ASTER GDEM V3(ASTER Global DEM)

Japan’s Ministry of Economy, Trade, and Industry(METI)
National Aeronautics and Space Administration (NASA)
Jet Propulsion Laboratory/California Institute ofTechnology

Prepared by Michael Abrams and Robert Crippen

User Guide
Version 1
June 2019

Copyright 2019 California Institute of Technology. U.S. Government sponsorship acknowledged.

ASTER Global DEM (ASTER GDEM)
Quick Guide for V3
1. Introduction
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument thatwas launched onboard NASA’s Terra spacecraft in December 1999 has an along-track stereoscopic
capability using its near infrared spectral band to acquire the stereo data. ASTER has two telescopes,
one for nadir-viewing and another for backward-viewing, with a base-to-height ratio of 0.6. The spatial
resolution is 15 meters (m) in the horizontal plane. Each scene consists of 4,100 samples by 4,200 lines
which corresponds to about 60 kilometers (km) by 60 km ground area.
The ASTER Global Digital Elevation Model (ASTER GDEM) Version 3 (V3) was generated using
1,880,306 Level 1A scenes acquired between March 1, 2000 and November 31, 2013. ASTER GDEM
was created by stacking all individual cloud-masked scene DEMs and non-cloud-masked scene DEMs,
then applying various algorithms to remove abnormal data. ([1], [2], [3].
A statistical approach is not always effective for anomaly removal in areas with a limited number ofimages. Several existing reference DEMs were used to replace residual anomalies caused by theinsufficient number of stacked scenes.
In addition to ASTER GDEM, the ASTER Global Water Body Database (ASTWBD) was generated
as a by-product to correct elevation values of water body surfaces like sea, rivers, and lakes. The
ASTWBD was applied to GDEM to provide proper elevation values for water body surface. The sea
and lake have a flattened elevation value. The river has a stepped-down elevation value from the upperstream to the lower stream.
2. Data Product Files
Each ASTER GDEM tile has two associated files: a DEM file with elevation information and a quality
assessment (QA) NUM file. Both files have a dimension of 3601 samples by 3601 lines, whichcorresponds to 1 degree by 1 degree data area. The names of individual data tiles refer to the latitude
and longitude at the geometric center of the lower-left (southwest) corner pixel. For example, thecoordinates of the lower-left corner of the tile ASTGTMV003_N00E006 tile are 0 degree northlatitudeand6degreeseast
longitude.
ASTGTMV003_N00E006_dem
and
ASTGTMV003_N00E006_num files accommodate DEM and QA data, respectively. The rows at thenorth and south edges as well as the column at the east and west edges of each cell overlap and are
identical to the edge row and column in the adjacent cell.
The data coverage is north 83 degrees to south 83 degrees. The GDEM collection includes 22,912
tiles.

3. Data Product Characteristics
Data product characteristics are summarized in Table 1.

Table 1 Data characteristics
Tile Size

3601 x 3601 (1 degree by 1 degree)

Posting interval

1 arc-second (30 m)

Geographic coordinates

Geographic latitude and longitude

DEM output format

DEM: GeoTiff, signed 16 bits, and 1m/DN for DEM;
NUM: GeoTiff; unsigned 8 bit number of individual
scenes used to compile each pixel (maxed at 50); andsource of fill for missing ASTER data (see index below)

Special DN values-9999 for void pixels and 0 for sea-level water body

Ellipsoid/Geoid

Referenced to the WGS84/EGM96 geoid

CoverageNorth 83 degrees to south 83 degrees, 22,912 tiles for
GDEM

4. Anomaly Correction
4.1 Initial Corrections
A cloud masking and the statistical approach were used to select the effective data for stacking. Cloudmasking cannot completely remove the clouds. In addition, the statistical approach is not always
effective for anomaly removal in the cases where a small number of scenes were used. At least threestacked scenes are necessary to apply statistical algorithms.
Existing DEMs were used as reference data to correct and replace anomalies or missing data. Table 2
shows the list of the reference DEMs used for ASTER GDEM V3 data. The anomaly data were filledwith the reference data according to the priority for usage shown in Table 2. When using GMTED2010
7.5 sec reference data, only an anomaly larger than 1 km² was filled with the reference data. Smaller
size anomalies less than1 km² were filled with two dimensional interpolation from the perimeter data.
The reference data shown in Table 2 collectively cover all ASTER GDEM voids. All of these reference
data are not anomaly free, but the initial ASTER GDEM V3 was nearly void free except for Greenlandand Antarctica.

