Background:
In Lecture you were introduced to some basic Remote Sensing Fundamentals where you learned about how objects emit energy; some we see, most we don’t You related this to other forms of remote sensing platforms such as manned aircraft and satellites. Today you will using an online application through an ESRI tutorial to view and work with Satellite Data. As you go through this assignment, think of how UAS data is similar yet different in how it can be and is applied. For example, you will be looking at some temporal aspects of the data. Think of how UAS data can be analyzed in the same way, but at what temporal scale. Also, you will be looking at different bands of satellite imagery. Again, think of UAS data, and what you can and cannot do with UAS data coming off an normal ‘color’ image; ie what kinds of sensors are needed to do the analysis you see here?
Objectives:
1. Objective #1: Discover, identify, and the apply the capabilities and options of working with a temporal raster data set.
2. Objective #2: Recognize, relate, and compare the similiarities and differences between UAS data and Satellite Data
3. Objective #3: Demonstrate proficiency and knowledge on how to effectively utilize spectral bands to given applications and identification of objects.
Results:
1. In Mangroves of the Sundarbans, Question 7 you look at color infrared.
Figure 1. Mangroves of the Sundarbans in infrared.
b. What is color infrared?
Color infrared combines near IR, red, and green.
c. What color is healthy vegetation?
Bright red
d. How does this help healthy vegetation stand out.
Its bright and catches your eye easily.
e. What portion of the EMS is this band?
Bands 5, 4, and 3.
f. Zoom over agricultural areas near Purdue and capture a picture to post it below.
Figure 2. Agricultural areas near Purdue band 5, 4, and 3.
2. In the Oasis view section, you are asked to view the satellite data as an index. An index is a way of simplifying what is otherwise complex information.
Figure 3. Oasis view section satellite as an index.
b. How does this index relate to some of the bands from what you viewed in color IR from above?
This index includes band 5 just like color infrared but it also includes band 6 and is put into a formula to produce the moisture index.
c. What jumps out when you view the agricultural band combination?
The bright green colors stand out that are coming from farm/agriculture lands.
d. How does this relate to the moisture index?
The healthy vegetation stands out in both, both must have a high moisture index.
e. Zoom over different agricultural areas in the U.S. such as the plains and areas of the desert SW (southern CA) and capture pictures to post these below.
Figure 4. Agriculture band satellite imagery over southwestern United States.
Figure 5. Another image of the agriculture band satellite imagery over southwestern United States.
3. In the track development on the Suez Canal section, you look at imagery through a temporal perspective.
Figure 6. Suez Canal through a temporal perspective.
b. Thinking of the temporal resolution here, along with spatial resolution, how might a collection of UAS imagery be used in a similar manner.
It is allowing you to see changes over a period of time. It increases the potential for imagery analysis even further. This is a great resource for the agriculture field to monitor cylces or patterns in crops.
c. What kinds of resources would be needed to accomplish this when thinking of digital transformation concepts.
Resources like drones, satelittles, infrared or special imagery cameras. You also need a way to process data so a powerful computer as well.
d. Examine Las Vegas, NV and Phoenix, AZ over time. Get pictures of now and from the earliest.
Figure 7. Las Vegas, now.
Figure 8. Las Vegas, then.
Figure 9. Phoenix, now.
Figure 10. Phoenix, then.
e. Think of how UAS imagery could complement such temporal data sets at a different scale.
UAS imagery could get closer to the ground than satelites therefore creating better resolution, so UAS could greatly complement temporal data sets.
4. In the ‘Create your own Spectral View’ section, you are asked to engage in some different band combinations.
a. Create your own table that provides each band of the imagery and what it shows
b. Engage in at least 5 different band combinations. Write down what the combination is you used and the region/phenomenon you looked at
1. Coastal(1), Green(3), SWIR1(6)
2. NIR(5), Red(4), Cirrus(8)
3. Green(3), SWIR2(7), Red(4)
4. NIR(5), Red(4), Coastal(1)
5. SWIR2(7), SWIRI(6), NIR(5)
c. Save an image of each band combination and area.
Figure 11. Coastal(1), Green(3), SWIR1(6).
Figure 12. NIR(5), Red(4), Cirrus(8).
Figure 13. NIR(5), Red(4), Cirrus(8).
Figure 14. NIR(5), Red(4), Coastal(1).
Figure 15. SWIR2(7), SWIRI(6), NIR(5).
1. What do satellite imagery, UAS imagery, and manned aircraft imagery share in common?
They are images taken from above and all use remote sensing. Each source is capable of taking IR imagery.
2. What advantages do satellites have over UAS? What about disadvantages?
They can capture more data in a shorter amount of time, as well as collect data in hard to reach place like the ocean or middle of the amazon rainforest. Since the images are taken far away you sacrifice resolution and pixel quality.
3. Thinking of both these questions, what niche does UAS fill between satellite imagery and manned aircraft imagery?
UAS is cheaper and easier to use. You can deploy it almost anywhere you’d like. UAS could argue that it is safer to use as well. Overall UAS is more efficient than other sources.
4. Finally, do you think UAS will eventually replace the need for satellite imagery? Why or why not? Please qualify your answer with a valid argument.
I think we will always have the need for satellite imagery. As I mentioned above satelittle imagery can reach places where humans or drones can not reach (i.e. middle of the ocean, top of mount Everest, amazon forest). Satelittes are also able to capture larger amounts of data in a shorter amount of time. So my argument is that we will always have a need for satellite imagery.
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