Thursday, November 16, 2017

GIS 4035 Introduction to ERDAS Imagine and Digital Data

Lab Number 5, Part A introduced students to using ERDAS Imagine. ERDAS Imagine is a remote sensing tool that has the ability to process raster files. It looks and feels very much like ArcMap, so it was nice to have that beginning familiarity. Calculations to determine electromagnetic imagery (EMR) behaviors were the first step of the lab. That was the first time I have seen 'complex' math in a few years, so it definitely threw me for a loop for moment! We then followed basic instructions to explore commonly used functions of ERDAS and learn small idiosyncrasies throughout the program, including an error. An image of forest in Washington state was used to then create a subset image (land use/cover are now classified). ArcMap was utilized to make final touches to our preprocessed image. I can see a great deal of use for ERDAS in a future career, so I look forward to working with it more!

Wednesday, October 11, 2017

GIS4035 Remote Sensing

Ground Truthing and Accuracy Assessment 
In this assignment, we used a previous Land Use, Land Cover Map to test the accuracy of the classifications chosen. In real life, ground truthing includes in-situ research (physically being there), but for this online program, we used Google Map's street view. The original image was hard to delineate certain government buildings (larger size) and apartments, hotels, etc. A high percentage of my classification "111" single- family units, was incorrect. I randomly selected 30 points. I realized how biased my "random" points were after I exported the map. First, I avoided the water, because I knew it would be hard to get a Google street view of such views. Second, I also chose to place the final points in spaces I realized were sparse. With all that being taken into account, the overall accuracy was about 30%. 

Tuesday, October 10, 2017

GIS 4035 Photo Interpretation and Remote Sensing

Land Use/Land Cover Classification Mapping 
Students were tasked with using a satellite image of Pascagoula, MS and digitizing polygons of different land use sections. After digitizing, we used the USGS Standard Land Use/Land Cover Classification System.
1.      I began with the top of the image and worked my way across (left to right) and then moved down and repeated the process. It was painstaking and honestly frustrating. I must have restarted my work four different times because I never clicked “save edits” each time I was in Editor.  I mostly used surrounding areas to help classify whatever parcel I was digitizing at the moment. With some knowledge of urban zoning patterns, I could assume that something like a school was not likely to be adjacent to what looked like an an industrial park. The colors (how green/healthy they appeared to be) of many of the open areas aided in my delineation of cropland or open field Some of the patterns, specifically for 41: Deciduous Forest and 61/61: Forested/NonForested Wetland, gave me clues at to the possible height of the trees/shrubs on said wetland. 

Tuesday, June 27, 2017

GIS 4043 Final Project

Intro to GIS (GIS 4043) tasked students with creating four different maps to help stakeholder's visualize a proposed transmission line's affects on the community and environment. This was a real proposal that extended through Sarasota and Manatee counties in South Florida. The map directly below is the basic basemap of the study area:
Next, you will see a map of environmentally sensitive lands and the the minimal imposition of the transmission line on said areas.
Finally, a map of homes, schools and daycares illustrates that the line does not impose on any schools or daycares and only homes within the buffer zone. 

The preferred corridor for the transmission line was deemed acceptable proposal. There are absolutely no schools or daycares affected, even within the 400ft buffer. Landowner’s parcels (aka people’s homes) that are affected are mostly within the 400 ft buffer. Compared to the total number of conservation lands within the study area, there is only a small amount affected. 


Sunday, April 2, 2017

Vector Analysis 2- GIS4043

This lab was the second half of student's work on Vector Analysis. There are countless impressive operations that are available for use in GIS analysis. Two of those operations this lab focused on were buffer and overlay. Students were introduced to ArcPy, as well. ArcPy is yet another important skill to possess in GIS expertise. Buffers are a simple concept: an area of certain proximity surrounding a specific map featrue. The map below shows buffers as "camp sites" and the specifications for said buffers are listed below, which was the first few exercises students performed. Next,  we created a script in ArcPy to complete multiple buffers at once- what a convenient tool! I am looking forward to utilizing ArcPy more in the future. The final steps of our map-making this week consisted of overlay analysis to combine information from two separate topics.We used the union tool to combine the water and roads buffers previously created, I chose to use the erase tool to exclude campsites from conservation areas, but I now think that symmetrical difference tool might have worked better. Visually, I do not like my map. I struggled to chose colors that worked well together and looked "outdoorsy," since this is a map of the DeSoto National Forest in Mississippi. Although I am not a fan of my own color choice, I tried to add a little bit of extra with the shadow effects.

Dot Density- GIS3015




This week's lab was Dot Density mapping. Dot mapping is widely used when raw data has been collected and the map is intended for illustrating the lack of uniformity in the data/phenomena. Each dot represents a certain amount and placed where the phenomena is likely to occur or has previously occurred. The class mapped 2000 census data for populations throughout south Florida. In addition to simply placing dots on the map (1 dot symbolizes 14,000 people) we used a mask feature to show patterns more effectively. The mask feature allowed us to specify that the dots were only placed where there is urban land. Many of the dots were initially on water and places that it's highly unlikely there was truly anyone living there! To give a reference of location we were instructed to chose 3 major cities and map those, as well. I think that in addition to reference, the end user could use these major cities and make inferences based on dot density and the relationship between population and major cities.
The masking feature takes up an enormous amount of processing  and we were warned to be patient. I noticed in the class discussion board that a lot of people were having trouble with ArcMap crashing, so I decided to build my map on a UWF campus computer instead of using the server at home. This helped a lot and I only had ArcMap crash once white it was masking and drawing all the data for surface water. I chose the blue background color because it shows the edges of Florida really well. The varying greens for the basemap and surface water are especially aesthetically pleasing.

Monday, March 20, 2017

Isarithmic Mapping- GIS3015

I found this lab especially enjoyable because I was given opportunity to map something I really enjoy and have a deep interest in. I consider it relatively common knowledge to anyone in the science field that precipitation levels change as topography changes. The state of Washington is known for it's rain levels, as well as the geography. This map displays exactly that phenomena (and the "precipitation" color ramp is beautiful when mapping this continuous data!) While mapping smooth, continuous phenonema is use in terms of meteorology, interpolation of such data is also crucial. We used PRISM  (Parameter-elevation Relationships on Independent Slopes Model) for our interpolation method. The map description explains this  method simply above. Using the "hillshade"effect in this map helped show the relief better on the display. Students made the first map with continuous tones and then evolved their second and third maps to hypsometric tints. Hyspometric tints were best for this map because it separates the contour values with the use of "stepped"color shading and contour values are a major component of this map's story. I look forward to mapping more data the shows relationships between geography and climate phenomena.