GIS4035 Module 5a - Intro to Electromagnetic Radiation (EMR)

This module marks the introduction to automated image classification using ERDAS Imagine in tandem with ArcGIS. After exercising the brain with a bit of mental Olympics for calculating EMR properties and an exploratory tour of ERDAS Imagine's eWorkspace, comparisons were made between Landsat Thematic Mapper imagery and Advanced Very High Resolution Radiometer (AVHRR) imagery. The map below was created in ERDAS Imagine by editing the attribute table for the provided imagery to add an area column. A subset of the imagery was created using an Inquire Box along with the Subset and Chip option. Ty discovered that the updated acreages did not transfer into ArcMap with the subset imagery and very graciously posted his solution to the problem for the benefit of the rest of the class. Thanks, Ty! His solution of creating another area column after creating the subset was very helpful. A basic map with the subset image and updated acreages was then created in ArcMap. Acreages were added to the legend through editing descriptions for labels in the symbology dialog box. Ideally a basemap would have been included as well, but eDesktop was not being very cooperative in that department this evening.

Land Classification for a Washington State site using ERDAS Imagine and ArcMap.

GIS4930 Module 2 - Mountain Top Removal, Appalachia Coal Region (Analyze)

Assessing the impact of Mountain Top Removal (MTR) mining on an area requires comparing imagery of that area taken after a period of time has passed. The Analyze portion of this module helped develop the skills to do just that. Utilizing data from SkyTruth, 2005 Landsat imagery was used in ArcMap to select training samples in known MTR polygons as well as in nonMTR areas. After changing class names, values, and colors for the samples they were compared for overlaps, and a signature file was created to be used in Supervised Classification. The same data was used in Erdas with the "Grow" tool to select several samples to add to the Signature Editor table.

The class was split into several different work groups of 3-4 students to analyze the 2010 data for SkyTruth. Each group was responsible for 2 to 4 images. After the preliminary exercise, the 2010 imagery was prepared by first using the Composite Bands tool to consolidate the 7 bands of the Landsat file and then clipping the imagery to the group's study area in ArcMap. In Erdas the Unsupervised Classification tool was used to create a layer with 50 classes. Areas which seemed to be part of MTR were classed and symbolized by color accordingly. Distinguishing the MTR areas was difficult for me. The remaining areas were classed as NonMTR and symbolized by a different color. Because identical spectral signatures can be assigned to very different types of features, many objects which are not MTR areas are included in the MTR class. This will be accounted for at a later time.

Back in ArcMap, the classified image was reclassified with MTR being assigned a value of 1 and all other Class Names assigned blank values. The resulting image is shown here:

2010 Landsat Imagery Classified for MTR
(NOTE: This image includes some nonMTR features with the same spectral signature as MTR.)

From this information, the MTR raster will need to be converted to polygons and smaller MTR areas removed. Buffers will be created around roads and rivers, and MTR areas within the buffers will be removed. An accuracy assessment will be done as well as a comparison with the 2005 dataset. Each group member will submit packaged data to the group leader for compilation into one dataset for the study area. Finally a map service to present the group findings online will be created. Look for this next week!

GIS4930 Module 2 - Mountain Top Removal, Appalachia Coal Region (Prepare)

This was a two-part lab preparing for a project concerning the coal-mining process of mountain top removal in the Appalachian Mountains of West Virginia. The first part of the lab involved learning about LiDAR as well as creating and viewing LAS datasets in ArcMap. Different point symbology and different filters impact how useful the information can be.

