EVALUATING UNMANNED AERIAL VEHICLE BASED CROP INDEXING
TECHNIQUES: MODIFIED CONSUMER GRADE RGB VS. MULTISPECTRAL
by

CHRISTOPHER F. SOLECKI
A THESIS SUBMITTED IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
BATCHELOR OF NATURAL RESOURCE SCIENCE (HONS.)
IN THE DEPARTMENT
OF
NATURAL RESOURCE SCIENCES
AT
THOMPSON RIVERS UNIVERSITY

THESIS EXAMINING COMMITTEE
John S. Church (Ph.D.), Thesis Supervisor & Associate Professor,
Dept. Natural Resource Sciences
David J. Hill (Ph.D.), Associate Professor, Dept. Geography and Environmental Studies
Garrett Whitworth (Ph.D.), Post-Doctoral Fellow, Faculty of Science
Lauchlan Fraser (Ph. D), Professor, Dept. Biological Sciences

Dated the 12th day of May 2017, in Kamloops, British Columbia, Canada

Abstract
Thesis Supervisor: Associate Professor John S. Church
The use of unmanned aerial vehicles (UAVs) in agriculture is a relatively new and rapidly
expanding concept. By using UAVs equipped with multispectral near infrared sensors, farmers
and land managers can detect intra-field crop variability which enables adjustments to be made
to crop applications and other management decisions. This type of management has been
termed precision agriculture and employs the use of various crop indices such as the
normalized difference vegetation index (NDVI). The NDVI is one of the most common crop
indices; and is used to measure relative chlorophyll content in green vegetation. In this study, I
investigated the sensing abilities of two aerial cameras to determine whether filter modified
consumer cameras can produce NDVI maps equivalent to those produced by a multispectral
camera. I compared a MicaSense RedEdge® multispectral camera to a modified DJI Zenmuse X3
camera, mounted simultaneously onboard a DJI Inspire 1 UAV, with respect to their ability to
generate reliable NDVI maps using Pix4D photogrammetry software. Through evaluation of the
index maps produced by the two cameras, the MicaSense RedEdge® was found to produce
index values that were more representative of the study site than the modified DJI Zenmuse X3.
Spatial vegetation patterns observed by the two sensors were also determined to be
significantly different. This study revealed that the two sensors did not produce equivalent
NDVI results, and multispectral cameras appear to be a more accurate tool for examining crop
productivity and variability.

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Table of Contents
Abstract ................................................................................................................................. i
List of Figures ....................................................................................................................... iii
List of Tables: ....................................................................................................................... iv
Acknowledgements: ............................................................................................................. iv
Introduction .......................................................................................................................... 1
Materials and Methods ......................................................................................................... 5
Image Acquisition and Processing ...................................................................................................5
Statistical Analysis .........................................................................................................................8

Results .................................................................................................................................. 9
Index Values ..................................................................................................................................9
Spatial Variations .........................................................................................................................12

Discussion ........................................................................................................................... 14
Processing Images ........................................................................................................................14
Index Values ................................................................................................................................15
Spatial Variation ..........................................................................................................................17
Image Format ..............................................................................................................................17

Conclusions ......................................................................................................................... 18
Literature Cited ................................................................................................................... 19
Appendix A.......................................................................................................................... 22
Appendix B .......................................................................................................................... 26
Appendix C .......................................................................................................................... 30
Appendix D ......................................................................................................................... 31

