1. Digital Elevation Models (DEM) / DTM
Uses in remote sensing:
queries and analysis, 3D visualisation, classification input
Fogo Island, Cape Verde Republic ASTER DEM / image
Banks Peninsula, Christchurch, New Zealand

2. Digital Terrain Models Photogrammetric
(and LiDAR)
Digital Surface Models
Spaceborne
(and LiDAR)

3. Almost all DEMs have been created from remote sensing:
a. DEMs from digitising contours (e.g. NTDB)
The earliest were created by digitising contours on maps into layers - not remote sensed
stereo photos -> contour lines -> digitised lines -> interpolate to raster grid
Arcmap: topo to raster PCI geomatica: vdemint

7. b. Digital Stereo photogrammetry: (e.g. BC TRIM)
Mass points (25 metre spacing) captured from aerial photographs
stereo photos -> mass points -> interpolate to raster grid
TRIM Specifications -

8. BC TRIM DEM – 25metre grid Interpolated to 25m grid
By 1:250,000 map sheets
(100 tiles assembled together)
Elevation in metres
= 16 bit DNs
0-65,535 (unsigned)
or ± 32,767 (signed)
Or 32 bit (real) - interpolated
-9999 for invalid numbers
e.g. across border into AB

9. Post 2000 methods c. Direct image grid DEM data from imagery
RADAR e.g. Shuttle Radar Topographic Mission (SRTM)
Stereo digital satellite imagery (adjacent or directed satellite tracks)
LiDAR and Digital photogrammetry
Issues: cloud cover and missing data
ASTER SPOT

10. SRTM (Shuttle Radar Topographic Mission) Feb 2000
90 metre pixels, 56ºS - 60ºN latitude e.g. Google Earth

11. ASTER GDEM global 30m pixels cloud cover issue and consistency
Global DEM (ASTER)

12. ASTER image and DEM : Svalbard, Norway (15 metre resolution)

13. DEM availability
A DEM is a continuous grid of elevation values (Raster/Vector) –
one height value per pixel ….. in a channel (not a band)
Resolutions and datasets available:
NTDB 25m (Canada)
TRIM 25m (BC only)
ASTER 30m (global) – with holes …
SRTM 90m (near global)
Download Canadian data (interpolated from contours) : geogratis.ca
* BC is provincial TRIM data (25m) resampled for NTDB

14. DEM - layers Elevation (‘DEM’)
DN = (metres, 16 bit):  represented onscreen as grayscale or pseudocolour tints;
DEMs are stored as integer elevations (metres) or 32 bit (after interpolation)
some (NTS) DEM tiles in Canada may still be in feet …. conversion = .3048

15. 2. Shaded relief (hillshade)
A cartographic layer, DN= (relative amount of light reflected),
as grayscale; light source can be selected, usually from the NW.
High values on NW facing slopes, low values on SE facing slopes.
Select light source azimuth and angle
Default = 315, 45
useful to detect errors and assess DEM quality

16. DEM data stored in ‘geographic’ (lat/long) must be ‘projected’
Reprojection can cause striping and artifacts 
Avoid reprojecting rasters if possible :
Group & Reproject vectors first
e.g. topo to raster

17. Stripes – often caused by
WGS84 -> NAD83
Holes: due to clouds (GDEM)

18. 3. Slope Calculated in degrees (0-90) or % (0 -> infinity)
slope is rise/run = vertical change over the horizontal distance
8 bit results should be adequate for most purposes

19. 4. Aspect: the compass direction a slope is facing DN = 0-359 (360)
Displayed as grayscale pseudocolour
Darker for lower DN (0-> ) , brighter for higher DN ( -> 359)

20. 4. Aspect: the compass direction a slope is facing
This raises three questions :
flat slopes have no value (they are given an arbitrary value, e.g. 510)
0-360 requires 16 bit data, so ….
Geomatica default converts to 8 bit by dividing by 2 (flat = 255)
north facing slopes have both extreme values, 0 and 359 (360)
… so aspect CANNOT be used as a classification layer
… instead, we use ->

21. 5. Incidence
DN is related to the reflection based on sun angle (0-90)

23. Incidence looks similar (inverted!) to shaded relief, but DN 0-90
the angle (degree) of light incidence, based on the sun position
Requires metadata for sun elevation and azimuth for the scene
Azimuth = sun’s compass direction
Elevation = height of sun
Solar zenith = 90 - elevation

24. We will look at these concepts later on – after the midterm
6. Curvature and Ruggedness
We will look at these concepts later on – after the midterm
Mixture of many terrain products (slope, elevation…) and filters

25. 6. 3D perspectives / fly-throughs ‘FLY’
e.g. Google Earth, ArcScene - Terrain Bender (open source).

26. Directed high res sensors- Ikonos 1999: 4 m

27. DEM from Photogrammetry: Tatras, Slovakia 2m

28. 7. Anaglyphs St-Pierre – Miquelon SRTM DEM

29. Klinakline Glacier, south Coast Mountains (ASTER DEM)

30. Hoodoo Mountain and Glacier, Coast Mountains (ASTER DEM)

31. 8. DEMs in Digital Image Classification strategies for reducing mountain shadows effect
Input channels for classification: (Remember same bit types - i.e all inputs 8 or 16)
Raw bands e.g. TM 3,4,5 / OLI 6,5,4 PLUS
Ratios / Indices
Transform components (e.g. Tassel Cap greeness, PCA)
3. DEM Elevation
Slope (gradient)
Incidence (not aspect)

32. Example: Land Cover Classification Using IRS LISS III Image and DEM in Rugged Terrain: A Case Study in Himalayas
Green
Red
NIR
MIR
NDVI
DEM

33. https://asterweb.jpl.nasa.gov/gallery-detail.asp?name=Helens