In This Space
Terrain roughness map (Source: WesternGeco)
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.
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 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.
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
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:051 Identification of fault lines
Input data sources
Optical: VHR1, VHR2, HR1, HR2
Radar: VHR1, VHR2, HR1, HR2, MR1, MR2
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:
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.
On-demand new acquisition
Delivery / output format
|Peer Reviewer:||Hatfield Consultants|
# of Pages:
Internal – Project consortium and science partners
External – ESA
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