Table 2 Reference DEM list
SRTM1 V3 [4](First priority)

Posting: 1 arc-second
Coverage: North 60 degrees to south 56 degrees

Alaska DEM [5] (Second
priority)

Posting: 2 x 2 arc-seconds
Coverage: All Alaska
territory

CDED [6](Canada DEM)
(Second priority)
GMTED2010 [7](Third priority)

Posting: 3 arc-seconds for latitude
3, 6 and 12 arc-seconds for longitude depending on latitude
Coverage: all Canada territory
Posting: 7.5 arc-seconds
Coverage: North 84 degrees to south 56 degrees (except
Greenland)

4.2 Final Correction
4.2.1

GDEM Error Mask Generation

The GDEM error mask was created in three major steps. First, the input GDEM, described above, (bothVersion 2 and Version 3) was compared to SRTM [4] and PRISM (30 m DEM available from Japan’s
JAXA, created from ALOS PRISM optical data [8]) for reasonable consistency. Second, the input
GDEM was evaluated for the likelihood of errors (such as clouds) based upon its own
morphology. Third, the composite mask generated from the first and second steps was expanded byfiltering to better include all input GDEM error pixels.
The first step followed qualitative logical probabilities. SRTM has no cloud errors, and the cloud
avoidance for PRISM appears to be more consistently effective than the cloud avoidance of the input
GDEM. However, both SRTM and PRISM have data voids, so are not always available for comparison
with the input GDEM. Also, SRTM can have interferometric unwrapping errors, although the mostsignificant of those were likely detected and were voided prior to comparison to the input GDEM (seeabove). In short, SRTM is generally considered reliable and PRISM is considered less error-prone than
the input GDEM, especially where two or fewer ASTER scenes were used to create GDEM. With thatin mind, this first step in creating the GDEM error mask followed the following logic:
1. SRTM and PRISM not void: Input GDEM pixel was rejected if it differed from both
SRTM and PRISM by more than 80 m.
2. SRTM and PRISM both void: Input GDEM not rejected.
3. SRTM void but PRISM not void: Input GDEM was rejected if more than 80 m different
from PRISM, unless input GDEM NUM pixel value was 3 or more.
4. PRISM void but SRTM not void: Input GDEM was rejected if more than 80 m different
from SRTM.
An initial rejection mask was thus created. The 80 m threshold was determined empirically fromextensive experience worldwide, maximizing cloud masking while minimizing the loss of obviously
real topography such as dendritic erosion patterns. The mask was then expanded to the eightneighboring pixels of any masked pixel in order to more assuredly include problem areas in the mask.
In the second major step, the mask was then additionally expanded to any pixel that differed “too much”
from any neighboring pixel (thus having a slope so high as to be more likely an error, such as a cloudedge, than to be natural topography). The threshold for “too much” was defined as greater than 100
meters in the north-south or east-west direction at the equator, and it was defined as 141 (about thesquare root of 2 times 100) meters in the northwest-southeast and northeast-southwest directions at theequator. Exceeding the threshold in any direction resulted in GDEM pixel masking. The threshold wasdecreased appropriately as a cosine function of latitude for all orientations (except north-south) because
longitude 1-arcsecond pixel spacings decrease with latitude. The (by-chance) round number 100 was(again) determined empirically. Lower thresholds lost too much good topography and higher thresholds
accepted too many cloud errors.
At this point a mask had been crated based upon two major steps: (1) comparisons of individual pixels
to themselves in other datasets, and (2) comparisons of individual GDEM pixels to their immediate
GDEM neighbors. Neither of these steps “look” across significant geography which was found to be a
problem. Often edges of clouds were masked but not the cloud interiors (which could be much lesssteep). Thus a third step, spatial filtering, was needed to “fill-in” the mask.
Extensive testing resulted in an effective filtering method, as follows. Any non-masked pixel was added
to the mask if masked pixels were found within a 50-pixel radius in at least 12 of 16 “spoke” directions(north, north-northwest, northwest, west-northwest, west, etc.). The concept here is that small areas
need to be additionally masked if they are enclosed by masked pixels or if they are almost (at least
12/16) enclosed by masked pixels. Note that this method does not expand the exterior edges of masked
regions (i.e., areas clearly exterior to clouds are preserved as unmasked).
A 5 by 5 median filter of the binary mask (masked versus not masked pixels) was needed to avoid somenoise from the 16 detector “spokes”. Previously identified “too steep” pixels were then restored to themask where eliminated by the 5 by 5 median filter. The mask was then complete and found to be
extremely effective.
4.2.2

