Child pages
  • Product Sheet: Terrain Roughness

Terrain Roughness


Terrain roughness map (Source: WesternGeco)



Component products


Terrain Information

  • N/A



  • Seismic Planning - Areas of poor coupling
  • Seismic Planning - Identification of adverse terrain for trafficability
  • Surface Geology Mapping - Lithological discrimination
  • Surface Geology Mapping - Terrain evaluation and geo-morphology characterization
  • Environmental Monitoring – Natural hazard risk analysis
  • Logistics Planning and Operations - Baseline mapping of terrain and infrastructure
  • Logistics Planning and Operations – Support to surveying crews

Geo-information requirements

  • Topographic information
  • Terrain information
  • Lithology, geology and structural properties of the surface


Terrain roughness products are delivered on a project/basin scale. A roughness map is a raster bitmap that highlights areas that are uneven or hazardous. The roughness is a calculation that can be based on elevation, slope, and or combined with optical or radar imagery interpretation to verify accuracy.

Optical-derived products:

Roughness tends to be unaffected by seasonal or temporal changes. A high resolution image will provide a snap shot of the roughness faced in the project area. Rocky outcrops, weathered/un-weathered exposed rock can be identified. Lower resolution imagery will be of limited use - a degree of detail will be lost, however if combined with radar images, its value will be increased.

Radar-derived products:

Radar images will show high scattering in rough areas. Radar images are not affected by cloud cover meaning that in areas where weather conditions reduce the availability of optical data, radar data can provide imagery that shows surface roughness.

Elevation data

DEM data (optically or radar derived) can provide detail of terrain roughness at different resolutions. Elevation derived products such as slope area also used. Roughness indexes such as relative topographic position, standard deviations of elevation and slope variability ways roughness can be derived from elevation data.

Known restrictions / limitations

Dense vegetation will mask rough ground and is a significant limitation; cloud cover will impact optical data but can be mitigated with radar data. If optical data needs to be programmed (i.e. not available in archives) then turnaround time can be up to 3 months depending on acceptance criteria (normally 90% cloud free image for example).

Roughness mapping from DEM data is limited by the availability of DEM data. DEM data derived from stereo pairs can have a lead time of 3 weeks, but has a higher degree of accuracy than freely available lower resolution DEM’s. Radar derived DEM data are available off-the-shelf, with accuracy affected in steep mountainous regions and densely vegetated regions.

Lifecycle stage and demand












  • Terrain roughness is useful to understand prior to a seismic survey. Help with the logistics planning of a seismic crew (where vehicles can safely operate), prediction of poor coupling, and potential for punctures. Surface roughness has a big impact on the speed/efficiency of a seismic acquisition and needs to be correctly modelled to match production expectations.


  • Terrain roughness can be used to complement geological mapping and for terrain evaluation. Help to predict trafficability, soil/lithology variations and rock excavatability (e.g. for pipeline trenches).

Geographic coverage and demand

Global coverage (with a few restrictions see below). Demand in remote regions is high with exposed non vegetated surfaces best suited.


OTM:058 Identifying ground conditions susceptible to poor coupling

OTM:043 Anticipating areas of high seismic impedance

OTM:046 Identifying variations in trafficability for seismic vehicle

OTM:051 Identification of fault lines

HC:1102 Identify rock-strewn areas to avoid point loading


Input data sources

Optical: VHR1, VHR2, HR1, HR2

Radar: VHR1, VHR2, HR1, HR2, MR1, MR2

Supporting data:

  • Digital elevation models (DEM)
  • Geological mapping
  • Vegetation/land cover mapping
  • Existing  GIS data such as infrastructure and assets
  • Local knowledge

Spatial resolution and coverage

Spatial resolution: 1 m – 1 km pixel size

Minimum Mapping Unit (MMU)

Variable, depending on source data resolution MMU as small as 0.5 ha is possible.

Accuracy / constraints

The geometric accuracy is usually comparable to the spatial resolution of the input satellite data, i.e. typically a few metres. The thematic (classification) accuracy (assisted with field verification) is in the range of 80–90% depending on the quality of the EO data.

Accuracies for a few off-the-shelf elevation products:

  • SRTM version 3: Absolute and relative vertical accuracy was anticipated to be less than 16 and 10 m, respectively
  • ASTER GDEM2 has a root-mean-square error (RMSE) in elevation between ±7 and ±15 m can be achieved with ASTER stereo image data of good quality.
  • WorldDEM (TanDEM-X) has a 2m relative and a 4m absolute vertical accuracy in a 12mx12m raster
  • WorldView Elevation Suite (for a 1m x 1m DEM) 30cm relative vertical accuracy and 50cm relative horizontal. However the accuracy is dependent on the quality of ground control points (GCP). Known locations need to be identified in the images.

Accuracy assessment approach & quality control measures

Validated by field visits and by comparison/extrapolation from published mapping or reports. Statistical confusion matrix with user’s and producer’s accuracy as well as kappa statistics for terrain roughness mapping.

Frequency / timeliness

Observation frequency: Archive imagery is usually OK. Repeat coverage is not usually required. New data collection may be required in some cases. The frequency is constrained by satellite revisit and acquisition, but also processing requirements. Depending on the requirements of the customer the best suitable satellite sensor has to be chosen regarding spatial / spectral resolution as well as revisit frequency.

Timeliness of delivery: Delivery in time with project planning requirements. Archive data can be used to good effect as the surface roughness for a region does not typically change much.


Archive data

On-demand new acquisition

Delivery / output format

Data type:

  • Raster formats
  • Vector formats (depending on customer needs)

File format:

  • Geotiff or shapefile (standard – any other OGC standard file formats)


 Download Product Sheet


Lead Author:WesternGeco
Peer Reviewer:Hatfield Consultants


Andrew Cutts

Document Title:

Terrain Roughness

# of Pages:



Internal – Project consortium and science partners


External – ESA



This page has no comments.