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List of Figures
Figure 1: Transmission curve for the modified DJI Zenmuse X3 camera following modification
with the band pass filter. Only red (660 nm) and near infrared (850 nm) wavelengths of
light can pass through the filter to the sensor (adapted from [Aerial Media Pros 2015])..... 3
Figure 2: Transmission curve for the MicaSense RedEdge® multi-spectral camera. Blue (475
nm), green (560 nm), red (668 nm), red edge (717 nm), and near infrared (840 nm) are
captured by the sensor (Adapted from [MicaSense 2016]). .................................................. 4
Figure 3: DJI Inspire 1 quadcopter with modified Zenmuse X3 and MicaSense RedEdge®
simultaneously mounted. ....................................................................................................... 6
Figure 4: Example of NDVI maps produced by pix4D photogrammetry software. Shown are
NDVI maps from August 23rd from the MicaSense RedEdge® (left) and the modified X3
(right). Green represents areas with high relative NDVI values while red represents areas
with low relative NDVI values. .............................................................................................. 10
Figure 5: Mean NDVI values for NDVI orthomosaics for the four sampling dates. Error bars
represent +/- 1 S.D. ............................................................................................................... 11
Figure 6: Example of NDVI maps once they had been separated in 20% quantiles and assigned
ordinal ratings of 1 to 5. Maps shown are from the August 23rd sampling date from the
MicaSense RedEdge® (left) and the modified X3 (right). ..................................................... 13
Figure 7: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3
(right) for July 11th, 2016....................................................................................................... 22
Figure 8: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3
(right) for July 13th, 2016....................................................................................................... 23
Figure 9: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3
(right) for August 19th, 2016 ................................................................................................. 24
Figure 10: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3
(right) for August 23rd, 2016 ................................................................................................. 25
Figure 11: NDVI maps once they had been separated in 20% quantiles for the July 11th, 2016
sampling date from the MicaSense RedEdge® (left) and the modified DJI X3 (right). ......... 26
Figure 12: NDVI maps once they had been separated in 20% quantiles for the July 13th, 2016
sampling date from the MicaSense RedEdge® (left) and the modified DJI X3 (right). ......... 27
Figure 13: NDVI maps once they had been separated in 20% quantiles for the August 19th, 2016
sampling date from the MicaSense RedEdge® (left) and the modified DJI X3 (right). ......... 28
Figure 14: NDVI maps once they had been separated in 20% quantiles for the August 23rd, 2016
sampling date from the MicaSense RedEdge® (left) and the modified DJI X3 (right). ......... 29
Figure 16: The DJI Zenmuse X3. This camera has a single lens and one 12 mega-pixel sensor with
a Bayer filter array. ............................................................................................................... 31
Figure 17: The MicaSense RedEdge® multispectral camera. This camera has 5 lenses, one for
each spectral band, and 5, 1.2 mega-pixel sensor chips with a separate filter for each chip.
............................................................................................................................................... 31

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List of Tables:
Table 1: Minimum, maximum, standard deviation and mean NDVI values for the MicaSense
RedEdge® and modified X3 NDVI maps for July 11th, July 13th, August 19th, and August 23rd..
................................................................................................................................................. 9
Table 2: Results of the Wilcoxon Signed Ranks test showing the standardized test statistic and
the significance of the test.................................................................................................... 30

Acknowledgements:
I would like to thank my thesis supervisor John Church for providing me with the project idea as
well as all the equipment that was needed to collect the data. Thank you John for including me
on the drone team, and for allowing me to be involved in various projects over the past couple
of years. I really appreciate all the help you have given me. I would also like to thank the rest of
my committee members. Thanks to David Hill for providing me with some guidance in finding a
way to compare these two cameras. Doing so would have been a lot harder without your
insight. Thank you, Garrett, for providing input and suggestions into my project along the way
and for sharing the MicaSense with me over the summertime. Thank you Lauch for your
feedback on the project that helped me add some extra strength to my statistics.

iv

Introduction
The use of unmanned aerial vehicles (UAVs) in agricultural management is a
relatively new and rapidly advancing field (Van der Meij 2016). Declining costs and
increasing capabilities of consumer-grade UAVs have made them more useful, convenient,
and accessible to consumers and farmers alike. UAVs provide an advantage over traditional
methods of acquiring remote sensing data (helicopters, fixed wing aircraft, and satellites) in
that operational costs are lower, they have more flexibility, and spatial resolutions are
greater (Matese et al. 2015). The integration of UAVs into agricultural management has
become increasingly popular with the emergence of what is termed Precision Agriculture
(PA), which uses technology to increase management precision and efficiency by
strategically placing crop additives, such as pesticides and fertilizer, in order to reduce
inputs while maximizing outputs (Candiago et al. 2015). This type of management strategy
uses ground sensing techniques such as multi-spectral imaging to evaluate plant
productivity and vigor, which is typically done through the generation of vegetation indices
(Candiago et al. 2015).
Vegetation indices are a way of using the reflectance of different wavelengths of
incident light to give us information about the characteristics of vegetation (Herring 2000).
The normalized difference vegetation index (NDVI) is one of the most commonly used
indices and looks specifically at the red (620 - 780 nm) and near infrared (NIR)(800 - 2500
nm) regions of the electromagnetic spectrum (Herring 2000). This index is useful for
determining the productivity of vegetation because (1) chlorophyll absorbs light strongly in
the red region of the spectrum while leaf cell structure reflects strongly in the NIR region
(Monteith 1972); and (2) the rate of photosynthesis is positively correlated with the amount
of photosynthetically active radiation absorbed by the plant (Pavlović et al. 2014). This
assumes that a higher rate of photosynthesis is indicative of higher chlorophyll
concentrations as well as productive vegetation; therefore, more chlorophyll indicates
greater plant productivity (Pavlović et al. 2014). Capturing reflectance data overtop of crops

