Cartoon contributions to modelling real world physics

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Movie physics

Numerical modelling is an important tool for most natural hazard researchers. Excellent codes now allow scientists and engineering practitioners the opportunity to simulate natural processes in both static and dynamic states. These codes typically grow from state-funded natural hazard research initiatives, and are principally aimed at understanding the 'bigger picture' in order to evaluate natural processes at the scale that poses a hazard to people and property.

Every now and then, however, it's worthwhile remembering the hazard researcher's not-so-distant, though often wealthier cousins in animation research. Although often dealing with smaller scale problems, the requirement for animators to come up with realistic-looking dynamic models for movies and computer games is currently driving a large amount of commercial research in scalable physics models. Physics-Based Animation is useful blog detailing recent advances in simulating physics for human visual consumption. This week Gizmodo featured work from the new movie Frozen, including an impressive Material Point Method snow simulation, applying real physical properties (compressibility, tensile strength, strain hardening, density, Young's modulus, and Poisson's ratio) to model snow as a dynamic, compressible granular material.

The models presented in the above video contain between 4x10^6 (snowplow) and 7x10^6 (rolling snowball) particles, sufficient for the evaluation of small to medium-scale rock or soil slope stability hazards, or possibly the design of protection structures which may interact with debris flows or snow avalanches. These simulations commonly use approximate solutions to replicate complex physical behaviors, however, as demonstrated in the above example, 'expert knowledge' (everyone's seen a snow plow in action) can provide good verification for such visual models.

As well as simulating situations with extremely large strains, models developed for animation purposes are also able to reproduce complex fracture behaviours, and examples such as that below may make their way into geohazard studies investigating, for example, particle fragmentation during rock avalanche, or rock slope failure as a result of earthquake shaking.

Blender - the free, open source alternative

Although the commercial nature of these codes often means the software itself is wrapped up in propriety licences, the maths and physics behind the above simulations are published in scientific journals (here and here). Blender is a free and open source 3D 'creation pipeline' (physics-based animation software) designed with fluid, rigid body physics, and particle tracking simulations in mind.

User-developed plugins and tutorials provide the opportunity for enthusiasts (or practitioners from generally unrelated fields) the opportunity to access these advanced simulation techniques, and drawing inspiration from commercial software, are often not far behind the state-of-the-art. Although limitations currently exist in terms of particle numbers and physical calibration, mean that animation software is currently no match for specifically designed and calibrated geohazard simulation software such as RAMMS, these are not insurmountable obstacles. As demonstrated by the simulation below, projects such as Blender may soon provide some exciting opportunities for research and mitigation of dynamic gravitational hazards, and at least so far, provides useful insight which may improve snowman mortality in the upcoming winter.  

Location and volume of the La Pintada landslide

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Heavy rainfall from hurricane Manuel initiated a large landslide which struck the village of La Pintada (Mexico) this Monday. 58 people have been reported missing in the small village, and at least 20 buildings have been destroyed. Although helicopters have rescued most of the villagers, 45 people remain, and authorities are concerned that "The rest of the hill could fall.". It is almost inevitable that such a large landslide in the narrow valley will cause major difficulties for rescue crews in the coming days, and access into much of the remote coffee growing region is likely to be challenging in the wake of the storm.

An aerial view of the La Pintada landslide

Source: Amador Narcia/Twitter

The above image is one of the first released of the region, and clearly conveys the devastating effect of the landslide. Getting information out to concerned parties can be critically important in such situations, although an accurate sense of scale and location data for early social media images is rarely available. Using the 'Add Photo' dialogue in Google Earth, however, we can use media images to locate, map, and make early decisions regarding ongoing risks and emergency response very soon after disastrous events. Using the Google Earth API we are able to approximately locate the position from which the above photograph was taken, as well as the position of an image which seems to provide an overview of the deposit area prior to the failure. The interactive map below represents an initial interpretation of the spatial extent of the landslide deposit (outlined in brown), as well as an initial guess as to what may be the source of the failure (outlined in grey).

