GIS 4035 - Module 6 - Spatial Interpretation

Process Summary Details

Exercise 1:

NOTES:

1.      Retrieving the data files from the website was at first difficult, until I used a proper browser and made sure that the Java extension was up to date. Otherwise, I did not run into too many problems when it came to downloading the image of Pensacola Bay. As instructed, I closed browser and used the image provided in the zipped folder of Module 6 content.

2.      I unzipped both files required for this task, and opened them in Erdas imagine by importing them according to instructions.

Exercise 2:

NOTES:

1.      I began by adding p011r061_nn80 to a main project in Erdas Imagine. When I applied the 3x3 low pass Kernel filter (using the Convolution tool), the image seemed overall brighter. The edges of individual features were not at all distinguishable, but larger ‘bulk’ features could be seen quite prominently. I repeated the same steps in the convolution tool, but instead filtered the image through a 3x3 high pass filter. Smaller individual features became a lot more defined and easy to see, but boundaries of larger areas became more difficult to define.

2.      I opened the same image in ArcMap, and used the Focal Statistics tool according to directions. The raster set created used the “Mean” statistical filter. It was filtered through the 7x7 Kernel (instead of 3x3 cells,) therefore the image came out looking less defined than before.

3.      Once again, I used the Focal Statistics tool to open the same image, this time through a 3x3 Kernel and with the statistical filter for “Range” which is often used to define edges in features by highlighting the difference in brightness between neighboring pixels and feature in question.

Exercise 3:

Write down every enhancement process that you run on this image, and describe any noticeable effects of each. (Consider this the most important part of the process summary – there should be a lot of detail here.)

NOTES:

1.      I came back to Erdas Imagine for the final task of this lab.

2.      I opened the l7_striping.img file as directed in the assignment. The image appeared visible and perfectly fine.

3.      I went into the raster tab and clicked on “scientific” under group.

4.      I clicked into Fourier Analysis and selected the Fourier Transform Editor.

5.      I opened the l7_striping.fft file and explored the image. It appeared as a several light lines of star-like images of varied radiance, stretching diagonally from the top to the bottom of the image.

6.      I scrolled all the way up on the image, and over to the center-top as directed.

7.      I used the wedge tool by selecting it, and clicking at the center of the upper-most star in the middle row and moving the mouse cursor over to expand the “V” shape. The first attempt created a very thin band. I figured that it would not be sufficient to mask the striping effect, so I clicked undo and tired again.

8.      The second time around, I attempted to create a much larger “V” shape, but it still came out slightly thinner than the one shown on the screen shot in the module 6 directions. I decided to keep it for the times being, and see what the striping might look like after application.

9.      Afterwards, I scrolled down to the very center of the image and selected the LowPass button. I held down the left mouse key over the center of the image, and extended a circle to the very edge of the screen. The resulting image was a very bright sphere; fading out at the edges, with two long triangular features extending from the center and out towards the edges.

10.  When I compared the finished product to the sample presented ion the assignment, I thought it to be quite similar.

11.  I saved as Fourier1.fft, and used the Run tool to create a new Img file. When I opened it in Erdas, the file seems to have been created successfully.

12.  Back in Erdas, under the Raster tab and Resolution group I selected Spatial-Convolution.

13.  I selected Fourier1.img as the input file and selected 3x3 Sharpen in the Kernel menu. I ran the filter. The Sharpen image seemed quite almost exactly the same, but the definition was slightly superior.

14.  I tried repeating the process and obtained following results when choosing the filter;

a.      11x11 LowPass – image was very blurry and difficult to interpret.

b.      3x3 Edge Enhance – image became very uniformly grey, and the lines became darker and quite pronounced.

c.       5x5 High Pass – Image became very dark, and only larger features became all grey in color. Very difficult to read.

d.      3x3 Haze Reduction – Fairly readable, but the dark lines become awfully thick on the left side of the image.

e.      3x3 Sobel – Image became monochromatic, and much too dark.

