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For better context, here is a 1:1,000,000 map of Clark County showing where the area in the previous post is located. The map we are trying to complete as Phase 1 of the Nevada Digital Dirt Mapping Project will be approximately 1:150,000. The tiling evident in this image is because the data are divided into the appropriate 100k sheets. For more background on the project, check out the Nevada Digital Dirt Mapping blog. Yes, there is some cross-posting going on…how else could I manage all of this stuff?

I have a tendency to map in great detail…even when it is unwarranted or relates to a largely inconsequential stratigraphic situation. This problem is proportional to the quality of the base imagery that I have or the intrigue-level of the units in the field. However, in my quest to lead the effort to develop a surficial geologic map of 10,000 sq. km. of dirt in Clark County in a compressed time frame (see:, I am learning that it is ok not to sweat the details, as long as you explain what comprises the mapped units. One thing that we have learned is that it is essential to develop an agreed-upon minimum map unit area (mmu). That is, the smallest polygon that is mappable at the chosen scale.

As far as I can tell, geologists are not very keen on the mmu, whereas the dirt mappers in the NRCS have codified the concept in their considerably more standardized procedure.
We have adopted a visual approach that is based on the basic legibility of a polygon at a specific scale, and have concluded that 10 hectares is a good minimum value to start with. 10 ha is 100,000 square meters…or a square that is approximately 316 meters on a side. Sounds kinda big, looks quite big in the field, and certainly looks mappable at 1:24,000. However, if you zoom out to 1:100,000, the story changes.
The map below shows a random area in the county at 1:24,000. A selection of polygons is labeled with respect to size in hectares.

Sure, those all look totally mappable, right? Well, not so much. Check out the area when outlined in a 100k map:
Now the story is different. Not only are the small polygons bordering on illegible, the scale of the task of mapping such small polys consistently in a reasonable amount of time is impractical without a huge expenditure of time.
There are some side effects of eliminating units below a certain size threshold. One is data loss. That will be handled by preserving the small polys as points. Thus their locations will be stored as will their attributes. This solution can also facilitate the mapping process by flagging those polys that need to be absorbed into larger, surrounding or adjacent ones. One other problem is the case of high-standing inselbergs. Some of these are very tiny, but protrude several meters above the surrounding surficial deposits. Thus, their omission is particularly notable when in the field. For example, in the photo below, the fairly conspicuous cluster of red sandstone inselbergs has an areal extent of approximately 10 ha.
Not mapping such a feature may seem like a total affront to the sensibility of a geologist, but there will be a point there in the dataset that indicates an awareness of the feature’s existence.
What do you think? Do you know of a standard mmu? Does 10 ha look to big?

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Yet another map for 2009…don’t get to excited, though. I won’t
subject you to my rant about how I get surprisingly little credit for
these gargantuan efforts until they are subjected to an external
review…that is for another day. Trust me, it will come. Probably in
mid January.

In any case, this new map includes a snippet of surficial mapping that
I and others did a few years ago in the entirety of Ivanpah Valley,
Nevada…’The Ivanpaviathan‘ with some minor changes.

The big story here is the huge amount of work that the first author
did in creating the bedrock mapping. This is a complicated area to say
the least (heard of the Keystone Thrust and its ilk?). Larry did a
fair amount of new mapping, but really went the extra mile in
compiling diverse scraps and swaths of mapping created over the years
by the other authors. No small task.

Now, about that missing cross section…

Kudos also to Irene for converting what was initially a ‘symphony in mustards’ to this nice map.

See also:

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Recall when I went on ad nauseum about my struggles with the Lower
Walker River map as I was trying to document (in part) the struggles
that the lower Walker River has had in dealing with its shrinking
lake? If you missed that fun, experience it here:

Well, I hardly made a peep about this map…mainly because it
was finished earlier and was out of the ‘buffer’ at the time. But now,
it has reached a comparable state of completion.

The twist with this map (which is along the lower Colorado River
between Hoover and Davis Dams) is that the river has lost its battle
with a lake by virture of having been dammed downstream. Thus, all of
the bluish-greenish units are submerged under the lake.

I was able to map these features with reasonable confidence using
sonar data and large-scale, pre-dam topographic maps of the valley.

Interesting side note: Since getting the map to this point, I have
gotten my hot little hands on a pre-dam aerial photo mosaic of the map

Revision time!

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It was recently brought to my attention that the graphing tool in ArcGIS could be really useful if you had the right type of data (thanks to ND at UO). Well, I spent most of today trying to refine a longitudinal profile of the Owyhee River from my coveted LiDAR data set, and it occurred to me that I had some useful data.

My goal beyond just examining the profile was to indicate the locations of major landslide complexes along the river corridor to investigate how they may influence the river’s gradient.  I actually extracted the profile data from the data using a tool in GlobalMapper which I like. I converted the data to an excel spreadsheet, opened the sheet in Arc and then exported it into my Geodatabase as a feature dataset. Once it was in there, I created a graph of the data (basically the profile) and began to select points on the profile along key reaches that I had mapped. Lo and behold, those points i selected on the map lit up in the profile graph. Sweet. This was huge. It goes both ways as well. Select points on the graph, and they light up on the map.





