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Objective #1: Discover, identify, and then apply basic functionalities of ArcGIS Pro Software.Objective #2: Recognize, relate, and compare the functionalities of multi-band UAS ImageryObjective #3: Demonstrate proficiency and knowledge on how to effectively utilize spectral bands to give applications and identification of objects using imagery tools in ArcGIS Pro.Vehicle: Bramor ppXSensor: Altum set to 1 ms and 16Bit TIFFFlight Number: 2Takeoff Time: 12:18 pmLanding Time: 12:35 pmAltitude (m): 121Sensor Angle: nadir 
Differences between red and red-edge? What is red-edge useful for?What is this band sensing? How does this differ from the NIR?This band deals with temperature and emissivity. This image is darker than the rest, showing that most of the vegetation and soil is cooler. This could be due to water content. Meanwhile what looks like to be walking paths are the brighest.LWIR and NIR bands measure different parts of the electromagnetic spectrum. LWIR bands detect heat being radiated while NIR bands detect reflected near-infrared lights.Now what features show up as the brightest?The image now shows that certain areas of once healthy vegetation is now burnt and am assuming gone. There are still vegetation areas that are darkest which we can assume were not affected as much by the fire. The walking paths near the fire seem to still be on the brighter side, which we can conclude people walked to put the fire out.
Creates a single raster dataset from multiple bandsWebsite link:
What features now jump out? What vegetation shows up pink? How about deep red? Take some screen shots and paste them in belowIn this image, I think you can see the healthier and less healthy vegetation better. The less healthy vegetation, or more sparese areas, appear to be pink or light red. The soil is showing up pale and a bit pink. I do not see any deep red in this image.What features now jump out? What vegetation shows up pink? How about deep red? Take some screen shots and paste them in belowThe areas that showed up the brightest in the post burn LWIR are showing up pink/red in this image. The surrounding areas are a mix of pale yellow, yellow, and almost green. I think the pale yellow is the soil, yellow is unhealthy vegetation, and green is healthier vegetation.This image has redder and pinker in it due to the burn.
Introduction to ArcGIS Pro and Multiband Imagery
Post-Burn NDVI Zoomed In
ArcGIS Pro is used for creating maps, performing spatial analysis, and managing geographical data. For UAS operators, this software tool can transform our imagery into geospatial data. It offers UAS operators data management, georeferencing, orthomosaic creation, surface analysis, and more! Multiband Imagery is data that is captured in multiple spectral bands, more specifically near-infrared, red-edge, and thermal bands. These bands are beyond what the human eye can see. Multiband imagery is an incredibly useful took as it can help monitor vegetation health, analyze water and soil, as well as detect changes in the environment.
In this document, I went through a series of steps to understand the ArGIS Pro software. I learned the software’s basic functions as well as began to recognize and relate the functionalities of multi-band UAS Imagery. I was provided with a data folder that contain a multitude of images that captures a field in Crownpoint Indiana. This folder contained images of the field before and after a burn.
Objectives
Metadata of the folder
Why is such information so important to record in the field related to these data collection missions? That is, why is this information of use to the person analyzing and working with the data?
Metadata provides the who, what, when, where, why, and how. This information is essential for interpreting and managing data collected. Metadata not only ensures policies and standards are kept but also allows for data to have context, which makes it easier to find and understand. In addition, this type of data allows processing software to compensate for variations, which will lead to more accurate and effective results.
Figure 1: Micasense RedEdge Peak Band Reflectance
Blue - 65504
Green - 65504
Red – 65504
Red Edge - 65454
NIR - 65377
LWIR_Thermal – 31602
LWIR – 30762
Figure 2. Blue Band Image
Blue band. What features have the brightest reflection? What has the lowest? Do any features jump out?
The features with the brightest reflectance seem to be the bare soil and rock. The lowest reflectance seems to be healthier vegetation. There is a trace around the perimeter and through the vegetation which I can guess is a path for people to walk through. I can assume that it is used quite often as it is very bright in this image. Blue band imagery reflects blue light, which can be corelated to water content.
Figure 3. Green Band Image
Green band. What features have the brightest reflection? What is the lowest? Do any features jump out?
The distinction in this image is not as much. Green band imagery absorbs red and blue light while reflecting green. Therefore, I can assume that the healthy vegetation has the brightest reflectance. The soil will have the lowest reflectance.
Figure 4. Red Band Image
Red Band. What features have the brightest reflection? What is the lowest? Do any features jump out?
