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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





Posted via email from Fresh Geologic Froth

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.



Posted via email from Fresh Geologic Froth

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:


Posted via email from Fresh Geologic Froth

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.


Posted via email from Fresh Geologic Froth

I have managed to steer largely clear of instant messaging (IM) for some time. This owes mainly to the lack of collaborators (beyond some family members) that are among the initiated. Well, it turns out that IM is pretty darn useful for communicating with colleagues,  particularly when you are both riveted to your computer screens working on a related topic (ideally, a geologic one). I have drowned in the Google Kool-Aid, so I obviously use Google Talk for this process. Recently, I had a friendly exchange with a colleague about getting some served imagery online. This has also been quite handy for quick, but mandatory queries about other topics with other collaborators. Sometimes a phone call takes too long and sometimes email languishes for days, why not try IM for some brief and instant results?

Fruitful IM Exchange about Geologic Mapping

Fruitful IM Exchange about Geologic Mapping

I added the Google Talk app to my Blackberry (yes)  in addition to a Google Map app that can show high-res satellite imagery keyed to my location (and, yes, I have used it in the field).  Recently someone at Google took the obvious step to approximate the combination of these two applications and developed Google Latitude. This application makes it possible (following a bit of setup) to make your location visible to a select group of collaborators. This has some pretty cool implications for mapping with a group. For one, if you are in an area with good coverage (and have an amenable provider) you can keep track of a mapping team and converse about what you may be checking out at the time. Of even greater interest is being able to remotely track a colleague who is in the field while you are in the office.

Google Latitude in my Neighborhood

Google Latitude in my Neighborhood

I recently approximated this approach with a combination of sms (text-messaging) and Google Maps. My geopal was down in southern Nevada looking for some key map units I had described. She sent me a text message requesting some coordinates. I was in my car more than 400 miles away, but headed to a coffee shop to log on to Google Maps, loaded the satellite view in the area and began to send key coordinates (decimal degrees if it matters) of key outcrops in the area as inferred from the imagery.  She hit the outcrops, took photos, geotagged them, and uploaded them to Picasa web albums that night. I checked them out the next morning. A sweet virtual mapping experience was had by all. Next time, we will try to directly incorporate Google Latitude into this process. We ran into coverage issues in this case, but it was still a success.

Howdy Dummies. Are you like me? Do you get so wrapped up in mapping lines on high-res imagery that you fail to judiciously attribute them? You know, that ‘oh man, I can just keep mapping this obvious contact until it disappears’ feeling. Do you do the same with label points (you do use label points, right?)? Well, you can control your attention deficit by selecting a key option in Editor>Options interface:

Once you select the correct attribution option, you will be interrogated by the program as to what the attribute of the feature you just created is. Yes, you will have to make the call then. You really don’t have time for that second, or third, or fourth sweep through the map do you? Do it right the first time. Be particularly judicious about your label points since those are much harder to formulate well after the fact.

I had no idea this option was available until fairly recently. If you knew of it, way to go. You are less of a dummy than I.

If you make your geologic maps using ArcGIS and work with nicely detailed color imagery, then you already know how useful a stretch is. If not, check this previous posts for dummies:

Now that you are back up to speed, I will share a simple trick I figured out by brute force that eliminates areas that may skew your stretch in an inconvenient way. Namely, large bodies of water. Right now, I am supposed to be finalizing mapping in the Spirit Mtn NW quad which includes parts of Nevada, Arizona, and Lake Mohave. Mapping along the lakeshore in the field is a joy; whereas compiling along the lakeshore is a pain in the neck…particularly when you use the standard deviation stretch restricted to the ‘current display extent’ which is usually the best option for contrast enhancement. The problem is caused by the black hole of lake pixels that dominate the statistics. The solution? Mask out the lake in a new raster using the ‘extract’ tool:

Here are the results from my current map area:

Epilogue. Someone with considerably more knowledge in GIS than I once explained to me how I could do this with raster math. I screwed around with that and failed. After numerous scans through Arc Toolbox (haven’t you scanned that stuff over and over looking for something?), I finally found some commands that sounded useful. Remember, this is digital geoscience for dummies.

Geologic mapping can span many scales of time and space. Some of the most complex linework can result from evaluating a fluvial system in great detail in a small area and over a geologically instantaneous period of time. In my case, this scenario corresponds to the Bill Williams River in Arizona. For several years, I have been compiling detailed geologic maps of channel change on that river since 1953. The result? A heaping plate of GeoSpaghetti.

The image above is an excerpt from a 35 mile stretch of river. Yes. The river has undergone some profound changes in the last 50 years or so. Exactly how and why is beyond the point of this blog. One day I will publish it if it matters to you.

The point of this entry is to describe the various tools and methods that I have employed in ArcGIS to compile the lines in a meaningful way and to turn the resulting spaghetti into a meaningful map or series of maps.

