GIS Internship: GIS Portfolio

     I have considered a personal website before and this was just the push I needed to commit to one. My portfolio gives me the chance to showcase my progress in UWF's Online GIS program to date. Those visiting my website can learn about me, download and review my resume and curriculum vitae, and peruse maps produced throughout the program. It is also possible to contact me via the contact page. I plan on continuously adding to and modifying my website. In particular, I plan on adding a page about my volunteer position at the Denver Museum of Nature and Science in Education Collections and other hobbies (like ceramics/pottery, embroidery, and nature photography). In fact, save the pictures of me, all the images I took on hikes or during fieldwork.   

     In putting together this portfolio I reflected on my time in the certificate program. I learned an incredible amount in the last year. Not only about making maps and GIS analysis, but about myself (career goals, time management skills). The volume of what I learned made it hard to choose what aspects of my work to highlight. I wanted to include every map I made in my portfolio. Each assignment taught me a new skill or required me to problem solve and consider map design. I still have two more courses left in the program but I hope those that visit my portfolio can see how much I enjoy GIS and map-making. 

     I provided a hyperlink above but in case this link is damaged, my site can be visited at: http://bburdgis.wix.com/bburd. 

GIS Day: Anytime, Anywhere

     GIS Day technically happens once a year. This year it formally takes place on Wednesday, November 16, 2016. For this internship course in the spring, however, we are honoring the day in our own way. Improvising a bit.  

     I am currently working for a cultural resource management company in North Dakota. Thus, my "office" ranges from the great outdoors to the hotel lobby to the desk in my hotel room. GIS has come up naturally throughout the work day. The company I work for employs GIS analysts to compile soil profiles and artifact density analyses post-excavation. When my archaeology and GIS worlds combine sparks fly. We are under a serious time constraint so conversations about GIS are informal and casual. 

     In an effort to devote a block of time out of my day to GIS (outside of this course), I invited some coworkers to listen to an informal presentation about what I have learned so about GIS. I showed them the ArcGIS interface and discussed automating geoprocessing tasks with Python. I also showed them maps I have made throughout the course via this blog. I particularly enjoy remote sensing (and space archaeology is a hot topic right now) so I spent a good chunk of time discussing remote sensing imagery and its capabilities. It was fun to share my experience with them. I am pretty sure I convinced a few people of the necessity for GIS literacy. 

     Happy GIS Day from the past! There are plenty of GIS Day festivities in Colorado and I hope to attend at least one. We'll see what November holds for me. Until then, I will keep discussing GIS to anyone who will listen.

Reviewing maps I have made over the
course of the program with coworkers. 

Remote Sensing: Module 10 - Supervised Classification

     This week is a continuation of techniques in automated digital image classification. Specifically, supervised classification is covered. Unlike unsupervised methods, this method requires the use of training sites (a priori knowledge) to guide classification. It also uses statistical methods as opposed to Euclidean distance data to assign pixels to information classes. To practice this technique, the lab assignment walks through a supervised classification the process for which is then reproduced to compile the weekly deliverable. The classification was performed in ERDAS Imagine and the map deliverable was made in ArcMap.
     The general method is to import an image, create AOI features surrounding training sites (using the region growing tools and drawing tools), record the signatures from these AOIs in the Signature Editor, perform the supervised classification (via the Raster tab), and then recode this information to represent final class designations. As a result of running the supervised classification, a distance image is also created. This image along with examining comparative class histograms and mean plots (options in the Signature Editor window) examine the accuracy of the classification.
     My map below shows Germantown, Maryland classified via a supervised classification. The Euclidean distance image is included as a reference to the degree of error. (The brighter spots indicate areas that are likely classified incorrectly.) Eight classes are ultimately used to represent the different land uses/land classes. Areas are also included and noted in the legend. The original band combination used is 5, 4, 6 as I felt these bands had the best separability between spectral signatures. As an aside, one of the more difficult things was creating training sites for roads. The borders between roads and everything else for in this image were quite pixelated. As a result, there is a fair amount of error in that class since my training sites may have included the occasional pixel belonging to a different class. When I was growing areas of interest for some of the other classes I also set higher distances between pixels to capture some of the variation in that feature and as a result introduced error into certain classes (particularly the fallow and agriculture classes) and that is validated by the bright spots in the distance image inset.

Map 1 An exercise in automated classification using a supervised classification technique

Remote Sensing: Module 9 - Unsupervised Image Classification

     This week we delved into digital image classification. Up first is unsupervised classification, the following module (10) covers supervised classification. Unsupervised classification is an iterative (that is, until a threshold is met) procedure involving the clustering of pixels (with similar spectral reflectance) into classes (a parameter determined by the user). Supervised image classification uses a priori knowledge to guide classification (by way of 'training sites').
     In this lab we took a high resolution aerial photograph of the University of West Florida campus and performed an unsupervised classification (using the ISODATA algorithm) in ERDAS Imagine. The result of which is a thematic raster with a lower spectral resolution than the original photograph. This image was classified into fifty categories. It was our job to reclassify and reduce these categories into one of  five choices: trees, grass, buildings/roads, shadows, and mixed (for pixels that fall into more than one category). This was done by selecting pixels and reassigning them into one of the categories and giving them a designated color. Once all fifty classes were reclassified, the Recode tool was used to reduce the number of classes into the final five. Descriptive data (in the form of areas and percentages) was calculated for each category and for permeable/impermeable surfaces. The map deliverable below displays the classified image and accompanying data.
     ArcGIS was used to make the final map product but it can also run an unsupervised classification by using the Iso Cluster tool and the Maximum Likelihood Classification tool. It is  necessary, however, to possess the Spatial Analyst extension.