15.  After many attempts, I decided to use my initial ‘sharpen1’ file from the early stage of the assignment.

16.  I added the essential map elements, after importing the raster file into ArcMap saved my lab assignment.

GIS 4035 - Module 4 - Ground Truthing

This weeks exercise was based off from the LULC shapefile created for Module 3.

Process Summary Details

For this exercise, in addition to documenting your technical procedures, make note of your thought process in deciding if your initial classification is correct. Your ground truthing of the photograph is much more important than documenting which tools you use to check the accuracy. That said, (1) your technical steps are still important, and (2) you do not need to record your processes for each and every point you check. Try taking notes on at least half of your points, as well as on features that are especially note-worthy or difficult to identify.

NOTES:

To begin the exercise, I created a new shapefile and called it Truthing. I dispersed the points myself, and attempted to create a 5x6 point grid. I did my best to keep the points marked in a systematic pattern. I clicked properties on the shapefile, and created two new columns in the attribute table as directed. Afterwards, I went over every feature point and tracked its location on google maps in order to verify its classification. Because the majority of the map on the left hand side falls onto a large body of water, many of the points fell onto a single classification.

Accuracy of truthing: 23/30=77%

At the very end, I went into the properties for the truthing shapefile. Under the symbology tab, I selected ‘caterogies’ and chose to add all values based on the True_Y/N value field. I chose a red symbol for the negative values and a green symbol for the positive.

GIS 4035 - Module 3 - Land Cover and Area Classification

Process Summary Details

Exercise 1:

For this exercise, in addition to documenting your technical procedures, make note of your thought process in deciding where you draw boundaries and how you identify features. Your interpretation of the photograph is much more important than which tools you use to draw the map. That said, (1) your technical steps are still important, and (2) you do not need to record your processes for each and every polygon you create. Try taking notes on at least one of each type of LULC, as well as on features that are especially note-worthy or difficult to identify.

NOTES:

1.      When drawing polygons around the features, I wanted to select areas that would be large enough to be legible in the exported JPEG file. I did my best to interpret areas based on shape, and size, shadow and pattern of surrounding features. Because the list of criteria is vast (even at level II) I did not spend a whole lot of time distinguishing between what may be a river, and what may be a canal.

2.      I created the LULC shapefile, imported it to the table of contents and began creating features.

3.      I began by using the draw tool to create a polygon around large bodies of water. I followed this up with drawing polygons around bare land masses on surrounded by water. I opened the attribute table, and added two columns according to the directions. With each new polygon drawn, I would assign the appropriate code as well as a code description.

4.      When the entire map was completed, I right clicked on my shapefile in the table of contents, and selected properties. Then, I modified the key to all my features and selected a color scheme. After creating a map legend, the Code as well as the Code_Dscr fields would become apparent.

5.      I added the essential map elements to my map, and changed a few characters to make the map legend a little more aesthetic. I saved the map, and exported the JPEG file like last time.

Q: List all of your LULC codes, Code_Descr, and a full description of what each is, what features are included in it, why you decided to classify it as such, etc.

A: The codes and code descriptions I used for the module are as follows:

11- Residential: Much of the map was marked as a single large feature shaded in yellow. Residential land was roughly recognized by the pattern of what seems to be houses, lots of small side streets and sparse vegetation that seems more aesthetically dispersed than natural.

12- Commercial Services: The polygons that fit this criteria were selected based on the proximity to a highway, and the size of parking lots. They seem to fall close to residential areas and are easily accessed from bigger roads.

122- School: There are only two polygons present under this category. They are shaded in orange, and both fall on the right side of the major road feature. The features were determined to be schools based on the size and shape of the building, and the parking lots. Also, the driveway designed for school buses is very easy to tell on an aerial photograph.

13- Industrial: The polygons that were placed under this criteria, had the look of industrial parks or construction areas from the top. It was generally the size as well as the appearance of the smaller features contained within, that lead to this classification.

15- Commercial/Industrial Mix: Only a single polygon falls under this category, and it is mostly due to the proximity to another large industrial feature.

