Use of aerial imagery to
detect N response in corn
following alfalfa.
FR 5262
Matt Yost
Stephen Palka
Project Description / Objectives:
The purpose of this project is to determine whether high spatial resolution aerial imagery can be
used to detect differences in corn yield due to nitrogen (N) fertilizer treatments applied to small
experimental plots within farmer’s fields. When researchers conduct soil fertility experiments in
crop fields such as corn, the yield of crops or plant samples are either measured by handharvesting small areas in each plot (20-40 ft. of corn row), with specialized plot combines, or
with large-scale commercial combines. Each of these methods has their advantages and
disadvantages. When hand-harvesting corn, the number and size of the samples are often limited
because of high labor and time requirements, whereas plot or commercial combines allow for
greater samples to be collected, but require large plots and strict calibration of yield monitors
and/or weigh wagons. If high spatial resolution aerial imagery could be used to estimate yield
[using greenness, normalized difference vegetation index (NDVI), or other indices], then the
imagery may be a tool for detecting treatments differences across experimental plots. This would
allow researchers to increase the number and size of their experiments and would save much
time and labor in the field. Furthermore, farmers and farm advisors could use aerial imagery to
estimate yields across their farms and could conduct their own experiments.
Methods:
In 2010, experiments were established on two Minnesota farms to determine the optimum
nitrogen (N) rate for first- and second-year corn following alfalfa. One field is located near
St.Rosa, MN and other is near Emmons, MN. At the Rohe farm near St. Rosa, manure was
applied to four of the eight main experimental plots in the fall before the corn was planted and 6
N rates were applied to the corn in the spring of 2011. At the Marpe farm near Emmons, MN,
there were 16 main plot treatments that were four replications of four treatments (combination of
two alfalfa regrowth (none and present) and two tillage timing (fall vs. spring) treatments). All
16 of these plots had 12 N rates applied to the first corn crop in 2010 and again in 2011 (192
small plots). The imagery and this analysis are for 2011 or the second corn crop following
alfalfa. At both fields, corn grain and whole plant yield was measured by hand-harvesting 10 ft
sections within each of the small plots. At both of these farms, aerial imagery was collected in
August of 2011. These images will be used to determine if corn yield differences due to N rate
treatments can be detected with NDVI.
Data Source
Aerial imagery was collected for both of these fields in the fall of 2011 shortly before the corn
had reached maturity and began the dry-down (close to the point of maximum growth). The
imagery is an RGB composite with one near infrared (NIR) band and spatial resolution of 1m.
The imagery was orthorectified by the company that collected the data and was projected in
NAD 83 UTM Zone 15N.
Process
The first step of the process was to import GPS points for each experiment area into ArcGIS.
These points were collected with a Trimble GPS unit at very a high degree of accuracy - around
10 cm (points were collected with real-time correction using the Minnesota Department of
Transportation - continuous operating reference stations (CORS) network).
The TIFF’s of the two experimental areas were then loaded into ArcMap. Polygons were created
to break the fields down into the their smaller experiment plots. These plots were based on the
GPS points that were previously imported. The fields were broken down in two ways. The first
was creating larger plots that represented main plot treatments [(manure application to first-year
corn (Rohe farm) and alfalfa regrowth and tillage treatments for second-year corn (Marpe
farm)].These polygons were created using the mid-point distance between the plot corners that
had GPS coordinates. The smaller polygons that represent the sub-plots in these fields (N
fertilizer rates applied to corn) were then created. These polygons were created using the
“Direction/Distance” tool in ArcGIS. All of the polygons that were created were then buffered
by -0.5 meters in order to be able to create areas of interest that would not have border effects
and would try to minimize the number of mixed pixels. The small plots at Rohe were 20 x 20 ft
and should have contained about 33 pixels after the 0.5m buffer. The small plots at Marpe were
only 15 x 17.5 ft and contained about 21 pixels after the buffer.
The feature classes containing each individual small and large plot border for both farms were
then exported to shapefiles as they would later be used to to create “Area of interest” layers in
ERDAS Imagine. The TIFF images were then opened in ERDAS Imagine. It was observed that
the bands were not ordered in the typical way. Band 1 was near infrared, band 2 was red, band 3
was green and band 4 was blue. In order to reorder the bands, the images had to be opened in
ArcMap again. Each band of the images were opened and then exported as their own file. An
“Image Stack” was then performed in Imagine. The image was stacked in the following order:
blue, green, red, NIR. This was done so that we could use ERDAS Imagine’s supervised
classification - NDVI, which is based on the bands being ordered as we stacked them.
These new images were opened in Imagine and the polygon shapefiles were also added to the
viewer. A new area of interest layer was also created for both the large and small plots. The large
polygons were then selected and copied to the area of interest layer. These area of interests were
again selected.
The signature editor was opened and new
signatures were added based on the AOI’s.
Minimum, maximum, mean, and standard
deviation statistics were added to the
signature editor. This created a table of
values of pixels that resided with the
AOI’s only. This process was repeated for
each set of polygons.
These values were also copied into a Microsoft Excel document so certain band ratios could later
be calculated.
From this signature editor table, the mean
band values for each small and large plot
were used to calculate these vegetation
indices (IR/Red, NDVI, and TNDVI).
