GIS 4048 Module 3: Natural Hazards - Tsunamis

Figure 1: Map showing radiation and runup evacuation zones surrounding the Fukushima II plant and along the coastline.

Module 3 of Applications in GIS continues the theme of natural hazards with a focus on the 2011 tsunami that struck Japan and created a radiation hazard from the Fukushima II nuclear power plant. This necessitated incorporating two hazard zones in the analysis and final map: the multiple perimeters of radiation exposure surrounding the plant and the runup from the tsunami along the coastline. An additional component of the module involved the proper organization of multiple datasets within geodatabases. This organization becomes crucial as the number of datasets and complexity of analyses increase, especially if projects will be worked on my multiple people and returned to over time. Finally, we utilized Modelbuilder to create a workflow that produced the needed datasets and that could be shared and modified.

The simplest step of the analysis depicted in the map above was the creation of the five radiation hazard zones using the Multiple Ring Buffer tool. This created a powerful visual of the potential radiation hazard facing populations and infrastructure at various distances. The tsunami runup hazard zones required more steps to create. In simplified terms, it is the result of combining layers depicting the lands affected by the tsunami with a DEM and categorizing the result into three zones based on elevation (0-10 meters, 11-40 meters, and 41-78 meters).

While the analysis portion of the assignment was a challenge, the requirements for the final map were just as challenging. The need to efficiently display two different hazard zones on the same map created many opportunities for symbologies to clash. After some experimentation, I decided to go with a patterned symbology for the radiation zones while keeping a simple color-based symbology for the runup zones. Each were given moderate transparency. This created a layout that communicated the information while not becoming cluttered. Major Japanese cities were added to the locator data frame to give context for the location of the Fukushima plant and tsunami damage.

GIS 4048 Module 2: Natural Hazards - Lahars

Figure 1: Lahar hazard assessment surrounding Mt. Hood, Oregon.

Having been away from GIS coursework for a few months, the first assignment of Applications in GIS was a welcome return to learning new GIS tools and skills. Specifically, this module focuses on the manipulation of raster data with a focus on tools used for hydrology as well as tools to perform mathematical functions on raster data. The purpose of using these tools was to produce a map depicting potential hazardous areas that would arise from lahars triggered by an eruption of Oregon's Mt. Hood.

Lahars are mixtures of material, generally mud and rock combined with water and volcanic material, that flow down the slopes of a volcano into surrounding valleys. The above map uses digital elevation model (DEM) data to predict the areas most likely to be affected by lahars originating at Mt. Hood. We proceeded through multiple steps to arrive at the final data product. First, two DEMs were mosaicked into one, then the Fill tool was used to "smooth" the raster data to diminish the potential for errors in the data affecting the subsequent analysis. The output of the Fill tool was the input for the Flow Direction tool, the result of which was a colorful raster wherein cells were categorized based on the direction water would flow over the surface. This output, in turn, was used with the Flow Accumulation tool. The output raster shows streams and rivers based on the previous calculations. The Int, Con, and Stream to Feature tools were then used, resulting in a line vector shapefile. The final step of the assignment was to create a buffer of 0.5 miles around this shapefile to catch the schools, cities and overall population (as represented by census block groups) that would most likely be at risk from lahars from Mt. Hood.

Although I do not know how often I will utilize the hydrology tools in my career, I am glad to have been introduced to them should the need arise.

GIS 5990 Final Project: Predictive Model of Neolithic Sites in Southeast Turkey

Figure 1: Overview of study area.

The aim of this project was to create a predictive model of Neolithic archaeological sites in southeastern Turkey. This area was chosen due to the location of Gobekli Tepe in the region. This site has become well known as one of the earliest uncovered monumental structures with some researchers (such as excavator of the site Klaus Schmidt) hypothesizing that mobile hunter gatherers constructed the site. The impetus for its construction may have been a change in ideology that also created fertile ground for the transition to sedentary, agricultural lifeways.

Figure 2: Site in the study area.

A successful predictive model would assist in explaining site location and help researchers in discovering new sites. Site data were acquired from The Archaeological Settlements of Turkey GIS (http://tayproject.org/giseng.html). Neolithic site data were manually entered into a new shapefile in ArcMap. This data is the weakest link in the analysis due to the potential for spatial inaccuracy.

The digital elevation model (DEM) seen in figures 1 and 2 were created by using the Mosaic tool on fifteen ASTER images acquired from the USGS Earth Explorer website (http://earthexplorer.usgs.gov/). With the image properly projected as a DEM, slope and aspect data could be generated in addition to elevation data.

Figure 3: Elevation data reclassified.

Based on the location location of Neolithic sites at lower elevations, the Reclassify tool was used to classify the elevation data into four equal categories. Category 4, representing the category with the highest probability of sites, was from 0-800 meters. In descending probability, category 3 was 800-1600 meters, category 2 was 1600-2400 meters, and category 1 was 2400-3200 meters.

Figure 4: Original slope data.

Figure 5: Original aspect data.

Figure 6: Slope data reclassified.

Figure 7: Aspect data reclassified.

Slope and aspect data were similarly classified into four categories of high to low site probability. The highest probability category for slope was defined as 0-5%. In descending order of probability, the remaining three categories were 5-15%, 15-25%, and 25-75%.

An aspect of 0 (or flat) was rated as the highest probability, with southern-facing aspects being the next probability category. East and west facing aspects were the next category, with northern facing aspects being of the lowest probability.

