What and why...

While randomly perusing Google Earth to find an image suitable for Where on (Google) Earth #182, I became re-enamored with the staggering array of complex and bizarre assemblages of landforms and landscapes on our planet. In particular, I find that the world's deserts are rife with landscapes and landform assemblages that approach the pathological* in their patterns and characteristics. But pathological landscapes are not the sole domain of the desert, they are azonal phenomena that occur across the planetary surface.

*pathological in the sense of being developed or expressed in such a degree that is extreme, excessive, or markedly abnormal.

The point of this blog is to develop an image catalog of such landscapes as well as constituent landforms, deposits, and processes that comprise and underlie them. Thus, the images can be derived from screen captures drawn from Google Earth, Flash Earth, Bing Maps, Google Maps, or your favorite GIS program. Field photographs of notably 'pathological' deposits or landforms are certainly appropriate as well.

We have all seen and, likely purchased, atlases of satellite imagery that emphasize the sheer beauty as well as weirdness of landscapes as seen from space. Now, with virtual globes, geobrowsers, and various GIS programs, we can break the constraints provided by the selection of images in those books and look anywhere we want to find intriguing visions of the Earth's surface.

Ideally, this blog will become a group effort with several contributors. In reality it may just stay one of my many side projects. I truly hope for the former, but won't mind the latter.

The only rules: provide the coordinates of the approximate center of the image; tag it by process[es] or deposit type[s]. Provide an extremely brief description of what is going on...not treatises here, just a brief description. Leave it to the interested viewer to explore the area of the image and come to terms with its scale...or provide a scale. The idea, however, is to not dwell too long on posting an image.

Here are some obvious tags: fluvial, aeolian, glacial, coastal, karst, thermal, mass wasting, erosion, anthro, tectonic, etc. Note that too many tags compromises clarity to some extent.

Impressive lava-river interaction in Argentina

Some images from Google Earth and Google Maps of an area of significant lava and river interaction in Argentina. Field Trip! The center of the image is approximately 36.40 S, 69.42 W.

This site was discussed in the following talk:

GEOMORPHIC HISTORY OF RIVERS DRAINING THE EASTERN ANDEAN CORDILLERA (34–37°S) CONSTRAINED BY TEPHROCHRONOLOGY, U-SERIES DATING OF PEDOGENIC CARBONATE AND COSMOGENIC 3HE DATING OF BASALT FLOWS

HYNEK, Scott A., Geology and Geophysics, University of Utah, 115 S 1460 E, Salt Lake City, UT 84112-0119, scott.hynek@utah.edu, MARCHETTI, David W., Geology Program, Western State College of Colorado, 600 N. Adams St, Gunnison, CO 81231, FERNANDEZ, Diego P., Geology and Geophysics, University of Utah, 115 S. 1460 E. Rm 383, Salt Lake City, UT 84112, and CERLING, Thure E., Department of Geology and Geophysics, University of Utah, Salt Lake City, UT 84112Alluvial deposits and associated geomorphic features are dated by their relation with volcanic rocks. The age range and geologic setting requires a broad approach to constraining the history of rivers draining the Cordillera. Maximum age estimates are provided by identification of the ~ 450 ka Diamante Tuff in fill terraces. Along the Río Diamante this ash bed is observed >100 m above modern river level. In the Río Papagayos and Río Atuel drainages, the Diamante Tuff is associated with alluvial surfaces much closer to modern river level. Coarse Diamante pumice in the Río Atuel implies significant changes to the headwater drainage system since 450 ka. The maximum age constraint implied by occurrence of the Diamante Tuff in fill terraces has been successfully combined with minimum age estimates from cosmogenic 10Be approaching 350 ka (Baker et al., 2009). The relatively old age of alluvial surfaces in the region is additionally supported by U-series age estimates derived from pedogenic carbonate in volcanic soils. A minimum age in excess of 100 ka is conservative. The dated surface is underlain by a pumice/lapilli tephra deposit and basalt flows both of which have the potential to provide maximum age estimates for the surface. Conversely, the U-series data implies that the basaltic volcanism is older than 100 ka. Our age estimates of flows in several drainages are much younger. Cosmogenic 3He concentrations in hornblende from basaltic-andesites erupted along the Río Salado indicate exposure, and therefore eruption, ages younger than ~ 6 ka. These flows temporarily dammed the Río Salado in one location and bedrock incision below the level of the flows has occurred since. 3He concentrations in olivine from basaltic rocks at northeastern Volcán Payún Matru indicate a shield-building phase at ~ 40 ka. Recent basaltic aa flows from multiple vents are morphologically quite young and 3He exposure ages are forthcoming for one of them. The Río Grande has incised the older flows, and provides an average incision rate over a full glacial cycle. Combination of geochronological data from the region indicates provides accurate, if not tightly constrained, ages for alluvial surfaces and identifies spatially variable geomorphic rates influenced, in part, by contemporaneous volcanism.


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Lava v. River example from British Columbia

Kathy C. turned me on to this example yesterday. I had no idea. Lava flow is only about 250 yrs old. It is in the Stikine Volcanic Field. There is a related, somewhat brief, paper:

Roberts, M.C. and McCuaig, S.J., 2001, Geomorphic responses to the sudden blocking of a fluvial system: Aiyansh lava flow, northwest British Columbia. The Canadian Geographer, v. 45, n. 2, p. 319-323.

The vent is located at 55.11 N, 128.89 W.

Field trip!

Posted via email from Arbitrary Frothings

Distilling LiDAR data with ArcGIS

Turns out that ArcMap has some very useful tools buried in the toolbox for evaluating the basic characteristics of LiDAR data. For example, using the 3D analyst extension, it is possible to collate the basic parameters of *.LAS tile sets. For example, the following steps:

3d analyst > conversion > from file > point file information

will generate a polygon shape file of the data tiles and will atrribute each tile set with measures of point count, point spacing, max z, min z, etc. This is useful because it is nice to have a simple polygon file that shows the extent of the data and some of the metrics in the attribute table are important to have for applying other types of processing to the data. In the figure above, the tiles are labeled according to object id (OID) rather than filename because the filenames are exceedingly cumbersome.

Posted via email from Fresh Geologic Froth