Remote sensing of sports grounds

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BENCHMARKING SPORTSGROUNDS IN AUSTRALIA
Being able to compare grounds without any preconceived ideas or perceptions offers an excellent
means of identifying practices that work, appropriate management strategies according to the
geographical area a site is located, how much play grounds can cope with under differing climatic
conditions or football codes and what factors have the most impact on surface quality during a
playing season?
Currently there appears to be no definitive answer to these questions because monitoring of
grounds is generally time consuming and costly, being done on the basis of ensuring that they
meet set pre determined criteria such as those set by the AFL.
As with all things there is no right way of carrying out ground maintenance but there are a lot of
“wrong” ways. We have spent the last twelve months investigating this issue and have developed
a means of benchmarking grounds against each other in both a quick and cost effective manner.
The end result is that at any given point in time we can now benchmark one ground against
another from the perspective of turf quality, wear damage and even irrigation distribution
uniformity.
Quantifying colour
Quite a few methods are being employed in order to quantify turfgrass colour objectively. One of
these gaining more credence is the use of a colorimeter which measures infrared and near-
infrared reflected light (Spectrum, 2006). The colorimeter emits a pulse of light and measures
reflected light from turfgrass in the red (660 nm) and near infrared (780 nm) spectral bands. The
colorimeter’s measurements are directly converted into the unit-less one to nine scale for colour.
We then correlated these measurements with the turf quality and percent cover data collected.
In relation to turf quality, a qualitative rating can be assigned with light green as a one and dark
green as a nine. Colour ratings need to be collected when the turfgrass sample is growing and not
influenced by any particular stresses.
Recently, it has been experimentally shown that a
colorimeter could be used to quantify the colour of
bentgrass cultivars (Karcher, 2003, p. 944). The values
generated by the colorimeter were well correlated with
evaluation of the turf’s colour averaged across five
assessors. Colorimeters have been successfully used in
order to assess differing turfgrass colour caused by
seasonal changes and variations among species and
cultivars.

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Actively growing plants therefore show a strong contrast between strong absorption in the red
and high reflectance in the near-infrared regions of the spectrum. The amount of absorption in
the red and reflectance in the near-infrared varies with both the type of vegetation and the vigour
of the plants. The natural change of a green leaf as it senesces is shown in Figure 1. We see that
the healthy green leaf has very low reflectance values in the red (600-700 nm) due to chlorophyll
absorption and very high reflectance values in the near-infrared (700-1000 nm). However, as the
plant begins to senesce, reflectance begins to decrease in the near-infrared and increase in the
red.
One way of characterizing this relationship with a single variable is by dividing the near-infrared
reflectance by the red reflectance (NIR/Red). The larger this ratio, the more photo-synthetically
active vegetation is present; the lower the ratio, the less photosynthetically active vegetation
present. Because these ratio values will vary considerably from one region to another, a way of
normalizing the ratio is called the Normalized Difference Vegetation Index or NDVI. NDVI = (NIR –
Red) / (NIR + Red).
The NDVI has been related to biophysical parameters like vegetation density, percent green
vegetation cover, biomass, fraction of absorbed photosynthetically active radiation (PAR) and leaf
area index (LAI). The relationships of NDVI and a biophysical parameter are often site specific and
vary with the soil and vegetation optical properties (e.g. leaf spectra, leaf angle distribution, plant
architecture). If there is no vegetation, the NDVI values are lowest for bright soils, and are
generally higher for dark soils with high organic matter contents.
We can therefore use this information to evaluate vegetation type, condition, or density and it is
this relationship, which lead us to commence a study which ran over the 2009 football season.
Soil Moisture
Maximized use of irrigation and rainwater is critical for proper turf management, especially where
water supplies or water quality are not ideal for turf. Water use is determined by many factors,
including weather, irrigation technology, budgetary constraints, turf maintenance practices,
topography and soil conditions. The turf manager’s ability to monitor soil water status is also
important. Measuring soil moisture content is one of the most effective ways to time irrigation
amount and frequency. Proper irrigation timing is, in turn, one of the best ways to promote turf
health.

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Proper irrigation produces strong root zones
and the highest turf quality. Improper irrigation
can increase water consumption, soil erosion, disease pressure and lead to a loss of vital nutrients
in the root zone. All of these factors contribute to higher maintenance costs for the property
owner and cause headaches for the turf manager.
The ability to anticipate and efficiently prepare for future drought conditions is essential.
Detecting soil moisture problems farther in advance with greater accuracy and improved geo-
referenced detail will be the crucial factor in making irrigation management decisions.
Distribution Uniformity or DU is a measure of how uniformly water is applied to the area being
watered, expressed as a percentage. Traditionally the accepted methology for calculating sprinkler
system performance has been to use catch can tests. This measures the actual precipitation rate
for each sprinkler zone and provides a measurement of how uniformly water is distributed by the
sprinklers comparing the average of the lowest quarter of readings to the overall average of the
readings. Commonly this is called the Lowest Quarter Distribution Uniformity of DULQ. The DULQ
is in turn used to calculate the irrigation water requirement to meet the plant water requirement
in the driest parts of the sprinkler zone. However, this method generally over estimates the
irrigation water requirement because it does not account for what happens to the drop of water
when it comes in contact with the thatch or the soil.
The higher the DU, the better the performance of the system. If all samples are equal, the DU is
100%. If a proportion of the area greater than 25% receives zero application the DU will be 0%.
There is no universal value of DU for satisfactory system performance but generally a value 70-80%
is considered acceptable.
Obviously there must be good DU, before there can be good irrigation efficiency, if the turf is to
be sufficiently watered. If distribution is poor then areas may be over watered and others under-

