Below is an image of Germantown, Maryland that features recoded LULC information.  I used tools and skills taught to us throughout the class, but primarily given to us in module 5 of GIS4035.  We generated seed classes using provided coordinates and region growing tools in ERDAS. We then used the maximum likelihood algorithm to extrapolate our seed classes to the entire image.  We then recoded our classifications into the combined classes you see below.  I actually combined Urban and Roads classes, although it was not part of the directive, to make the image more visually accurate.  I did this because there was some spectral confusion between those two classes, likely due to my error in generating seed classes.

GIS4035 Lab 4

The following map shows three images depicting three separate geographic features using the spatial analysis tools available to us in ERDAS Imagine, and using the skills taught to us in lab 4 and throughout the semester in GIS4035.

Here is a brief description of each of the images and how I found each of these features:

Water Body: The feature creating this data spike in layer 4 is a water body, and judging from its geographic location it appears to be the Puget Sound.  I found this feature by opening the metadata for the given image and examining the histogram for layer_4.  I then looked for obviously dark areas in the true color image and used the inquire tool to find the specific DN information for the suspected areas in layer_4.  I dragged the cross-hairs over multiple locations within the large water body and found that the DN values all fell within 12 and 18 as described in the lab directions.  I also dragged the cross-hairs to dark areas outside this water body, but couldn’t find any areas outside that fell within the range (aside from other small water bodies).  I changed the band combinations to False Color Infrared because water stands out so well in this type of band usage.  The water body should be easily discernible in my map.

Snow-Capped Peaks: The feature matching these data spikes were snow-capped mountains.  I found this feature by using the same method as with the first feature (opening the metadata for the given image and examining the histogram for layers 1-6, and looked instead for obviously bright areas in the true color image).  When using the inquire tool I paid attention to the differences between layers 5 and 6, and layers 1-4 to make sure they were corresponding with their respective spikes when in this obviously bright area in the true color image.  I then set green band to layer_3, the red band to layer_5, and the blue band to layer_6. I selected these because I wanted one layer to be in the bright range and the other two to be in the dark range of these data spikes.  This layer combination turned out to be great for accentuating the desired feature as bright green against a deep red/purple background. 

Contaminated Runoff: I happened to notice that some areas of water stood out as a bright green when I changed the color bands to red/layer_5, green/Layer_3, and blue/layer_6.  This seemed fishy, so I went ahead and checked out those areas first.  My suspicions were confirmed when I moved the Inquire cursor over those areas.  Layers 1-3 became much brighter (mid-80’s to mid-90’s), layer 4 became somewhat brighter (mid-20’s), and layers 5 and 6 remained in the 10-12 range.  The area I selected appears to be caused from drainage on the south end of the Sound.  I chose to represent this area using the following layers: red/layer_2, green/layer_3, blue/layer_6.  I played around with different band combinations a bit, but this combination made the area of interest a bright yellow/green against a dark blue background.  Having the red being set at layer_2 also made the surrounding water darker and allowed the yellow/green to contrast better with the non-contaminated water.

This map represents a subset image of Thematic Mapper data provided in a lab exercise that allowed us to familiarize ourselves with ERDAS Imagine.  We selected this area and used ERDAS Imagine to calculate the area of each vegetation type in hectares.  We then transferred that image and it's associated data over to ArcGIS Pro and created this layout.  I listed the vegetation types in the legend from low to high in terms of total hectares.  I rounded up to the nearest hectare to keep it looking neat.

This map is a representation of LULC for Pascagoula, Mississippi.  We were given an aerial image and used that image to select and differentiate land use and land cover using the USGS Land-Use/Land-Cover Classification System.  We defined this map to Level 2 classifications, so it is a rather broad generalization, but also a very fun exercise to practice with.  Enjoy!

The image below shows five areas identified by texture and 5 areas identified by tone.  The photo is from the USGS and since the characteristics are somewhat arbitrary, I used the photo itself to provide a range of possible characteristics from which to base my selections.

The image below shows point descriptions based on pattern, shape or size, shadow, and association.  Some of these object can be identified using more than one method and some are best identified by using multiple methods together. 