Interactive map indicating the estimated source (grey), and mapped deposit (brown), as well as two photographs covering the area of the damaged village (double click to view). An updated .kmz file (including additional aerial photographs and and revised map of the scarp from 21/09/13) is available as a Google Earth download here

As is evident above, the landslide initiated on a partially vegetated slope to the north of the village, and as reported, appears to have buried much of the eastern side of the community. Survivors report a loud rumbling, and very rapid failure, which is evident from the map as at least one building seems to have been pushed around 30 m from it's foundations (white arrow).  A depression formed at the back of the deposit (adjacent to the hillslope) indicates the energetic landslide mass probably scoured sediment out from the toe of the slope, and did not stop moving until the whole mass reached the valley floor. Calculation of the landslide volume using a combination of the measurement tools in Google Earth and an estimation of the deposit thickness from the aerial photograph suggests approximately 100,000 m^3 of debris remains in the village, although the failure may have been larger as there has been some erosion of the deposit adjacent to the river channel. 

An image taken looking east, across the village prior to the landslide. The mass travelled from left to right, through the buildings in the farground.

Source: gatopelu008\Panoramio

While it is not immediately apparent why this particular slope failed during the storm, there appears to be some evidence for planar failures on similarly oriented slopes elsewhere in the valley. Judging from the weathered appearance of the landslide deposit, it seems likely that bedrock degradation has been ongoing as part of the landscape's natural weathering and erosion cycle. As in many sub-tropical regions, it is possible that either historical, or recent, removal of vegetation led to a local acceleration of these natural processes, decreasing the stability of slope materials, and may have increased the chance of failure during the extreme environmental event.

Map of La Pintada landslide deposit (brown) and assumed source area (grey) derived from the above Twitter photograph

Although there is definitely room to streamline the process of mapping from media photographs, combining Google Earth with social media in this case provides important quantitative insights into the landslide occurrence in a relatively short period of time (~1 hr). Given the limited resources available in many remote or undeveloped regions, these technical aspects are often not addressed, and even more rarely published. Expanding on methods such as those presented here could therefore be a useful means for both local authorities, and the wider research community to investigate and document natural disasters in the future. 

Three simple ways to embed maps in a website or blog

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Last week a major debris flow struck the town of Kedarnath, an important Hindu pilgrimage destination in the Uttarakhand region of northern India. One week on, details of the event are ever so slowly beginning to emerge, although the remoteness of Kedarnath, ongoing flooding within the region, and significant damage to infrastructure throughout Uttarakhand, means that much of the information necessary to evaluate both what took place, and assess ongoing risks, remains unknown. The government currently estimates perhaps 10,000 people remain isolated in the valley, while the number of casualties is likely to be in the thousands, and the nature of the hazard remains speculative.

A social media image of the temple at Kedarnath following the debris flow last week

Source: http://www.twylah.com/divakarssathya/tweets/347957857922187264

Unfortunately this imprecise picture is common in the period immediately following such disasters, as the chaotic nature of the event disrupts critical infrastructure and information channels. Recently, however, social media networks have begun to fill the gap, as the unregulated nature of the networks allows the information channels to form somewhat organically, as in the case of Uttarakhand, where feeds from people on the ground via Twitter, governmental agencies on Facebook, affected communities using web resources such as Google Person Finder, and data collation on informal blogs are playing a critical role in shaping the response of the region.

The community-driven nature of these information channels means that they can better respond to suit community needs than more formally structured official information channels. Although both methods of communication are important, they serve different purposes, and in some cases the rapid evolution of community-developed information feeds can provide a better, more locally relevant service, than that provided by the state. An example of this is the Canterbury Quake Live website, which was privately set up after the 2010 Canterbury earthquake to collate and disseminate information from three government agencies. Despite these agencies all providing excellent online services, the unique combination and intuitive presentation of the information on a single site suited the residents of Canterbury - This played a key role in educating and informing residents, and three years on, this site remains a principal source of earthquake information, receiving hundreds of visitors daily.

