Lab 12 Volumetrics

Lab 12- Volumetrics

Introduction:

Volumetrics is an essential component and skill in the evolving field of remote sensing and geospatial analysis because it gives the user to make 3D calculations using 2D aerial photos and a Digital Elevation Model (DEM).  One of the most useful applications of this technology is in the mining industry where it may be used to calculate volumes of stockpiles, tailings piles, and the amount of material removed from a mine pit. In the past, and in many cases today, large feature volume calculations are performed by a survey crew on land.  However, in cases where the target area is a large mine sites with many stockpiles, this method is very time consuming. With the use of UAV technology, large areas may be covered quickly and efficiently while producing equal to greater quality data as traditional land survey techniques.  Fortunately, there is an abundant number software packages available that are capable of performing such operations.  For the sake of this lab, we compared two different volume calculation methods within ArcMap 10.3.1 in addition to the volume calculation feature within Pix4D mapper.

Methods:

Pix4D Mapper Volume Calculation:

Due to a corruption in the Pix4D file, no analysis was able to be made on its ability to calculate volumes.

Arcmap Raster Clip Volume Calculation:

To perform the volume calculation using this technique, the piles selected for volumetric analysis were clipped from the surrounding raster data.  To do this, a loose polygon was drawn over each of the pile (Photo 1), and the arcmap tool "Extract by Mask" was used to remove the surrounding raster data (Photo 2,3,4). 

Photo 1: Screenshot of each pile selected
for Volumetric Analysis and their associated polygon
drawn over the top of it. Pile 1 (green), pile 2 (purple),
and pile 3 (turquoise).   

Photo 2: Showing pile 1 after extraction
from the original raster. Image is not to scale

Photo 3: Showing pile 2 after extraction
from the original raster.  Image is not to scale

Photo 4: Showing pile 3 after extraction from the
original raster. Image is not to scale.

To calculate volume, the "surface volume" tool was used.  The surface volume tool works by calculating the area and volume of the region between a surface and a reference plane (Diagram 1).  The reference plane, in this case, is the polygon drawn around the designated pile. As can by seen by photo 5, the height of the plane (elevation at the base of the pile) must be inputted into the tool wizard before running the calculation.  The overall process used to calculate the volume using this method is modeled by photo 6.  

Diagram 1: Visually showing how the surface volume tool calculates volume.
The blue line represents an imaginary refrence plane at the base of the object in which
the tool uses as a lower limit for its calculation.  Diagram obtained from pro.arcgis.com

Photo 5: Showing the software wizard for the Surface Volume tool.
It is important to note that the tool saves the volume calculation as a text file,
separate from the feature class.  

Photo 6:  Showing the different feature classes and tools used to calculate the final volume.

Arcmap TIN Volume Calculation:

To perform the volume calculation using the TIN file method, the raster clips created in the last method were first converted to a TIN file using the "Raster to TIN" tool.  The advantage of using the TIN file is that it produces a better surface definition using triangulations.  As may be seen from Photos 7,8 and 9, a TIN file may be used to create a better fitting polygon around the base of the object.  Next, the "Add Surface Information" tool was used attribute the newly drawn polygons with the TIN surface area information where the two objects intersect (Photo 10).  

Photo 10: Showing the Add Surface Information tool wizard and the
different output properties that can be created with the tool.  

Lastly, the volume calculation was performed using the "Polygon Volume" tool.  This tool is similar to the "Surface Volume" tool, but is made specifically for TIN feature classes, and is capable of utilizing average surface elevation calculated with the add surface information tool as opposed to using the estimated value used in the previous method.  The overall arcmap process used to calculate the TIN volume is modeled in photo 11

Photo 11:  Model showing the different feature classes and tools
used to calculate the TIN volume.

Results/Discussion

Map Set 1: Displaying the extracted rasters
for Pile 1,2, and 3 and their calculated volumes.

Map Set 2: Displaying the standalone TIN feature classes converted
 from the raster clip (left), the TIN feature class overlain by the
polygon used in the volume calculation (right), and the calculated volume.  

Table 1: Comparing the volume calculations between the
Raster Volume and TIN Volume method.  

Map Set 1 and 2 display the resulting data obtained from the two different volume calculation methods within the Arcmap Software. One weakness of the raster clip method is that the user needs to draw the polygon with enough space around the pile so that surface elevation may be estimated using the information tool later. Consequently, that reference plane then adds volume of material outside of the pile area, and overestimate the total volume.  The two methods are compared side by side in Table 1, and show that the volume calculations of the two largest piles differed by 2000 cubic meters while the smallest pile (pile 2) was only differed by 250 cubic meters.  The volume comparison, combined with weakness of the raster clip method as explained above, infers that the larger the object surface area, the larger the volume overestimation.  

