Module 5: Unsupervised and Supervised Classification

 Module 5: Unsupervised and Supervised Classification

This weeks lab we learned how to use two different ways to look at Land Use\Land Cover (LULC) by unsupervised and supervised classification. Image classification is the process of giving discrete and unique values to all pixels in a raster image.

First we learned how to do unsupervised classification with ERDAS. The important part of this portion was trying to understand that as the GIS analyst you need make sure the pixels actually make sense. When creating the pixels we were told to do 50 different classes with a 95% reliability of the pixels. We opened the attribute table to change the different pixels to certain colors. While doing this you could see how some of the pixels were showing up in places that did not make sense. An example was when doing the grass class there were some pixels where it showed up on the roof of a building. Also, the grass was considered a shadow class. Even though unsupervised classification is faster it does not always show up in the correct pixels.

Second we learned how to do supervised classification with ERDAS. In this portion of the lab the supervised classification gives the GIS analyst a little more control on what the different colors are going to be. There were two tool methods for creating spectral signature. One of the tool methods you can use is using the inquire tool and put in the coordinates you need for the location in UTM. Then under the Drawing tab go to the Geometry tab and click the polygon button. You then will draw your own boundary. After that you go to the Signature editor and click on Create New Signature(s) from AOI button to make your new class. The second tool method is using the inquire (legacy) tool and once again you put in the coordinates you need for the location in UTM. Then under the Drawing tab go to the Geometry tab and click on the Grow tab and click on Growing Properties. Once in there make sure to click on the At Inquire button to get you to the location you put in the inquire (legacy) box. When looking at the box you can look at the table and see if there are any other colors in the box. You can change the Spectral Euclidean Distance and Neighborhood boxes. Also, you can move the box to smaller if you just want one color. The same as the other method you go to the Signature editor and click on Create New Signature(s) from AOI button to make your new class.

The image below is a supervised classification of Georgetown, Maryland and the current LULC. While doing this image I did the steps for the supervised classification, but there were some hiccups. The major hiccup I had was making sure the signature file I created was big enough that the color was correct on the image. Another hiccup was even though I had everything that was big enough there were two classes that kept getting mixed up in the image. I tried to create the signature file a couple of times, but I was not able to get it correctly. The example for this was how the roads and Urban\residential area kept getting mixed up in the image.

Module 4: Spatial Enhancements, Multispectral Data, and Band Indices

Module 4: Spatial Enhancements, Multispectral Data, and Band Indices

In this weeks lab we learned even more in ERDAS Imagine and how to interpret our data we are given from different websites. First we learned how to take data from Glovis and EarthExplorer on USGS. When looking at websites it is important to know where it is coming from and that it is a credible source. Also, many of the data is already in the source we need it for ERDAS but it is important to make sure they are .img so they can be looked at in ERDAS. Lastly, we need to know what processing they used to make the images and if we need to do anything else such as taking the haze out of the photo. 

The second thing we learned was using spatial enhancements in both ERDAS Imagine and ArcGIS Pro. In ERDAS and ArcGIS Pro you are able to have the image go through different filters, such as low filter and high filter. Both of the filters do important things to the image depending on what you need it to look like in the final version. The low pass filter allows low frequency data, or data that does not change much from pixel to neighboring pixel, to pass through, removing the high frequency data, or data that changes rapidly from pixel to neighboring pixel. The visible result is the image appears blurred or smoothed. High pass filter allows high frequency data to pass through, or data that changes rapidly from pixel to neighboring pixel, suppressing low frequency data, or data that does not change much from pixel to neighboring pixel. High pass filtering can be useful for finding edges, enhancing lines and edges, or sharpening an image. Being able to do these can help with what it is in your image and show it easily in your final version. Lastly, you can sharpen an image to help get the boundaries of rivers and buildings more precise.

Next we learned how to look at the Histogram data to understand what the image statistical data to help us interpret the data. This is important because it helps the GIS analyst represent the data in a manner that everyone can understand. Also, we learned how to look at the spectral characteristics of the different band combinations to show either True Color, False Color IR, and False Natural Color. Being able to know the different band combinations helps because depending on what you are trying to represent you need to change the image. Lastly we learned calculating differences between different spectral bands, an index can be created to further enhance the appearance of certain features. We created a Normalized Differential Vegetation Index (NDVI) to help distinguish clearcut areas. Knowing how to do this can help enhance features within the image.