A snapshot of 30 day TRMM rainfall anomalies projected onto Google Earth using the techniques described in this post (data credit: NASA, NOAA)

As natural disasters are inherently widespread events; accurate, clear, and up-to-date presentation of spatial data is an important component of developing information channels. Today we are fortunate to have a number of freely available global mapping services which offer the opportunity for users to relatively simply become involved in the mapping process, and in the case of natural disasters, develop tools to compile or present relevant data in real-time. Although the process is not difficult, these services still appear intimidating to most people. In this post I hope to show how simple it can be to take control of online geodata by presenting three ways to incorporate data on a website using the Google Maps API.

Static Maps API

The Static Maps API allows you to embed a Google Maps image on your webpage without requiring a screenshot or complicated code. This uses a single line of code (see the snippet below the map), within which you can include a number of parameters to style the map appearance. Google provides a handy description of the available parameters on their developers site, however, to quickly get started you can simply copy the code from below and paste it into your website.

Changing the numbers following the 'zoom=' (values from 1 to 21+) increase the zoom from the globe to individual buildings) and 'center=' (as decimal latitude and longitude) parameters will reposition the map. The 'maptype=' parameter is consistent for all the Maps API services, and allows you to change the style of the displayed map from 'roadmap' to 'terrain', 'satellite', or 'hybrid'.


Click to expand/collapse the code for the above map

Static maps code
<img src="http://maps.googleapis.com/maps/api/staticmap?maptype=hybrid&amp;size=640x480&amp;zoom=18&amp;center=30.734605,79.066804&amp;sensor=false"/>

HTML iframe

An HTML iframe is probably the most efficient means of embedding a custom interactive map into a website. As opposed to the static map above, using the API within an iframe allows users to interact with the map, scrolling, zooming, and changing view modes within the same map. The html required to embed an iframe map in a website also consists of a single line of text, which in this case you can generate using the 'Get link' button on the Google Maps page.

The frame provides a virtual container in which the map code can run, making layout simple, and reducing compatibility issues (see Line & Pixel for a more detailed description of iframes for Google Maps). The iframe uses the full Google Maps API, with abbreviated parameter names to keep the html short. In the example script below I specify the latitude and longitude of the map center following the 'll=' identifier, and use 'q=' to add a pointer at the same location. Strangely I can't find the Google link listing the available parameters, but they are described on this page.

Click to expand/collapse the code for the above map

HTML iframe code
<iframe frameborder="0" height="480" marginheight="0" marginwidth="0" scrolling="no" src="http://maps.google.com/?t=h&amp;z=18&amp;ll=30.734605,79.066804&amp;q=30.734605,79.066804&amp;output=embed&amp;amp" width="640">

Google Maps API

The third possibility is by far the most versatile, though requires a small amount of scripting to set up. This allows you to make full use of the Maps v3 API from Google, providing a large amount of creative control with the ability to incude features such as map overlays, historical imagery, and kml layers; although for (some complicated) compatibility issues an extension of the API is required to access to Google Earth functionality within the map window. The trade-off is a reduction in compatibility, as some mobile platforms do not fully support the additional data types, and the Earth API requires a free plug-in to operate in some browsers. One of the easiest resources to begin learning about Google Maps API is the tutorial at w3schools.com, their introduction is clear and simple, and combined with the fantastic example codes at the Google Code Playground provides more-or-less all the necessary details to start users creating their own maps.

