February 15, 2019

A LiDAR perspective on a 1965 geologic map

Posted by larryohanlon

By Philip S. Prince, Virginia Division of Geology and Mineral Resources

The enhanced ability to see the bare Earth surface provided by high-resolution LiDAR imagery provides an exciting new tool to geologists, who can now quite literally see features that were once effectively “invisible” to human observers on the ground or at any altitude.

Little Stone Mountain’s sedimentary “skeleton” (meaning the resistant layers that support it) is easy to see here, but it’s nothing but trees to an observer on the ground or in the air. Contacts between layer packages are obvious in this image, but their ground exposure is limited. The roadcut at bottom right is a large, 4-lane highway.

A reasonable question to ask is how much existing geologic maps, particularly those produced without any digital topography or remote sensing, could be enhanced by checking them against LiDAR hillshade. The answer varies, and to continue the Powell Valley Anticline (PVA) discussion, I draped a 1965, hand-drafted geologic map over the new LiDAR hillshade background (Big Stone Gap 1:24k, Ralph Miller, USGS, 1965). This map turns out to be remarkably spatially accurate, with a number of geologic contacts clearly drawn on marker beds (layers) that can be seen in LiDAR imagery. Other areas could be improved by LiDAR comparison in various ways.

I show several examples below. Admittedly, this is probably of most interest to folks who work with geologic maps, but I think the concept of taking a 54 year old, handmade map and seeing how well it does in 2019 is worth a look. The examples may be difficult to appreciate without the ability to fade geology on and off yourself, but I have tried to produce a “cutaway” effect where the map pattern can easily be compared to the surface expression it should match. A video showing geology faded in and out is also linked here; it shows the basic idea of the geologic map-topography comparison:


First and foremost, the map is entirely effective in communicating the structure of the PVA and showing that topography is completely dictated by geologic structure in this area. Contacts and map units match topography well, and the Mississippian/Pennsylvanian limbs and Silurian/Ordovician core of the PVA look great over LiDAR with Google Earth topography at the large scale.

The map pattern accurately captures the structural style that produces the ridges and valleys in this quadrangle, and the color field appears to match topography nicely at this zoom.

In areas where units are strongly expressed in topography, some of the 1965 mapping is amazingly spatially accurate. The northwest limb of the PVA on Little Stone Mountain, shown below, is a good example.

It looks like the color fields were drawn over the patterns seen in the bare Earth hillshade. They likely were, to the extent that the patterns are visible in contour topography and aerial photos. Actual field data from stream valleys would have supplemented these observations, but this is still remarkably impressive hand drafting.

The transitions seen in LiDAR match map transitions almost perfectly. The dark green layer near the bottom of the slope is the Mississippian Price-MacCrady Group, overlain by the Mississippian Greenbrier limestone.

Silurian Units match up equally well in most places. The Hagan Shale, marked by Sch on these images, makes a narrow band of muted topography near the summit of the Silurian ridge at center.

The Hagan Shale (Sch) matches the subtle topographic stripe just to the right of the ridge crest. It’s not well expressed in the contour lines, and the map does not indicate what, if any, outcrop data supports how contacts were drawn. Again, very impressive drafting!

The purple Sch band on the map matches the hillshade topography almost perfectly. The “knobs” of Reedsville Shale down the ridge to the right also match the orange outcrop field nicely. The Hagan Shale makes no discernible topographic footprint on the map, so this is very good mapping work and drafting on the part of the author(s).

One of the most interesting (and subtle) agreements on the map is the contact between the Hardy Creek (pink and white striped) and Edinburg (pink) limestones. A subtle but visible “line,” which is some sort of slightly less erodible limestone layer, can be seen in the hillshade imagery as an extension of the contact between the map fields on the image below. Some of this may be attributable to coincidence, but the distinct contact layer is very much a real thing, and it’s exactly where it should be according to the map.

The contact-marking bed is subtle, but it’s there. I presume it might have been visible if this area was bare agricultural land. Many carbonate units have very clearly visible bedding in 1-meter hillshade in this region, even if they are not exposed on steep cliffs.

This contact marker can be seen elsewhere in the hillshade images, and typically matches as well as it does here. I presume this bed was exposed in pastures and streams to permit it to be identified and located so confidently.

