Minimum mappable unit blues

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 10,000 sq. km. of dirt in Clark County in a compressed time frame, 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. Ten hectares covers 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:

(note: larger images viewable at:
http://geofroth.posterous.com/minimum-mappable-unit-blues)

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.

More on this later.

Posted via email from Fresh Geologic Froth

Generalized Parent Material Map Progress

We recently generalized the existing bedrock geologic data for Clark County into 19 general lithologic categories. Our goal is to provide clear context for evaluating parent rock materials for the array of surficial deposits that we are compiling and mapping in more detail.

The units have been preliminarily divided into the following 19 categories (2 not yet used on the map):

Sedimentary Rocks (S)

Carbonates (Sc)
Limestone (Scl)
Dolomite (Scd)
Interbedded limestone and dolomite (Scld)

Siliciclastic sedimentary rocks (Ss)
Mudrock and shale (Sssh) (not yet used)
Chert and argillite (Ssc) (not yet used)
Sandstone and coarser (Ssss)
Interbedded shale and sandstone (Ssshss)

Interbedded carbonates and siliciclastic sedimentary rocks (Scs)

Igneous Rocks (I) Plutonic (p) Volcanic (v)

Felsic igneous

Intrusive (granite) (Ipf)
Extrusive (rhyolite and tuff) (Ivf)

Intermediate igneous
Intrusive (diorite) (Ipi)
Extrusive (andesite) (Ivi)

Mafic igneous
Intrusive (gabbro) (Imi)
Extrusive (basalt) (Ivi)

Mixed volcanic rocks (Ivx)

Metamorphic (M)

High grade (crystalline rocks) (Mh)
Low grade (phyllite, argillite, quartzite) (Ml)

It may be that this is more detail than is warranted...or maybe it is not quite enough...opinions will vary. The data are structured in such a way that ratcheting the detail up or down is not complicated. The goal is to work with the existing data in a consistent way across the study area.

Also, note that this project does not involve remapping any bedrock units other than where the boundaries with the Q deposits can be improved.

Correlating units from many maps

We reviewed the nomenclature for the existing maps that cover Clark County and correlated those units to the newly-developed Clark County nomenclature based on deposit type, what materials it is composed of, and any age constraints. In cases where the units do not correlate well, we simply added additional units to the Clark County nomenclature to accommodate the existing map’s nomenclature.

The following maps were compiled:
Las Vegas 100k: usgs sim 05-2814
Lake Mead 100k: usgs ofr 07-1010
Mesquite Lake 100k: usgs ofr 06-1035
Ludington, unpublished data
Death Valley ground-water model area, 250k: usgs mf 2381
Colorado, White River, and Death Valley groundwater flow systems, 250k: nbmg m150


This list of units with brief descriptions is the current Clark County nomenclature.

The following correlation diagrams are split by deposit type and show how all the compiled maps correlate to each other and to the new Clark County nomenclature.

Making one map from six: Fun with ArcGIS!

It took a great deal of experimentation to get the final data sets merged and ready to work with. Below are the steps we used to do this.

Downloaded original data.

Checked projection. Is it defined? If not, define it (define projection tool).

Several data sets were in UTM NAD 27. Our final map will be in UTM NAD 83. So, re-projected NAD 27 data sets to NAD 83 (project tool).

Where data extends beyond the county, we clipped it (clip tool). Where data sets overlap, we chose to keep the larger-scale data and clipped the smaller-scale data to it.

Imported the data sets into an sde, creating line and poly feature classes for each map.

Attributes: To each line feature class, we added attribute fields named CC_ltype, orig_OID, orig_ltype, and source. To each polygon feature class, we added attribute fields named CC_surf, CC_rox, orig_OID, orig_unit, and source. Orig_OID, orig_ltype, and orig_unit were populated from the original data as a way to preserve and refer to the original data. CC_surf was populated based on unit correlations. All attribute fields besides the ones listed above were deleted.

All of the line feature classes were then merged into one line feature class, and all polygon feature classes into one polygon feature class. The merge tool is pretty straight forward, but behaves in ways I can’t claim to fully understand. Even though all the data sets had the same attribute fields, the merge tool appears to be picky as to the order in which you add data sets that you want to merge. For example, I was unable to merge all of the data sets at one time. I had to merge smaller groups of data sets and then merge the merged groups. I’m not sure why, but it worked.

We are now ready to start adding our own lines and modifying the existing data if needed!

First glimpse of the unified surficial geologic map of Clark County

This map represents the results of our attempt to unify the surficial geology and geomorphology of Clark County as expressed in available digital data sources spanning most of the county. Much work remains to be done to fully unify the various sources, but this is a big step.

Soon we will develop a 'postable' figure showing our interpretations of the published data that were unified, thus explaining the colors on the map. In the example above, the bedrock is not shown (note that the dark green is vegetation indicated on the base map).

Realizing Full Coverage of the County at 100-150 k

A recent increase in dirt mapper activityhas resulted in a collective dataset covering ~75% of the county. These data from USGS and NBMG sources are presently being unified into a single classification scheme based on common process, material, and geochronologic characteristics. We will soon post the unifying scheme for comments.

We also are pursuing a couple of leads that include mapping of the remaining part of the county. We currently have our hands on a hard copy of a generalized bedrock map of Southern Clark County that we know will suit our needs well.

Geochronology Catalog in Google Earth

The Nevada Digital Dirt team has initiated a compilation of published geochronological data for Quaternary deposits in Clark County, NV. We are using the Google Spreadsheet Mapper tool to depict the data. Once we reach critical mass on the number of points from the available published sources, we will share the kml file and request suggestions for correcting any positioning or misattribution. So far, everything is under control with respect to the latter, but the former may need some work.

As you can see, it is a very intuitive way to depict and explore the data. Imagine if it were done for all the rock and dirt dates in the western US...