One issue with the TIN method is that it takes more time to complete. Not only is the flow model longer (Photo 11), but in order to utilize the higher surface detail, the user must construct a detailed polygon around the object as opposed to a loose polygon with four or five points.  As a whole, the process is by far more accurate, but consumes more time and presents more opportunity for error.To help alleviate this issue, ArcMap includes a feature called model builder, which allows the user to visually see the process before actually preforming any spatial analysis.  Not only does this speed the process up for multiple calculations, but helps eliminate human error.

Conclusion
 It was found that the TIN method produces a higher surface definition, and makes it easier for the user to draw a more accurate polygon around the object.  The higher surface definition, as seen by the far more detailed map legend in Map Set 2,  allows for the user to therefore calculate a more accurate volume.  Although the TIN method produces a more accurate volume calculations, it involves a few more steps in the process.  In this lab, only three volumes needed to be calculated, and it is easy to see that the TIN method could take a long time if the process needed to be repeated to calculate many more volumes.

The ability for UAV to calculate volumes through aerial imagery is a powerful asset to spatial analysts because it greatly increases applications in which this technology can be used.  According to my own interests, volumetrics may be best applied for mining operations. Not only does aerial photography greatly expedite the process for large operations, but also improves safety for mine operations that must hire 3rd party individuals with little actual mining experience to walk out on the tailings and storage piles.

Lab 11: Adding GCP's to Pix4D Software

Introduction:

Ground control points are necessary for the geometric correction of images, which improves the accuracy of the maps derived from aerial images.  The degree of accuracy and precision in which data are improved is therefore dependent on the accuracy and precision of the device used to establish the coordinates of the designated GCP locations on the ground.  The purpose of this lab is not only to demonstrate the consequences of not using GCP's, but also to show the importance of using high quality GPS units when establishing the points.  Pix4D software allows an individual to upload Ground Control Points, georeference the imagery, and generate an orthomosaic according to the adjusted tie points.  To accommodate for situations in which a user may or may not know the geolocation or coordinate system of the imagery/GCPs, there are three methods that GCP's can be added to Pix4D.

The first method is only used when the image and GCP geolocation and coordinate system is known.  To perform this method, add GCP data with the GCP/Manual Tie Point manager and run the first step to complete initialization, Next, mark the GCP's with the rayCloud so that the imagery is pinned to the proper coordinate.  Lastly, run steps 2 and 3 to generate an orthomosaic.

The second method may be used if the initial images are without geolocation, or when either the initial images or GCPs are in a local coordinate system.  To perform this method, run step 1 before adding and marking three of the GCPs in the rayCloud.  Then, add and mark  the other GCPs with the Manual Tie Point Manager and rayCloud editor, respectively, before running Steps 2 and 3 to generate the DSM and Orthomosaic.

The last method may be used for any situation, but requires more time in order to mark the GCPs on the images.  To perform this method, add and mark all GCP's with the Basic GCP/Manual Tie Point manager and run steps 1, 2, and 3.

Study Area:

Photo 1: Map area outlined by yellow rectangle.  Map area is located between 
Fairfax Street (East of baseball fields) and Hester Street (northwest corner of the map), 
just south of South Middle School.  

Data collected on September 30, 2015. Weather conditions upon data collection: Sunny/Cloudless, and a temperature of about 60°F. 

Methods:

For this Lab, photos obtained from the SX260 camera were uploaded to the software using the new project wizard on the welcome page.  The image geolocation and GCP coordinate system is known, so method 1 was used to process the data.  Because this specific camera contains a GPS unit, the geographic locations of each photo are stored with the image and the program is able to automatically detect the coordinate system and camera information. Before finishing the new project wizard, it is important to indicate that the exported data should be in the UTM NAD 1983 Zone 15 coordinate system so that the imagery aligns with the GCP data.  Next, a text file containing the latitude and longitude of each ground control point was imported to Pix4D using the "GCP/Manual Tie Point Editor" tool (Photo 2).  Using the "rayCloud Editor", tie points may be adjusted by pinning the GCP coordinates to the center of the GCP in each image (Photo 3).  The more images that are adjusted, the more accurate/precise the overall data will be.  For the purpose of this lab, at least ten images were adjusted for each GCP.

