Introduction to Arc GIS Pro and Multiband UAS Imagery

Introduction:

In this lab we were introduced to the Arc GIS Pro program in order to analyze data collected via UAS. ArcGIS Pro is a powerful program that allows for detailed analysis of imagery. It can be used with various industries and fields to gather data that would otherwise not be visible through basic means of imagery. The data collected was aerial imagery of a field near Purdue's campus, before and after it was burned. In order to analyze this data, we took advantage of various spectral bands to identify key areas of the imagery and understand what each band will show the user. Through this understanding, teams can collect information that is immensely helpful for individuals who own the land and want to know the health of the vegetation before and after burns.

Objectives:

#1: Discover, identify, and apply basic functionalities of ArcGIS Pro Software.

#2: Recognize, relate, and compare the functionalities of multi-band UAS imagery.

#3: Demonstrate proficiency and knowledge on how to effectively utilize spectral bands to given applications and identification of objects using imagery tools in ArcGIS Pro.

Methods/Assignment:

At the beginning of the assignment, we looked at META data that was included with the imagery in order to gain a better understanding of the operation. Through this, we knew the vehicle, the sensor, the time of takeoff and landing, altitude, and the flight number. This information is crucial when analyzing data collected with UAS because it gives the individual a better understanding of the type of data. For example, the takeoff time of this operation was 12:18pm. By knowing this, we understand the height of the sun and how that could possibly effect the lighting and the outcome of our data.

Vehicle: Bramor ppX

Sensor: Altum set to 1ms and 16Bit TIFF

Flight Number: 2

Takeoff Time: 12:18pm

Landing Time: 12:35pm

Altitude (m): 121

Sensor Angle: nadir

Next we reviewed a figure to understand the peak reflectance for bands 1-5 on the Altum Sensor. Below is the graph that shows the highest reflectance of each band in comparison to the others. (Figure.1) Blue 450-500, Green 525-625, Red 650-700, Red Edge 700-750, and NIR 800-850.

(Figure.1 Micasense RedEdge Peak Band Reflectance)

After reviewing the peak reflectance for each band, we opened the imagery and began adjusting the different bands and analyzing what aspects of the imagery each one highlighted. The first band that we enabled was the blue band. (Figure.2) This band really highlighted the field circled. That field has the highest reflectance when compared to the trees around it as well as some of the other fields.

(Figure.2 Blue Band)

Next we disabled the blue band and turned on the Green band. With this band, the image seems brighter and a lot of the features are harder to distinguish from the others. (Figure.3) The fields that were dark before are now much brighter. The area that stands out to me the most is the bottom right corner. There is a patch in side the otherwise dark trees that is much brighter than it was before. This area and the fields mentioned above, have a higher reflectance than the trees on the left of the image.

(Figure.3 Green Band)

The red band was enabled next and it was very similar to the blue band in terms of saturation. Unlike the green band, the red band made the image have distinguishable sections. (Figure.4) The fields were very distinct and the trees were much darker than in the green band. The area that stuck out to me the most was the rectangle in the top of the image. In the previous bands it was very hard to make out especially in the green band. In the red band, the rectangle was quite visible.

(Figure.4 Red Band)

Next we enabled the NIR band which was even more saturated than the green band. The small fields in the center of the image were once distinct but are now blending together. (Figure.5 ) The trees around the fields are also very saturated indicating that they have a higher reflectance than the other areas. The top left of the image, in the section of trees, was very bright and stood out to me in comparison to the other parts. Something to note as well is the rectangle that I pointed out in the red band. This rectangle is now immensely darker than before and the area around it is brighter. This band seems to flip the reflection of the map when compared to the other bands.

(Figure.5 NIR Band)

After reviewing various bands and identifying what they show, we then explored the data layer properties in order to understand some of the different values like pixel reflectance, cell size, radiometric resolution, and coordinate systems. This is important because it gives the user a detailed understanding of the data that they are analyzing and allows them to analyze it more efficiently. 

All of the above bands displayed the image without color which gave us a good idea of the reflectance for each band in respect to the different areas of the fields. In order to better understand the bands and what they can show, we created a false color infrared band using Band 5 for the color red, Band 3 for the color green, and Band 2 for the color blue. The false color IR highlighted the imagery in a few different colors that really identified areas of vegetation. While before the trees were still distinct, the false color IR highlights them in pink which identifies them as vegetation. Some of the areas in the field are a lighter shade of pink since the vegetation is not as dense. (Figure.6)

(Figure.6 False Color IR)

Conclusion:

ArcGIS Pro is an amazing program that has the capabilities of displaying an immense amount of information when combined with UAS imagery. In this lab we were able to take images and apply various bands that highlighted key portions of the map in order to better understand what we need. In this case, we were able to see the amount of vegetation before and after the burns.