41- Deciduous Forest Land: Determined by the color of the trees in largely overgrown areas. There are two features along the shore which seem to fit the category, as well as three more which are deeper inland.

43- Mixed Forest Land: Only a single feature falls into this category. Mostly due to the context, and its proximity to a lake where I would expect a mixed variation in trees and brush.

51- Streams and Canals: Used to define the body of water to the left side of the map, mostly due to its many offshoots which cut around the islands. The body of water is very large when compared to features like residential properties, but it was much more convenient and consistent to throw the entire area into the same category instead of splitting it up more specifically.

52- Lakes: There are two very small lakes, easily distinguished by shape, uniformity of color and by their proximity to tree cover all around them. They seem to be exceptionally small, and could have been interpreted to be ponds as well, but I would expect denser vegetation when mapping a pond.

621- Marsh: The marsh category was applied to everything on the map, that is located close to a body of water and has a difficult to distinguish type of vegetation. Most on the marshes marked fall on the left side of the map, along the large river. Some of them, also fall on the shore of the mainland.

Amadeusz Zajac

GIS4035

Remote Sensing and Photo Interpretation

9/5/2016

Module 2 Process Summary

Map 1

After opening the 7KGY10042_060.tif file in ArcMap, I never saw the window that would ask me if I want to build pyramids. I opened the image, and studied the aerial for exceptionally bright features. I selected a small white feature within the upper right-side quadrant of the photograph as my object of focus. It is an elongated rectangular building, among a cluster of similar looking buildings.

*I have decided to select small features for the first part of this lab, because I thought that the shade tones were most defined in smaller land marks.

I proceeded to draw a polygon around my feature with the drawing tool. I made the outline bright pink and left the inside of the feature hollow. Afterwards, I repeated the process four more times as directed and selected the following features to correspond to the brightness criteria:

Light: A T-shaped building along a major road in the upper right-side quadrant. (Close to the center of the map.)

Medium: A rectangular medium-grey feature in an empty lot, by a small woodland. Located within the upper right quadrant of the map.

Dark: A small, rectangular building feature in the upper-right quadrant of the map.

Very Dark: A small semi-rectangular body of water located off a road in the lower left side of the quadrant.

I converted my drawn outlines into a shapefile as the instruction suggested, and deleted the drawings from my aerial photograph all together. I began the editing session by right-clicking my “Tone” shapefile in the table of contents, and named all the features according to the appropriate tone. Afterwards, I exited the editing session.

I repeated the above steps to create five texture features based on the following criteria:

Very Fine: A body of water in the lower-left quadrant of the photograph.

Fine: A field in the bottom center of the map.

Mottled: A small field in the bottom left quadrant of the photograph.

Coarse: A patch of woodland in the left center of the photograph.

Very Coarse: A rectangular, semi-overgrown block located in the bottom-right of the map.

Afterwards, I simply added a few of the essential map elements to my work and exported as a JPEG file.

Map 2

For the second assignment, I searched for three features that could be recognized mainly by their shape and size. Afterwards, I marked them with a green point using the drawing tool and converted them to a set of features like I have in the previous exercise. The features selected were;

1.       A docking pier

2.       A swimming pool

3.       A parked truck

I created two more sets of features in the same fashion, according to the lab instructions. I have located three features that were recognizable primarily by the shape of their shadow, and marked them with blue points. The features were;

1.       A water tower

2.       A cluster of trees

3.       A directional road sign

(The last one is the least certain, due to its specific location in front of a building. But I feel that an argument can be made based on the shape of the shadow alone.)

Finally, I have located two features that could be identified based on their pattern. (Other features closely associated.) They were;

1.       A school (based on the size, shape as well as the driveway up front that seems to be designed for buses.)

2.       A residential neighborhood (Identified by many medium-sized buildings located next to one another in rows, and right along streets.)

Once all the features were labeled, I finalized the project by adding a few essential map elements and exporting it as a JPEG file.