ArcGIS Spatial Analyst – Dealing with NoData

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When using Spatial Analyst I regularly have issues with NoData values cropping up in the data.

Various tools appear to introduce NoData values regularly.

These values then cause issues with other tools and have to be eliminated with reclassification, usually resulting in other problems.

How should NoData values be dealt with? Is there a simple way to replace NoData values with zero? Can NoData be avoided altogether?

Best Answer

I would have used the raster calculator with the following expression:

Con(IsNull([raster]), 0, [raster])

It assumes that your raster is called 'raster'.

The 10.x Raster Calculator tool syntax is slightly different. In this example, "raster" is a layer in the map:

Con(IsNull("raster"), 0, "raster")

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I wouldn't clamp the values, as suggested by the pseudocode, because the curvature artifacts do not necessarily correspond to high-curvature areas. Better cures for the problem include:

Consider smoothing the DEM before computing the curvature. This would slightly reduce most curvatures but might give reasonable curvature values at all locations. Smoothing the DEM can be done with local medians or means.
Find a robust way to estimate curvature. (You won't find this in any Arc* software, but with the availability of general-purpose tools like R, this is a feasible recommendation.) In general, curvature is estimated by fitting a quadratic (or some specialized low-order polynomial) in each neighborhood. The usual fitting method is least-squares, which can be sensitive to slight local variations. By expanding the neighborhood and using a fitting method that downweights outlying residuals (such as IWLS), you might be able to get representative and accurate curvature estimates everywhere.
Replace the extreme curvature values with NoData. This is still a draconian measure, but would be appropriate if the extreme values are really artifacts of the interpolation (and don't, therefore, reflect truly high curvatures).
Choose an interpolation method that produces smoother surfaces!

The 'map algebra' syntax for replacing values with NoData uses the SetNull command, as in

SetNull(Abs([Curvature grid]) > 10*Abs(m), [Curvature grid])

By the way, m should not be the median curvature: the median often will be close to zero and won't reflect typical large curvatures (positive or negative) A large multiple of the median absolute curvature might be a reasonable choice. Better yet, examine the histogram of curvatures to find where to cut off the values.

If the SetNull approach pokes too many NoData holes in the grid, think about whether the curvature grid should be smoothly varying. If so, and if those holes tend to be isolated (or in narrow stripes--which would be a strong indicator that a poor interpolator was used), you can fill them by computing a focal mean and pasting the mean values over the holes. The pasting is done with the conditional operator, as in

Con(IsNull([Modified curvature grid]), [Focal mean grid], [Modified curvature grid])

Note the use of IsNull to detect the NoData cells.

[GIS] Calculating flood volume using LiDAR DEM and HEC-RAS floodplain polygon

As mentioned in my and Tomek's comments above, this analysis can be completed by interpolating a sloping raster from either existing HEC-RAS cross sections, or by creating your own cross sections by querying your lidar surface. If you have access to the original HEC cross sections (i.e. the lines used to create the floodplain extent polygon), you can use these as contours in the Topo to Raster tool. The resulting surface will be a sloping raster that represents the floodplain inundation surface, which you can then use in your Cut/Fill calculations to determine the flood volume.

Alternatively, in the absence of the original cross section data, you can create your own cross section lines, and populate them with Z-values from your lidar surface. To do this, digitize lines perpendicular to the floodplain, terminating at the outer extents of your floodplain polygon. Create these lines at intervals sufficient to capture the variability of the down-valley slope (this approach can become fairly tedious if you have a large study area). You can then query the lidar elevation values at the intersection of your newly digitized XS lines, and use those values to populate an elevation attribute for your XS lines. Elevations at either end of your lines may not match exactly, but averaging the values should approximate the inundation surface elevation. From here you can perform the Topo to Raster and Cut/Fill analyses as mentioned above.

Best Answer

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