Photo 2: Displaying the GCP/Manual Tie Point Manager. The green number
on the left side of each row indicates the number of images adjusted to the X,Y
Coordinates.  Key functions of this window include the "Import GCP" button on the
upper right hand  corner, and the "rayCloud Editor" on the
lower left corner.

Photo 3: Displaying the rayCloud Editor window within the Tie Point Manager.
The lower right window of the editor populates all the images containing the designated
 GCP, and allows the user to pinning the coordinates to the image by clicking on the
center of the ground control point

After using the rayCloud editor to align images to the GCP coordinates, the data was processed to produce a georeferenced orthomosaic.  For the sake of demonstrating the importance of utilizing GCPs, an orthomosaic was generated without using the GCP data for comparison. The processing reports are linked below, and provide indication of the amount of geolocation error, amount of overlap, and number of images processed.  

Pix4D- 3D Animation

GCP Initial Processing Report
GCP Orthomosaic Final Report
No GCP Initial Processing Report
No GCP Orthomosaic Final Report

Results/Discussion:

Comparison Between GCP and Non-GCP Orthomosaics

Results from the orthomosaics generated with and without the GCP points are shown in Photo 4 and 5, respectively.  Visually, it is quite obvious that the imagery processed with the GCP's is far more accurate, and aligns far better to the basemap than the imagery processed without the GCP's. It may be seen from photo 5 that the ground GPS units indicate that the imagery is substantially pulled northwest of the actual GCP locations.   This is likely a result of the poor quality GPS in the SX260 camera which is unable to provide the precision required for high quality and survey grade maps.  

Photo 5: Showing the SX260 imagery not
corrected using ground control points. Satellite
base imagery shown for comparison.The GCP
numbers follow a counter clock-wise order
starting at the cars in the northwest corner (1). 

Photo 4: Showing the SX260 imagery corrected
using ground control points in Pix4D. Satellite
base imagery shown for comparison.  The GCP
numbers follow a counter clock-wise order
starting at the cars in the northwest corner (1).

All images taken on September 30th, 2015 at the Southside Community Gardens in Eau Claire, WI 

with a SX260 Camera for the purpose of learning how to input Ground 

Control Points in Pix4D software.  

An analytical representation of the error is possible by calculating the root mean square (RMS) for each transformation performed.  The RMS equation uses residuals, which is the measure of the difference between locations that are known and the the locations that have been digitized, to give an indication of how accurate the derived transformation is.  ArcGIS automatically displays the RMS value when using the georeferencing tool to translate points from the "Non-GCP Orhomosaic" to GCP Orthomosaic (Photo 6). 

Photo 6: Showing the georeferencing function and the resulting RMS calculation
 (upper right hand corner of  grey box).  

Comparison of Ground GPS Units

It was assumed that the Dual Frequency Survey Grade GPS is the most accurate, and the coordinates obtained from the device were used to pin the GCP's to the image in Pix4D. The Dual Frequency Survey Grade GPS is represented as a red circle the photo, and lies directly over each GCP.  Comparatively, the iphone Collector was unsurprisingly very unreliable.  Although it occasionally came very close to the actual mark (as in the case of GCP 2), other locations show it to be the most inaccurate unit (GCP 5 and 6).  With regards to the Bad Elf Survey Collector, I was surprised to see that two of the points (GCP 1 and 4) were way off point while the rest of the points were fairly accurate. Regardless, the Bad Elf Surveyor proved incapable of living up to their claim that it provides accuracy within 1m (Photo 7). Overall, the other GPS units appeared to have relatively the same amount error and variation between points, with the Garmin GPS being the most consistent device.  

Photo 7: Measuring the distance between the Dual Frequency Survey Grade
GPS (Red Circle), and the Bade Elf Surveyor (Blue Hexagon) at GCP 2.
The error at this GCP is over 2.5m.  

Software Discussion:

Adding GCP information to the imagery is easy and intuitive with the use of Pix4D software. I found that when pinning the UTM GCP coordinates to the images, the ability for the program to recognize and populate images saved an enormous amount of time.  However, if the user is unaware of what the program is doing, it is easy to click through the various windows and select a different coordinate system for the imagery than the GCP's.  Using two different coordinate systems is possible, but not recommended because this will result in completely useless data. 

Although this lab has proven the importance of using GCP's in aerial photography, it does have its limitations.  One major struggle in this lab was simply processing the data because it took between 2-3 hours to generate each orthomosaic.  In the real world, it may prove difficult to perform the data analysis if a company does not have access to the appropriate computer processing power.  Furthermore, the resolution of the data combined with the GPS data sets made this project extremely hard to work with in ArcGIS because the program had to continually re-draw the raster data at each click of the mouse.

