GIS 5990 Module 4: Predictive Modeling

Weighted overlay depicting a predictive model for site locations.

Module 4, stretching over two weeks, required us to progress through the multi-step process of creating a predictive model for archaeological site location probability. A predictive model combines known site locations with variables (generally environmental) to create a classified raster categorizing the probability of finding sites in different regions. Common variables to consider are elevation, slope, aspect, and distance to water sources; these were the variables used for our project. In order to create data layers that can be used in the Weighted Overlay tool to produce the predictive model, the required data must be located, acquired, clipped, merged, reclassifed, and/or converted. As an example, we must decide how many classifications we will have in our weighted overlay layer; we used three for our assignment. Thus, once we extracted an aspect layer from the DEM using the Aspect tool, we then used the Reclassify tool to create a new aspect raster with only three classifications. We had to similarly edit and create the slope, elevation, and water source distance layers for use in the Weighted Overlay tool.

This assignment, although lengthy, was an enjoyable practice of something I have been doing in my internship with the BLM. Something both this project and my internship have driven home is the need for a good project plan and good data for any predictive model. The sheer number of variables and different potential classifications and weightings for those variables, as well as varying responses to those variables by different human populations in different times and environments, requires careful consideration if one has any hope of producing a useful predictive model.

GIS 4035 Module 5a: Introduction to ERDAS Imagine and Digital Data

Subset of a classified image selected from ERDAS Imagine and exported to ArcMap for layout finalization.

Our module this week was divided into two topics. First, we dove deeper into the details of the electromagnetic spectrum, including the relationship between wavelength, frequency and energy along with how to calculate each. While we need not be physicists to use remotely sensed data, we do need to have an understanding of the structure of the electromagnetic spectrum and the type of data we can extract from different wavelengths.

Our second topic was an introduction to the ERDAS Imagine program. I am glad to finally be learning this program, in part due to the positive things I've heard of it from others but also as a change of "scenery" from ArcMap. Our deliverable (above) was simply a selected section from a classified image exported and finalized in ArcMap. Most of the assignment was designed to get us familiar with the program for use throughout the semester and, hopefully, our careers.

An issue that often arose as I worked with ERDAS Imagine was the difference between Imagine 2011 (the basis for the assignment) and Imagine 2014. The majority of changes I encountered were simple to recognize and overcome. The only major problem I had was in exercise 3; the subset image I created using the inquire box never updated the area attribute. I made several attempts without success. Most likely I was in error but I could never locate the source. In the end, I calculated the updated area totals in ArcMap using the Count field of the attribute table.

I enjoyed working with Imagine as the program seems to be much more efficient than ArcMap in simple manipulations of raster data (such as panning and zooming). I am looking forward to learning more about what the program can do.

GIS 4035 Module 4: Accuracy and Ground Truthing

Land use land cover classification of Pascagoula, MS with accuracy of thirty sample points symbolized.

Our task for week 4 of Aerial Photo Interpretation and Remote Sensing was to check the accuracy of last week's efforts to classify an aerial photograph. The best methods for ground truthing such a classification involve direct testing sample sites in the field, but the online nature of the course prevents such tests. Instead, we utilized Google Maps (and especially Street View) to test the classification accuracy of thirty sample sites. I roughly followed a stratified random sampling pattern based on classification category. However, small or homogeneous categories were given fewer samples (e.g., the large body of water in the west, the cemetery) while large, heterogeneous categories were allotted more samples (e.g., residential, commercial and services). The accuracy of my classification turned out to be about 73%. The main error sources were misclassifications of bodies of water and of forest cover. While my misclassification of Krebs Lake as a bay could, perhaps, be forgiven, my misclassifications of forest cover were due to not correctly distinguishing deciduous and evergreen trees. As a result of this assignment, however, I believe I would have a higher accuracy percentage on a similar aerial photo.

GIS 5990 Module 3: Mayan Pyramids and Angkor Wat, Analysis and Report

Supervised classification of Angkor Wat, Cambodia based on SWIR composite.

Google Earth image of potential pyramid locations in El Mirador.  Potential sites, difficult as they are to see, are in red.

Google Earth image of various layers of El Mirador overlaying the standard Earth view.  The NDVI layer is visible.

Google Earth image of El Mirador supervised classification overlay with legend.

The final module of our initial project involved two main tasks.  First, we were to export the layers created in the first two modules into a format that could be displayed and shared in Google Earth.  This was a relatively simple task using the 'Layer to KML (Conversion)' tool for individual layers and the 'Map to KML (Conversion)' tool for entire data frames.  The results can be seen above.  I was disappointed in the appearance of my potential pyramid layer and some of the colors of my classification layer are off, but in general the process was a success.