In this image, the soil and unhealthy vegetation have the brightest reflectance; meanwhile, the healthier vegetation has the lowest reflectance. Again, the path of people walk is very prominent in this image.
Figure 5. Red-Edge Image
Red-Edge band. What features have the brightest reflection? What has the lowest? Do any features jump out?
The unhealthy vegetation is the brightest reflectance while the healthier vegetation has less reflectance. The Red Edge band is a narrower region, which makes it more sensitive to changes in vegetation health. You can tell the difference between the red edge and the red band, as the red band picked up on the soil while the red edge band did not. This image was brighter than the red band.
Figure 6. NIR Image
NIR band. What features have the brightest reflection? What has the lowest? Do any features jump out?
Healthy vegetation is the brightest when using the NIR band. However, I think the healthier soil has the lowest reflectance.
Figure 7. LWIR Thermal Image
LWIR (long-wave infrared band) for Doaksburn_thermalprocess_lwir. What features have the brightest reflection? What has the lowest? Do any particular features jump out?
Figure 8. LWIR Post Burn Image
LWIR (long-wave infrared band) for Doakspostburn_band6_mosaic_lwir. What features have the brightest reflection? What has the lowest? Do any particular features jump out?
Figure 9. Reflectance Values
Reflectance value
Blue - 65504
Green - 65504
Red – 65504
Red Edge - 65454
NIR - 65377
LWIR_Thermal – 31602
LWIR – 30762
Figure 10. Raster Information For Blue Band Image
Blue Band Raster Information
X Cell: 0.05626 Y Cell: 0.05626 (meters)
Radiometric resolution: 16 bit
Projected coordinate system: WGS 1984 UTM Zone 16N
Thermal NIR
X Cell= 0.85791 m Y Cell= 0.85791 m
Why is it important to always look over the properties of the data before working with it in GIS/Remote Sensing?
It is critical to look over the details and properties of data before working with it in GIS or Remote Sensing because we want to ensure that our data is accurate and will produce valid results.
Composite Bands
Summary of what a composite band is according to website:
Why might this tool be useful?
Tool is useful because: This tool can also create a raster dataset containing subset of the original raster dataset bands. This is useful if you need to create a new raster dataset with a specific band combination and order.
Figure 11. Pre-Burn Image
Figure 12. Post-Burn Image
Pre-Assigned Bands
Figure 13. Bands
For the Pre and Post Burn Images, the software, ArcGIS Pro, pre-assigned bands to interpret the data. As you can see in Figure 13, red was assigned band 1, green was assigned band 2, and blue was assigned band 3. In the software you can assign new values to these bands. The software automatically set all the bands to 1.4. These set wavelengths produced the images you see in Figures 11 and 12.
Assigning/Changing Bands
Pre Burn
Figure 14. Pre-Burn Image Bands
I wanted to see what would be produced if I changed the wavelength on the bands. I changed the red band to be 5.0, green band to be 3.0, and blue band to be 2.0. Below, in Figure 15 you can see how the image changed due to the manipulation of the wavelengths assigned to each band.
Figure 15. Pre-Burn Image with Bands Adjusted
Post-Burn
Figure 16. Post-Burn Image with Bands Adjusted
Just like the Pre-Burn, all the wavelengths for each band were automatically set at 1.4. You can see such a difference between Figure 12 and Figure 16.
Post-Burn NDVI – Multipart Color Scheme
Figure 17. Post-Burn NDVI
Figure 18. Post-Burn NDVI Zoomed In
The burnt vegetation shows up darker green/yellow, the healthier vegetation or vegetation safe from the fire is red/orange while the soil is showing up as yellow/green
Pre-Burn NDVI
Figure 19. Pre-Burn NDVI
How do the pre and post burn NDVI images relate and the color patterns?
The Pre-Burn NDVI shows a lot greener than the post-burn. The Pre-Burn NDVI Imagery shows a lot healthier vegetation than the Post-Burn Imagery. In The Post-Burn NDVI the areas that are showing up as yellow/green may indicate the once stressed vegetation, that was burnt. Meanwhile in the Pre-Burn NDVI Imagery, we can see those stressed areas showing up as red and also revealing soil and rock.
Creating my own band combination
I wanted to experiment and create my own band combination, Figure 20 and 21. I changed red to band 4, green to band 3, and blue to band 2. I then added the values 5 to red, 3 to green, and 2 to blue. In Figure 20, you can see that general light red/pink color is shown. The areas of stressed vegetation show up as light green/yellow. The walking path is less prominent in this band combination.
Figure 20. Personal Band Combination
Figure 21. Personal Band Combination Values
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