Conclusion:

Establishing GCP's is necessary in order to georeference data, even when a camera such as the SX260 has a GPS built into the system. Not all GPS systems are created equal, and therefore caution should be taken when selecting the device for a survey project.  Although the Dual Frequency Surveyor is the most accurate, it is also a large device, which makes it more cumbersome and time consuming to use in difficult terrains.  Overall, the accuracy of the GPS is directly related to the accuarcy of the end product.  Fortunately, Pix4D software makes it incredibly easy to add GCP data and adjust imagery to align to the correct coordinates.  

Lab 10- Construction of a point cloud data set, true orthomosaic, and digital surface model using Pix4D software.

Introduction:

Pix4D is powerful mapping program capable of converting images taken by drone to highly precise georeferenced maps, mosaics and 3D models. This program is unique compared to  other processing software (such as the GEMS software) used in previous labs because it has the power to orthorectify data using digital elevation models.  Pix4D also comes equipped with editing tools such as the rayCloud Editor, which combines the 3D point cloud and images to allow the user to assess the quality of their results (Photo 1).  Other tools such as the Mosaic Editor and the Index Calculator allows the user to improve the quality orthomosaics and generate NDVI vegetation maps, respectively.

Photo 1: Showing the densified point cloud from the RGB GEMS data set. 
 Green dots indicated where the drone is, and blue dots indicate
 where the drone should be at the time of the image capture.  Red dots indicate
 data that was discarded. 

Other aspects that separate Pix4D from other software packages is the its ability to process multiple flights and oblique images.  However, it is important to note that if the pilot is intending to process data from multiple flights, that he/she must ensure enough overlap between images, and that the two flights are conducted under as similar conditions as possible (sun direction, weather, no new objects, etc.). Similarly, when constructing an image acquisition plan for reconstruction 3D buildings, it is important for the pilot to take one image every 5-10 degrees to ensure enough overlap for processing in Pix4D.  Additionally, it is important to note that Pix4dmapper only generates a point cloud for oblique images, and does not produce an orthomosaic.  

Study Area:

Data were obtained at the Eau Claire Soccer fields southeast of the intersection of Craig Rd and Hamilton Avenue in Eau Claire Wisconsin on September 23rd, 2015. Weather conditions during data collection were Mostly Cloudy-Overcast,72°F, and the wind speed 5-8 mph from the southeast.  

Photo 2: Base map of study area obtained from ArcMap GIS 10.3.1

Methods:

Pix4D integrates an extremely user friendly interface and performs most functions at a click of a button.  For this Lab, photos obtained from the SX260 camera were uploaded to the software using the new project wizard on the welcome page (Photo 3).  Because this specific camera contains a GPS unit, the geographic locations of each photo are stored with the image and the program is able to automatically detect the coordinate system and camera information.  Therefore, no additional settings were altered in the "new project" wizard.  Comparatively, the GEMS camera stores the geographic coordinate information separately from the image data as a text file (Photo 4).  However, before loading the txt file, it is important for the user to input the camera specs first (Photo 5).  After the camera information is loaded, load the txt file found within the original data collection folder so that the images may be georeferenced.

       

Photo 3: Displaying the uploaded photos in the
new project wizard.  

      

Photo 4: Txt. file
showing the GEMS images
and their associated
Latitude and Longitudes

Photo 5: Displaying the edit camera
menu within the new project wizard.
To add information for the GEMS
camera, RGB was selected under the
"Bands" drop down bar, focal length
was set as 7.7, and the sensor width was
set at 4.8.

After all necessary information is entered, it is crucial to double check that the latitude and longitude of the images are in the ball park of what is expected. If all information is accurate, finish the new project wizard and run the data analysis of the images to create a digital surface model, point cloud data set, and orthomosaic.

Pix4D generates a data quality report after analysis to provide the user with information regarding the number photos calibrated and georeferenced, the amount of overlap, and geolocation error.  Links to the quality reports from this lab are posted directly below:

SX260 Quality Report (Link)

Photo 6: Calculating volume of shelter
from SX260 camera data set. Edges of the
shelter are highly pixelated, making it
 difficult to capture the full shelter volume.

The SX260 data set is much smaller than the GEMS data set, but the quality report shows that 32 out of 32 total images were processed and referenced.  Due to the lack of images, the number of overlapping images averages around 3-4.  Problems occurred when attempting to calculate volume in Pix4D for this data set because pixel resolution was too poor to capture the full volume of the shelter (Photo 6). To improve the data set, images should be taken more frequently to increase the amount of overlap. Video 1 shows the SX260 data as it is represented in Pix4D.