Below is a link to my Story Map created for the experience in GIS 4043.  Enjoy!

https://pns.maps.arcgis.com/apps/Cascade/index.html?appid=9c0d1ec7e7684cfabb36b91137f7d848

Below is a link to a webmap that I published this week in order to complete the geocoding lab assignment for Module 6 in GIS4043.  The lab allowed me to explore raw data manipulation and proper formatting in Excel that, in turn, allowed me to add it to an ArcGIS Pro project.  I gained some experience with Excel functions I had not used before and I also learned some of the basics of geocoding within ArcGIS.  The real world applications are obvious and I felt like this was a very good lab to bring together some of the basic concepts and processes we had been working on throughout the semester.

Please use the following link to view my webmap:
https://www.arcgis.com/home/webmap/viewer.html?webmap=14d5f6772d424a81966c0f39a88c1675

The map below was created using selection and overlay tools to create a layer that expressed suitable campsite locations in the De Soto National Forest in Perry County, Mississippi.  The criteria used to determine these areas were: within 500 meters of a river or within 150 meters of a lake, and within 300 meters of a road.  Once these parameters were set and associated in a new layer, I removed any of these areas that overlapped with designated conservation areas.  The final layer was then split into 4 groups based on area in hectares.  I made a progressive color scale base on 200 hectare increments with larger areas being darker as those were preferred. The final map shows these areas along with the road and water layers for reference.  I made my scale bar in kilometers because the areas are in hectares and the map is based in meters.  The inset map shows an expanded view of Mississippi for reference.

I created the following map to show how three different projections impact land area calculations in square miles.  This information can be used to illustrate the importance of using consistent map projections when analyzing multiple data sets.  This also illustrates the importance understanding your projections and using the correct projection for the type of information you want to convey.  Inconsistencies in use of projections or a lack of understanding in how projections work can lead to errors in data analysis and promote erroneous ideas.  The State Plane projection appears to be a nice balance between the Albers and UTM projections in terms of consistency when analyzing data for the state of Florida on the county level.  Topography along county lines and coastline areas may influence how these projections calculate area and effect the final result.

Prior to making this map, I collected data on safety signs in Fort Davis, Texas.  The exercise of collecting data helped my to understand how real world conditions can impact data collection and allowed me to think through potential pitfalls that I would not have if I had not done this exercise.  Also, the experience with using different file types and interactive applications was very beneficial for when I might apply these principals in the real world.

The map I created of UWF Main Campus uses Ariel font for all titles and descriptions contained on the map.  I followed the lab guidelines for sizing and location and used the insert rectangle method for all text boxes.

I placed the scale bar approximately centered below Escambria County to make it easier to reference scale for the area of interest.  I placed the Logo beneath the inset map and made it approximately the same size because I found it to be the most aesthetically pleasing.  I placed the north arrow in the top left because it added a sense of balance to the map.

I placed my name, sources and date in small font at the bottom left of the page so that it didn’t detract from the main areas of the map.  I manually entered the information using a text box.

The legend contains the important reference layers we worked with, including: UWF campus location, Interstate Highways, Rivers, Cities, Escambria County and the adjoining Florida Counties layer. 

I chose a light green color for Escambria County because I thought it offset the gray color of the adjoining Florida Counties layer nicely.  I used the same colors in the inset as I did in the main map to make the relationship easily recognizable. I chose a large yellow star symbol for the campus location and placed the city labels to the lower right so that they did not overlap any other important symbols or features.

This week's lab experience was great for me to go through the very basics of map making.  Just the experience of activating layers and using multiple layers with unique data sets was very helpful. I think this will make future maps easier and help me get into a workflow that I am more comfortable with.  

This is the map created for the Week 1 Lab assignment.  It features cities of the world represented by black dots and a green color scale representation of 2007 world population using 7 natural breaks. Population increases from light to dark.

Hello and welcome to my GIS blog!  I do not have any GIS content yet, but look forward to providing project links and discussion as I progress through my GIS education and career.