GDEM Void Filling

The masking of errors in GDEM creates voids that need to be filled. Preferably these voids are filledwith an alternative DEM rather than via interpolation. For consistency, the best DEM to fill the latest
GDEM is an earlier GDEM. That results in a final GDEM that is maximally from ASTER and thus
most inherently an ASTER product with minimum elevation data from other sources. Using GDEM V2
to fill voids is possible because errors in GDEM V3 were not always spatially coincident with errors in
GDEM V2. The newer GDEM uses the ASTER scenes used in GDEM V2 plus additional (more recent)scenes. In general, having more scenes results in fewer errors. However, inclusion of additional cloudy
scenes occasionally introduced errors that did not occur in the older GDEM.
Thus, GDEM V2 was the first choice as the elevation model for filling voids of the newer GDEM. Since
GDEM V2 has errors too, the same error masking procedure (above) was applied. SRTM, whereavailable, was the elevation model of second choice to be used as fill This was the reprocessed (e.g.
ICESat adjusted) SRTM that was concurrently (circa 2017) generated for the NASADEM Project. The
original SRTM had been the primary fill for all prior versions of GDEM. As described above, someinterferometric unwrapping errors were removed from SRTM but no further masking wasapplied. PRISM AW3D30, where available, was used as third choice, after it was subjected to a masking
routine similar to that applied to the GDEM elevation models. Masking PRISM AW3D30 was a
precautionary step. Note that after masking GDEM2, all of the filler elevation models had voids (GDEM
V2, SRTM, and PRISM AW3D30), either inherently or from masking, or both.
The void fill routine is a modified version of the Delta Surface Fill method of Grohman [9]. The
published method was modified in order to (1) use filler DEMs with voids, (2) reduce the spread oferrors from void edges into the void fill, and (3) improve processing speed. (See Appendix 1 for further
details of the modified procedure).

With the delta surface calculated and filled, each intermediate DEM (from each filling step) is
generated such that (1) the output is the primary DEM where it exists, (2) the output is void where
both DEMs are void and (3) the output is the secondary DEM shifted by the filled delta surface where
the primary DEM is void and the secondary DEM is not void.
To summarize, the initial GDEM3, the GDEM2, and the PRISM DEM were error-masked via selectcomparisons to each other and to SRTM. The masks were then expanded to also remove DEM
morphologies more likely to be clouds (and other errors) than real terrain. Specially designed filtering
improved the masks. Voids in the masked initial GDEM3 were then sequentially filled with themasked GDEM2, SRTM, and the masked PRISM DEM (in that order). Interpolation filled any
remaining voids. Results for all quads outside Antarctica and Greenland were visually inspected andfound to be satisfactory to excellent, with some exceptions. Results for major ice sheets (often with
few ASTER scene inputs) often have poor quality and limited utility.
An example of the results of the procedure is shown in Figure 1. A 400 by 400 pixel subarea of tile
N60E05 was processed using the above process. The input was the GDEM provided by the contractor,
who applied some additional void-filling corrections that introduced new artifacts (left image), partially
due to use of poor quality GMTED data. The right image shows the final, fully corrected GDEM V3
tile. Note that his area is above 60 degrees north latitude, where there are no SRTM data.

Figure 1. GDEM V3 tile N60E05, 400 by 400 pixel subarea. Left: Input GDEM with errors and artifacts,
both original, and introduced in early attempt at correction; Right: Final GDEM tile after corrections as
described in Section 4.2.
5. NUM File
A NUM (number) file is provided for each tile to describe the number of images (or swaths) stacked
to derive the elevation values from ASTER (GDEM2 and initial GDEM3), PRISM, and SRTM. The
ASTER scene count is limited here to 50 for both GDEM2 and GDEM3 (in some areas the actualscene count exceeds 100). The actual maximum scene count for PRISM is 54 (37 in non-polar areas)
and is fully indicated. The actual maximum swath count for SRTM is 23 and is fully indicated. Someother NUM values simply indicate the data type previously used as fill sources in GDEM2 and theinitial GDEM3 (identified by a single value). See Table 3. The NUM files are presented as single-byte(8-bit, 0-255 DN) images for easy visual display. The tile size, posting interval, and geometric formatsof the NUM files are consistent with the DEM files described in Table 1.
Table 3. Contents of NUM file showing number of DEM tiles used, and sourceof void fill. *0=Unspecified; **National Geospatial-Intelligence Agency (filled
SRTM).
NUM

