GIS 5990 Module 8: Biscayne Shipwrecks, Prepare Week

Five Biscayne National Park shipwrecks in relation to nautical charts (modern and historic) and bathymetry.

Module 8 begins our three week investigation of shipwrecks within Biscayne National Park. This week of preparation involved locating, downloading, and properly preparing bathymetric data as well as historic nautical charts. The National Oceanic and Atmospheric Administration was the source for both. The Historic Map and Chart Collection (http://historicalcharts.noaa.gov/) provides a wealth of historic charts, with the only caveat being they must be georeferenced once acquired. Although initially frustrating, this proved to be simpler than I had assumed. Having the data represented in the historic chart (in this case from 1856) is important if one is interested in knowing the quality of data available at the time of a shipwreck. 

The bathymetric data proved more troublesome to locate. After trying several downloads (including multibeam data that I may not have processed properly), I finally chose the image above featuring good detail but covering only the western portion of the Park. This image as well as the historic chart were clipped to the boundary of Biscayne National Park. 

This was an enjoyable module that proved slightly frustrating when it came to finding the best data source. Nonetheless, I am glad to get more experience in locating and processing data for a project.

GIS 4035 Module 8: Thermal and Multispectral Analysis

Imagery combining the thermal infrared band (6) with the shortwave infrared bands (5 and 7) to highlight fires.

The eighth module of the course focused on using thermal infrared imagery to extract data unavailable from other EMR wavelengths. The general concepts and tools were similar to previous exercises, yet thermal infrared imagery presents unique challenges. First, thermal infrared EMR is emitted, not reflected; the amount of thermal infrared EMR emitted by a feature is a combination of the amount of energy absorbed, the feature's composition and surface characteristics, and the sensitivity and exposure length of the image (among other factors). In simple terms, thermal infrared EMR reflects temperature, but we cannot assume a direct correlation without calibration.

Our deliverable for the week was more open-ended than usual; we were to choose an area or feature in one of the two composite images created for the module and create an image that highlights the chosen area or feature. The thermal infrared band needed to at least be used to identify the feature even if it wasn't used in the final layout. While we had already identified select fires in the imagery, I was struck by how defined the fires' core extents appeared when the thermal infrared band was combined with the shortwave infrared bands. After applying a Gaussian stretch in Imagine and a minimum-maximum stretch in ArcMap, the fires popped out from the background significantly. This did allow me to identify a third, small fire southeast of the largest fire that I had not noticed previously.

I enjoyed the experimental nature of this module and learning of the unique data one may extract from thermal infrared imagery. I am still not confident in my ability to manually manipulate histogram breakpoints to achieve fruitful results, but this module did help me significantly understand the areas I need to work on.

GIS 5990 Module 7: Scythian Mounds Report Week, Regression Analysis

Results of ordinary least squares regression analysis on Scythian mound data.

The third and final week of our work with Scythian mounds near Tuekta, Russia allowed us to dig deeper into statistical analyses. I am glad to get more practice with statistics; perhaps eventually I will come to understand statistics and how to apply statistical tests in practical situations. Until then, the detailed guidance in our assignment instructions on interpreting statistical test results was most welcome.

We began the assignment by creating 100 random points as examples of locations without sites; these were then merged with our point shapefile digitized previously representing mound locations. We then needed to include the category of elevation, slope, and aspect in which each point was located in the merged shapefile's attribute table. I used the Extract Multi Values to Points tool, as described in the instructions. This was much simpler than manually entering the data, although it did entail deleting superfluous fields in the attribute table.

After the data were prepared properly, we ran the statistical tools of Ordinary Least Squares Regression, Spatial Autocorrelation, and Hot Spot Analysis. The results of these analyses indicate all three environmental variables affect mound placement (with elevation being the most significant) and give a 99% confidence value that mound placement is not random. The detailed results of the Spatial Autocorrelation analysis is included in the above map.

This module taught me much on the proper way to set up regression analysis. I will likely use this module for reference in my internship and future work until I become more comfortable with statistical work.

GIS 4035 Module 7: Multispectral Analysis

Feature 1 multispectral analysis: Deep water.

Feature 2 multispectral analysis: Snow.

Feature 3 multispectral analysis: Shallow water.

Our task this week was to practice using different methods of multispectral data analysis to find three features with particular EMR signatures. The methods practiced in the assignment included histogram analysis and manipulation, displaying different combinations of bands as composites, creating indices based on different bands (e.g., Normalized Differential Vegetation Index), and using the Inquire Cursor to get detailed data on individual pixels. In order the find the three features, I primarily used the Inquire Cursor method based on histogram analysis.

