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

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

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

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 - http://geobc.gov.bc.ca/base-mapping/atlas/trim/specs/Gridded_DEM_Specificationc_ver1_1.pdf

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

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

SRTM (Shuttle Radar Topographic Mission) Feb 2000 90 metre pixels, 56ºS - 60ºN latitude e.g. Google Earth http://srtm.csi.cgiar.org/

ASTER GDEM global 30m pixels cloud cover issue and consistency Global DEM (ASTER) http://asterweb.jpl.nasa.gov/gdem.asp

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

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

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

2. Shaded relief (hillshade) A cartographic layer, DN= 0 - 255 (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

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

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

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

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)

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 ->

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

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

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

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

Directed high res sensors- Ikonos 1999: 4 m http://www.satimagingcorp.com/galleryimages/ikonos-high-resolution-dem-eritrea.jpg

DEM from Photogrammetry: Tatras, Slovakia 2m

7. Anaglyphs St-Pierre – Miquelon SRTM DEM

Klinakline Glacier, south Coast Mountains (ASTER DEM)

Hoodoo Mountain and Glacier, Coast Mountains (ASTER DEM)

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)

Example: Land Cover Classification Using IRS LISS III Image and DEM in Rugged Terrain: A Case Study in Himalayas http://www.geocarto.com.hk/cgi-bin/pages1/june05/33_Saha.pdf Green Red NIR MIR NDVI DEM

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