1

can therefore allow us to derive measures of crop productivity using NDVI and similar
indices.
Multi rotor UAVs offer an affordable and user friendly platform to mount airborne
sensors on to capture images of crops. Typically, an automated grid pattern is flown over
top of the target area with images being captured at predetermined intervals with a set
overlap and height. Once images of a crop have been captured, processed into an
orthomosaic and an index map is created, the information contained in the index map can
be quantified into different regions for application. By examination of the NDVI and other
similar indices (such as the green normalized difference vegetation index and the soil
adjusted vegetation index) managers are able to identify variability within crops; and make
adjustments to applications rates of crop additives such as fertilizer and pesticides by
employing variable rates of application (Candiago et al. 2015; Matese et al. 2015). The
ability to detect and account for intra-field variability, and employ variable rate spreading
instead of uniform application over the entire crop, has the potential to save costs on crop
additives, lower environmental costs induced by leeching of excess nutrients and chemicals,
and increase crop yields (Matese et al. 2015).
Modified cameras, such as the modified DJI Zenmuse X3 (Appendix D, Figure 15)
used in this study, refer to standard consumer digital cameras (capturing red, green, and
blue bands of light; RGB) that have been modified by altering the filter configuration (Taylor
2015). The modified camera used in this study has had the infrared blocking filter removed,
and a lens with a band pass filter added which allows only red and near infrared
wavelengths to pass through the lens at 25 nm band widths for each band. This
modification allows near infrared light to pass through as well as the red band so that NDVI
can be calculated (Aerial Media Pros 2015). The Zenmuse X3 camera has a 12 megapixel
complimentary-metal-oxide-semiconductor (CMOS) sensor with a Bayer filter array (DJI
2017). Once the new filter is installed, the camera effectively has two filters: the band pass
filter and the Bayer filter array which is situated directly on top of the sensor. The
transmission curve for this cameras lens is shown in Figure 1. This transmission curve shows

2

the specific bands of light that are able to pass through the filter band pass filter, however,
it does not show the transmission of the micro-filters on the Bayer filter array.

Figure 1: Transmission curve for the modified DJI Zenmuse X3 camera following modification with
the band pass filter. Only red (660 nm) and near infrared (850 nm) wavelengths of light can pass
through the filter to the sensor (adapted from [Aerial Media Pros 2015]).

Multi-spectral cameras like the MicaSense RedEdge® (5-band)(Appendix D, Figure
16) simultaneously capture discrete bands of light through separate lenses (MicaSense
2016). The MicaSense RedEdge® captures 5 bands of light in the blue (475nm), green (560
nm), red (668 nm), red edge (717 nm), and near infrared (840 nm) regions of the spectrum
(Figure 2). This camera has a 1.2 megapixel CMOS sensor for each of the five bands that it
captures (2017 personal communication with MicaSense support).

3

Figure 2: Transmission curve for the MicaSense RedEdge® multi-spectral camera. Blue (475 nm),
green (560 nm), red (668 nm), red edge (717 nm), and near infrared (840 nm) are captured by the
sensor (Adapted from [MicaSense 2016]).