Click to expand/collapse the code for the above map

Google Maps API code

Note: To use the Google Maps API, ensure the image background is set to transparent in the website CSS. This can be changed with the user interface in Blogger by selecting Design->Template->Customise->Advanced->Images->Background colour->Transparent <div id="map" style="width:640px;height:480px;background-color:#FAFAFA"></div><br /> <!--Google maps API script library--> <script src="http://maps.google.com/maps/api/js?sensor=false" type="text/javascript"></script> <!--Google JAVA API script library--> <script src="https://www.google.com/jsapi?" type="text/javascript"></script> <!--Google Earth plugin script library--> <script src="http://google-maps-utility-library-v3.googlecode.com/svn/trunk/googleearth/src/googleearth-compiled.js" type="text/javascript"></script> <script type="text/javascript">  google.load('earth', '1');  var map;  var googleEarth;  function init() {    map = new google.maps.Map(document.getElementById('map'), {    zoom: 18,    center: new google.maps.LatLng(30.734605, 79.066804),    mapTypeId: google.maps.MapTypeId.SATELLITE     });      // define a marker to highlight the location of the village   var marker = new google.maps.Marker({    position: new google.maps.LatLng(30.734605, 79.066804),    draggable : false,    title : 'Kedarnath'   });   // add the marker to the map environment   marker.setMap(map)      googleEarth = new GoogleEarth(map);   google.maps.event.addListenerOnce(map, 'tilesloaded', addOverlays);  }  google.maps.event.addDomListener(window, 'load', init); </script>

Spatial data for the Uttarakhand monsoon

Although any of these methods can be useful for quickly presenting spatial information, the Maps API is particularly useful for bringing together some of the complex factors associated with the natural disaster. Below is an example created for the Uttarakhand monsoon using a slightly more complicated implementation of the API described above to present data from the Tropical Rainfall Measuring Mission (TRMM) alongside pre- and post-event Landsat 8 imagery provided to the Landslide Blog by Robert Simmon of the Earth Observatory at NASA. TRMM produces live updates of historical and forecast precipitation information as kml files which I have directly linked to the below map. The individual overlays can be switched on and off using the check boxes below, and viewed in 3D using the Earth style - this is particularly useful for the satellite images, though you need to zoom in to the placemark to see them. Any new geodata (such as new satellite images from the region, transport hazards, or flooding information) can be included with this method, which I think could relatively easily be adapted to provide diverse spatial information to people such as those in the Uttarakhand region.

A full-screen version of the map is available from this link.

Additional layers

[-]

Prev 30 day rainfall anomalies
Prev 72 hr rainfall
Experimental 24 hr prediction
Satellite image 22 May 2013 (pre-event)
Satellite image 23 June 2013 (post-event)

Disclaimer: All the code in this post was put together with just 1 week of internet research. I am not an HTML programmer, and welcome any comments to improve the content.

Five minutes from layout to landscape

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The 'Image Overlay' tool is the key to an extremely simple, surprisingly powerful, and somehow little known trick in Google Earth. The tool allows anyone to copy maps out of print documents, drop them onto the terrain model in Google Earth, and then browse the maps in 3D - something that even the authors were often not able to do. It takes less than 5 minutes to import the maps in this way, and as it's so easy, I often use it while reading scientific papers or reports in order to better understand the sites and context of the figures. In the example presented here, I refer to a useful paper from G.J. Hearn (2002) summarizing the practical challenges of mapping and road engineering in some of the most difficult terrain in the world. The experience and insight conveyed in papers like this are very important for the future avoidance and mitigation of geohazards, a quality that becomes particularly apparent when reading the paper in association with 3D maps created using the Image Overlay tool.  

The steps required to transfer a map from paper (or almost-paper) to a 3D relief are described below.

The .kmz maps for this example can be downloaded for Google Earth here. As well as these being nice examples of geomorphological mapping for geotechnical applications, it's interesting to compare the proposed  roads to those now constructed (see the GE 'Roads' layer), and in particular see how both projects were directed by the results of the mapping investigations.

So, while this isn't a difficult process, hopefully the comparison of the 2D map in the first image with the 3D representation above gives an idea how useful this technique can be. And as usual, (thanks to Google) this technique is free for everyone to use, provided the original publishers are wise enough to embrace the benefits of open access - but that's another story!