The strongly-bedded Trenton limestone also shows a well-placed contact with the top of the Edinburg. This contact was almost certainly identified in the small drainage left of the center of the image, but no data point is recorded. Recorded point data is actually quite sparse on the map, and I am sure this drainage was visited during field mapping.

The boundary between the clearly bedded Trenton Limestone and the Edinburg Limestone (often a calcareous shale) below it is clear in the hillshade, and matches the map contact nicely.

Like many carbonate units in the region, the Trenton often looks very crisply bedded in the 1-meter imagery, and contrasts sharply with the underlying Edinburg and overlying Reedsville Shale.

Bedded Trenton also reveals some mismatches, like the one shown below.

The clearly layered Trenton should match the orange band in this case. Here, LiDAR would increase accuracy of projecting contacts in areas with no good outcrop. Despite the obvious layering seen here, these rocks are likely not exposed at the surface.

The Trenton-Reedsville contact should move uphill, to where the strongly layered area meets the nearly featureless area. Back and to the left, the Hagan Shale is again perfectly matched to its topography near the ridge crest.

The greatest misatch on the entire map is on the Mississippian section on Cliff Mountain. I presume these contacts were estimated from topography and thickness due to the steepness of Cliff Mountain.

The upper contacts are good, but the base of the Greenbrier and underlying formations don’t match well.

The contacts at the top of the mountain match the hillshade well, but things go bad at the base of the Greenbrier.

This area would be extremely difficult to map with spatial accuracy on foot due to surficial deposits and the dangerously steep terrain. Here, the hillshade could improve things.

Note also that the landslides and debris flows were not identified despite being so clearly visible in the hillshade. They are ubiquitous along the base of the steep topography along Cliff Mountain.

A couple more comparisons are shown below.

In the low ridge at the center of the image, subtle patterns of layering are seen. This is a narrow, small-scale anticline in the core of the PVA.


The anticlinal hinge marked right up the center indicates the small anticline seen in the bedding in the previous image. Again, these areas were probably partially exposed as grazing land.


Little Stone Mountain’s moderately dipping beds and alluvial fans match the map very well.


Pennsylvanian sandstone caprock (blue) supports broad, low-angle flatirons above rugged gorges.

Altogether, there is not much to do to this map using newly available imagery. I think that the tweaks that could be made show what high-res hillshade imagery is very good for, at least in this region:

–Surficial deposits. Many were identified, and many that are plenty think and extensive enough to impact development were not identified because they just can’t be seen due to vegetation. Using LiDAR hillshades or slope-shades in this way is whole new ballgame.

–Projecting contacts. It will always be necessary to do some interpolation at the 1:24k scale, and great surface imagery makes this go better. Dips and thicknesses can change quickly along strike in this fold-thrust setting, and assuming constant dip and thickness can put a map off hundreds of feet. Matching surface expression with map outcrop fields is a good use of the technology.

–Remotely visiting rough country. I like a good adventure, but mapping the face of Cliff Mountain would be quite an undertaking. It can be done much more accurately using the 1-meter imagery.

–Communication. Looking at hillshade in a setting like this makes the geology seem “real.” A non-specialist can see rocks in roadcuts and on occasional cliff exposures, but the concept that the moutains and valleys are completely rock type-controlled is sometimes hard to articulate to folks outside of the discipline. Making the connection between an outcrop observation, the 1,000 ft sequence it is part of, the mountain the 1,000 ft sequence supports, and how the mountain structurally connects to its neighbors goes better with good pictures that show what the mountains are “made of,” so to speak.

Hwy 58 descends Little Stone Mountain towards Big Stone Gap, Virginia. LiDAR hillshade brings geologic control over this topography to life. Note where the roadcut passes through large debris cones at center.

Incidentally, the only change that probably should be considered for this map is in the cross section. At present, the cross section shows the PVA fold extending down into, and presumably beyond, the basal glide plane, to suggest possible basement involvement. Today, a horse/duplex solution would be used, with the horse’s basal fault climbing to the Devonian glide plane under the forelimb.

The black line shows a fault/ramp that should probably be drawn on the cross section. Pink Ordovician beds would be flat to the left of the ramp, which cuts them off. As drawn, the structure implies basement involvement, which is not consistent with the bulk of data collected in this area.

This post was originally published on The Geo Models blog