The proposed advantage of modified cameras is that they offer similar results to
multi-spectral cameras at a much lower cost; and essentially any digital camera could be
turned into a tool for precision agriculture by simply removing the infrared blocking filter.
An Aerial Media Pros modified DJI Zenmuse X3 retails for $1740.66 CAD (Aerial Media Pros
2015), while the MicaSense RedEdge® multispectral camera retails for $7906.00 CAD
(MicaSense 2016). While modified cameras may offer a lower cost of data collection in
terms of initial capital acquisition, the reliability of data acquired from these cameras has
not been well demonstrated in the literature (Lebourgeois et al. 2008; Klaas 2016).
Matese et al. (2015) present evidence that the UAV platform is most useful on
smaller scales (5 ha or less) as they provide greater resolutions, and thus are more capable
of detecting variability within crops with high levels of heterogeneity. While comparisons
between various sensing platforms (UAV, aircraft, and satellite) have been conducted (Nex
and Remondino 2014; Matese et al. 2015), and have identified UAV’s as a cost-effective
4

method of data acquisition, there is debate over whether low cost, modified consumer
grade RGB cameras can offer reliable NDVI results as compared to NDVI produced by multispectral cameras. Previous work has found varying results when analyzing crop indices from
modified cameras with some supporting their use (Lebourgeois et al. 2008), and some
finding spectral contamination in modified cameras significantly reduces accuracy (Klaas
2016).
For this study, I examined the NDVI maps that were produced by the MicaSense
RedEdge® 5-band multi-spectral camera and compared it to a modified DJI Zenmuse X3 to
determine whether the modified X3 camera will provide a similar output with respect to
NDVI values, and the spatial patterns in vegetation productivity that are recognized by the
sensor. My null hypotheses are that (1) the modified camera will produce NDVI index maps
that are equivalent (not statistically different) to those produced from the MicaSense
RedEdge®, and (2) the same spatial variations in vegetation will be recognized by both
cameras.
The objectives for my study were to:
•

simultaneously capture images with both cameras overtop of the plot area;

•

generate normalized difference vegetation indices (NDVI) from the images obtained
by both cameras

•

determine whether NDVI values differ between camera;

•

determine whether the same spatial variation in vegetation is recognized by both
cameras

Materials and Methods
Image Acquisition and Processing
All images acquired from the cameras were taken over top of a square 250x250m
grass forage plot located at Tatalrose Ranch inc. in Grassy Plains, British Columbia. The plot

5

was seeded with a forage mix of 21% smooth brome (Bromus inermis), 9% orchard grass
(Dactylis glomerata), 2% timothy (Phleum pretense), 29% 4-star alfalfa (Medicago sativa),
32% red clover (Trifolium pratense), and 7% yellow blossom alfalfa (Medicago falcata). The
approximate location of the plot is 53°58'55.69"N, 125°58'42.66"W. Both cameras were
mounted simultaneously on the DJI Inspire 1 quad-copter for all flights. The modified X3
was mounted on the aircrafts factory gimbal and the MicaSense RedEdge® was attached
using a custom mounting plate securing it to the back of the aircraft (Figure 3). Grid flight
patterns overtop of the plot were generated using the Pix4D mapper capture application
and were flown at a height of 40 m. Images captured by the modified X3 were saved by
default in 8-bit JPEG format while the images captured by the RedEdge® were stored as 16bit RAW Tiff files. The X3 was powered by the battery on the UAV, while the RedEdge® was
independently powered by a GoPro battery.

Figure 3: DJI Inspire 1 quadcopter with modified Zenmuse X3 and MicaSense RedEdge®
simultaneously mounted.

Prior to initiation of all flights, weather conditions were recorded including wind
speed, temperature, and cloud cover. Time since last precipitation, time of flight, and the
average maturity of the crop were also recorded. Immediately prior to the first flight on
each day, images of a spectralon calibration target were taken to calibrate images to the
degree of incoming solar radiation. As cloud cover was generally high, and persisted for the
majority of the field season, I attempted to select days with similar cloud conditions (based