Reference:
Hearn, 2002, Engineering geomorphology for road design in unstable mountainous areas: lessons learnt after 25 years in Nepal. Quarterly Journal of Engineering Geology & Hydrogeology v. 35 no. 2 p. 143-154 doi: 10.1144/1470-9236/2000-56

Geo-guesses, and Geo-information in Street View

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GeoGuessr provides a natural change of pace for a Sunday blog, though it's also a great chance to highlight the growing coverage and interesting applications of Google Street View. GeoGuessr is a Google Chrome experiment from Anton Wallén which allows you to test your geographic knowledge against the Street View data by dropping you into five random scenes, and asking you to guess where in the world the scene belongs. It's simple, but absolutely addictive as you can finally put all that random geographic knowledge to good use.

"Where in the world is this?"

Anton uses the Google Maps API to implement the interface, this is a really nicely designed and documented piece of software, and it's fantastic that Google encourages people to start using this, and other mapping products. The use of these products is initially free, and with the official support of Google Earth Outreach, can remain free for non-profit websites. 

In addition to addictive map-guessing games, the current coverage of Google Street View provides an important visual record across more than 400,000 km of roads, which as in the case of the 2010 Maierato landslide in Italy, can provide useful information on the condition of roading infrastructure prior to hazardous events.

source: http://news.bbc.co.uk/2/hi/europe/8517378.stm

As this event was unfolding, the global community was able to both locate the failure, and identify precursory signals (such as cracked and repaired pavements) which may have been associated with the developing slope-wide failure. Although such information would, of course, not have been new to local authorities, the ability to discuss it in an open forum has both scientific and social merit as communities become more aware of environmental signals indicating potential future hazards. An interesting article in New Scientist discusses recent initiatives to crowdsource roading repairs using similar techniques, and although an assessment of potential landslides from Street View images may not be on the horizon, Street View data certainly provides a useful tool for everyday users to begin investigating environmental hazards across ever-increasing regions of the globe.

Integrating GIS with field investigations

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Things are moving fast in the mobile world, and the last couple of years have been particularly progressive in the rather specific, but somehow exciting world of mobile GIS. This is allowing practitioners to tighten the loop between desk investigation, field study, and reporting, as mobile devices provide access to traditional reference data in the field, data can be digitally recorded and georeferenced 'on-the-fly', and mobile internet access allows instant updating of web-based GIS systems. 

While this may all sound a bit esoteric, the potential is exciting - as groups of engineers and geoscientists involved in disaster response, for example, can be deployed independently while working collaboratively on live geotechnical or geohazard databases. Colleagues with access in either field or office environments can then undertake live data analysis or consultation, focusing field programs without interrupting the investigation process. The last couple of years have seen some huge steps toward this goal, but every new technology develops in proportion to it's user base, so perhaps now is a good time to highlight some particularly impressive advances.

                   
Google Earth Mobile now allows custom .kml files to be loaded

                   

Lambert allows structural measurements to be simultaneously recorded, 

geolocated, and plotted for analysis

The BGS has released iGeology 3D, an augmented reality app for Android that overlays geological data on images viewed through a mobile phone camera (credit: www.bgs.ac.uk)

ESRI has released a couple of mobile ArcGIS apps (credit: www.esri.com)

                     

And Fulcrum allows the development of customized geodatabases for mobile devices

This last offering from the people at Fulcrum is particularly exciting, as working closely with the open-source GIS community, the developers have produced a beautiful, functional means of quickly creating customized geodatabases for both iOS and Android devices. The mobile data can be instantly synced with an intuitive web platform, and data can either be exported manually, or integrated with custom software using their own API. The service has been available for just over a year, has a number of basic subscription-based plans available to commercial users, as well as a free (almost full-featured) option available for occasional users. 

