Special Topics: Module 11 - Final Project (Modeling Cave Sites)

     This final project is the culmination of the work we have carried out in the name of GIS for archaeology. We have modeled Scythian landscapes, areas potentially containing shipwrecks, and site probability in an archaeological district. For my final assignment I wanted to model potential areas where ritual cave sites could be located in Greece. As it turns out, my model fails to reject a null hypothesis of random site distribution. My data is not clustered or spatially autocorrelated. This is likely due to a small known sample size and a simplistic model based upon only three explanatory variables (slope, aspect, and elevation). Each map below represents progress in the development of a predictive model.
     Data for this assignment is freely available and was compiled from several sources. Sacred cave data was partially derived from The American School of Classical Studies at Athens. Additional cave sites were compiled from Wikipedia and a caving enthusiast's website. Digital elevation data was derived from ASTER tiles downloaded using USGS's Earth Explorer. (When downloading lots of data be sure to use their handy bulk downloading tool).

Study Area
    The extent of the study area was determined by the cave data. An arbitrary polygon was drawn around a portion of mainland Greece and Crete. The cave data represent multiple periods of ritual use (from Neolithic to Roman). The point locations are also generally approximate and unverified. I used the Mosaic to New Raster tool to stitch all 30+ ASTER tiles together into one raster. I then clipped the raster to the study area polygon. The cave shapefile was a product of the ASCSA data and the other site data. I used an Excel spreadsheet to add XY data and merge it with the ASCA data. Voila! More data (relatively speaking, my sample size went from 10 to 21). 

Map 1. An overview of the study area with a DEM
overlaid atop an Esri imagery base map.

Map 2. An overview of the study area with sacred
cave sites noted. 

Primary Coverages
     Before secondary coverages can be generated, it is necessary to examine patterns in existing data. This helps in deciding weights to assign when data are reclassified. Here we see a slope, aspect, and contour coverage of the study area. Each are derived using the DEM and their respective Spatial Analyst tool (Slope, Aspect, Contour). There were not any obvious patterns in the data particularly concerning aspect. The caves were almost equally distributed between northerly and southerly aspects. Most caves were located between 0 and 600 meters in elevation. They were also predominately located in areas with a slope between 10 and 20 degrees. There were outliers though. The Cave of Zeus was located at a much higher elevation and slope than any of the other caves (only the highest and slopiest for old Z-man). This information was taken into consideration when developing weight classes for data ranges.
   

Map 3. A slope surface derived from DEM data.

Map 4. An aspect surface generated from a DEM.

Map 5. A map showing contour lines in 300 meter intervals.
Cave sites are shown to demonstrate the process of
noting characteristics of cave sites.

Secondary Coverages
     Once the data have been examined and weights decided upon they are used to develop secondary coverages. This is done using the Reclassify tool in ArcMap. Here we see the results of that process. Each map shows probability represented by three classes. Areas possessing similar characteristics to known sites are symbolized in red denoting a higher probability of finding sites, moderate probability areas are in yellow, and low probability areas are in green. These coverages will then be weighted to develop a predictive model.

Map 7. Slope reclassified. 

Map 6. Aspect reclassified.

Map 8. Elevation reclassified. 

Predictive Model
     These secondary coverages are now ready for use in deriving a predictive model. I generated multiple predictive model outputs using different weights assigned to each secondary coverage using the Weighted Overlay tool. Each model emphasizes one coverage over the others. The exception is Model D which weights each coverage equally. This comparative exercise shows what happens when weights of explanatory variables are altered. When using predictive models to inform survey and archaeological investigations it is important to generate a solid predictive model. These models have common areas of high probability but differences could mean a lot when time and money for a project are of the essence.

Map 9. The predictive models. Each one of these models are the result of assigning different weights to the secondary coverages. Weights assigned are noted beneath each data frame. High probability areas are symbolized in red.