The assignment helped me greatly in understanding histograms, although I am still not confident in manipulating them for a better image. By looking at the histograms of each band, one gets a good sense of how the data will display. I also appreciated the practice in combining bands into custom composites to highlight particular data. This was something I did not quite grasp in ArcMap, but the methods in Imagine made the concepts clearer.

GIS 5945: Dream Job

It is difficult for me to pin down exactly what my dream job would be.  Any archaeology position in the western portion of the United States (and I even include Alaska) could be a dream job. However, I am not limiting myself to archaeology positions.

A job I recently applied for on USAJobs.gov was with the Forest Service in the Gila National Forest, Silver City New Mexico. The official job title is "Interdisciplinary Soc/Bio/Phy Information Specialist." The job description is to support "managers and resource specialists in the use of Geospatial Technology and Geographic Information Systems (GIS) for land, natural resources and ecosystem management." It is the interdisciplinary aspect of the job that appealled to me; working with a variety of experts from different disciplines to help solve problems and overcome challenges using GIS. It also helps that I am familiar with the region; the archaeology field school I attended for two summers was half an hour from Silver City. It is a region I came to enjoy and would not mind at all living in.

Because of its interdisciplinary nature, the job application requirement list consisted of numerous "or" statements. Essentially, the job required a Bachelors degree in a hard science, resource managment, or a social science (or equivalent experience). It further required one year of graduate education, specialized experience, or "superior academic achievement." I would qualify under "superior academic achievement," and my graduate coursework plus my internship would give me some additional points. 

The archaeology-focused courses in the UWF program would likely be the most beneficial to performing this job. While the projects may be more natural resource oriented rather than cultural resource, many of the same tools and techniques would apply. Although remote sensing expertise was not mentioned specifically, such a skill might also prove useful in the position.

GIS 5990 Module 6: Scythian Landscapes, Analyze Week

Geographic variables and Scythian mound locations.

This module continued our exploration of Scythian burial mounds near Tuekta, Russia. The majority of the assignment involved creating layers representing geographic variables that may have affected where mounds and other sites were placed. As seen in the map above, these variables include the topography, slope, aspect, and elevation of the region. The raw rasters representing slope, aspect, and elevation were reclassified to show the regions most and least likely to feature sites. The most favorable areas will be relatively flat, low elevation, and facing south (if they are on a slope at all). We also digitized points representing the centers of mounds in the provided aerial imagery. I was surprised to find so many, as previously I had focused mostly on the line of largest mounds in the eastern portion of the image. I ended up digitizing 66 mounds, although there are likely more. The pattern immediately seen is that the mounds form a series of lines running roughly north-south, with most of the lines trending towards a NNE-SSW orientation.

GIS 4035 Module 6: Spatial Enhancement

Landsat 7 image run through Fourier transformations, sharpening, and statistical filter in ERDAS Imagine.

Past modules in this and other courses have mentioned that GIS operators often must correct acquired imagery for various errors prior to using it for their analyses. This module introduced us to some of the ways such corrections can be done. We explored the tools available in both ERDAS Imagine and ArcMap, including Fourier transformations, high pass filters and low pass filters. Low pass filters generate output that appear smoother and less detailed than the original image; noise in the image is also removed, according to the size of the kernel chosen. High pass filters create high contrast and noisy output that highlights the edges of features.

For the assignment, we were to experiment with Imagine and ArcMap to create an enhanced version of a Landsat 7 image that minimizes as much as possible the scan line corrector failure striping without removing too much detail. The assignment walked us through using the Fourier Transform Editor tool as the initial step in the process. While I experimented with different wedge placements in the editor, I was unable to create an image that was significantly better than my first attempt. After experimenting with various filters and settings in Imagine and ArcMap, the final image used above was the result of sharpening and a statistical filter in Imagine. Other filter and setting combinations created images similar to this, and a few were significantly worse. I did not experiment much with the histogram, however; I hope to learn more about histogram manipulation in future modules.

GIS 5990 Module 5: Scythian Landscapes, Prepare Week

Layout from the first week of the Scythian Landscapes project.

After our two week module on predictive modeling, we begin another three chapter project investigating a particular topic. This time, we are considering the theoretical perspectives contained within landscape archaeology and, with GIS as the tool, looking at Sythian burial mounds in southern Russia (near the borders of Mongolia and Kazakhstan).

Our tasks for the first week were relatively light. To prepare for next week's tasks, we prepared a mosaic of four DEMs that were then clipped to a polygon representing the study area. We were provided with an aerial photo of the mounds near Tuekta, Russia that needed to be georeferenced to the clipped DEM. Our layout was required to include the clipped DEM along with an inset of the georeferenced aerial photo. I added a small-scale reference inset to further orient the viewer; the arrows I added from Adobe Illustrator to make the relationship between the insets clear.