The Fulcrum project setup combines an intuitive drag-and-drop interface with
powerful requirement and visibility settings for each field

The drag-and-drop interface allows customizable (text, numeric, multi-option, single-option etc.) fields to be added and structured within the app, and with the help of some clever requirement and visibility rules, designers can ensure only relevant fields are available for a specific situation, while at the same time data such as soil or rock descriptions cannot be saved without ensuring standard recording requirements have been fulfilled (handy for forgetful or rushed investigators in the field). The platform is specifically configured for work in regions with no, or intermittent mobile coverage, and the open-source base maps can be supplemented by user-generated maps (up to 100 Mb for the free service). This is one of the first systems to implement a new open source MBtiles API, which with the help of free software such as TileMill and QGIS, allows users to upload their own maps to mobile devices by circumventing an inherent bottleneck associated with opening large files on relatively low-performance devices. 

                 

The ability to upload user-generated maps and fully customizable database setup are

major strengths of the Fulcrum system

A single button in the mobile interface can be used to sync the database settings, user-generated content, and online data for all members of a group - it really couldn't be easier. The online map interface is easy to use, though unfortunately at the moment there seems to be no way to display user-generated base maps on the website. Overall I'm impressed, the system seems effective, and not overly complicated, the integration with open source products and simple data export options highlights the community-driven nature of the service, and seeing such an effective application focus on environmental management and disaster relief makes me feel like we've come a long way in the last few years. 

The online interface is clear and effective, allowing users to display data in map and tabular formats (credit: fulcrumapp.com)

For me, all of the above offerings are exciting, and I expect they will soon change the way geoscientists and engineers get things done. While this is good for the practitioners, I think the faster and more focused investigations could be great for affected communities, and I look forward to seeing interesting future applications and outcomes.

Mapping the Gyama (Jiama) mine landslide from press images

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Just under three weeks ago a catastrophic landslide claimed the lives of 83 workers at a copper/gold mine in China's Tibet Autonomous Region. The victims are reported to have been local workers employed by a Chinese national mining company. Many details of this tragic event are described in news reports, and summarized on Dave Petley's landslide blog, where a recent post sparked my interest. Dave makes reference to official reports that attribute the cause of the event to fracturing of natural gravel on the slope, but notes on his blog that recent Google Earth images from May and August last year show clear evidence of mountain top mining and end-tipping of waste onto a slope in the region. Dave suggests that the unusually long runout of the event may have been aided by the fractured nature of the gravels in the initiation zone, and suggests this mining activity may have had something to do with it.

A press photo of the rescue crews working at the site of this very large, highly mobile landslide. 

Although there is understandably a large amount of press coverage regarding the event, there has been very little information on the landslide itself, and most reports focus on the sometimes thousands of rescuers who have been working at the site. Images in the media do, however, show large parts of the landslide, and offer a good opportunity to illustrate how tools such as Google Earth can be used to map 3D structures from such 2D photographs. While there are a number of commercial and non-commercial software packages that currently allow users to generate 3D point clouds from multiple photographs, there are few that allow details from photographs to be projected onto digital elevation models. Some architectural software such as Sketchup allow the mapping of photographic textures onto simple geometries, but as far as I know there is nothing currently available for geotechnical applications. 

The above press photo overlaid on a Google Earth terrain model using the Add>Photo dialogue.

The Add>Photo dialogue in Google Earth is one tool which provides a simple means of combining on-site photographs with spatial geodata. This first requires that a user can reasonably accurately locate the point from which a photograph was taken. In this case, the task was not too difficult, as the existing mining roads provided me with features to "walk along" using the 'Ground level' option in GE. I could then use features in the photos to locate the most likely observer positions.  

Options in the dialogue allow full control over image positioning and transparency.