Analyses
     Predictive models cannot be taken at face value. They must be statistically examined. Turns out, when I probed my data it was found wanton (but that doesn't mean it isn't informative). The data is random and not spatially autocorrelated. It overpredicts sites in low elevation areas especially on the smaller islands and Crete. I performed an Ordinary Least Squares regression (in order to perform these analyses I had to generate random non-sites as a proxy for known non-sites in the study area), spatial autocorrelation, and a hot spot analysis. The results of these tests are seen in the final map product for this assignment.

Map 10. OLS, hot spot, and spatial autocorrelation results. 

Special Topics: Module 9 - Biscayne Shipwrecks (Part 2)

     All that preparation last week set the stage for analysis this week. So we'll dive right in. First, I defined a 300 meter radius surrounding the shipwrecks to examine the benthic environment surrounding each wreck. Then, reclassified rasters were generated for the benthic and bathymetric data. These were used in conjunction to create a simplified weighted overlay/predictive model.
For this analysis, there was not enough data for a linear regression or a statistically meaningful analysis of the shipwrecks (we were working with n = 5) but we had enough data to guide survey efforts.
     As in previous weeks regarding predicitve models, essential tools were the Feature to Raster tool,  Reclassify, and the Weighted overlay tool. As well as the workhorses of geoprocessing: the Buffer and the Clip tool.
     The three map deliverables below each show a different aspect of the analysis. Map 1 is a summary of the benthic characteristics within the study area (300m radius) surrounding each shipwreck. Map 2 shows a comparison of the reclassified data used to derive the predictive model. Map 3 is the predictive model. In Map 2, areas in red denote high probability while those in dark green represent the lowest probability of finding a shipwreck site. The areas in red in Maps 3 are thse areas where the model predicted areas with a high likelihood of containing a shipwreck. That is those areas most resemble the conditions surrounding the known shipwreck sites.

Map 1 shows the benthic environment surrounding the study area of each site.
It also contains an overview of the park.

Map 2 shows the reclassified raster data used in the predictive model.

Map 3 is the end product of our analyses. This is the predictive model
generated by using reclassified benthic and bathymetric data.

Special Topics: Module 8 - Biscayne Shipwrecks (Part 1)

     Time to sink in (bad joke) to some shipwrecks. That's right. These ships went down near Biscayne Bay, Florida. They don't make jokes about you for nothing Florida. Way to s(t)ink, I kid. I love my home state. This assignment made me feel a twinge of home sickness. Luckily I will be heading that way soon and maybe snorkel this maritime heritage trail.
     This week I explored two websites looking for data to use for this marine archaeology project. The first website provided the historical chart while the latter is where I acquired DEM data for Biscayne National Park. Point data for the heritage trail was provided to us care of UWF as was the benthic data.  Finding the DEM was a bit of a chore as the data I thought I was accessing led to another website/tab several times before I actually reached the data. And, often that data wasn't what I was looking for. Perseverance is the order of the day. Once I downloaded the historical chart I georeferenced it. The DEM/bathymetric data was clipped to a digitized boundary of the park. The data is now prepped for use in the coming weeks.
     The final map deliverable is seen below. It shows the benthic data, historic chart, and DEM data all clipped to the park boundary. The shipwreck locations display in each data frame giving them context in different visualizations within the same space. A short description is also included on the map that briefly discusses the map content.

A map deliverable displaying data for Biscayne National Park, Florida
 prepared for analysis in the coming week. 

Special Topics: Module 7 - Modeling Scythian Landscapes (Part 3)

     This wraps up our discussion of Scythian landscapes. We prepped data, generated secondary coverages, and this final week we used Ordinary Least Squares (OLS) regression to explore our data.
The first task was to generate non-sites (though this is not an ideal method as it is not ground truthed data). ArcGIS has a nifty tool that generates random points across your data. The Create Random Points (a Data Management tool) accomplishes that end. Once the points were generated they were merged with the Tuekta mounds identified last week. The attribute table was then populated with the reclassified slope, aspect, and elevation numbers from the secondary coverages.
     With the attribute table populated, I ran an OLS linear regression using these sites and 'non-sites.' Turns out the model, as it stands, explains about 74% of variability in the data. (Elevation had the highest coefficient of the three variables.) We also checked the results of the OLS regression against the results of a Spatial Autocorrelation and a Hot Spot Analysis. The results are summarized in the map deliverable. Two data frames display the OLS and Hotspot analyses over a DEM of the study area. The results (a low p-value and a high Z-score confirm the results of the regression - the data is clustered and non-random) of the Spatial Autocorrelation are shown at the bottom of the map. Adding additional variables could improve the predictability of the model, however, these variables are enough to parse the statistics and learn a bit about probing landscape level archaeological data.