DEM Source

0-50

GDEM3 (0* to 50+ scenes)

60-110

GDEM2 (0* to 50+ scenes)

131-184

Prism (1 to 54 scenes)

201-223

SRTM (1 to 23 swaths)

231

SRTMv3 from initial GDEM3

232

SRTMv2 from initial GDEM3233

SRTMv2 from GDEM2

234

SRTM with NGA** fill from GDEM2

241

NED from GDEM2 (USA)242

NED from initial GDEM3 (USA)243

CDED from GDEM2 (Canada)

244

CDED from initial GDEM3 (Canada

245

Alaska DEM from GDEM2

246

Alaska DEM from initial GDEM3250

Interpolation

6. Key Processing Factors
The changes in the key processing factors from Version 1 to Version 2 to Version 3 are listed in Table
4.

Table 4. Key processing factors
Key processing factor
Number of input scene DEMS
Posting interval
Correlation kernel size
Minimum water body detection size

Version 1
1,264,118
1 arc-second
9 x 9 pixels
12 sq. km

Version 2
1,514,350
1 arc-second
5 x 5 pixels
1 sq. km

Version 3
1,880,306
1 arc-second
5 x 5 pixels
0.2 sq. km

Water body post-processing

Not applied

AppliedFiltering threshold value
Offset

40 m-5 m offset
observed
June 200940 m
V1 offset removed

Separation of rivers
from lakes
40 m
---

October 2011

June 2019

Release date7.

Validation

Japan Spacesystems evaluated GDEM V3 against the 10 m Japan digital topographic data set.
Geolocation error, compared to GDEM V2, improved from 0.11 arc-seconds (3.3 m) to 0.01 arc-seconds(0.3 m) to west; and from 0.20 arc-seconds (6 m) to 0.18 arc-seconds (5.4 m) to the north. The standard
deviation of the elevation error was 12.1 m. Horizontal resolution remained at 2.4 arc-seconds (72 m).
(See Table 5)
Table 5. Summary of validation in Japan
Version 1(Kernel Size: 9 x 9)

Version 2(Kernel Size: 5 x 5)

Version 3(Kernel Size: 5 x 5)

0.82 arcsec to west
0.47 arcsec to south

0.11 arcsec to west
0.20 arcsec to north0.01 arcsec to west
0.18 arcsec to northoffsetAbout 6 m

About -0 m

N/A

SD

14.8 m

12.6 m12.1 m

3.8 arc-sec. (114 m)

2.4 arc-sec. (72 m)

2.4 arc-sec. (72 m)

Geolocation Error
Elevation
Error
Horizontal
Resolution

8. Data Product Access and Citation
The following tools offer options to search the LP DAAC data holdings and provide access to thedata:
Bulk download: LP DAAC Data Pool and DAAC2Disk
Search and browse: NASA Earthdata Search
Citing ASTER GDEM V3 data:
NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team. ASTER Global
Digital Elevation Model V003. 2018, distributed by NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/ASTER/ASTGTM.003
9. References[1] Fujisada, H., G. B, Bailey, G. G. Kelly S. Hara, and M. Abrams, ASTER DEM performance,
2005, IEEE Transactions on Geoscience and Remote Sensing, vol.43, no.12, pp.2707-2714.
[2] Fujisada, H. Urai, M. and A. Iwasaki, Advanced methodology for ASTER DEM generation, 2011,
IEEE TGARS, vol.49, no.12, pp.5080-5091.

[3] Fujisada, H., M. Urai, and A. Iwasaki, Technical methodology for ASTER global DEM, 2012,
IEEE Transactions on Geoscience and Remote Sensing, vol.50, no.10, pp.3725-3736.
[4] NASA JPL. NASA Shuttle Radar Topography Mission Global 1 arc second. 2013, distributed byNASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MEaSUREs/SRTM/SRTMGL1.003[5] USGS. USGS Alaska Digital Elevation Models (DEM) 5 meter. 2013, distributed by USGS The
National Map.
[6] National Resources Canada. Canadian Digital Elevation Data (CDED) 3 arc second. 2008,
distributed by Open Government of Canada GeoBase.
[7] USGS. Global Multi-resolution Terrain Elevation Data 2010 7.5 arc second. 2010, distributed by
USGS EarthExplorer.
[8] JAXA. ALOS Global Digital Surface Model 1 arc second. 2017, distributed by JAXA Earth
Observation Research Center.
[9] Grohman, G., G. Kroenung, and J. Strebeck, 2006, Filling SRTM voids: The Delta Surface Fillmethod. Photogrammetric Engineering and Remote Sensing, v. 72, no. 3, p. 213-216.
10. Acknowledgement
Work by M. Abrams was performed at the Jet Propulsion Laboratory, California Institute of Technology
under contract with NASA.