Next, using the 'Photo' tab of the 'Add Photo' dialogue, I changed the heading, tilt, roll, and field of view, to match features in a semi-transparent photo with those on the Google Earth model. By zooming out of the photo a little I could then approximately map the landslide extents 'through' the photos using the Add>Polygon tool. This step required some trial and error, as for some reason it only allowed me to move nodes in the lower left corner of the screen, however, in an hour I was able to map the region of the landslide (indicated in grey in the lower figure). Although the photographic coverage of the valley is incomplete, I could also infer a potential source region (in orange), and derive the pre-mining valley long-profile indicated in the figures below (see my previous post for info on this tool). A .kmz file containing the data for Google Earth is available for download at the bottom of this entry (I take no responsibility for it's accuracy), to view photos from the photographer positions simply click on the camera icons once the file is loaded in the Google Earth browser. 

A map of the visible landslide deposit (grey) and source area (orange) inferred from the 
run-up of landslide debris in the valley. Note the steep grey and white slope related 
to recent mining activity at the inferred source location. Cameras and text 
indicate the location of images used to 'map' the landslide.

A long-profile of the landslide source and runout zones. Note the slope gradient prior 
to the dumping of spoil was ~50% (22.5 deg). This is already approaching that of 
many natural talus slopes containing loose gravel (typically <35 deg) and clearly 
does not leave much room for storage of non-engineered mining waste on the slope.

This analysis provides some interesting insights into the event. The BBC video image looking toward the landslide source appears to show that debris ran from left to right across the valley floor, and partially into the right-hand side catchment before flowing down-valley. This suggests the source was in fact from the upper left, and may correspond to the location of what appear to be end-tipped gravels in recent mid- and late-2012) satellite images. In addition, these images show that at least late last year, there was no settlement capable of supporting 80 workers within the landslide runout zone. The majority of structures are located on the ridge crest, and close to the excavation site. While it is possible that a new camp was erected after the satellite images were taken (there seems to be some ongoing road construction), it would be very disturbing to discover that any, let alone so many, workers were operating on such a steep end-tipped gravel slope adjacent to the mining site. 

Exploring the Mt Dixon rock avalanche

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In my last post I discussed the growing volume of geodata available online - and in particular ways in which it can be accessed by freely available GIS software. This kind of accessibility has great potential for geoscientists, and among other uses (e.g. teaching, structural geology) perhaps is a step toward 'crowdsourcing' geotechnical expertise for geohazard identification and emergency response. We can take a look at the 2013 Mt Dixon rock avalanche to see an example of this (see Dave Petley's landslide blog for a very interesting discussion on this event).

A NASA EO-1 ALI image of the Mt Dixon rock avalanche deposit (credit: The Landslide Blog)

By draping the NASA image over topography in GE, and including the geological units, fault traces, and structural measurement layers from the GNS repository we can get some understanding of the lithological and structural setting of the failure.

Satellite image overlay of the rock avalanche including structural and lithological data

Click here to download the data for Google Earth 

The light blue shading indicates the failure occurred in interbedded greywacke and argillite (perhaps no surprise there), and we might infer that structure in the region of the landslide is possibly affected by the westerly-dipping faults present on the lower valley wall. The orange markers indicate the location of structural measurements, and support a regional dip azimuth of between 260 and 330 deg, with an inclination of between 60 and 80 deg. This is a similar orientation to the planar rock slope immediately south-east of Mt Dixon, and would indicate the initial movement direction was perpendicular to the local bedding orientation. Using the "Add path" tool in GE, we can draw a long profile to investigate the pre-failure topography of the runout zone. If we select the new path in the menu, the "Show elevation profile" tool will produce a plot of elevation vs. distance along the path.

Mt Dixon rock avalanche runout path, indicating an elevation loss of ~890 m, and reach angle of 29% (16 deg). The red arrow on the map indicates the transition to deposition, and is reflected by the vertical grey marker on the elevation profile. This appears to be the base of a crevasse field marking a steepened section of the glacier surface.