A second task required of graduate students was to repeat the supervised classification process with Angkor Wat, a well-known archaeological complex in Cambodia.  We were to locate and download appropriate Landsat imagery, then classify the image based on a false color, NDVI, or SWIR image.  As La Danta pyramid was the focus of our El Mirador classification, the monumental core of Angkor Wat would be our focus here.  The difficulty resided in the different land cover types that are included within the core.  Every training sample I tried over-represented the likely locations of additional remains.  However, my best classification restricted the core sample to the vegetation overlying the structures.

This was an enjoyable module that would have been more so if my computer had not expired in the middle of the assignment.  Even so, I believe I was mostly successful in completing the two tasks.  As always, however, more time could be spent on tweaking the training sample for a more refined classification.

GIS 4035 Module 3: Land Use Land Cover Classification

Aerial photo of Pascagoula, MS with land use/land cover classification overlay.

This week for our Photo Interpretation and Remote Sensing lab we were given the challenge of classifying an aerial photograph based on land use and land cover.  While we were not required to get too detailed with our classification, we were required to classify everything.  We were to create a new shapefile and then create polygons over each classification.  I extensively used the "trace" and "clip" tools from the editor toolbar as I classified the image to ensure everything was properly classified with no gaps remaining between polygons.

This was a rather challenging lab assignment, due both to its intrinsic difficulty as well as to the catastrophic failure of my computer.  It was poor timing, but I believe I was successful with the above map in the end.

GIS 5990 Module 2: Identifying Mayan Pyramids, Data Analysis

The second week of our GIS Special Topics in Archaeology course continues our experimentation with remotely sensed data from Landsat 7 in our search for Mayan pyramids.  In this assignment, however, we focused on the infrared bands in looking at the electromagnetic radiation signature of the region's vegetation in the hope of locating archaeological remains hidden underneath.  We first utilized our false color composite from last week to create a normalized difference vegetation index (NDVI), which represents the difference between the red and infrared bands.  This is intended to show the plant life as communicated by EMR.  The next map we created was a composite consisting of Landsat bands 4,5, and 1.  Incorporating two bands of infrared data, this composite represents more detailed information on the health of plant life.  The final map is the result of a supervised classification of the 451 composite, created from a training sample focused on locating more remains based on the EMR signature of the La Danta pyramid.

I made several goes at the training sample before I reached one I was reasonably satisfied with.  My final classification over-represents bare ground, but it is less egregious than my earlier ones.  Not many areas returned positive hits for pyramids which is better than too many hits.  I do believe the areas returned as possible archaeological remains would be worth a look, although further data and analysis would always be welcome.

GIS 4035 Module 2: Visual Interpretation

The first two modules of our Photo Interpretation and Remote Sensing course focused on the background and fundamentals we need to build on throughout the semester.  In particular, the two maps above show our experimentation in module two with the different criteria by which we interpret remotely sensed data (specifically aerial photography).  In the first map, we were required to identify five categories of tone (from very light to very dark) and five categories of texture (from very fine to very coarse).  Our next task, reflected in the second map, was to use four additional identification criteria (shape and size, shadow, pattern, and association) to interpret the photograph.  The most difficult aspect of this task was attempting to consider each criterion in isolation; in normal interpretive practice we obviously use all available criteria.  However, considering them singly was helpful; I had not considered how useful shadows might be to correctly interpreting aerial photography.

These introductory modules provided a good foundation in the science of remote sensing and the basics of interpreting the resulting data.  I look forward to learning much more throughout the semester.

GIS 5990 Module 1: Identifying Mayan Pyramids, Data Preparation

The first module of our Special Topics in Archaeology course begins a three module project using remotely sensed data to locate Mayan pyramids in dense jungle terrain.  As seen above, we produced three rasters depicting a single area in El Mirador, Guatemala, with the intent of noting any noticeable characteristics in the raster data indicating the presence of the La Danta pyramid.  The Landsat 7 data were downloaded from the USGS Earth Explorer website (http://earthexplorer.usgs.gov).  The first raster we looked at was composed solely of Landsat 7's band 8.  This panchromatic (sensitive to visible light) band is of a higher resolution than the other bands (15 meter versus 30 meter), yet the La Danta pyramid is far from obvious in the raster.  We next combined Landsat 7 bands 1, 2 and 3 into a composite raster to replicate natural colors.  Again, the pyramid does not show up as terribly obvious.  The finally raster produced this module was another 3 band composite, only this one utilized bands 2, 3 and 4 to create a false color raster showing near-infrared data (band 4) as red in the final image.  While perhaps a slight improvement over the previous two rasters, I was still unable to clearly identify the pyramid in the false color raster.

In addition to the bands utilized in this module, Landsat 7 data include three additional bands of short-wave and thermal infrared remotely sensed data.  Further raster composites are possible utilizing these bands for more analytical tasks in the infrared wavelengths.  Perhaps these bands will draw out the La Danta pyramid more clearly in the next two modules.