Figure 8: Water buffer of 500 meters.

Data on rivers and lakes, acquired from Eurostat (http://epp.eurostat.ec.europa.eu), were buffered to 500 meters. A raster was then created by using the Feature to Raster tool with probability defined as binary categories. Areas within the 500 meter buffer were considered high probability with all else being low probability.

Figure 9: Weighted Overlay.

After the data on the four variables were suitably processed, the Weighted Overlay tool could be used. Elevation and water source distance were each weighted 35% while aspect and slope were each weighted 15%. The result can be seen above.

Figure 10: Ordinary Least Squares analysis.

In order to test the weighted overlay predictive model, an ordinary least squares analysis would be conducted. First, the Create Random Points tool was used to created over 100 points to function as locations without sites. This was merged with the original site shapefile. The Extract Multi Values to Points tool was used to incorporate data from the variable rasters into the merged point shapefile. The Ordinary Least Squares tool can then be run.

Unfortunately, the statistics indicate the model is not successful in explaining site locations. Only elevation returned as a significant variable. There are several explanations for why the other variables were not significant. First, as noted above, site location accuracy is not assured based on the data source used. A more robust model would incorporate verified site locations. Additionally, the reclassifications of slope, aspect, and water source distance may have been incorrect. Other classifications may function better. It is also possible that slope and aspect do not in fact help explain site location.

We might also attempt to incorporate paleoenvironment data, including on water sources. Using modern hydrography as a proxy for ancient water sources may not be justified.

Another potential variable to include in future models is viewshed and site visibility. Figure 11 shows an experiment with the Observation Points tool depicting raster cells visible from five well-known sights in the study area. Gobekli Tepe in particular has a wide view to the south. There is potential here for future model attempts.

Figure 11: Observation points for five site in study area.

GIS 4035 Module 10: Supervised Classification

Supervised classification and spectral distance file of Germantown, Maryland.

The task for module 10 was to utilize ERDAS Imagine to create, analyze, and edit supervised classifications of multispectral images. We began by reviewing the fundamentals of the Signature Editor tool (to create and edit the training samples used for a supervised classification) and creating an AOI (area of interest) layer. We then used the Inquire tool to locate specific points on the image on which we created polygons to capture to areas to be used as the spectral signature of a class. We also reviewed the use of the Seed tool; this tool automatically creates a polygon capturing pixels of a set spectral distance from the initial point based on input parameters. I preferred using the Seed tool to manually inserting polygons as it gives more control over keeping each class distinct. We also spent some time analyzing the histograms and mean plots of spectral signatures. These tools give use a more precise way to ensure our classifications capture unique features and are not spectrally confused.

The assignment required us to use all the tools reviewed in the module to create our own supervised classification of an image of Germantown, Maryland. We were given coordinates of three urban features, two fallow field features, four agriculture features, and one each for grasses, deciduous forests, and mixed forests. We also were to create signatures for water and roads. With this completed, we then needed to recode the number of classes down to eight, consolidating the categories with multiple classes into one each. We also needed to include the spectral distance file in our final map; this image shows us at a glance the pixels in the image that are furthest away from any of our classes. The final layout was completed in ArcMap.

GIS 5990 Module 9: Biscayne Shipwrecks, Analyze Week

Five shipwrecks in Biscayne NP with 300 meter buffers showing benthic zones.

Reclassified layers showing benthic zone and bathymetric data; these layers were used in the weighted overly below.

The output of the Weighted Overlay tool using the reclassified benthic zone and bathymetry layers.

This was the second week of our three-week project focusing on GIS use in analyzing shipwrecks in Biscayne National Park. This module was similar to our previous work in creating predictive models, only this time we processed data relating to the sea floor. As seen above, the variables included in our weighted overlay were benthic zones and bathymetry. Interestingly, the bathymetric data seemed to contradict the actual locations of known shipwrecks; most shipwrecks were not located in the shallowest waters. To account for this, the weighting of the bathymetric data was lowered to 30%; the benthic zone data comprised the majority 70% weighting.

For the most part, this was an enjoyable assignment to complete. I did run into a frustrating difficulty at the end as I attempted to run the Weighted Overlay tool. Many, many attempts either ran into errors or resulted in a raster output that was not classified correctly. Eventually, I checked the properties of my input data, the reclassified benthic zone and bathymetry rasters. I discovered the latter raster was lacking a defined coordinate system; fixing this allowed the tool to run quickly and correctly. The experience reinforced the lesson that I must always check my data to ensure everything is in order.

GIS 4035 Module 9: Unsupervised Classification

Five category unsupervised classification of the UWF campus based on true color imagery.

The ninth module of the course guided us through performing unsupervised classifications of imagery in ArcMap and ERDAS Imagine. An unsupervised classification takes basic guidelines from the user (such as the number of desired categories) and creates categories based on the appearance of each pixel. A perfect classification would, for example, classify all water in a category, all trees in another category, and so on. There has likely never been a perfect classification, however, so the resulting classified image must be edited to better capture the desired categories.

In order to create the above classified image of the UWF campus, the original true color image was run through Imagine's Unsupervised Classification tool to create fifty categories. The resulting image (not shown) looked very similar to the original image. We then reclassified each of the fifty categories into the five classes of trees, grass, buildings/roads, shadows, and mixed (grass/urban). The main source of error was the overlap of bright grass and ground areas to some urban areas; this created the need for the "mixed" class. 

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.