GEMS Quality Report (Link)

Pix4D processed 215 of the 220 images for the GEMS data and geolocated 219 of the 220.  As represented under the computed Image/GCPs/Manual Tie Points Positions heading, the only images discarded were those taken directly after the roto-copter took off from the ground. The remaining photos were properly processed, and had a high average number of overlapping images (5+).  The quality report shows that areas that only have three overlapping images or less are located predominately at the edges of the map area, as expected. Overall, the quality of this data set is extremely good.  Video 2 shows the GEMS data as it is represented in Pix4D

Post-processing geometric calcuations

Calculating length of polylines, areas, and volumes using Pix4D is relatively simple due to the tools integrated into the software.  After drawing the line or polygon in the rayCloud editing tab of Pix4D, the software will automatically determine the length, area, or volume (Photo 7).  

Photo 7: Displaying the surface area calculation of one of the
soccer fields at the Eau Claire Soccer park. 

Results/Discussion:

The data obtained from this lab may be utilized/analyzed using a number of different methods. One objective of this lab was to learn how to calculate distances, surface areas, and volumes of aerial images.  The calculated lengths, areas, and volumes of the objects measured (The shelter as shown in photo 6 and the soccer field in photo 7) are represented in table 1 and represented visually on the maps in photo 8. 

GEMS
Bike Rack Polyline (length):	2.77m
Soccer Field Area:	327.29m2
Shelter Volume:	6.48m3
SX260
Sidewalk Square Polyline (length):	2.64m
Soccer Field Area:	323.74m2
Shelter Volume:	8.13m3

Table 1: Lists the measured objects and their associated calculated 

length, area or volume. The bike racks were not shown in the SX260

data within Pix4D, so the length of one sidewalk square was 

measured instead.  

Photo 8: Comparing final maps between the GEMS and SX260 Cameras. 

All images taken on September 23rd, 2015 at the Eau Claire Soccer Fields 

with a GEMS and SX260 Camera for the purpose of learning how to capture 

aerial field data and process it using Pix4D software. Cartographer Nick Matula

The results from table 1 show that there are some differences between calculations. The soccer field areas differed by approximately 3.5 meters squared, and the volume of the shelter differed by 1.5 meters cubed.  These differences may be accounted for by the differences in data quality.  The GEMS data set had a substantially larger number of images, which allowed Pix4D to more accurately line up features within each image. It is important to note that the DSM images are also very different from each other.  In the SX260 DSM, the main pavilion is not shown as having the highest elevation, which further indicates to a weaker quality of data.  As a general rule, the recommended overlap for most cases is at least 75% frontal overlap (with respect to the flight direction) and at least 60% side overlap.  However, when the study area is a uniform field or covered by snow, 85% frontal overlap and 70% side overlap is recommended.  It is suggested therefore, that in the future that images are taken more frequently for fuller coverage.  Although more data will result in longer processing times, it is necessary in order to obtain reliable data from volume calculations.


One aspect of the Pix4D that raises concern is that it is almost too intuitive.  As a result, an unsuspecting user that doesn't know what is going on can easily end up with inaccurate results.  For example, when georeferencing the GEMS images it was important to look at the order that the latitude and longitude were recorded and stored in the .txt file.  Instead of listing the latitude first and the longitude second, the order of the values are switched.  It is easy to assume that the program will correct the problem itself, but in reality it is necessary to tell the program the data from the .txt file is listed in the reverse order so that the images are referenced properly.  Without having a general idea what the latitude and longitude should be in a target area, an unsuspecting user could easily georeference the images backwards and result in improperly located data.

The applications for aerial photography volumetrics is enormous in the mining industry.  Specifically in sand mining, the size of storage piles are regulated by the DNR.  Currently, surface area is being calculated using time consuming traditional survey techniques.  With aerial photography and processing software such as Pix4D, storage pile volumes and surface areas could not only be calculated more efficiently, but also then used to show growth/decline of product reserves over time.  Furthermore, aerial technology could also be used to assist reclamation by calculating excavated volumes and the area of lands environmentally disturbed by mining processes.

Conclusion:

Pix4D is a dynamic program of producing orthomosaics and calculating distances, surface areas, and volumes of objects.  One strength of Pix4D is that it is extremely user friendly, and simplifies the process of georeferencing and inputting camera specifications if it is not stored with the images. If processing images without geolocation, ground control points should be used so that scale, orientation, and absolute position may obtained in the Pix4D software.  Without geolocation, the data is unable to be used for mining, agriculture, and other applications.