6

on visual observation) in order to keep variability of incoming solar radiation low. Flights
consisted of 4 – 150 x 150 m grid flights at 40 meters above ground level, resulting in a 300
x 300 m square area being covered. 300 x 300 m was used to ensure that the entire plot
area (250x250 meters) was encompassed by the flight. All flights were initiated between
12:00 pm and 1:00 pm local time, and flights were completed within 1 to 1 ¼ hours of the
start time.
Images were then processed to produce orthomosaics using Pix4D photogrammetry
software. Images were calibrated using the images of the spectralon calibration target and
the Pix4D camera calibration tool. Once step 1 of processing in Pix4D had been completed,
five 3-dimensional ground control points (GCPs) were added to each map (each GCP was
marked on a minimum of two images) to increase the positional accuracy of the maps.
Pix4D is a software package that allows users to take georeferenced images captured with a
UAV or aircraft, and stitch these images together into orthomosaics, digital surface models,
terrain models, and point clouds (Pix4D 2017a). The software is meant to allow consumer
level UAVs to be used as mapping and surveying tools (Pix4D 2017a).
The orthomosaics created in Pix4D were used to generate a reflectance map from
which the NDVI map was obtained. The NDVI formula that was used in the Pix4D raster
calculator for the MicaSense RedEdge® is as follows:

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =

𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 − 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌
𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 + 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌

Where NDVI is the normalized difference vegetation index, 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 is the near infrared band,
and 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 is the red band.

The formula used in the raster calculator for the modified Zenmuse X3 camera was

slightly different, as the camera captures the reflectance data from the near infrared band
in the location that the standard, unmodified camera captures reflectance data for the blue
band and is as follows:

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =

𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 − 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌
𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 + 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌
7

Where NDVI is the normalized difference vegetation index, 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 is the band storing

information for the near infrared band, and 𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌 is the red band.

Statistical Analysis
In order to test for significant differences in NDVI values between the two cameras
and between the four sampling dates, a two-way ANOVA (α=0.05) was done using IBM SPSS
statistical software (IBM Corp. 2016). Prior to the two-way ANOVA, each of the groups of
the two independent variables were tested for normality and homogeneity of variance
using SPSS (IBM Corp. 2016). Camera and date were used as the two factors for the test;
and 1100 sample pixels were randomly selected from each of the index maps using ArcMap
software (ESRI 2016).
To determine whether the two sensors were recognizing the same spatial patterns
in the vegetation across the plot, further processing of the orthomosaics was needed. A
GeoTiff of each index map was exported from Pix4D software and loaded into an R-Studio
working directory (R Core Team 2016). The GeoTiff files contained coordinates and an NDVI
value for each pixel. To statistically compare each set of NDVI maps the index values from
each map were separated into quantiles of 20 percent for a total of five categories. Once
the quantiles were created, ordinal values from 1 to 5 were assigned to each category of
values with five representing the highest NDVI values and 1 representing the lowest NDVI
values (e.g. if an NDVI value was 0.92 and 20% of all the index values for the map were
above 0.90, then it would be designated as a five). The transformed index maps were then
exported from R-Studio as a GeoTiff.
GeoTiffs were then imported into ArcMap GIS software (ESRI 2016). Using the pixel
inspector tool and the X,Y coordinate locator, random samples were collected from each of
the modified X3 index maps. A sample from the same coordinates were then taken from the
corresponding MicaSense RedEdge® index maps for each date. Sample coordinates were
selected using a random number generator to produce a latitude and longitude within the
map extent. A total of 5, 220 pixel samples were drawn from each map for a total of 1,100

8

pixel values. SPSS statistical software was used to compare paired samples from each map
using a Wilcoxon matched pairs signed ranks test (α=0.05).

Results
Index Values
Table 1 displays the minimum and maximum values as well as the mean value for
the entire raster obtained from each of the NDVI maps generated from the modified DJI X3
and the MicaSense RedEdge® images. An example of the NDVI maps produced by Pix4D for
both of the cameras can be seen in (Figure 4). Green indicates areas that have higher NDVI
values while red indicates low NDVI values. The NDVI maps for both cameras for all four
sampling dates can be found in Appendix A. The mean NDVI values were higher for the
MicaSense RedEdge® for all index maps and all values obtained from the modified X3 were
negative. Mean values from the MicaSense RedEdge® decreased with each successive
sampling period but this trend was not present for the modified X3. Standard deviations for
index maps from the MicaSense RedEdge® were also larger.

Table 1: Minimum, maximum, standard deviation and mean NDVI values for the MicaSense
RedEdge® and modified X3 NDVI maps for July 11th, July 13th, August 19th, and August 23rd..