Map 1 Unsupervised Digital Image Classification of the University of West Florida Campus

Remote Sensing: Module 8 - Thermal Imagery

     In continuing with digital image processing, thermal imagery is introduced. Thermal remote sensing is unlike other forms of remote sensing in that sensors record emitted energy as opposed to reflected energy. A general rule of thumb when examining thermal data is that good absorbers are good emitters of thermal radiation while good reflectors are poor emitters of thermal radiation. Thermal characteristics will also vary by time of day and season which is taken into consideration when planning a study or recording data for a site.

     Thermal imagery has a coarser spatial resolution than other bands because it has a larger instantaneous field of view. This came into play during the lab as there were two choices of imagery for analysis. I initially chose a different image than I ultimately used because I thought it had obvious features (like recently irrigated fields) to identify using the thermal layer (which was the goal of the assignment). The lab, however, wanted the feature highlighted in the map and when I attempted to make the map the irrigated field was too pixelated for useful visual analysis. Thus, the map deliverable below. It shows an image with a better spatial resolution (and I chose a large feature to eliminate scale issues altogether). On the left side of the map is the feature visualized in a band combination (4, 5, 1) that highlights its extent. On the right side of the map is the thermal layer (Landsat ETM+, Band 6) displayed using a red, yellow, and blue gradient. 

     The thermal image shows a distinct spot near the coast that is much warmer than the surrounding water (also note the red areas as evidence of cultural activity/urbanization). When it is visualized in the multispectral image it shows as a bright red swath along the coast. The red is a result of reflected near infrared energy. This could indicate some photosynthetic activity or agricultural runoff. A combination of histrogram analysis/manipulation, viewing the image in greyscale for each band, and the inquire tool (in Imagine) were used to analyze this image.

Map 1: An Exercise in Thermal Image Interpretation

Remote Sensing: Module 7 - Image Preprocessing Part 2

     This assignment continues examining image processing techniques by way of multispectral analyses. Different band combinations can be used to tease out information that may be missed when viewing an image in true color. In other instances, true color is the best option for viewing a given feature. What band combination is best is ultimately determined by what feature is under examination.
     Three features were described in our lab assignment and we were tasked with identifying them based on the given criteria. Histogram spikes and trends helped indicate whether a feature has dark or bright pixel values. From there, examining greyscale versions of each band in conjunction with multispectral versions of the image aid in visually assessing trends and confirming patterns in the histogram. In ERDAS Imagine, the Inquire Cursor is invaluable. It provides information about a pixel for each band including its frequency and LUT value. The Inquire Cursor helped confirm the features described.
    Below are the map deliverables for this assignment. Each map shows one identified feature displayed in a band combination that highlights that aspect. An inset map is included in true color to provide a true-to-life context. Without giving away too much detail, the first feature is a water body as the pixel characteristics matched the histogram characteristics provided for a certain layer. The second and third features are snow atop a mountain peak and sediment in water, respectively. The pixel values also fit the given description. Band combinations are noted on each map with a brief note on why that combination is used. t

Feature 1

Map 1 Feature Identification Exercise Using Multispectral Analysis -- Water Bodies 

Feature 2

Map 2 Feature Identification Exercise Using Multispectral Analysis -- Snow

Feature 3

Map 3 Feature Identification Exercise Using Multispectral Analysis -- Variation in Water

Remote Sensing: Module 6 - Image Preprocessing Part 1

     This modules covers spatial enhancements and image preprocessing. The exercise introduces the USGS Global Visualization Viewer where satellite and aerial data can be browsed and downloaded. These satellite images can then be imported into ERDAS Imagine for analysis. Some of the Landsat imagery exhibits striping due to a sensor malfunction and in this lab we attempted to correct for this phenomenon with spatial enhancements. These can be performed in either ArcMap or Imagine. Edge detection, Fourier transformations, and other techniques help in getting the most out of remotely sensed data.
     Two maps below show my attempts at image enhancement. The first one is the map deliverable for the exercise. I used the Focal Analysis tool in Imagine iteratively. That is, I performed a focal analysis calculating the mean of a 3x3 kernel (excluding 0 values from the calculations) and applying those values to pixels with a value of 0. Performing this analysis to each end product of the previous analysis (using the same exact settings) results in gap closure. The image, however, is not perfect and there is a disconnect between the gap filled areas and the original data. causing it too look like an improper join. This image is a composite between the original banded image and the modified image. Other methods attempt to fill missing data by using Fourier transformations in conjunction with other filters (low pass, high pass, range/edge detect, sharpening).

Map 1: Image Enhancement Exercise Using Landsat Satellite Imagery

     The second map I created shows some of the end products of combinations of filter effects and enhancements including focal statistics, histogram adjustments, and altering raster display settings. The top left image is the original, unaltered image with gaps. The top right is the image I used in my final map above. The bottom left and bottom right images are composites using the banded image. The bottom left image uses a Fourier transformed, sharpened image. The bottom right image was made by using focal statistics (maximum) and a Fourier transformation. 

Map 2: Various Attempts at Image Enhancement Using Different Methods

     While you can perform these enhancements in ArcGIS, I predominately used ERDAS Imagine. Both programs are great but I like that I could open four views in Imagine and compare them simultaneously. Imagine also lets you preview the alterations which I found helpful.