Lastly, we took all the things we learned from above to make three different images. This was to help us understand what we could do to data and present it to the public. First image is a False Natural Color to help show the water feature but not be too bright like False IR. The second image is a False Color Infrared to distinguish the mountain top compared to the rest of the landscape. The last image is a True Color to show the different areas of the water feature and how they change colors.

Module 3: Intro to ERDAS Imagine and Digital Data

 Module 3: Intro to ERDAS Imagine and Digital Data

In this weeks lab we learned about how to calculate the Electromagnetic Radiation (EMR) and understanding how EMR transmits/travels through the landscape. Also, in this weeks lab we learned how to use ERDAS Imagine and why one would use it. Lastly, in this weeks lab we learned how different passive and active sensors can present their data in ERDAS Imagine.

It is important to note that EMR energy comes from the sun and is a continuous spectrum of energy. Also, EMR has different wavelengths and frequencies that range from a longer wavelength equals a lower frequency to a shorter wavelength equals a higher frequency. Nowadays the equations to know the frequency and wavelengths are done automatically through the remote sensors. We did learn how to do basic equations called Maxwell’s wave theory: C = λν and Planck Relation: Q = hν. The Maxwell's wave theory is the relationship between the wavelength and frequency of the EMR. The Planck Relations is the amount of energy is inversely related to the wavelength of the light. This means that the shorter the wavelength, the greater the energy of each photon of the light. Being able to understand how the remote sensors interpret the data can help with the GIS analyst knowing what EMR energy is being collected.

I have been using remote sensing machines for awhile but I have always used ArcGIS Pro after the data was processed. This weeks lab showed another way to look at data and that is ERDAS Imagine. This was supposed to help us understand how to look at large-data sets from remote sensors. First we learned how to put in Raster data into the data layer. The raster layers can be raw/multiple layer continuous data, single layer panchromatic continuous data, or categorical/single layer thematic data. This is important because the raster data is meant for many different sensors. Also, when presenting the Raster Options and make sure to use the correct Display As tab. They are True Color, Pseudo Color, Grayscale, and Relief. In the lab we used Pseudo Color because it displays thematic images by associated each class value a color or single layer. Another tab we need to click on is Multiple and click on the Multiple Independent Files because it brings in files, individually, as separate layers that can be arranged or deleted separately from the other files in the Viewer. Lastly, click OK button and it will present your data on the page. Many of the other features are the same as ArcGIS Pro when zooming in and out as well as clicking on the different tabs to get information you may need to understand the data.

While being in ERDAS Imagine you can create an image that can be put into ArcGIS Pro. The way this is done is by opening the data imagine you want onto the layer. Since much of this data has a lot of information you will want to only take a portion of the image out. This is done by clicking on the Home tab and hit the button next to Inquire tab until you see Inquire Box. When it opens up you can change the size of the box and have that as the boundary. In the Raster tab, under the Geometry group, you will expand Subset and Chip menu, and select Create Subset Image. Then you will press the From Inquire Box button to set the appropriate area. Lastly, under the Output File navigate to your Output folder and name the file. Also, in this image we needed to get the Area of the different classification categories. This was done by right clicking on the layer I wanted and clicking Display Attribute Table and it will show up on the bottom of the screen. Then you will click on the Table tab and hit the Add Area tab. It will then show up and you can pick different ways of presenting the data but we chose hectares. The image below is how I presented the data in ArcGIS Pro.

Lastly, in this weeks lab was looking at the layer information, spatial resolution, and radiometric resolution. To see the layer information make sure to click on the layer you want and click on the Metadata tab. It will present a lot of data starting with File Info: which shows the basic information about the imagine including file type and file path. Next it is Layer Info: which shows the width and height in pixels, Type (if it continuous or thematic), and Data Type (how the pixel data is stored). Also, this is where you find the radiometric resolution that is usually expressed as a bit number (Unsigned 8-bit). Next tab is Statistics Info: which shows the min and max of the pixel darkness (min) or brightness (max). Also, shows the mean, median, and mode which the values are the measures of the average brightness of all pixels in the layer. Whereas the standard deviation  represents how close most of the pixels fall to the mean value. Next tab shows Map Info (Pixel Center): which shows provides the spatial coordinates of the corners of the image in the projection and the height and width of each pixel in the image. The last tab is Projection Info: tells you how the data is being projected and if it is not geo-rectified it will be blank in this portion. Knowing how to read the metadata can help understand how the data was collected and if it was projected correctly.