Max
-0.16

Std. Dev.
0.0151

Mean
0.901

RedEdge®
Min Max
0.67 0.96

Std. Dev.
0.0232

-0.54

-0.04

0.0195

0.901

0.67

0.90

0.0199

-0.136

-0.98

-0.01

0.0236

0.589

0.24

0.87

0.0778

-0.149

-0.98

-0.02

0.0420

0.366

0.00

0.76

0.0994

July 11th

Mean
-0.164

Min
-0.60

July 13th

-0.174

Aug. 19th
Aug. 23rd

X3

9

Figure 4: Example of NDVI maps produced by pix4D photogrammetry software. Shown are NDVI maps from August 23rd from the MicaSense RedEdge®
(left) and the modified X3 (right). Green represents areas with high relative NDVI values while red represents areas with low relative NDVI values.

10

Figure 5 shows mean NDVI values from Table 1 plotted over time. NDVI value was
significantly related to the camera model that captured the images (P<0.0001), and NDVI
values for the MicaSense RedEdge® were higher than the X3 for all dates. NDVI value was
also significantly related to the date that the image was captured on (P<0.0001), and NDVI
values for the MicaSense RedEdge® declined as date progressed. There was also a
significant interaction between camera and date. Index value by date was significantly
related to camera model that captured the images (P<0.0001).

Figure 5: Mean NDVI values for NDVI orthomosaics for the four sampling dates. Error bars
represent +/- 1 S.D.

11

Spatial Variations
Figure 6 shows an example of the NDVI index maps once they had been separated
into 20% quantiles and assigned values from 1 to 5. The scale bar in the upper right corner
shows the ordinal rating scale and black and white color palette where a 1 (white)
represents areas of low relative NDVI values while 5 (black) represents areas of high relative
NDVI values. The spatial patterns that were recognized by the MicaSense RedEdge® were
significantly different (P<0.0001) for all dates except for July 13th (P=0.08). A summary of
results from the Wilcoxon signed ranks test between the two cameras on each date can be
found in Appendix C, Table 2.

12

Figure 6: Example of NDVI maps once they had been separated in 20% quantiles and assigned ordinal ratings of 1 to 5. Maps shown
are from the August 23rd sampling date from the MicaSense RedEdge® (left) and the modified X3 (right).

13

Discussion
In this study, I looked at the differences in NDVI maps produced by two different
cameras to assess whether they are producing similar NDVI values. Multi-spectral imagers
have been looked at before in comparison with modified consumer cameras, and have been
proven to produce values that are close to that of a field spectrometer (assuming the same
filter configuration), which is regarded as the most accurate way to measure reflectance in
the field (Nebiker et al. 2016). Previous studies have found mixed results when examining
modified consumer cameras, with some finding modified cameras very similar to
multispectral cameras with respect to quantitative and qualitative measurements
(Lebourgeois et al. 2008); and some finding that only qualitative analysis is possible from
these cameras (values are different than field spectrometer measurements but patterns are
still recognized correctly) (Klaas 2016; Nebiker et al. 2016).
In summary, the main results from this study were as follows:
•

NDVI maps produced by the MicaSense RedEdge® and modified X3 camera did not
display similar index values. The MicaSense produced positive values while the
modified X3 produced negative values;

•

NDVI values were significantly correlated with the camera that captured the images;

•

The two cameras did not recognize the same spatial variation in NDVI values.

Processing Images
The MicaSense RedEdge® had greater ease of processing than the modified X3
camera. Data processing for the modified X3 was more difficult as the Pix4D
photogrammetry software produced more errors in processing of the images obtained by
this camera which resulted in issues with stitching images together. The issue may be that
the program recognized the modified camera as an unmodified Zenmuse X3. As the
modified camera has a different lens and filter configuration than the factory model,
adjustments to camera parameters in the pre-processing stage had to be made (2016 email

