation surfaces developed are distributed as GeoTIFF files, which can be read in any Geographic Information System software.

Meyer, T. H., Roman, D. R. & Zilkoski, D. B. What does height really mean? Part I: Introduction. Surv. L. Inf. Sci. 64, 223–233 (2004).

Zilkoski, D. B. Vertical Datums. In Maune, D. F. (ed.) Digit. Elev. Model Technol. Appl. DEM users manual., chap. 2 (American Society for Photogrammetry and Remote Sensing, 2007), 2nd edn.

Lemoine, F. G. et al. The Development of the NASA GSFC and NIMA Joint Geopotential Model. In Int. Assoc. Geod. Symnposia 117, 461–469 (1997).

Farr, T. G. et al. The Shuttle Radar Topography Mission. Rev. Geophys. 45, 1–33 (2007).

Abrams, M. et al. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) after fifteen years: Review of global products. Int. J. Appl. Earth Obs. Geoinf. 38, 292–301 (2015).

Tadono, T. et al. Generation of the 30 M-MESH global digital surface model by ALOS PRISM. In Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. - ISPRS Arch., 41, 157–162 (2016).

Sickle, V. J. GPS for Land Surveyors 4th edn, (CRC Press, 2015).

Meyer, T., Zilkoski, D. & Roman, D. What Does Height Really Mean? Part II: Physics and Gravity. Surv. L. Inf. Sci. J. Am. Congr. Surv. Mapp. 65, 5–15 (2005).

Meyer, T., Roman, D. & Zilkoski, D. What Does Height Really Mean? Part IV: GPS heighting. Surv. L. Inf. Sci. 66, 165–183 (2006).

Meyer, T. H., Roman, D. R. & Zilkoski, D. B. What does height really mean? Part III: Height systems. Surv. L. Inf. Sci. 66, 149–160 (2006).

Featherstone, W. E. & Kuhn, M. Height systems and vertical datums: A review in the australian context. J. Spat. Sci. 51, 21–41 (2006).

Milbert, D. G. & Smith, D. A. Converting GPS height into NAVD88 elevation with the GEOID96 geoid height model. In Gis Lis-International Conf., 1, 681–692 (1996).

Smith, D. A. et al. Towards the Unification of the Vertical Datum Over the North American Continent. In Int. Assoc. Geod. Symnposia 138 Ref. Fram. Appl. Geosci., 138, 253–258 (2013).

Véronneau, M. & Huang, J. The Canadian Geodetic Vertical Datum of 2013 (CGVD2013). Geomatica 70, 9–19 (2016).

INEGI. Cambio al Dátum Vertical NAVD88 en Información Geodésica Oficial. Tech. Rep., Instituto Nacional de Estadística y Geografía (2016).

Smith, D. & Bilich, A. The VERTCON 3.0 project. NOAA technical report NOS NGS 68. Tech. Rep. (2019).

Zilkoski, D. B., Richards, J. H. & Young, G. M. Results of the General Adjustment of the North American Vertical Datum of 1988. Surv. L. Inf. Syst. 52, 133–149 (1992).

Aguado, F. J. R. Vertical Geodetic Network of Mexico. In Drewes, H., Dodson, A. H., Fortes, L. P. S. & Sánchez, L., S. P. (ed.) Vert. Ref. Syst. Int. Assoc. Geod. Symp. 124, 44–49 (Springer, Berlin, Heidelberg, 2002).

Zilkoski, D. B., Balazs, E. I. & Bengston, J. M. Datum definition study for the North American vertical datum of 1988. Tech. Rep., National Geodetic Survey (1991).

INEGI. Guía Metodológica de la Red Geodésica Horizontal. Tech. Rep. (2015).

INEGI. El Geoide Gravimétrico Mexicano 2010. Tech. Rep., Instituto Nacional de Estadistica y Geografia (INEGI) (2015).

Villasana, J. A. Geodetic Networks in Mexico. Can. Surv. 28, 452–456 (1974).

GRASS-Development-Team. GRASS GIS software. Tech. Rep., Open Source Geospatial Foundation (2019).

Carrera-Hernández, J. & Gaskin, S. The Basin of Mexico Hydrogeological Database (BMHDB): Implementation, queries and interaction with open source software. Environ. Model. Softw. 23, 1271–1279 (2008).

R Core team. R: A Language and Environment for Statistical Computing (2019).

Pebesma, E. J. Multivariable geostatistics in S: the gstat package. Comput. Geosci. 30, 683–691 (2004).

Bivand, R. S., Pebesma, E. & Gómez-Rubio, V. Applied Spatial Data Analysis with R. (Springer, New York, New York, NY, 2013).

Conway, J., Eddelbuettel, D., Nishiyama, T., Kumar, S. & Tiffin, N. RPostgreSQL: R Interface to the’PostgreSQL’ Database System. R package version 0.6-2 (2017).

Bivand, R. rgrass7: Interface Between GRASS 7 Geographical Information System and R. R package version 0.2-1 (2019).

Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag, New York, NY, 2016).

Carrera-Hernández, J. A tale of Mexico’s most exploited-and connected-watersheds: the Basin of Mexico and the Lerma-Chapala Basin. Wiley Interdiscip. Rev. Water 5, e1247 (2018).

Hudson, G. & Wackernagel, H. Mapping temperature using kriging with external drift: Theory and an example from scotland. Int. J. Climatol. 14, 77–91 (1994).

Carrera-Hernández, J. & Gaskin, S. Spatio temporal analysis of daily precipitation and temperature in the Basin of Mexico. J. Hydrol. 336, 231–249 (2007).

Goovaerts, P. Geostatistics for Natural Resources and Evaluation (Oxford University Press, 1997).

Isaaks, E. H. & Srivastava, M. Applied geostatistics. (Oxford University Press, New York, NY, 1989).

Cressie, N. A. C. Statistics for Spatial Data (Wiley & Sons, 1991).

Zambrano-Bigiarini, M. hydroGOF: Goodness-of-fit functions for comparison of simulated and observed hydrological time series. R package version 0.3-10 (2017).

Willmott, C. J. & Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30, 79–82 (2005).

Höhle, J. & Höhle, M. Accuracy assessment of digital elevation models by means of robust statistical methods. ISPRS J. Photogramm. Remote Sens. 64, 398–406 (2009).

Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).

Carrera-Hernández, J. Mexico’s vertical datum transformation grids. figshare (2020).

Hijmans, R. J. raster: Geographic Data Analysis and Modeling (2019).

Watch the video: How To Download SRTM Dem 30m Data Directly From QGIS. Digital Elevation Model Data Download QGIS (September 2021).