The data from the GEMS data averaged an image overlap of five images while the SX260 data averaged an average of three images.  The lack of overlap in the SX260 data effected the ability of Pix4D to calculate geometric accurately, and demonstrates the need to maintain at least 75% overlap of images. When high quality data is combined with an expansive analyst tool set, Pix4D becomes a powerful tool to process aerial photography.

Lab 6: Using the GEMs software to construct geotiffs, and to field check your GEMs data.

Introduction:

The GEMS (Geo-localization and Mosaicing Sensor) is a multispectral payload designed to be mounted on a flat portion of a small UAV with the cameras facing down. The sensor is capable of  capturing RGB, NIR, and NDVI imagery simultaneously during flight, which allows the data to be used in a larger range of applications one processed.  The sensor has a 1.3MP RGB and MONO camera resolution, and a ground sampling distance (GSD) of 5.1cm at 400ft ad 2.5cm at 200ft.  

The accompanying software (GEMS Tool) is capable of displaying the data as a georeferenced mosaic for the RGB, NIR, or NDVI band spectrums.  The program, however, cannot orthorectify the data because this process, as described by the ArcGIS desktop help, requires the use of a digital elevation model in order to correct distortions due to topographic variation.

Software Procedure:

The GEMS Tool software is provided with the GEMS hardware kit, and is used to produced georeferenced mosaics from data obtained using the sensor. To access the data within program, locate the .bin file within the Flight Data folder on the usb storage drive and load it in the GEMS Tool software. The flight data folder is labeled with using the following format to display the GPS time the data was collected: Week=A TOW=H-M-S.  Upon successful upload, the program immediately translates that data and displays it visually on the screen as the flight path taken during data collection (Photo 1). 

Photo 1 showing the flight path in Lab 4 after loading
 .bin file into the GEMS Tool software. 

 Prior to generating the mosaic, it is important to run the NDVI Initialization tool and allow the program to perform a spectral cross-band alignment.  As seen in photos 3 and 4, the Normalized Difference Vegetation Index is represented in two different color schemes.  The first scheme (Photo 3) represents vegetation as hot colors (orange-red) and non-vegetation as cool colors (green-blue). Photo 4 shows the same data but  reverses the color scheme to provide a more intuitive color map where vegetation is represented as green.

Photo 3: NDVI FC1, displaying vegetation
as orange, and non vegetation as green

Photo 4: NDVI FC1, displaying vegetation 
as green, and non vegetation as red

After the NDVI Initialization process is complete, run the mosaic tool.  For this assignment, we selected the fine alignment option to obtain the best possible results.  This tool will generate a folder with a series of "tiles" for each spectrum band that the mosaic tool generated (Photo 5).

Photo 5: Displaying one tile generated for the RGB spectrum using the mosaic tool.

 The overview mosaics, which combine the tiles into a single image, are also found within the tiles folder in the jpeg and .tif file formats. When producing the final maps, the .tif files were used because of their ability to be easily read by GIS tools such as ArcMap.  As supplied below, a final map was produced for NDVI FC1, NDVI FC2, Mono Fine, NDVI Mono Fine, and RGB.  A basemap using world imagery of the location was added for comparison. The software also has the capability to export to Pix4D, which would allow a geographer to then take the data obtained from the GEMS sensor and measure volume extractions or stockpiles in the mining industry.

Photo 6: NDVI FC1- NDVI color scheme indicates
vegetation in orange/red, and non vegetation in green/blue.

Photo 7: Mono Fine- This map displays the raw,
black and white, imagery mosaic of the target area

Photo 8: NDVI FC2- NDVI color scheme indicates vegetation
in green, and non-vegetation in red.  This color scheme is more
intuitive to geographers.  

Photo 9: NDVI Mono Fine-  NDVI Color scheme
indicates white as vegetation, and black as
 non-vegetation.
  

Photo 11: RGB-  Map showing the colored,
imagery mosaic of the target area.

Photo 10: World Imagery- Basemap of target

area obtained from ESRI ArcMap

GIS 13.4.1 Software

Photography completed at the Eau Claire Soccer Parks,

Eau Claire WI, 54701 on Wednesday September 23rd, 2015

for the purpose of exploring and reviewing the GEMS 

hardware and associated software.