The TNDVI was an option in Imagine
and is simply a square root
transformation of NDVI in the case that
the values are non-normal. We also tried
two other methods of calculating NDVI
in Imagine. A supervised classification of
NDVI was performed for the whole
image at Marpe and for only the area of
interest at Rohe. We were interesting in
comparing whether the mean values for these plots would be different this way. We found that
the difference between using the signature editor and supervised classification was minimal if the
whole image was classified with supervised classification at Marpe (r2= 0.99).
However, at Rohe where we used supervised classification for only the area of interest, the
correlation between the two NDVI values was not as good (r2=0.91). This indicates that area of
interest supervised classification is not as accurate. We decided to use the mean NDVI values we
calculated for the remainder of the analysis.
Results:
The average NDVI values calculated manually with the signature editor were lined up with the
corn grain yield from both farms and the corn silage or whole plant yield from Rohe. The
average NDVI values at the Marpe farm were lower than the NDVI values from Rohe. The
correlation between corn grain yield and NDVI, IR/Red, and TNDVI was poor at the Marpe
farm; the highest r2 value was only 0.005. However, the correlations at the Rohe farm were
actually quite good (r2 values between 0.42-0.45) for grain yield. Silage yield at Rohe had the
higher correlation to the indices that grain yield (r2 values between 0.48 and 0.53). The
difference in correlations between yield and vegetation indices was small at both farms, but the
IR/Red index typically had the highest correlation. We are not certain why the correlations of
yield and vegetation indices at Marpe were much lower than Rohe. It may be due to more mixed
pixels at Marpe because of the smaller plot size (22 pixels compared to 33 pixels at Rohe), but
other field characteristics may have affected the values.
Although the IR/Red index was slightly more correlated to yield than NDVI, the difference
between this and NDVI was likely not significant and so we decided to first use NDVI to try and
detect corn yield differences due to fertilizer N treatments. To test whether NDVI could detect
treatment differences across the plot areas we used yield and NDVI as independent variables in
an analysis of variance. The experiment was designed as a randomized complete block design
and was analyzed separately by farm with main (manure, alfalfa regrowth, and tillage timing)
and sub-plot treatments (fertilizer N rates) as fixed effects and blocks within location as random.
The significance (P < 0.05) of the main and sub-plot treatments were the same for yield and
NDVI at both farms (Table 1); both analyses showed that fertilizer N rate was the only treatment
that had a significant effect on corn yield or NDVI. These results are very encouraging because
they signify that the aerial image alone could detect yield differences due to N treatments.
Table 1. Analysis of variance results for the fixed effect of
manure, regrowth, and fertilizer N on corn yield and
normalized difference vegetation index (NDVI).
Farm Main effect Grain yield Silage yield NDVI
-------------- P > F -------------Rohe
Manure
0.484
0.164
0.326
N
0.003
0.025
0.042
Manure*N
0.515
0.339
0.952
Marpe
Regrowth
N
Regrowth*N
0.177
0.021
0.234
-
0.656
0.018
0.835
The next step forward in using NDVI to detect treatment differences in small plot experiments, is
to determine whether NDVI can be used to predict the level of response to a treatment.
Therefore, the regression models for yield and NDVI were evaluated for both farms and are
shown in figures on the next page. The slope of NDVI and grain yield could not be compared for
Rohe because the grain yield fit a quadratic regression model and NDVI was linear. The slope of
silage yield response to fertilizer N, could significantly fit a linear model, so there may be
potential to relate the silage or whole plant yield to NDVI. The grain yield response to fertilizer
N at Marpe fit nicely to a linear regression model, but NDVI had poor correlation with fertilizer
N rate (r2=0.26). This may be small plot size, more mixed pixels, or other characteristics about
the corn or growing conditions.
Rohe – grain (left), silage (right), and NDVI (bottom) response to fertilizer N.
Marpe – grain (left) and NDVI (right) response to fertilizer N.
Conclusions:
The results of this study show that NDVI can be used to detect differences in corn grain and
silage yield due to fertilizer N rates applied near corn planting. Therefore, aerial imagery could
be a potential tool for researchers and farmers to use in adjusting N rates applied to corn. This
tool may especially be useful for corn after alfalfa because the corn often requires no fertilizer N
to maximize corn grain yield. A few N rates could be applied to many fields across the state and
then aerial imagery could be used to detect when there was an N response to fertilizer. This
would allow for many field trials to be conducted at a very low cost. If the relationship between
N response in corn and response in NDVI could be validated across more fields, an online tool
could be developed to help farmers turn in GPS points of their plot corners and details of their
treatments, and then NDVI differences could be analyzed across plots. This would be especially
useful for farmers that don’t have expensive yield monitors on their combines.
What We Would Have Changed
If we were to redo this project there are many factors that we would change. The imagery we
used only had four bands available. We had originally wanted to use a greenness index using a
tassel cap classification to see if this would have given us a better representation of the health
and yield of a certain plot. However, we did find that in order to calculate this index you needed
7 bands and seems to be specific to the Landsat TM sensor or IKONOS. It would also have been
better to have larger experiment areas. The areas that we had to use for AOI’s were very small,
containing only 20 - 30 pixels. Larger field plots would have also enabled us to use a sensor with
a coarser spatial resolution. This would have given us access to more information. This would
have created the possibility of letting us look at the different index values at varying times
throughout the growing season to see if that would have given us better results.