Week 6: Final project, An Unsupervised Classification of Lake Tahoe Basin Land Use/Land Cover

Hello all, this is my last post for a while since I won't be taking another GIS class until the fall next year. 
My task:

The Lake Tahoe basin has been subject to many fires over the past few years and the Forestry Department is interested in seeing how much of the south western portion of the basin has been affected in the past 20 years. However, the Forestry Department realized that they do not have LULC data for 1999. I have been tasked with performing a LULC analysis of the Lake Tahoe region in order to assist land managers with identifying the amount and pattern of forest within the California portion of the Lake Tahoe basin. I have been provided Landsat 7 data from 1999 in order to complete this analysis.

Imagery Utilized:

I used the provided landsat_img_nad27.img which contains an 8-bit Landsat 7 ETM image with 6 bands of the Lake Tahoe Basin and was taken 04-15-1999. Meaning that this image has very radiometric and spectral resolution. The image’s projected coordinate system is NAD 1927 UTM Zone 10N with a projection in Transverse Mercator. The image’s geographic coordinate system is GCS North American 1927 with a spheroid of Clarke 1866. The image was sourced through UWF’s GIS Drive however it could also be sourced through the USGS Global Visualization Viewer (GloVis).

Project Outcome:

I created one map that illustrated everything I needed to at once. With a little knowledge of mountainous landscapes, I created 8 categories for the image: bare land, coniferous forest, grass, mixed forest, scrub brush, space forest, snow and water. Everything is pretty much self-explanatory but I personally consider mixed forest a mix of trees, scrubs, grass and sometimes snow or sparse forest.

The area of my study area is 62487.78 Hectares. And the percentage of land cover is as follows:

•                    Bare Land: 8717.7 hectares, 13.9%

•                    Coniferous forest: 11444 hectares, 18.3%

•                    Grass: 1455.5 hectares, 2.3%

•                    Mixed Forest: 8091.3 hectares, 12.9%

•                    Scrub Brush: 6281.3 hectares, 10.1%

•                    Sparse Forest: 11025.7 hectares, 17.6%

•                    Snow: 7029.21 hectares, 11.4%

•                    Water: 8463 hectares, 13.5%

My concerns for this project are that the image of the area that is photographed is taken in April which is a time of year that still has snow on much of the mountains. So it is possible that the areas categorized may change in a few weeks to months. It is difficult to tell the difference between what could be dense scrub brush and grass, mixed forest and scrub brush, and barren land can sometimes be as white as snow. Not only that but the greenery is often sparkly forested or shrub brush may look like trees. Meaning that this is not entirely perfect as mountain landscapes are very complex, that I am not a professional and these are just the numbers I got doing my best work.

Another thing is that I chose this particular area because I started trying to classify the entire image and ran into several issues where urban was also snow, grass was also water, sage wasn’t really showing up as its own thing. So to avoid the urban area and the issues it presented I chose this chunk of land and made a partially true story. The area is under conservation and there have been many fires which has affected the land and caused fine sediments to drain into the lake due to small amounts of desertification caused by fires. The U.S. does seem to make a yearly GIS maps based on areas impacted by fire, conservation issues, protection plans, land use and many other subjects. I am sure that they have a 1999 LULC map of the area but I wanted this project to be all my work and not use their data as a crutch.

Map:

It is below and I hope that it is as self-explanatory as can be. The large mage on the left is my unsupervised classification of land use and land cover for Lake Tahoe’s basin. The inset map is to show the original true color landsat 7 image and the images as well as the study area’s location in terms of the United States Map because it is one thing to say Lake Tahoe but not everyone knows where it is located exactly or that it is in two states. The scale bar for my LULC image is to the bottom left because it didn’t look ok anywhere else and the north arrow is in the lake itself so it didn’t conflict too much with my map. I feel like I should have added the area of each classification but it just made the map look too clustered.

Week 5: Supervised Classification

Hello all!
This will be a short explanation because I have been burning the candle from both ends for several days and I'll soon crash.
So this week I preformed a supervised classification of land use for Germantown, Maryland using Erdas and its supervised tool.
An AOI image was added to the provided aerial image, and using a mix of the Inquire (legacy) cursor, region growing properties tool, signature editor, offered coordinates and classifications, a heavy dose of eyeballing and referring to examples classified the land use. Then I recoded the classes to be a single number, re-added the names of the classes that correspond to the number, and add area.
It sounds easy but between being so tired and being slightly unsure if I was doing things right I re-did and deleted things several times until I was satisfied. Which only took 7 hours just for the last exercise before I transferred the image to Arc GIS Pro and created the map which is below.

Week 4: Image Preprocessing

Hello all,
 This week ERDAS Imagine was used to process and look at a raster image and identify 3 different aspects of said image.
Feature 1: Using the inquire tool I found that a spike in pixels between 12 and 18 in Layer_4 is water. In my opinion it was easier to see the Water in the raster band combination Red: 4, Green: 3, Blue: 2, or near infrared because water shows up as a dark blue or green in this band combination whereas the land is a range of red colors.
Feature 2:  Using the inquire tool and histogram I found that Snow on the mountain had small spike in layers 1-4 around pixel value 200, and a large spike between pixel values 9 and 11 in Layer_5 and Layer_6. I used the  raster band combination 1, 1 , 1 or grey scale to make the brightness in the pixel values to shine through the most.
Feature 3:Using the inquire tool and histogram I found that the water of a small lake has much higher pixel values in layers 1-3 while layers 5 and 6 remained relatively unchanged. I used the true color combination or Red: 3, Green: 2, Blue: 1 to make the bright blue reflection of the lake to be seen the easiest.