14

from Pix4D support). The quality report that summarizes Pix4D processing of orthomosaics
identified high relative differences between optimized and internal camera parameters for
the modified X3. The modified X3 had an average of 16.87% difference between initial and
optimized camera parameters, even once the recommended adjustments to camera
settings were made. In comparison, the MicaSense RedEdge® had an average of only 0.32%.
This may be a reason for processing errors experienced with the Modified X3 (Pix4D 2017b).
From visual observations of the index maps it appeared that the modified X3 camera
was impacted largely by vignetting effects. Vignetting refers to the reduction in sensor
saturation as you move farther away from the center of an image; meaning that as you get
closer to the edges of the photo, digital numbers (DN)(a variable value that is assigned to
each pixel) will decrease, and the image darkens (Pix4D Support 2016). Pix4D software
automatically corrects for vignetting effects when generating the reflectance maps (Pix4D
Support 2016), however it appeared that effects from vignetting were still prevalent
following processing; and this likely had an impact on generation of index maps as well as
NDVI values (Lebourgeois et al. 2008).

Index Values
NDVI values from the MicaSense RedEdge® camera followed a logical trend as time
progressed throughout the field season (Table 1 and Figure 5). Following seed production
and maturation of grass, chlorophyll concentrations begin to decrease and dry matter
content within the plant increases (Cordon et al. 2016). It was noted on August 12th that the
grass crop had reached full maturity and had begun to senesce. As the chlorophyll
concentrations decline, we should see a subsequent decline in NDVI values. The MicaSense
RedEdge® NDVI values decreased from the July 11th flight onward which is logical, however
the modified X3 NDVI values did not follow this trend. Based on this information the
RedEdge® appears to be more effective at recognizing chlorophyll concentrations based on
NDVI.
Negative NDVI values are characteristic of areas with no vegetation such as bare soil
or rock, at least when the true reflectance is captured (Candiago et al. 2015). The modified
15

DJI X3 (modified X3) produced all negative values for all index maps while the MicaSense
RedEdge® produced all positive values Table 1 and Figures 7, 8, 9, and 10). The proximal
cause of the negative values produced by the modified X3 is the low level of near infrared
light that is being registered by the sensor relative to red light. In green vegetation, there
should be more near infrared light reflected than red light and NDVI values are expected to
be above 0 (Herring 2000).
Low relative levels of near infrared light could occur for more than one reason.
Lebourgeois et al. (2008) attributed low DN’s of near infrared light to underexposure of the
images resulting in low values in the near infrared region. A more likely cause however is
that the overlapping of spectral channels in modified cameras can cause near infrared
values to be lower than expected relative to other wavelengths, which has been
documented in other studies (Verhoeven et al. 2009; Nebiker et al. 2016). Spectral overlap
occurs because the infrared blocking filter has been removed, and because silicon sensors
are very sensitive in the near infrared region of the spectrum (Darmont 2009), it allows the
photodiodes of the sensor to absorb photons in the near infrared region. Photodiodes that
register red light are typically the most sensitive of the visible bands in the near infrared
region so red sensors will be registering both red light and a high level of near infrared once
the near infrared sensor is removed (Verhoeven et al. 2009). The blue band, which is the
band being used for near infrared in the modified X3, will not absorb as large a portion of
near infrared light (Verhoeven et al. 2009). This would result in higher values being
registered for red light by the modified X3 and thus a negative NDVI value because it
effectively changes the formula for the modified X3 camera. The formula will change
because the red band is really registering red plus NIR, while the blue band is only
registering NIR. The resulting formula is then:

𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =

𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌−(𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌+𝜌𝜌𝑛𝑛𝑛𝑛𝑛𝑛)

 𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁 =

𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌+(𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌+𝑛𝑛𝑛𝑛𝑛𝑛)

16

−𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌
2𝜌𝜌𝜌𝜌𝜌𝜌𝜌𝜌

Spatial Variation
The patterns observed once the index maps had been separated into quantiles were
significantly different between the two sensors (Table 2 and Figures 11, 12, 13, and 14).
Upon visual inspection of the index maps, either before or after transformation to the
ordinal rating scale, the two maps do not appear similar and the same trends in vegetation
are not present. Vignetting effects would likely have an impact on this as NDVI values were
lower in areas where vignetting was most apparent. Vignetting was also only present in
NDVI maps from the modified camera, resulting in large differences in values in some
locations, which likely had a sizable impact on detecting spatial patterns.