The second part of the lab involved creating stream and basin features from USGS Digital Elevation Models (DEMs). A mosaic of four DEMs which Group 3 will be using this module was created and clipped to the extent of the class's study area. Several tools of the Hydrology toolset of Spatial Analyst were used then:

1. Fill tool to fill in low spots/pooling areas

2. Flow Direction tool to assign values to each pixel indicating direction of flow across the individual pixels 

3. Flow Accumulation tool to indicate the number of other cells flowing into each cell (to show possible streams)

4. Con tool to eliminate features which likely are not streams

6. Stream to Feature tool to create streams layer

7. Basin tool to create watershed layer

The created streams and basin layers are depicted in this map:

Group 3 - Streams and Basins

Appalachian Mountains

GIS4035 Module 4 - Ground Truthing and Accuracy Assessment

Land Use / Land Cover Classification Accuracy
Pascagoula, Mississippi

This module included a lot of completely new material for me. Although the lab exercise was straightforward and rather easy, the preparatory information was rather overwhelming. The lab exercise itself involved using last module's classification map and adding sample points. The actual land use or land cover for these sample points was determined using Google Maps Street View where possible or zooming in on Google Maps aerial view. This was compared to the classification selected last week for the same points. The results were tabulated for accuracy. Best accuracy for this map was determined for non-forested wetlands, probably because they were so easy to distinguish. Barren lands had lowest accuracy because of having classified open areas as barren lands when a better classification would have been herbaceous rangeland. The lab itself was a fun exercise. The resultant map with sample points is shown here.

GIS4930 Network Analyst - Report Week

Producing maps with the routes created last week was the focus of this week's lab. The patient evacuation maps were inserted into a pamphlet template which included information for the patients and their families. Each of the emergency supply routes to the evacuation shelters was shown on its own sheet which included turn-by-turn driving instructions for the National Guard crew members and other emergency workers. These maps were specifically created in grayscale for ease of reproduction as requested; following that same thought process, the maps were created in an 8 1/2 x 11 format for easier handling, storage, and reproduction. 

Emergency Supply Delivery Route Map
with Turn-by-Turn Directions

The map shown here is representative of the supply route maps. It was not a favorite to complete, but is included as an honor for having the distinction of providing plenty of opportunities for me to learn from my mistakes. I feel so much smarter already. There were two particular challenges. One challenge encountered with this part of the project was trying to ensure that there was suitable contrast between the different map elements. The other challenge was fitting all the required information on a single sheet and still retaining legibility.

The final map was created for media distribution to the public. Because the map was requested for use in print media as well as on television, simplicity was expected. The map shows the shelter locations with primary streets along with the addresses of the evacuation shelters and some miscellaneous shelter-related information obtained from the City of Tampa and Hillsborough County. The analysis aspect of this map was completed in ArcMap using Network Analyst and the map was finished using Adobe Illustrator.

The entire project from start to finish was very comprehensive, very practical, and very educational.

GIS4035 Module 3 - Land Use / Land Cover Classification Mapping

Land Use / Land Cover Classification
(Pascagoula, Mississippi)

Creating a classified land use and land cover (LULC) map from an aerial image was the goal for this lab. After a polygon feature shapefile was created and additional fields were added to the attribute table for LULC code and description, Editor was used to create features by digitizing polygons for different categories of land use and land cover. Vertex and edge snapping were used to digitize adjacent polygons, but eventually some issues involving the Snapping Toolbar were encountered that could not be resolved promptly. The ability to clip out polygons from within other polygons is a skill that will be handy for future projects. The identify tool was very helpful while clipping out polygons.

Although time-consuming, especially because of the scale and zoom level I selected (it seemed like a good choice initially), this was a very enjoyable lab to complete.

GIS4930 Network Analyst - Analyze Week

The analysis portion of the Tampa Hurricane Planning project built on the information prepared in the previous module. The map shows routes from the National Guard Armory to three schools serving as hurricane shelters for emergency supply distribution runs and the routes from Tampa General Hospital to Memorial Hospital and St. Joseph’s Hospital for the evacuation of patients prior to the hurricane’s arrival. The map also outlines the areas to be served by the three emergency shelters (identified on the map).

Tampa Hurricane Planning - Routes

To prepare this map, a new network dataset was created from the streets layer prepared earlier with attributes selected to create the fastest, simplest routes rather than shortest-distance routes with flooded roads closed to the public but possibly traversable by emergency vehicles.