Module 2: Land Use\Land Cover

 Module 2: Land Use\Land Cover

In this weeks assignment it was about learning how to interpret a map with using Land Use\Land Cover (LULC) classification criteria. 

LULC is important to learn because humans have been changing the landscape throughout time. Being able to look at an image and know how the area is being occupied throughout history for planning purposes. The image I was looking at was in Pascagoula, Mississippi. This area is constantly changing either by new people coming into the area for jobs or wetlands slowly going away through time. Many cities are planning different things such as making roads bigger. If the area did not have a lot residents the traffic would not be bad and not necessary for the roads to change. As you can see in the image below there are a lot of residents and it is likely the roads will need to change to help with the flow of traffic.

Another portion of LULC is doing the ground truth of the area. In order to make sure what the map is representing you can look at different areas on the map. It would take too much time to do the whole area. Being able to do sporadic testing across the whole map is important because it makes sure one area is not the main focus. Also, it is harder to get to other areas such as the wetlands on the left side of the image. This could be "ground truthed" by looking at the National Wetland Inventory (NWI) on there website. The only other way would be through getting on a boat to make sure it is correct.

LULC does give the GIS analyst time to understand the landscape and make sure they take their time to interpret the map. Also, it is important to note that the image is a snapshot of the area and can constantly be changing. Even though I had 80 percent accuracy, which is good, the image was older and some of my areas changed because of residential expansion and not because the area was incorrect.

Module 1: Visual Interpretation

 Module 1: Visual Interpretation

In this weeks lab we learned how to understand what remote sensing does to help us understand our landscape. Also, remote sensing can be passive (i.e. photos) or active (i.e. LiDAR). This is important to know because it helps us know how the data is being represented and how we can read the data. Lastly, we learned that the data can be automatic but it is up to the GIS analyst to understand the data and show how it is represented on a map.

In the lab module we had three objectives we needed to learn. In the first objective we needed to show the tone and texture of a USGS picture. We needed to make the polygons a feature class in the geodatabase. Since the map is not projected the features were not automatically projected. You could choose the "current map" and it is what is currently on the map. The tone of the map is trying to represent how dark or light different portions of the map are to the GIS analyst. The texture of the map is how close or smooth of different areas on the map. The image below shows how I as the GIS analyst saw the different portions of the USGS image.

In the second objective was to take another USGS image to understand how to look at an image and see how one could understand the image by Shape/Size, Shadow, Pattern and Association. First the shape and size features I choose was a building, house, and road. I choose each of these because it was very obvious of the size and shape of the feature on the image. Second the shadow features I choose was a water tower, light poles, and tree. I choose the water tower because it was the easiest to see and the shadow. The light pole was a little harder to see but when looking at the shadow you knew it was the light pole. Lastly, choose the tree because it was obvious to know what it was by the shadow. Third the pattern feature I choose was a parking lot, beach, and neighborhood. The parking lot was the easiest pattern to see because of the parking lines on the asphalt. The beach was a pattern of sand all together creates the beach. The final pattern was a group of houses would create a neighborhood. I thought a pattern could also be multiple houses and not just lines indicating a parking lot. Lastly, the association features I choose was a  hotel and pier. I could tell it was a hotel by the association of a parking lot, multiple balconies, and a pool. All of those identifying features helped me know it was a hotel. The second one was a Pier and that was the location of the feature, being in the water, and the wood planks on the pier. Looking at those identifying features I was able to know it was a pier for Pensacola Beach. The image below shows how I understood the image based off the different features.

The final objective was looking at two different images to see the true color and see how our eyes see different colors. The second part was looking at a False color infrared image to see how those the different colors change. Knowing these changes is important because it shows how remote sensing represents colors from our eyes to an image. An example of this is when you look at the trees in the in the true color image it is green, but when you look at the false color infrared image it shows up as red. The reason for this is because green is our "visible" color and red is the color what the trees give off to the remote sensing tool.

Final Project

 Final Project

In the final project for this class was taking everything we have learned the past seven weeks to answer different questions about a transmission line within Manatee and Sarasota County, Florida. The four objectives of the project were: Identify environmentally sensitive lands, Identify home/parcel locations, Identify school locations, and Identify the length of the transmission line and cost.