Appendix 1
In simple terms, the original Delta Surface Fill method is intended to create a void-free DEM from (1)a primary DEM that has voids and (2) a secondary DEM that does not have voids. The secondary DEM
fills the voids seamlessly by being adjusted vertically to compensate for height differences (the delta)
between the DEMs, with particular focus on the primary DEM’s void edges. Elevations of the secondary
DEM within the voids of the primary DEM (where deltas can only be estimated) are adjusted verticallyby deltas interpolated from those void edges. Thus, a full delta surface (the DEM difference) is
calculated as a simple subtraction where outside the voids, and it is filled by interpolation within thevoids. That void-free delta surface is the spatially variable vertical shift that is applied to the filler DEM
as it fills the voids of the primary DEM.
As implemented for some steps (not the final step) for NASADEM, the delta surface has voids not only
from the primary DEM but also from the secondary (filler) DEMs. This often means that someelevations (including void edges) in the primary DEM correspond to voids in the secondary DEM. This
is not a severe problem because, unless the DEMs are badly corrupted, all of the delta surface values
across a region should be nearly a constant. The delta surface measures general vertical differences
between the DEMs (such as reference datum errors, which are spatially constant), various possible
warps, and local vertical differences between the DEMs (associated with measurement errors ofindividual or neighboring pixels, thus having high spatial frequencies and no widespread trends). In
short, estimating delta surfaces for voids in the primary DEM is usually performed by interpolation of
the delta surface from the edges of those voids. But if it is performed by interpolation from further away,
namely at the edge of the void of the filler DEM, there is usually no large adverse impact. The fill may
be somewhat less “seamless” at the edge of the void in the primary DEM, but that “seamlessness” mayjust have been corruption of the fill to better match errors at the void edge. It may look better (smoother)but be less correct in terms of actual elevations. Thus, applying the Delta Surface Fill method with afiller-DEM that has voids does work, but it will (of course) leave remnant voids where both DEMs arevoid.
The delta surface (as with nearly any difference image) is typically quite noisy. The vast majority of theelevation signal is cancelled and only differences (e.g. errors and possibly temporal differences) in theelevation measurements remain. Systematic errors (reference datums and warps) will remain, and these
are what the delta surface is intended to extract and then suppress in the DEM merger. However, random
errors also exist pixel-by-pixel or over small areas. This may be especially true at void edges next topixels that were voided because they were clearly erroneous or at least unreliable. Interpolation fromvoid edge pixels can therefore result in a delta surface fill that is particularly noisy. To suppress this a
5 pixel by 5 pixel median filter was applied to the non-void pixels (only near voids, forspeed). Interpolation across the delta surface voids then proceeded using these filtered void-edge pixels.
However, the delta elevations are typically still quite noisy, so an interpolation method is needed thatsuppresses local noise and keeps it local. Consequently, a repetitive edge growing interpolator was used.
Pixels in the void directly adjacent to the void edge were interpolated, and then the void pixels next tothose were interpolated from the first interpolated pixels. The edge growing interpolator was run forfive iterations. This greatly smoothed the void fill. For speed in global processing, remaining void pixels
used direct interpolation and skipped edge growing.
In both the edge growing interpolator and the interpolator without edge growing, an inverse square rootof distance interpolator that looked in 16 directions (north, south, east, west, and approximately westnorthwest, northwest, north-northwest, etc.) was applied. Using the inverse square root of distance may
be unique. Interpolators often use the inverse square (not square root) of distance to more heavily weight
nearby reference points, especially the single nearest reference point. But (to some degree the opposite
approach was desired. To de-emphasize noise in the very nearest reference points. Distant pixels are
generally less relevant in most interpolations. But recall that the delta surface generally measures afairly uniform value (the elevation signal cancels out). Distant reference pixels are more scattered andtherefore less redundantly subject to a single noise source. Giving them extra weighting helps to reducenoise. This is particularly true in the edge-growing interpolator because typically half of the 16 lookdirections find pixels that are only a very few pixels away, and close pixels (due to their quantity)
already get heavily weighted because of that. The inverse square root of distance formula helps balance
the weighting of reference points around the void.