The Project:

Map the bottomland geomorphology of the Bill Williams River at specific points in time using a chronology of orthorectified aerial photographs. At this point, I have mapped six generations of the valley bottom. The resulting plexus of lines is a logistical nightmare to a certain extent, but I believe I came up with a reasonable way to deal with them. If you map similar things and have better ideas or suggestions, please let me know.

  1. Set up a geodatabase…yes you need to know the basics of this fundamental operation. Add your lines as classes in a geology feature dataset.
  2. Determine a boundary to which you will be mapping and stick with it. Note that as you map different generations of lines, you will want to alter the boundary…you just will. However, unless it is a major issue and you will diligently propagate that alteration through all of your line layers, resist the temptation
  3. Develop a line and polygon attribution scheme that is flexible and systematic. Important: this scheme needs to be logical and transferable to each generation of linework. Certain generations may require specific types of lines and polygons, but try to adhere to a common conceptual base so that it makes sense all the way through. Record the nomenclature in a spreadsheet and update it when you inevitably revise or add to your units. The spreadsheet can be a life-saver if you tend to work on too many projects and put this one down for a few months.
  4. Begin mapping the earliest generation of photograph if possible. It is best to map the images in chronological order for reasons that will soon become clear. Map lines NOT polygons. Starting with polygons is whacked. You can build them from lines in a matter of seconds.
  5. Once the earliest generation is mapped (and you have attributed the ‘proto’ polygons with a point feature class…post coming if this is news to you) and the topology is all correct (you did build and check the topology, right?) copy it and rename it. Use this dataset as a starting point for the next generation of photos. Note: the tediousness is about to set in or get worse.
  6. Yikes. Your map is already a mess. Now you need to mesh the data in a logical way. You have added lines that preclude the existence of some of the previous generation’s lines, right? All of the precluded lines need to be removed (don’t worry the originals still exist…remember, you copied them).
  7. Luckily, you have already built and analyzed the topology of your first layer, right? Well now build and analyze the topology of the second layer for laughs. The only rule you really need is the ‘no dangles’ rule. If you have the topology built and analyzed, you can use the ‘Planarize’ tool to break selected lines (even all of them) at each intersection. Then you can sweep through and select and delete all of the (now) superfluous lines.

That sounds easy right? It is easy, but really really tedious. Also, unless you have taken some preliminary precautions, you may lose all of your careful attribution. For better or worse, when you set up the geodatabase, you have many, many, options to ignore or address. Some of these are very useful to know about. One is ‘Default value’. What you choose here is the default attribution given to any piece of data that you enter. In the case of the Bill Williams map, setting the apyear (aerial photograph year) to the appropriate year was essential and useful. In other cases, I bet you can come up with some examples of your own where this would be useful.

You may also find yourself splitting and merging many lines. Unless you establish ‘split’ and ‘merge’ policies, you may get some disconcerting results…like total loss of attribution that you didn’t find out about until you split 10s to 100s of lines:

It is best practice to attribute your geolines immediately upon drawing them unless it is really ambiguous and you have a firm follow-up plan. Thus, choosing a default value for a line that requires some scientific judgment may not be the best idea. In the (recent) past, I have had a tendency to map many lines without attribution, assuming that I will do it in a ‘second pass’ through the data. Yikes. That is really stupid. For one thing, once you have drafted the line, you have covered it; for another, the ‘second (or third) pass’ idea isn’t very efficient and just effing snowballs up on you.

So what to do?

Option 1: Diligently attribute each line after you draft it.

Option 2: Have the program force you to attribute the line, or point, or poly, once you draft it.

Option 2 is the most efficient way to go. I just discovered this one.

Stay tuned for updates as to the progress of the Bill Williams River map…polygons coming next.

Smooth Your Image:

To get the most most out of your base imagery, you need to experiment with different image enhancement tools in Arc. The basic manipulations can be found under the ‘display’ and ‘symbology’ tabs found under ‘layer–properties’ (right-click on the layer of interest). To smooth the image without any negative effects, choose the ‘bilinear interpolation’ option and then click ‘apply’. This will smooth your image in a visually satisfying way. Other resampling options may result in bothersome artifacts in the typical types of imagery that geologists use for mapping.

Stretch Your Imagery:

Stretching your image can create levels of contrast and color balance that you will appreciate. For details, consult a remote sensing textbook. For now, just accept the fact that you can vastly improve an image’s appearance by applying a standard deviation stretch to your data. Start with n=2 and experiment with increasing and decreasing this value. Also, if you limit the stretch statistics to the ‘Current Display Exent’ you will get ~local results that typically improve the contrast of the image. This will vary with the absolute range of values present in the current display. Experiment with other stretches.

Both of these enhancements are useful for b/w DOQQs, color DOQQS, and Quickbird data among (presumably) all other remotely sensed base (photo-like) imagery. It is not useful for DRGs.