Map deliverable showing the results of both the OLS and
hotspot analyses overlaid atop the study area DEM.
The Spatial Autocorrelation results are also shown for reference.

Special Topics: Module 6 - Modeling Scythian Landscapes (Part 2)

     The week prior focused on data preparation for this lab. This week secondary coverages were generated for slope, aspect, and elevation. In addition, contour lines were also derived from the DEM. The georeferenced image of the mounds was used to generate a shapefile of the point locations of the mounds. This was great practice for identifying archaeological features in the landscape and also to discern any patterns these features may or may not possess.  The general trend seems to be that the mounds are found in areas with low elevation, facing in southerly directions (SE, S, SW), and they lie at slopes between 0 and 10 degrees. Next week, the lab will statistically explore the data and we will 'publish' it.
     The map deliverable below shows the several coverages that were generated along with a shapefile showing the point locations of some of the Tuekta mounds. The coverages were generated just as was done in preparation for the Tangle Lakes project weeks prior.

Special Topics: Module 5 - Modeling Scythian Landscapes (Part 1)

     This module is all about data preparation (for labs in the coming weeks) and starts our foray into landscape archaeology. We walked through gathering data for international landscapes from the USGS Earth Explorer. In particular, locating DEM data for an area in the Altai Mountains of Russia that contains known Scythian mound burials (also known as kurgans). For this exercise we used ASTER derived data. The Mosaic to New Raster tool was used to stitch together four raster datasets and from there we digitized a boundary and clipped the larger DEM down to something more manageable.
     The map deliverable shows the subset of data examined by way of a DEM symbolized with the elevation color ramp. It also highlights the area where the mounds are located with a georeferenced JPG.

This week's deliverable highlighting a region in the
Altai Mountains, Russia that contains burial mounds.

Special Topics: Module 4 - Predictive Modeling

     Predictive models are another tool in the archaeologists toolbox and this week we ran a simple predictive model for an archaeological district in Alaska. Predictive models can take many factors into account but for the purposes of this lab we used four components/generated surfaces: slope, aspect, elevation, and proximity to water. The Spatial Analyst extension is key in implementing a predictive model in ArcMap, particularly the Reclass, Surface, and Overlay toolsets (for the Weighted Overlay tool).
     While predictive models are great for informing survey decisions and streamlining cultural management processes they're aren't a panacea. They can in no way serve as substitute to boots on the ground, they can merely supplement and inform survey and project planning. There is no model, no matter how thorough, that can say that there are no archaeological materials in an area. These models represent probability and likelihood of the presence of archaeological remains.
     I am currently at the Great Basin Archaeological Conference and one of the presentations I was able to see discussed a predictive model for an area of National Forest that I work in. The model had a greater than 90% ability to predict the known site locations (it was thoroughly cross validated and I have spent the summer collecting data in some of the unsurveyed areas...it will be neat to see if the model predicted a comparable amount of density in that region). That being said, it's those sites that appear in the unlikely areas that become of great interest (in their deviation from a pattern).
     Yes, predictive models do not tell the whole story but they go a long way in helping, in my instance a National Forest, determine how best to allot limited resources (time, money, seasonal employees).
     Below is my map deliverable showing the areas that are likely to contain sites in green. That means their combination of slope, aspect, elevation, and proximity to water are ideal.

Map deliverable displaying the results of a predictive model
run using slope, aspect, elevation, and proximity to water. 