After these features were found I created a subset of the image then imported the images to create a map for each of the features in Arc GIS Pro which are below.

Week 3: Intro to ERDAS

Greetings on this chilly blustery day,
So this week was an introduction to ERDAS Imaging 32 bit. It was not all that bad for a first time, the interface reminded me of windows 64 a little bit but it functioned pretty smoothly. I can't say that I like the little round about ways to save or work on things but I like the save session option. I can see its applications and favor ability but it will take a while to get used to the new program.
The task this week was to navigate around ERDAS and get comfortable with the program. Then take a provided raster image, edit the bands to be a TM false natural color band combination, add an area column to the attribute table then crop the image to size using the inquire option, that the image and export it to Arc GIS Pro and make a map with legends for area size and colors.
This is said map.

Week 2: Land Use/Land Cover

Hello again,
       This week was about land use and land cover where I took a provided image and made a feature class (LULC) to create polygons in order to class II classify the land usage of the image using ArcGIS Pro. The levels were as follows: 

1. Urban or built up land
1. 11: Residential
2. 12: commercial service
• 129: vacant
3. 13: Industrial
4. 14: Transportation, Communications, and Utilities
2. Agricultural Land
1. 21 Cropland and Pasture
3. (4)Forest Land
1. 41 Deciduous Forest Land
4. (5)Water
1. 51 Streams and Canals
2. 52 Lakes
3. 54 Bays and Estuaries (did not have enough time)
5. (6)Barren Land
1. 73 Sandy Areas other than Beaches

After the digitizing and categorization was done I created a point shape file to plot points at semi-random to check for accuracy. My method was kind of used a grid where I went left to right and clicked at random on that line motion. Then to check I used an "in/on field" accuracy assessment using google maps street view. I was unsure how to calculate accuracy and I forgot to add the accuracy to the map. But I was unsure of the numbers anyway. If we count the water areas I did not digitize then I probably got around 60-65%, and if we ignore that I think I would say 70-75%. I may be wrong however. 

     The colors I chose to categorize was from the random assortment of bold colors with slight tweaking so green corresponded to trees and blue to wetlands and all set at a transparency of 40-50% depending on the vibrancy.

     Below is the finalized map, I probably spent too long on the digitizing but it looks good.

     Thank you for reading and hopefully you stay tuned for next week.

     ~Jo Snow. 

GIS 4035 Beginning!

Hello all,
     I am back at it again in GIS at UWF this fall with an 8 week course in Remote Sensing and Photo Interpretation. I am excited but also anxious to get back into the swing of things considering I have had 5 months to forget a lot.
     Anyway, in this week's lab (week 1) I looked at an image and then distinguished as well as labeled tone from very light to very dark, and distinguished as well as labeled texture on a scale from very fine to very coarse. This was done by using the definition of the terms and creating polygons to illustrate the areas by their term. Then, identified objects by shape and size, shadow, pattern and association on a greyscale areal image; this was done through definitions of each criteria and plotting points with labels to illustrate which criteria it falls under as well as what it is. Finally, the last objective was to see the difference between true color and false color images; this was done by identifying 5 features in true color then looking at the same feature in false color. There was no deliverable for this part.
     Thank you for reading and see you next week,
      ~Jo Snow.

Final Project Presentation

Abstract:

      I am overweight, do not like using a gym and would like to get out of the house. I am also curious about the statistics of how many Americans exercise regularly to see how I stand in the exercising realm. Using ArcGIS I would like to look at the demographics of how many adults exercise regularly in 2018, compare myself with this data, and plan a camping trip to Blackwater River State Park.

Processes that were used are: project, buffer, clip, screen digitizing with the edit tool, label (I know that is not a tool but it annoyed me and I spent a lot of time with it), and add data.
      My area of interest is Blackwater River State Park that is in Santa Rosa county, Florida. The park has 30 camping sites, restroom and shower facilities, picnic pavilions, hiking and walking trails, water and other outdoor activities. There are plenty of hiking and walking trails that vary in length available at Blackwater River State Park to spearhead my weightless goal. Using an ArcGIS living map I found that I am  on par for average exercise for Americans on the National and for my zip code scale, I am above average for the county scale and for and under average for my specific block.
      Data and information was sourced from FGDL, FDOT, Esri, www.floridastateparks.org, and floridahikes.com.
      Below is my PowerPoint Presentation for my project (The link). I hope you enjoy.

https://drive.google.com/file/d/1DrSaLgwRFIsn1dfePA4x2YAdzH2BI7hy/view?usp=sharing

Note: The linked powerpoint is honestly difficult to read and I apologize. Also for some reason when downloading to my google Drive it chopped up the work Fin on the last slide.