While these observations are relatively simple, I hope they provide some inspiration to realize the potential for what is a growing volume of readily-accessible geodata, particularly when it can be incorporated in a free 3D mapping platform such as Google Earth.

Visualizing geodata in 3D

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After spending some time working on the Bavaria maps pages (both the Earth and Maps pages work now!), I recently went in search of a way to project the same data in a vector format. At the moment I use the GDAL library (specifically gdalwarp and gdal2tiles) to convert georeferenced .tiff files to a .png format for projection in Google Earth (GE). These are raster files, just the same as the .jpg's that come from your digital camera, and each pixel is assigned a different colour. Using the data in this way has some issues, however, as the resolution of the images is limited, there's no way to attach metadata, and serving up all the image files puts a reasonable load on the server.... In contrast, vector formats allow us to define a shape by coordinates, then tell the computer a texture or colour to fill the shape, and  present the same data with essentially has no resolution limits (though it of course depends on the accuracy of the data collection). In addition vector files can contain metadata with links to external webpages, and are usually about 10% the size of the raster images. 

After passing through OneGeology.org, I landed at the GNS GeoServer website. OneGeology is one of those 'logical' collaborative efforts much like Wikipedia or OpenStreetmap for geological maps... for some time now, Geological data has been provided online using the .wms format as a means of connecting remotely served geological data to whatever GIS software you're running on your PC (see here for an example). It's really a great step forward, I think you could say that this initiative now provides geological data of the whole world at some resolution for free. It is, however, still a bit slow and somewhat disorganized at the moment, at least on the OneGeology portal... But as it's a true cutting-edge combination of science and IT, I think the fact that it's there is really fantastic.

The OneGeology portal links .wms data from various servers around the world, one of which is located at GNS in New Zealand. While I'm new to this, the GNS site is one of the best geodata services I've come across. The portal (once you click on Data>layer preview) provides access to 1:1M and 1:250k geodata for the whole country in a wide range of formats... including .kml for GE. This can be accessed as network links streamed off the server (for GE, first download the .kmz file by clicking here), or (for example) as a .kml file to download to your computer (this is a bit faster, check the portal). 

Google Earth screenshot showing vector-based geology of the Mt Cook region 

(data credit: GNS & Google)

I'm told by the guys and girls at GNS that the data-serving side is a true 'work in progress' (a client upgrade will happen next week), and I think the future looks exciting for the users throughout the geoscience world.

It's time to do this!

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Hi all,

Over the last few years I've spent a lot of time working in the Alps - learning about the valley geomorphology, slope instabilities, bedrock properties... enjoying the steep valleys, long trails, alternately hot and freezing weather, and supermarkets closed at lunchtime. 

A German version of steep valleys and freezing weather;

A view from the top of the inaccessible (for this scientist) north wall of the Zugspitze this Feb.

At the same time I've become reliant on web-based GIS as a means of maintaining field data, using tools such as Google Earth to keep it accessible even when I wasn't in the office - or when the office was not as ordered as it should have been. As I begun to generate modelling data, the simple format, and flexibility of .kml (the .xml?-based language of GE) lent itself to quickly producing results out of a number of softwares... 

The ability to compare field data to model results in a 3-D GIS environment, and share and discuss results with colleagues around the world without requiring any special software, provided some of the greatest insights during my time in Zurich.

I now have a reasonably large library of code, tools, and .kml-based geo data that grew with the project, but never really reached a 'mature' stage... Over the next months I hope to begin outlining some of the methods and tools I found most useful... and while I'm not an html-monkey I'll try to present some of the ideas, concepts, useful websites, and codes using the awesome google .api library.

To start, I've linked the 1:500 000 geological maps of Switzerland, (Liechtenstein), and Germany as Google Earth and Google Maps overlays on my "Online maps" page. In the next days I'll describe where to find the data, how to adapt it, and some of the benefits of having something like this available in a 3-D open source platform!

3D Geology of the Swiss alps looking out over Thun, Tichino, and south toward Italy