Discussion and Final Opinion

The GEMS Sensor and associated software provides a practical and versatile data analysis package that is relatively easy to use. The individual tiles/photos produced by the sensor are of only low to moderate quality, but were more than sufficient to meet the needs of this assignment.  In the business world, however, it is easy to see how a more advanced sensor with higher resolution may be required. It is appreciated, however, that the NDVI is represented in two different color schemes.  Although it may not be a critical aspect of this assignment, the NDVI FC2 color scheme is more intuitive, and makes it easier to distinguish between the vegetation and non-vegetation within the map area.

 Overall, the mosaic tool did an good job of aligning most of photos together to form the RGB mosaic, which is seen by its comparison with the World Imagery Maps (Photos 10 and 11). However, it is seen in NDVI final mosaic images that the software struggled to align the edges of the tiles.  As result, the final images have large streaks running northeast/southwest across the photo.  It is acknowledged that some discrepancies are unavoidable but for use in the agricultural industry, this might to problems because of the large areas that are smudged by the mosaic tool. 

In conclusion, the GEMS hardware and software combo is user friendly, excels at allowing inexperienced users to producing basic mosaic images.  However, in my opinion, the sensor should receive an upgrade for its 1.3MP camera so that it can begin to compete with other sensors such as the Canon SX260 and the GoPro which have 12.1MP and 12MP cameras, respectively.   I would likely  only use this program again due to its ease of use, and its ability to mosaic photos in a relatively short time.  The NDVI images do have some alignment errors, and would likely not be acceptable in real world applications.  Although the sensor was suitable for the purposes of this lab,  in the world of mining problems in alignment could calculation errors that ultimately cost lives.

Lab 5:  Obliques for 3D-Model Construction

Introduction:

Nadir photography is utilizes single point perspective, meaning that the photo is taken from a completely vertical position. This perspective allows for the scale to remain relatively constant and therefore make measurements easier than working with oblique photographs.  In comparison, oblique photography is characterized by tilting the camera at an angle between the optical axis and the nadir line.  As a result, the scale varies with the angular orientation in addition to the topographic relief.  Oblique images are useful for providing overviews of an area and easier to distinguish and recognize objects.  For this lab exercise, the focus was capturing oblique images for the purpose of processing them into a 3D-model at a later date.  

Study Area:

The study area was the pavilion at the center of the Eau Claire Soccer Park, which is southeast of the intersection of Craig Road and W Hamilton Avenue (Photo 1 &2).  Weather conditions were beautiful, with light scattered clouds and a temperature of 57°F.  Record of the weather conditions may be found in the Field Notes section of this blog. 

Photo 1: Google Maps view showing area of interest at the Eau Claire Soccer park.

Photo 2: Ground view of the pavilion at the Eau Claire Soccer Park.  

Methods:

Both the Iris and the Phantom drones were used during this exercise.  The Iris was programmed using the "Structure Scan" mode on the mission planner tablet app to make circular paths around the pavilion starting at 5m and climbing to 26m at 4m intervals.  The GoPro camera on the Iris was set to the "narrow" lens view, and programmed to take a photo every 2 seconds.  After the Iris completed the mission, the drone was flown in manual mode at a height of approximately 2.5m above the ground to ensure that the entire building was captured by the drone.  

In comparison, the Phantom was used solely in manual mode, and each member of the class was given an opportunity to take photos of the pavilion by circling drone around the building.  Photos were taken using a variety of angles and heights to ensure full coverage.  

Oblique photography data collection for 3D modeling is different than Nadir (mapping) because shadows and the number of angles play a major role in how much detail is captured about the object being modeled.  For that reason, multiple photos of the same spot must be taken at different angles and locations to ensure that the model can be rendered in the proper detail.  In addition, more photos are needed (relative to nadir photography) because oblique photography tends to have high scale variations and radial distortions.

Discussion:

Data for this lab is not yet processed, so discussion will focus on the data collection experience.

During the data collection portion, I found oblique photography to be much more complex because many more variables are involved.  In Nadir photography, the camera angle and height above the ground is fixed, whereas in oblique photography both aspects are altered.  As a result, it may not always be practical to fly pre-planned missions where the camera shutter is set at a specific interval. For example, as seen in photo 2, the pavilion has an overhang that covers part of the lower building.  For this reason, the Iris was flown manually at 2.5m off the ground because it was believed that the original mission flown by the Iris (starting at 5m) was unable to capture the lower part of the building covered by the overhang.  The object chosen for 3D modeling in this lab is relatively simple, however it is easy to see how a more complex structure could require precise angles, and therefore manual mode, to prevent gaps in the data. 