The areas to be served by each of the storm shelters were created in Network Analyst with values selected to avoid accidentally excluding any of the study areas from the analysis and to clearly identify only one storm shelter for any area.

As done in the preparation lab, a map package was created for the map and zipped together into one folder with the metadata files, routing map image, and the process summary. Again, a sample email detailing this information and process was created as if a co-worker would be completing the project. The email included the zipped folder, details of what the City of Tampa had requested, what had been accomplished during this part of the project, and what needed to be completed yet. Items to completed next are different information brochures along with a map and supply list for emergency supply distribution. These will be done during the Report portion of the project.

GIS4930 Network Analyst - Prepare Week

The focus of this module was preparation of data to be used for the production of a map and public information brochure by the City of Tampa in preparation for a hurricane. The City is especially interested in routing for evacuation, emergency access, and post-hurricane supplies, taking into consideration road closures. Elevation zones for the basemap were produced from a Digital Elevation Model which was classified into distinct elevation classes and symbolized. The flood zone for this hurricane (expected surge of 5.5 feet) is symbolized in red to stand out. Higher elevations are shown in other colors with 1-foot increments. Shelters, hospitals, fire and police stations, the National Guard armory, and roads are shown on the basemap as well. Symbols for facilities were shaped distinctly different from each other and generally included an associated letter designation to ensure that there was no confusion caused by color blindness or lack of familiarity. This would be important to anyone already experiencing the duress of an evacuation before an impending disaster.

Tampa Hurricane Planning Basemap

The streets layer was prepared for use as a network dataset with the addition of attribute fields for length in miles, travel time in seconds, and whether the road is in the flood zone. Interstates received the "not flooded" attribute because they are elevated. This information will be utilized in the next module.

A map package was created for the map and zipped together into one folder with the metadata files, basemap image, and the process summary. A sample email detailing this information was created as if a co-worker would be completing the project. The email included the zipped folder, details of what the City of Tampa had requested, what had been accomplished to date, and what needed to be completed yet.

GIS4035 Module 2 - Aerial Photography Basics & Visual Interpretation of Aerial Photography

Map 1 - Degrees of Texture and Tone in an Aerial Image

Module 2 focused on the basics of aerial photography (vantage points, cameras, filtration, and films) along with how to use recognition elements to interpret aerial imagery. Recognition elements include location, tone and color, size, shape, texture, pattern, shadow, height/depth, and site, situation, and association. The first map depicted here focuses on the identification of different degrees of tone (from very light to very dark) and texture (from very fine to very coarse). Degree of tone indicates the degree of energy reflected by surfaces. Degree of texture is relative for any particular image. For this specific image, the texture ranges from very fine for water to very coarse for residential neighborhoods. For another image, these descriptions may vary significantly dependent on the nature of the subject of that particular image.

Map 2 - Features Identified by Visual Attributes

The second map focuses on utilizing other recognition elements to identify specific objects in the image. Shape and size were utilized to identify roads (long and linear), a pool (roman shape with rectangular diving board), and cars (rectangular shapes associated with large, open parking area). Shadows were used to identify a water tower (long, thin shadow with flared bottom and topped with bulbous shape), trees (slender trunks topped by clusters of fronds/branches), and a warning siren (horn-like shadow at top of tall, slender shadow which was distinctly different from the shadows of other poles in the image). Objects identified by patterns included parking spaces (herringbone pattern), utility poles (regularly spaced tall, thin objects with cross bars at the top), and houses (buildings of similar size, height, and spacing with a driveway to each structure). Some features were identified by their association with other features. This included the pier (a long, thin structure projecting from the shoreline out into the water with a shadow indicating height above the water) and a driveway (a short linear object which did not cast a shadow and leads to a house).

From personal and professional (surveying) use of aerial imagery, identification of objects is a rather comfortable practice. An area of growth for me was demonstrated in the third part of the lab which focused on comparing objects between a true color image and a false color infrared image. Along with learning more about the use of recognition elements in identifying objects in imagery, learning to interpret information from natural color and false color IR images is a skill I look forward to developing.