Input feature data
The input features containing the z-values to be interpolated into a surface raster.
Each feature input can have a field specified that contains the z-values and one of six types specified.
There are nine types of accepted inputs:
Output cell size
The cell size of the output raster that will be created.
This parameter can be defined by a numeric value or obtained from an existing raster dataset. If the cell size hasn't been explicitly specified as the parameter value, the environment cell size value will be used if specified; otherwise, additional rules will be used to calculate it from the other inputs. See the usage section for more detail.
|Analysis Cell Size|
Extent for the output raster dataset.
Interpolation will occur out to the x and y limits, and cells outside that extent will be NoData. For best interpolation results along the edges of the output raster, the x and y limits should be smaller than the extent of the input data by at least 10 cells on each side.
The default extent is the largest of all extents of the input feature data.
Margin in cells
Distance in cells to interpolate beyond the specified output extent and boundary.
The value must be greater than or equal to 0 (zero). The default value is 20.
If the Output extent and Boundary feature datasets are the same as the limit of the input data (the default), values interpolated along the edge of the DEM will not match well with adjacent DEM data. This is because they have been interpolated using one-half as much data as the points inside the raster, which are surrounded on all sides by input data. The Margin In Cells option allows input data beyond these limits to be used in the interpolation.
Smallest z value to be used in interpolation
The minimum z-value to be used in the interpolation.
The default is 20 percent below the smallest of all the input values.
Largest z value to be used in interpolation
The maximum z-value to be used in the interpolation.
The default is 20 percent above the largest of all input values.
The type of drainage enforcement to apply.
Primary type of input data
The dominant elevation data type of the input feature data.
Maximum number of iterations
The maximum number of interpolation iterations.
The number of iterations must be greater than zero. A default of 20 is normally adequate for both contour and line data.
A value of 30 will clear fewer sinks. Rarely, higher values (45–50) may be useful to clear more sinks or to set more ridges and streams. Iteration ceases for each grid resolution when the maximum number of iterations has been reached.
The integrated squared second derivative as a measure of roughness.
The roughness penalty must be zero or greater. If the primary input data type is Contour, the default is zero. If the primary data type is Spot, the default is 0.5. Larger values are not normally recommended.
Discretisation error factor
The discrete error factor is used to adjust the amount of smoothing when converting the input data to a raster.
The value must be greater than zero. The normal range of adjustment is 0.25 to 4, and the default is 1. A smaller value results in less data smoothing; a larger value causes greater smoothing.
Vertical standard error
The amount of random error in the z-values of the input data.
The value must be zero or greater. The default is zero.
The vertical standard error may be set to a small positive value if the data has significant random (non-systematic) vertical errors with uniform variance. In this case, set the vertical standard error to the standard deviation of these errors. For most elevation datasets, the vertical error should be set to zero, but it may be set to a small positive value to stabilize convergence when rasterizing point data with stream line data.
This tolerance reflects the accuracy and density of the elevation points in relation to surface drainage.
For point datasets, set the tolerance to the standard error of the data heights. For contour datasets, use one-half the average contour interval.
The value must be zero or greater. The default is 2.5 if the data type is Contour and zero if the data type is Spot.
This tolerance prevents drainage clearance through unrealistically high barriers.
The value must be greater than zero. The default is 100 if the data type is Contour and 200 if the data type is Spot.
Output stream polyline features
The output line feature class of stream polyline features and ridge line features.
The line features are created at the beginning of the interpolation process. It provides the general morphology of the surface for interpolation. It can be used to verify correct drainage and morphology by comparing known stream and ridge data.
The polyline features are coded as follows:
1. Input stream line not over cliff.
2. Input stream line over cliff (waterfall).
3. Drainage enforcement clearing a spurious sink.
4. Stream line determined from contour corner.
5. Ridge line determined from contour corner.
6. Code not used.
7. Data stream line side conditions.
8. Code not used.
9. Line indicating large elevation data clearance.
Output remaining sink point features
The output point feature class of the remaining sink point features.
These are the sinks that were not specified in the sink input feature data and were not cleared during drainage enforcement. Adjusting the values of the tolerances, Tolerance 1 and Tolerance 2, can reduce the number of remaining sinks. Remaining sinks often indicate errors in the input data that the drainage enforcement algorithm could not resolve. This can be an efficient way of detecting subtle elevation errors.
Output diagnostic file
The output diagnostic file listing all inputs and parameters used and the number of sinks cleared at each resolution and iteration.
Output parameter file
The output parameter file listing all inputs and parameters used, which can be used with Topo to Raster by File to run the interpolation again.
Profile curvature roughness penalty
The profile curvature roughness penalty is a locally adaptive penalty that can be used to partly replace total curvature.
It can yield good results with high-quality contour data but can lead to instability in convergence with poor data. Set to 0.0 for no profile curvature (the default), set to 0.5 for moderate profile curvature, and set to 0.8 for maximum profile curvature. Values larger than 0.8 are not recommended and should not be used.
Output residual point features
The output point feature class of all the large elevation residuals as scaled by the local discretisation error.
All the scaled residuals larger than 10 should be inspected for possible errors in input elevation and stream data. Large-scaled residuals indicate conflicts between input elevation data and streamline data. These may also be associated with poor automatic drainage enforcements. These conflicts can be remedied by providing additional streamline and/or point elevation data after first checking and correcting errors in existing input data. Large unscaled residuals usually indicate input elevation errors.
Output stream and cliff error point features
The output point feature class of locations where possible stream and cliff errors occur.
The locations where the streams have closed loops, distributaries, and streams over cliffs can be identified from the point feature class. Cliffs with neighboring cells that are inconsistent with the high and low sides of the cliff are also indicated. This can be a good indicator of cliffs with incorrect direction.
Points are coded as follows:
1. True circuit in data streamline network.
2. Circuit in stream network as encoded on the out raster.
3. Circuit in stream network via connecting lakes.
4. Distributaries point.
5. Stream over a cliff (waterfall).
6. Points indicating multiple stream outflows from lakes.
7. Code not used.
8. Points beside cliffs with heights inconsistent with cliff direction.
9. Code not used.
10. Circular distributary removed.
11. Distributary with no inflowing stream.
12. Rasterized distributary in output cell different to where the data stream line distributary occurs.
13. Error processing side conditions—an indicator of very complex streamline data.
Output contour error point features
The output point feature class of possible errors pertaining to the input contour data.
Contours with bias in height exceeding five times the standard deviation of the contour values as represented on the output raster are reported to this feature class. Contours that join other contours with a different elevation are flagged in this feature class by the code 1; this is a sure sign of a contour label error.
|Label||Explanation||Data Type||Output surface raster|
The output interpolated surface raster.
It is always a floating-point raster.