The first concern of the transmission line is how it is going to disturb environmentally sensitive lands, such as National Wetlands and Conservation Lands. The data from the conservation lands was able to calculate how much was being disturbed by the transmission line. The data from the National Wetlands Inventory (NWI) was able to help calculate the impact of the transmission line on the wetlands and uplands. This is important because wetlands are protected, and the energy company need to know how much they would be disturbing. The uplands are not protected but the energy company needs to make sure they are not disturbing too many acres. The way I was able to get to the results was by using the overlay tools in the geoprocessing pane to figure out which portions were intersecting with the transmission line. Also, to figure out how many acres were being disturbed I needed to add an “Area” field in the attribute table and then calculate geometry in acres. The results for the conservation lands are a total of 10,394 acres within the study area and 163 acres within the transmission line corridor. The results for NWI wetlands and uplands within the study area were a total of 6,566 acres. The uplands were 5,652 acres and the wetlands were 914 acres. We were then told to figure out the percentage of the uplands and wetlands which were 14% wetlands and 86% uplands.

The second concern were homes/parcels and how they would be impacted by the transmission line. Before adding in the home feature class I had to do the 400-ft buffer surrounding the corridor. This was used to know the “proximity” of houses and parcels within the area. The data for the homes were from the jpg aerials of both Manatee and Sarasota Counties surrounding the transmission line. I had to make my own feature class for the homes to place them on the landscape myself. I made the judgement call on the houses. Also, to get the parcels within the transmission line and the 400-ft buffer I used the overlay tool that intersected the corridor and the 400-ft buffer separately to get the total count. The results for the homes were a total of 74 homes with 23 homes within the transmission line and 51 homes within the 400-ft buffer. The results for the parcels are a total of 421 parcels in both counties with Sarasota County had 90 parcels and Manatee County had 334 parcels. Then within each county I went further to find what was within the corridor and within the 400-ft buffer. Sarasota County had 32 parcels within the corridor and 55 parcels within the buffer. Manatee County had 132 parcels within corridor and 202 parcels within buffer.

The third objective was knowing which schools would be impacted by the transmission line. The school location data from the Florida Department of Education website. After cleaning up the data, I geocoded the data for both Manatee and Sarasota Counties. The reason to do this was so you can get the exact location of the schools with a bigger data set quicker than trying to find them individually. Also, if the data does not show up in the county or is a P.O. Box you can fix it easily within GIS. The results of the data were that there were zero schools within the corridor and the 400-ft buffer. There was a total of 5 schools with 4 being Manatee County and 1 being Sarasota County.

The last objective was the transmission line length and cost. The transmission line length can be measured with the centerline. The way I did this was done by using the Polygon to Centerline tool. There was a problem with doing it originally with a shapefile that was projected in the wrong projection. I had to make sure it was projected in the Albers Conical Equal Area [Florida Geographic Data Library]. After the centerline was made, I had to add another field for the “Length” and calculated geometry in miles to get the exact distance. The total length is 25.8 miles. Also, I wanted to learn how to do cost. Based off the PowerPoint that was given to us I decided to choose Double circuit, Strung on both sides, Lattice Tower at 230kV will be $1,600,000. I choose this one because it seems that this transmission line is necessary for two counties to have energy for their area. The other information was on if it was hilly, flat, or mountainous. Also, if the area is urban population or suburban population. Lastly, you need to know the length to add in the extra 1.2 or 1.5. For me the formula is 1,600,000 x 25.8 x 1.2 x 1.5 = $74,304,000. I am not completely sure if I did this correctly because the formula said less than 10 miles it is 1.5 and 10-20 miles it is 1.2 but this one is 25.8 miles. So that was my best educated guess.

The impacts of the transmission line on the communities, landowners, parcels, and environmentally sensitive lands. The communities and landowners were a total of 74 homes (Within Transmission Line: 23 homes and Within 400-ft Buffer: 51 homes). There was a total of 421 parcels (87 parcels in Sarasota County and 334 parcels in Manatee County). Environmentally Sensitive Lands: There was a total of 10,394 acres of conservation lands within the study area and a total of 163 acres within the transmission line. There was a total of NWI 6,566 acres within the corridor (5,652 acres were uplands and 914 acres were wetlands). The location of this transmission line was reasonable for the schools and homes, but more impact on the parcels and wetlands. Wetlands need to be minimally impacted because we need them for our environment, and they are protected. Lastly, using GIS was very important because we are able to answer all our questions in one place and make an easy representation of the data.