Conclusion:

Oblique aerial photography captures objects at an angle, giving viewers a 3D representation  of an object that is easier to identify than nadir photography.  However, oblique photography should not be used for mapping or measurements because it has a larger degree of distortion that prevent applying the traditional nadir mapping equations.  Full coverage is important in order to obtain a complete rendering of the target object, meaning using mission planner or similar software may not be applicable in situations that require a more delicate touch.  Lastly, oblique photography requires more photos over a smaller areas because variables such as shadows and camera angles are no longer fixed.  

Lab 4: Gathering Ground Control Points

Introduction:

The purpose of this lab was to learn how to establish ground control points (GCPs).  GCP's are used to georeference and geometrically correct images taken from unmanned aerial systems.  Therefore, the accuracy of the map is only as good as the accuracy and precision of the GPS used to establish the position of a given GCP.  In this lab, obtained the location of the GCP's using various GPS platforms so that a comparison in their accuracy could be assessed.  A minimum of three GCP's is necessary to adjust an image to a reference coordinate system but in general, the more topographically variable a location is, the more GCP's that will be required (Aber, J.S., Marzolff, I., and Ries, J.B., 2010, 124-128). Lastly, when placing GCP's, it is important to ensure that the GCP's are spaced out and not at the edge of the map area.  This is because the further you get from the center of the map area, the more distorted the map features become.

Map Area:

Photo 1: Map area outlined by yellow rectangle.  Map area is located between Fairfax Street and Hester Street, just south of South Middle School.  

Photo 2:  Ground view of map area taken from the cul de sac at the end of Hester Street. 

Conditions during the lab period were Sunny/Cloudless, and a temperature of about 60°F. Documentation of the weather conditions in addition to a hand drawn map may be found under the field notes section of this Blog.

Methods:

Before a mission could be flown using the Matrix Quad Copter, ground control points were established on the ground.  Based on the size of the map area and relatively flat topography, six ground control points were established (GCP locations visually represented on hand drawn map under Field Notes section of blog).  The GCP's consist of a black and white tarp approximately one meter wide and one meter long so that they can be seen from aerial photo (Photo 3).  As each point was established, the location was taken using the Dual Frequency Survey Grade GPS.  This device has millimeter accuracy, and served as the baseline for the other GPS devices to be compared to.  

Photo 3:  Using the Dual Frequency Survey Grade GPS to pinpoint Ground Control Point locations.  

Once the GCP locations had been pinpointed using the Dual Frequency Survey Grade GPS ($12,000), the latitude and longitude were taken again using the following devices: Bad Elf GNSS Surveyor GPS ($600 and claims sub-meter accuracy), Bad Elf GPS ($125), Garmin GPS ($100), and a Smart Phone GPS.

The final objective of this lab was to fly a mission over the map area, so that the images could later be georeferenced using data from the different GPS's listed above.  As shown in photo 4 and 5, a mission was planned using the mission planner software, and executed using the Matrix Quad Copter.  

Photo 4:  Showing the mission created
using the mission planner software

Photo 5: Showing the Matrix Mid Fligh

Discussion:  

The data obtained from this lab is not yet processed, so the discussion shall focus on the GCP's and different GPS's used.

In a commercial setting, it is easy to see how the Dual Frequency Survey GPS would be necessary for certain projects requiring the the most accuracy.  However, not all people an afford $12,000 equipment to do their mapping.  This is why technology is moving toward a more portable option such as the Bad Elf Surveyor.  Although the Dual Frequency Survey GPS provides high quality data, it is rather large, and may pose mobilization issues when collected data from areas with difficult terrain.  Therefore, in some circumstances it may be more practical to utilize more compact equipment such as the Bad Elf GNSS Surveyor if the results defend its claim to sub-meter accuracy.  Using the Bad Elf GPS (Photo 5) with the Tablet made data collection very quick and easy, and was by far more portable than the Dual Frequency Survey GPS.

Photo 5, Showing the compact shell of the Bad Elf GPS

Lastly, Ground control points could become less practical if the mapping area is covered by heavy foliage, and is extremely large.  As learned from the lab, establishing GCP's takes a substantial amount of time.  If the same process is scaled up to large area with tough terrain and foliage, the process would prove extraordinarily time consuming.  

Conclusion:

This lab taught me which factors to consider when placing Ground Control Points so that the aerial photos could be properly georeferenced when processed. Overall, we placed six GCP's around the map area, and made sure to pick locations equally spaced from each other, away from the edges, and on varying levels of topography.  In addition, it was interesting to explore the strengths and weakness of each GPS device, and I am interested in analyzing data obtained from the Matrix Quad Copter.