Image Format
Capturing images and storing them in jpeg format is important to address, as this is
the format that images from the modified X3 camera were stored in. It is well established in
the literature that capturing images in jpeg format is not recommended when radiometric
fidelity is desired, and that storing of images in RAW format is preferable (Verhoeven 2009).
RAW format refers to image storage where DNs are not compressed prior to storage to
preserve all characteristics of the image (Verhoeven et al. 2009). Klaas (2016) found that
observed differences between modified and multispectral derived NDVI were due to
spectral contamination due to overlapping bands rather than the loss of spectral artefacts.
Additionally, Lebourgeois et al. (2008) reported that using unprocessed images did not
improve results of image analysis; and capturing RAW images didn’t change the correlation
between spectral bands and surface characteristics. While jpeg compression is likely to
impact the ability to derive detailed quantitative information from the sensor, it does not
explain the low relative values obtained for the near infrared band and resultant negative
NDVI values produced by the modified X3.

17

Conclusions
In conclusion, this study showed that there were significant differences between
NDVI maps produced by the modified DJI Zenmuse X3 RGB camera and the MicaSense
RedEdge® multispectral camera. The MicaSense RedEdge® appears to be a more accurate
means of producing NDVI when compared to the modified X3 camera because it identified
logical changes in chlorophyll throughout grass maturation that was observed between
sampling dates. As the reflectance of red light increased with senescence, NDVI values are
expected to decline, as they did for the RedEdge®. While I have speculated about the cause
of the low values for near infrared light collected from the modified X3, to really determine
the cause of this difference, a spectral response curve should be generated from the
modified X3 using the process outlined in Verhoeven et al. (2009). This would allow the
sensitivities of the camera bands to different wavelengths of light to be known, and thus
would potentially provide a more complete explanation of the observed differences.
The modified DJI Zenmuse X3 RGB camera does not appear to produce equivalent
NDVI maps to those produced by the MicaSense RedEdge®. The RedEdge® recognized
expected patterns in grass maturation correctly, as well as produced values characteristic of
green vegetation, while the modified X3 camera did not. The vegetation sensing capabilities
of the modified DJI X3 camera does not appear to be equivalent to that of the MicaSense
RedEdge® multispectral camera with respect to the NDVI produced; and therefore I reject
my null hypothesis that the modified DJI X3 RGB camera is capable of producing equivalent
NDVI index maps to those produced from the MicaSense RedEdge® multispectral camera, as
well as the null hypothesis that the two cameras will recognize the same patterns in spatial
variation in vegetation.

18

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Appendix A

Figure 7: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3 (right) for July 11th, 2016

22

Figure 8: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3 (right) for July 13th, 2016

23

Figure 9: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3 (right) for August 19th, 2016

24

Figure 10: NDVI orthomosaics from the MicaSense RedEdge® (left) and the modified DJI X3 (right) for August 23rd, 2016

25

Appendix B

Figure 11: NDVI maps once they had been separated in 20% quantiles for the July 11th, 2016 sampling date from the MicaSense
RedEdge® (left) and the modified DJI X3 (right).

26

Figure 12: NDVI maps once they had been separated in 20% quantiles for the July 13th, 2016 sampling date from the
MicaSense RedEdge® (left) and the modified DJI X3 (right).

27

Figure 13: NDVI maps once they had been separated in 20% quantiles for the August 19th, 2016 sampling date from the MicaSense
RedEdge® (left) and the modified DJI X3 (right).

28

Figure 14: NDVI maps once they had been separated in 20% quantiles for the August 23rd, 2016 sampling date from the MicaSense
RedEdge® (left) and the modified DJI X3 (right).

29

Appendix C
Table 2: Results of the Wilcoxon Signed Ranks test showing the standardized test statistic and the
significance of the test.

Date
July 11th
July 13th
August 19th
August 23rd

Standardized Test Stat
18.586
1.752
19.725
-6.824

30

Significance
P<0.0001
0.08
P<0.0001
P<0.0001

Appendix D

Figure 15: The DJI Zenmuse X3. This camera has a single lens and one 12 mega-pixel sensor with a
Bayer filter array.

Figure 16: The MicaSense RedEdge® multispectral camera. This camera has 5 lenses, one for each
spectral band, and 5, 1.2 mega-pixel sensor chips with a separate filter for each chip.

31