Restrict the displayed points on the graph to those selected on the map and you can export them as a subset of the data. This step comes in really handy for plotting the exact position of the landslide complex-reaches on the overall profile figure. Previously, I had stupidly brute-forced this process. Typical. The result is below:



Also very useful is to plot the profile data in the form of cumulative distance vs. slope of channel segment. This graph immediately indicates important trends and anomalies in the data. Turns out that the anomalously high slope values and negative slope values relate, in this case, mainly to inadvertently collected data from vegetated bars, extremely coarse gravel bars, and even wave trains at some of the rapids. Thus, an important and informed QA step can be taken to clean out the riff-raff. In general, though, you can see how useful this method is for zeroing-in on areas of key interest. For example, many of the points on the map below correspond to rapids





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I was in the field yesterday doing some mop-up following a field review of a map. While checking a contact I had mapped that was queried by one of the reviewers, I happened upon this blunt assessment of my interpretation rolled up in a desert shrub.

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Just happened upon a sweet and simple geobrowser called Flash Earth…very smooth and easy to understand. Added bonus for me is that it links to high-res images of my favorite field area that are available only in Yahoo and Bing Maps:


Seems my pals at Google still just don’t care about SE Oregon. Anyway, I found the site by perusing the details in an exif header in one of my geotagged photos. Was checking that out in Irfan View, a program I was aware of but hadn’t tried yet. Turns out, it is well worth a look:



Which led me to the GeoHack wiki:



The internets are amazing, no? Totally cool.



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It is tortured river season in my office. Lately, I have been tackling Nevada’s mighty Walker River and its shrinking terminal lake (new term is terminus lake…but that is a bit soft); and Oregon’s Owyhee River and its travails with lava and landslides; but now I am back on to the Mighty Bill Williams River of Arizona. You know, the Bill Williams River.

Included below is a snippet of the map I am working on. Shown are 6 generations of lines that document major changes in the channel, most since a dam was finished in the late 60s. One day soon, this map will actually make sense, I promise.


The BWR is a special case. It is a roughly 35 mi stretch of river that traverses the hot desert below the confluence of two rivers that collectively drain more than 5000 square miles of western Arizona. Alamo Dam sits just below the confluence and traps essentially all of the sediment that would otherwise have gone down the BWR and to the Colorado River (well, at least to Lake Havasu). Also important to note is that the pre-dam BWR could attain peak discharges ranging up to 100,000 cfs, whereas the post-dam BWR can hardly exceed 7000 cfs owing to the outlet works of the dam. Thus, large runoff events that would have otherwise blasted through the system in a week or less (Spikes) are now converted to protracted, flat-topped hydrographs that lumber through the channel for up to several weeks to months (Bricks). Recall that these bricks are also sediment-free except for the sed picked up in the channel below the dam.


The result is an interesting experiment in channel change, sediment budgeting, and inadvertent (or otherwise) tamarisk farming.


I won’t be posting daily updates of this map, so don’t worry. Be assured, however, that I will make a lot of noise when I finally finish it. This one is a long, long, long, time coming. Just ask the sponsors.


Some other BWR info:


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Last week I had the chance to explore the upper McKenzie River valley in the Oregon Cascades. My tour guide was a UO PhD student in volcanology. She was showing me the range of interesting lava-water interaction features that characterize the valley. A very cool and unbelievably scenic place. 

Luckily, she has some LiDAR data of the area. This was my first chance to hit the field with LiDAR in hand of an area covered by old-growth forest. In other words, entirely non-trivial vegetation cover that is disconcerting in both its density and its scenic values. Two main conclusions: 

1. It is amazing how well the LiDAR data reveal the topography through a dense forest cover. I knew this, but living it for a day was very convincing. Many small to medium scale details in the surface of a thickly forested Holocene lava flow are painfully obvious in the imagery (below). For the desert rat in me, they were easier to see in the imagery than they were on the ground at first. Eventually, I was able to relate the two once I could see past the forest, but the imagery was far more revealing of the local geomorphology. 

Typical scene in the field area…locally, forest cover is thicker. Photo from surface of the lava flow evident in imagery below.

2. The LiDAR in this case also revealed some major features that had previously gone unnmapped at a fundamental level. We found / explored a very prominent volcanic feature in the midst of the lava flow that, according to the 24k USGS base and the 10 m DEM, does not exist. However, it is almost absurdly obvious in the LiDAR data. Mind you, this feature is not trivial in scale. It tops out at nearly 90 m above the valley floor and is has a 150 by 200 m footprint. It (the ‘Pimple’) is extremely steep-sided (as we discovered climbing to the top). It is also enigmatic geologically…potentially a glacially modified and exhumed volcanic fissure. Not sure on that.

Looking down from the Pimple. A seriously steep hike.

The 24k topographic map rendition of the area shown in the two LiDAR images.

Slope-shaded map of the LiDAR data

Hillshade map of the LiDAR data.

The point is that this very conspicuous feature is very mappable, but was overlooked in the development of the 24k map. A bit surprising in that it corresponds to a major ‘peak’ in the forest canopy. I will admit, however, that if you were out there in the rain, you could walk right by it. Certainly makes you wonder what else out there has gone unnoticed by the USGS topographic maps we once relied so heavily upon. Yikes…

Thanks to ND for the field trip and the LiDAR map snips.


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