State of the Clark County map, March 25, 2010

This one is huge, huge. This map is the focus of the Nevada Digital Dirt Mapping Project. It has a team of as many as 10 (now) at any given time. Two of us are 'master' mappers, the others are learning fast and occupying lower levels. We are seeking another 'master', trust me.

The county map below provides some context for the density of linework...much of it compiled from published sources. However, most of those sources are quite general and particularly weak on the Q geology...which is our focus. Note, a couple of them are strong on Q, which is good news for us.

The second image shows a close-up in the NW corner of the county. The mismatch you see is dominated by our refinement of the bedrock-alluvium contact. Other mismatch related to incomplete (in progress) carving out surficial units. In other words, the polys are based on the original linework...haven't rebuilt the entire set yet.

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State of the Owyhee River map, March 25, 2010

Believe it or not, this one is almost done. I have posted proof of that before. What remains is the correct attribution of all of the lines that you see in red. I am systematically going through the map and fixing all of these. I started at the east end. Don't ask why so many lines are unattributed...long story.

The first image shown here is the extent of the mapping taken to the source vents of the intracanyon lava flows. In this version, only the lava flows and landslide complexes are shown in color. The second image hones in on a small part of the area of detailed mapping (i.e., the river corridor where I have LiDAR and have logged many, many miles of reconn). It has the same color scheme but reveals that there are many other types of units...mainly fluvial deposits.

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State of the Reno / Truckee River map (a small part of it), March 25, 2010

This effort is a compilation / map improvement process. I have been tasked with correlating and refining the Quaternary units in the Truckee Meadows...the large floodplain where most of Reno sits. This process is being enhanced considerably with georectified aerial photos from 1939. And to think, many folks wondered why the airport flooded in the 1997 flood. 

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More LiDAR...different river

Another example of the of the standard hillshade vs. 'isotropic' hillshade (isoshade) concept previously described with reference to the Owyhee River, OR. These are from a couple of reaches of the the Bill Williams River, AZ. The BWR data set is fine until the trees get in the way. Both image variations are pleasing. The standard hillshade looks 'natural' but the isoshade image reveals more intricate details.

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Useful New LiDAR love tips

I am late on reporting these useful tidbits and for that I apologize. I learned of these techniques from Ian Madin from DOGAMI while I was at the AASG meeting in Park City way back in June. Ian is my LiDAR hero for the time being. Basically, he showed me some simple tricks that make complete sense in hindsight but struck me as nothing short of revolutionary when I applied them to my data set. Before I get into it, I will say again that LiDAR changes everything. It is a truly revolutionary tool for geologic mapping of any kind, but particularly for surficial geologic mapping.

OK. So you have your LiDAR and you love the super neato hillshade images that it can be used to generate. But, hey, what about those damn shadows in areas of key interest? Well, you can apply a redundant brute force approach to making hillshade images with different solar geometries...but that would be downright nutty. You could crank it up a methodological notch and use GlobalMapper or Surfer to create these images far more quickly and choose your favorite to export...but that would be silly as well (but kind of fun...except for the exporting part). 

Step back and think about what you are trying to visualize with the hillshade....wait for it....slopes, right?!. So, what you do is effectively create a universal \ isotropic (?) hillshade image by using the 'slope' tool in the ArcGIS toolbox (3D analyst\ raster surface\ slope). Trust me, it works. However, you can't just go with the default settings. You have to stretch the resulting data (std dev works best for me), invert the grayscale ramp (important) and sit back and take it all in. Sweet! But wait, you need to overlay a slightly tranparensized color ramp of the elevation data (stretched as well for simplicity) to make it tasty. Now you have it all. For some real fun, change the n value in the standard deviation stretch and see what happens (maybe stay between 1 and 3).

Click through the photoset for some comparisons and you just may become a believer. Obviously, having all of these visualizations at your immediate disposal is the way to go...the beauty of GIS for geology, no?

Maybe you noticed that the last one has a comfortably smooth contour overlay...how the hell did that get there? Stay tuned for a tip that even took an ESRI LiDAR braniac by surprise at the Users Conference.

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