Entry Name:  "UKON-Schlegel-MC3"

VAST Challenge 2017
Mini-Challenge 3

 

 

Team Members:

Udo Schlegel, University of Konstanz, udo.3.schlegel@uni-konstanz.de PRIMARY

Alexandra Diehl, University of Konstanz, diehl@dbvis.inf.uni-konstanz.de

 

Student Team: YES

 

Tools Used:

Tableau

Python

D3

SIZE: Satellite image zooming and exploration (Udo Schlegel, University Konstanz)

Approximately how many hours were spent working on this submission in total?

50

 

May we post your submission in the Visual Analytics Benchmark Repository after VAST Challenge 2017 is complete? YES

 

Video

Provide a link to your video.  The video is a mp4 file that is part of the submission: Link.

 

 

 

Questions

1Boonsong Lake resides within the preserve and has a length of about 3000 feet (see the Boonsong Lake image file).  The image of Boonsong Lake is oriented north-south and is an RGB image (not six channels as in the supplied satellite data).  Using the Boonsong Lake image as your guide, analyze and report on the scale and orientation of the supplied six-channel satellite images.  How much area is covered by a pixel in these images?  Please limit your answer to 3 images and 500 words.

With the tool, the user is able to insert the image of the Boonsong lake into the different satellite images. Afterwards he can drag, drop and scale it to any point and any size possible. With a bit of exploration and scaling he can find the lake located in the south-west. However, the tool already suggests the location based on a previous exploration.

The image used of the Boonsong lake is cropped to a height of 231 pixel and fits the 3000ft as good as possible now. It is scaled to 0.14 to fit the location on the satellite images. The orientation of the lake is the same orientation as the satellite images. So, the satellite images are also oriented North-South.

This results in 3000 / ( 231 * 0.14 ) = 92.7 = 93. As the satellite images have a size of 651 pixel it covers 60389 feet or 11.4 miles in height. If we assume it’s also the case for the width, we get 93^2 = 8649 feet per pixel. We also have used Tableau to validate findings and it shows a more narrow estimation of the lake height of 31 pixels, considering only the highest intensity value. The user knows the lake size is 3000 feet, so each pixel has 97 feet height, or 9409 square feet.

This discrepancies let us to the conclusion that a good approximation for the area would be 93^2 feet per pixel with an error of 0.5% approximately.



 

2Identify features you can discern in the Preserve area as captured in the imagery. Focus on image features that you are reasonably confident that you can identify (e.g., a town full of houses may be identified with a high confidence level). Please limit your answer to 6 images and 500 words. 

There are some features identifiable on the images like lakes and streets, but there are more than just these features.

1. Farmland

Figure 1  The pictures on top show the plant health images (B4, B3, B2) from September 2015, June 2016 and September 2016. On the bottom are the corresponding true color images.

On the top, circled in red, there are some changes other time. With the plant health images, the shape and the development, it is possible that these objects are fields of corn or crop. They have a rectangular shape and different colors in different seasons.

 

2. Houses

Figure 2  The images show the plant health (B4, B3, B2) from August 2014, June 2015, September 2015, June 2016 and September 2016.

Along the farmland, there are objects, which differ from the farmland and have a similar color to the streets. So, you can suspect that these objects are houses of the farmers.

 

3. Rivers

Figure 3 The images show the flood (B5, B4, B2) images for August 2014, June 2015, September 2015, June 2016 and September 2016.

On the flood images, it is possible to find rivers (around an island in the middle) either floating into or out of one of the lakes. It is only possible to distinguish these rivers on the flood images, because of the small resolution.

 

4. Mountains

Figure 4 The top images and the first on the left of the second row shows the snow (B1, B5, B6) images of March 2014, December 2014, February 2015 and December 2016. The one in the middle of the second row is the true color image of December 2014 and the last one is the plant health (B4, B3, B2) of August 2014.

With these images, it is possible to identify mountains in the satellite images. In the snow images, it is possible to spot, which areas have more snow then others and through this can be speculated to be higher than the surroundings. In the plant health images, red shows vegetation and chlorophyll, so it is also possible to say that these mountains aren’t high enough to consist only of a stone surface.

 

5. Streets and hiking paths

Figure 5 The two images show the true color and the plant health (B4, B3, B2) of June 2015.

On the different images, it is possible to recognize streets and hiking paths throughout the whole map. The transit street in the middle is the most recognizable of all, but there are more than just this one. Right next to the transit street and beneath the lakes, there are a lot of different smaller streets and hiking paths, which are lighter in the images.

 

6. Lakes

Figure 6 The two images show the flood images (B5, B4, B2) from March 2014 and December 2014.

There are five lakes on the satellite images, easily identifiable with the flood images.

 

 

 

3There are most likely many features in the images that you cannot identify without additional information about the geography, human activity, and so on.  Mitch is interested in changes that are occurring that may provide him with clues to the problems with the Pipit bird.  Identify features that change over time in these images, using all channels of the images.  Changes may be obvious or subtle, but try not to be distracted by easily explained phenomena like cloud cover.  Please limit your answer to 6 images and 750 words. 

1. Mining location changes

Figure 7 The images show the plant health images (B4, B3, B2) from August 2014, June 2015, September 2015, June 2016 and September 2016.

On the plant health images, there are some locations, which change their color during the time. The color, the two locations change from and to, is quite greenish and indicates, with the help of the primer, vegetation color and mineral deposits. You could suspect that there is a changing in locations for mining, which could interfere the bird population.

 

2. Road expansion construction site

Figure 8 The images show the plant health images (B4, B3, B2) from August 2014, June 2015, September 2015, June 2016 and September 2016.

By examining the plant health images, it is possible to identify that an already existing road in the beginning gets expanded. It is quite small in early 2014 and gets larger in 2016.

 

3. Strange new mineral deposits

Figure 9 The images show the true color image from February 2015, the plant health (B4, B3, B2) from June 2015, September 2015, June 2016, September 2016 and the true color image from December 2016.

In the plant health images, there is a strange new greenish field in the last two images of 2016. With the help of the spectrum primer from the data, the greenish color means there is an either a new vegetation or a new mineral deposit. This could also interfere with the birds. It could be a plant, which is foreign and not liked by them or an illegal digging location on a place, where they breed.

 

4. One of the lakes changes from 2014 to 2016

Figure 10 The images show the true color images (B1, B5, B6) from December 2014 and December 2016 and the corresponding snow images from December 2014 and December 2016.

One of the lakes changes from Winter 2014 to Winter 2016. In the true color images on top, it is already possible to see that there is something strange in there. On the snow images, it is even easier to spot differences. One of the lake has changed in general. On the left side are the images from December 2014 and on the right, are the ones from December 2016. The highlighted lake should have the same color as the one above it. But a good part of it is more blueish. By the bands (B1, B5, B6) this means there is no ice there and the soil mineral content changed. This could be the case because the lake was used to deposit trash or other substances. Or it could just be a landslide.  This could be another cause for the leaving of the birds.

 

5. Global warming

Figure 11 The images show the snow images (B1, B5, B6) from December 2014, December 2015 and December 2016.

During the winter or snow images, it is possible to see a decreasing on the intensity of the reddish coverage. In December 2014, red is a major color in the image. While in December 2016, there is not much left of it. Red corresponds to general visible brightness and should have a high value if there is white or more general snow. Even in the image from November 2015, where a lot of clouds hide most of the surface, it is more red.