I have included the link here of the transcript for the final project: 

https://docs.google.com/document/d/1--DkfQyJsPPIdNK1MwzeTZfeZLXhaFnl/edit?usp=sharing&ouid=100825180926986160891&rtpof=true&sd=true

https://drive.google.com/file/d/1-BUO0jz895XKbMo-6lEj6QKPqUnnfjli/view?usp=drive_link

Module 6: Georeferencing, Editing, & 3D

 Module 6: Georeferencing, Editing, and 3D

In this weeks lab assignment we were taught how to georeference jpg photo, editing those photos, and make a 3D scene from LiDAR data. 

Being in archaeology I have taken many jpg photos and georeferenced it to current landscape. In this module I learned about the RMS errors and the different transformations you can do when georeferencing photos. When georeferencing photos will always have a 0.0 RMS error because there is not enough information to know if it is on the landscape correctly. Also, when trying to do control points for the image it is important that the points are in different parts of the image so it can get the length and width of the whole image. Lastly, when georeferencing the photos they either need to be in a jpg or a tiff for the images to show up in ArcGIS Pro.

The second part of the module we learned how to edit features to add more data on to the map. When editing you want to make sure to hit the Create button under the Edit tab. Then you will click on the feature you want to create and can draw any image you want. After drawing the image you can edit the feature and put the data in the attribute table. Another feature we edited was the UWF Roads and used the snapping tool because it helped connect between two roads without going over the line. Knowing how to edit the features is important because you can add more data to your images without having to make a new feature class. Lastly, we needed to make a multi-ring buffer around the Eagle Nest that is on UWF campus. We needed to make a 330 foot and 660 foot buffer because conservationist wanted to make sure the eagles nest does not get bothered. Also, we were able to add an image on the GIS data point that in ArcGIS Pro. The image below represents three different georeferenced jpg photos and the edited features.

The third portion of this module was making a 3D Scene from LiDAR data. This LiDAR data was from USGS National Map Viewer. You need to go to the Insert tab and on the far right click New Map Arrow and click on the New Local Scene. It will open a new map that says Scene and will be blank. Make sure under the Contents Pane to uncheck the World Elevation3D/Terrain3D. Next you need to add UWF_lidar.lasd layer. This can be done by dragging it in from the Catalog Pane or by the add data button on the Map tab. Since it is LiDAR data it might take a second before loading on the page. It is likely that the data will show up as a blank red box. Make sure to click on the data in the contents pane and then the LAS Dataset Layer tab will show up after the share tab. Then under the Filters section on the right you will click LAS Points arrow and click on Ground. Afterwards you will click on the Analysis Tab and click the Tools button. That is when the Geoprocessing pane will open on the right.  In the search bar type in LAS Dataset to Raster to where you will input the data needed to get the LiDAR points to show up on the screen. Input LAS Dataset: UWF_Lidar.lasd and Output Raster: UWF_DEM. Then click Run at the bottom. You can change the color to what you want but to use common look of LiDAR data you want to right click on the data and change the symbology to “Surface” This is when the data will show up for you to see the information better.  Make sure to add the UWF DEM data on the scene map. The way to do this is by going to the Map tab and under the Layer group click on the Add Data arrow and click Elevation Source Layer. Then you need to go to where you saved the data. The UWF DEM view shows the LiDAR data but as a picture instead of specific dots on the page. It makes it easier to have a picture on top and still show the changes in the landscape. For this map we did add the other data such as buildings, UWF roads, uwf_n, and uwf_s1 to be on top of the UWF DEM map.  In order to get the buildings 3D we needed to click on the Buildings feature in the contents pane. Once that is clicked the Feature Layer tab will show up after the Share tab. On the far right in the Extrusion group click on the Type and select Max Height (third from the top). Lastly, to the right click on Field and select height as the option. Then the features will become 3D on the map.  Lastly, it is important to note that you can change your Elevations Surfaces in the Contents tab to be either UWF_DEM or World Elevation3D/Terrain3D. I chose the UWF_DEM one because it showed the elevation change better than the World Elevation3D/Terrain3D. The map below is showing all of the data above.