Spectral Classification

For this week's lab, we learned how to complete an unsupervised and supervised classification on an aerial image in ERDAS Imagine. First, we practiced classifying an unsupervised image of UWF campus by accurately classifying different spatial and spectral resolutions. To finalize this, we reclassified and recoded images from the spectral signatures created in an AOI layer. 

After learning the steps needed to create a supervised classification map, I used the inquire tool to enter in the coordinates for all the features to begin creating spectral signatures (except for the roads and water-I created them without the inquire tool). When I completed creating the spectral signatures, I evaluated them using the Histogram and mean plots. The bands with the greatest distance from each other determined the color band that was used. For this map, I used R4, 5G, 3B. The output distance file did show some bright pixels that let me know the classification was not correct. I had to go back to my spectral signatures and adjust the Fallow, Water, and Road area. I could never get the Road and Urban area perfect but I finally got an image that made sense.

Spatial Enhancements & Multispectral Bands

For this week's lab, we learned how to apply spatial enhancements, perform a multispectral analysis, and create band indices.

The spike on the histogram between pixels 12 and 18 is very tall so that means the feature is very large. I viewed in multiple color combinations and greyscale and noted features I was confident with identifying such as water, mountains, town. When I moved the breakpoint on the histogram between 12 and 18 pixels, the rivers and lakes remained dark so I was able to conclude that the feature was water. For the bands, I used the TM False Natural Color 5,4,3 Landstat 4 TM-6 Bands because this combination band makes water delineation stand out.

I viewed this image in greyscale and different multispectral bands and it was white in Layers 1-4 and dark in Layers 5-6. When I viewed the Histogram, there was a small spike on the right which means it is very bright.  I was able to identify the feature as snow. I used the True Color combination 3,2,1 with the Landstat 4 TM-6 bands because the white snow stood out in natural color. 

Since I knew this feature was going to be water as well, I just had to deduce what type of water. Layers 1-3 became brighter in certain areas and somewhat brighter in certain areas in Layer 4. I was able to quickly identify the feature as being shallow water because the shorelines were brighter and then what appeared to be a landlocked water basin was very bright as well.  I chose the TM False Natural Color IR 4,3,2 combination with Landstat 4 TM-6 bands.

GIS Event Day

For my GIS Day Event, I decided to do something personal and informal. This is partly due to the fact that I have a ton on my plate right now and then partly because I am a sensitive creature. I decided to invite my family to be guests for my event and shared a story map I made earlier this year about my experiences in 2018. It was an assignment where I created something really personal for myself and had not yet shown anyone (that I know personally). I wanted to share with my family that one of the reasons why I love GIS so much is how versatile it is for everything. It geographically and spatially relates ANY kind of data in a manner that drives industries. But it can also be used as a creative expression! I went to my mother’s house and showed her. I had hoped for a larger audience but Jim was hunting and my sister ended up not being able to make it. That’s ok because my Mom made me talk about GIS LOL. She works as a freight broker and said that she is glad I understand how to use GIS because it is beneficial to her. Like, when she has an oversized load and needs me to look up low clearance maps from TxDOT on arcgis, so she can route her driver properly. We had an emotional moment after she viewed my story map. She appreciated how it created a timeline of my life. Here is the link for my story map: https://arcg.is/1bXSrT

ERDAS Imagine

 For Module 4, we were introduced to ERDAS Imagine and how to use basic tools and view data. This lab really jump started our work with remote sensing. We learned about EMR, electromagnetic radiation, and how to calculate its wavelength and frequency based on Maxwell's Wave Theory. The size of the wavelength is related to the amount of energy and the frequency of that energy. The amount of energy is inversely related to the wavelength of the light: the shorter the wavelength, the greater the energy of each photon of the light. Maxwell's Wave Theory, which is the speed of light = (wavelength in meters)*(frequency in Hz), and Planck's Relation, energy of photon/quantam in Joules = (Planck's constant- 6.626*10^-34 J*s)*(frequency in Hz), calculate and derive variables that explain EMR. This is important to understand when studying remote sensing because it helps you identify features in the photography. In ERDAS, we learned how to view raster layers and understand the data being displayed. Raster layers can be raw/multiple layer continuous data, single layer panchromatic continuous data, or categorical/single layer thematic data. Continuous data is quantitative data that has related, continuous data. Thematic data is qualitative data that is categorical. We compared AVHRR images to TM (thematic mapping) images to determine that the former has a coarser spatial resolution than the latter. On the TM image, many features can be identified, so we changed the band combinations for multispectral data to enhance vegetation differentiation, snow, water delineation, and bare ground/urban areas. We also learned that to switch between the TM False Natural Color and TM False Color IR, the former makes the bare ground more distinguishable. These are important tools to use when identifying features. For the map delivarable this week, we used ERDAS Imagine to create a subset image to use in ArcPro to make a map.

Land Use/Land Cover Classification and Ground Truthing

For Module 2, we utilized the identifying features we learned last week, such as shape/size, color, shadow, pattern, and association, to help locate features in a natural color aerial image and create a land cover/land use map in Pascagoula, MS. Land cover is the biophysical description of the Earth's surface. Land use is how humans interact with the landscape and therefore change it. I used Level I and Level II classification scheme to create codes for different features I found. There are 9 Level I classes and I used 6 of them: Water, Urban, Agricultural, Wetland, Barren Land, and Forest Land. The trickiest part of this lab was getting comfortable creating polygons and editing the vertices in a way to keep overlap at a minimal.For the ground truthing calculation, I created 30 random sample points by distributing them as evenly as possible across the map. Then, I utilized Google Maps street view to check the accuracy of each point to polygon. In the shapefile attribute table, I used Yes or No to confirm the accuracy.