Special Topics: Module 3 - Finding Maya Pyramids (Part 3)

     This module wraps up our discussion of and practice with locating archaeological data with satellite imagery. Specifically, this week focused on disseminating our results with a wider audience through the publication of layers and maps for use in Google Earth (as it is user friendly and has a wide usership - hah wasn't sure that was a word...).
     Two tools in the Conversion toolbox create file formats (.kml/.kmz) that can be opened in Google Earth. These tools are the Layer to KML and Map to KML. Below is a screenshot of one of the map open in Google Earth. It is quite handy. You can move between the layers of your map to toggle them to display or not. We've used these tools in the program before when we displayed our dot density maps of South Florida for the Cartography course. I also use the Layer to KML tool in work to share non-sensitive data (usually fire related) information within the Forest Service. Putting what you learn to use is always exciting. I am very much looking forward to the next module on predictive models because I am going to using those skills at work too. Fun, fun.

Screen capture of my map product displaying in Google Earth.
The false color (4, 3, 2) composite is showing. Vegetation is in red.

Special Topics: Module 2 - Finding Maya Pyramids (Part 2)

     Last week we used Landsat 7 ETM+ data to examine the vegetated area obscuring the La Danta pyramid and archaeological complex (a Maya site located in Guatemala). Since the location of the complex is known it was used as a control to see if any other areas could be hiding, based on spectral similarities, archaeological structures. First, a new spectral band combinations (new as last week we looked at a false color and natural color composite) and a normalized difference vegetation index (NDVI) image were generated. Once these images were generated, I performed a supervised classification to search for probable site locations to survey.
     I used the NDVI tool in the Image Analysis window to generate the NDVI image. This index is use to determine relative biomass and health of vegetation. Differences in plant growth patterns or in plant health can be used as indicators of the presence of material remains. The NDVI image can help in detecting these differences.
     I used the Composite Band tool again to generate two different band combinations - 7, 5, 4, and 4, 5, 1. The first band combination is ideal for penetrating atmosphere/smoke and highlighting the difference between bare soil and vegetation. The latter band combination is idea in detecting the various stages of plant growth (though precipitation impacts these results).  To my eye, the pyramid was still difficult to discern in all of the images. Thankfully, owing to the known location of La Danta a pyramid training sample can be generated and the results can identify areas of similar spectral characteristics. These locations, once refined and examined further, can narrow down the search area for archaeologists.

Map deliverable for this module. showing three renderings of a Landsat image. 

     The supervised classification was generated with the help of the Image Classification toolbar in ArcMap. The classes I used were dense forest, forest, water, urban, bare earth, cloud, shadow, and the La Danta area for 'pyramid.' Despite many attempts I was unable to go without multiple classes of the same type. That is, when I combined my training samples (for instance, the two bare earth classes) it introduced greater error in the resulting image. My final classification shows some areas where pyramids could potentially be found. I think, in addition to other contextual visual information, the results could help in determining potential areas to survey.

Special Topics: Module 1 - Finding Maya Pyramids (Part 1)

     This assignment takes me back to the Remote Sensing course but now I get to apply those principles to finding Maya pyramids. Space archaeology, hooray! This first assignment sets the stage for the next two weeks by preparing the data for a supervised classification (next module). We use past (2001) Landsat 7 ETM+ data to examine the location of a known Maya pyramid complex (La Danta, discovered in 2009). Using different band combinations (see below) we looked for any distinguishing characteristics around the pyramid that might help in identifying the locations of other pyramids in the jungle (in the next module).

     My map shows a full extent panchromatic image (band 8, 15m resolution) of the broader area surrounding the pyramid, La Danta (a Maya pyramid). A true color and false color image of the area in the immediate vicinity of the pyramid are shown. The true color (band combination: 3 - visible light [red], 2 - visible light [green], 1 - visible light [blue]) and false color (band combination: 4 - near infrared, 3 - visible light [red], 2 - visible light [green]) images were generated using the Composite Bands tool (Data Management toolbox). Both the contrast and the brightness were altered to enhance the image and the images were pansharpened using the panchromatic band (band 8). The pyramid is difficult to visualize at these band combinations as it was, at the time, obscured by vegetation.

A map showing a full extent panchromatic image of an area of jungle in
 Guatemala containing several Maya pyramid complexes. Two composite
images (one true color, one false color) highlight an area where a pyramid,
La Danta, is obscured by dense vegetation.