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watered. This may lead to an increase in irrigation volume which may increase waterlogging in
localised areas and associated issues such as Poa annua invasion or disease etc.
A different and newer method for determining the efficiency of the irrigation system other than
using catch cans is to determine the efficiency of the system by using a Time-Domain
Reflectometer (TDR) to record the soil moisture. A distribution uniformity calculation can then be
carried out from the recorded soil moisture data.
A similar process as discussed earlier is used to compute the DULQ of the soil moisture data. The
data is ranked from lowest to highest soil moisture content. The average of the lowest quarter of
readings is divided by the overall average. The result is presented as a percent. The value of this
calculation, compared to the catch can DULQ, is that it is a measure of water in the soil profile as
opposed to water applied to the turf surface. Factors such as compaction, thatch, slope, and
moisture re-distribution can cause the variability pattern to be very different between the
application (catch can) and soil moisture measurements.
The use of Time Domain Refractometry (TDR) has become increasingly accepted as being an
accurate means of determining volumetric moisture content over the last decade. TDR measures
the electrical properties of a given volume of soil. The volumetric soil moisture content (VMC) of
the soil is proportional to its dielectric constant as measured by TDR. Electromagnetic waves are
propagated along a wave guide, so by timing their return the dielectric constant of the soil is
determined. It has been shown that the dielectric constant is strongly correlated to soil moisture
content, with little effects from texture, material, temperature or salinity. Thus, water content in
the surrounding media can be determined (Topp et.al., 1980).
Based on the results gained the VMC% can then be compared to the theoretical field capacity and
wilt point for a specific soil type, and thus it is possible to gauge much more accurately whether a
soil is being over or under-watered.

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Methology
Throughout the 2009 NRL season regular NDVI and moisture readings were taken to determine
the impact of wear on surface quality and the existing moisture status on a number of grounds on
the eastern seaboard on Australia. This comprised travelling a 10metre x 8 metre grid over the
entire playing surface with readings being recorded and mapped using a GPS unit.
The same monitoring protocols were followed for both NDVI measurement and moisture
monitoring. In the case of the latter short 7.5cm tines were used to record volumetric soil
moisture content (VMC%) with this being logged using a Time Domain Refractrometer (TDR).
Following this proprietary mapping software was used to create visual images of the surface at
that time.
The results gained were then graphically represented as both comparative NDVI, soil moisture and
distribution uniformity readings. In the case of the NDVI and moisture maps the darker the colour
of green in the case of NDVI and blue in the case of the moisture readings the higher the turf
quality or soil moisture content respectively.

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The end result is the development of a database for grounds throughout NSW, one ground in
Canberra, one in Newcastle and one in QLD which enables grounds to be mapped and then
benchmarked vs existing grounds in the database. This is not meant as a tool to enable direct
comparisons between say a top NRL ground such as Skilled Park on the Gold Coast, and a council
ground in Sydney (although this is possible), but instead enables turf managers to be able to
interact and through the exchange of results and ideas be better able to determine what
strategies at say these respective venues are working and which are not.
Results
The results gained are shown below graphically and also in tabular form.
Quality/VMC % Colour Legend
Low NDVI Moisture
High

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NDVI Readings
Minimum Maximum Average
NDVI mean 0.37 0.78 0.66
NDVI highest readings achieved 0.694 0.807 0.742
NDVI lowest readings achieved 0.167 0.755 0.586
Experience of using the NDVI technology in the field has lead to our establishing that low readings
are generally associated with bare or thin vegetative coverage i.e. high wear areas give low
readings. The graphical representation below in Figure 2 clearly shows that Knox Grammar and
also Skilled Stadium on the Gold Coast in August 2009 after were showing the lowest readings.
Both of these results can be explained as being directly associated with excessive wear. In the
case of Knox these readings were directly related to the main entry point onto the surface which
was subjected to excessive traffic. In the case of Skilled Stadium these readings were a
combination of excessive use (it was being used by the Titans, Gold Coast United, and also for
training by both).
Between March-August 2009 there had already occurred 67 hours of training which equates to
3.35 hours a week on average. Bearing in mind that UK figures determine a two hour
football/rugby training session to equate to 1 game then these figures change dramatically when
the actual games played is also taken into consideration.
Event Games Time (weeks) Hours per week
Training March - August 67 20 weeks 3.35
Games March - August 32 20 weeks 1.6
Intense period May-July 13 8 weeks 1.625
The data gained from Skilled Stadium was especially useful as in fact two sets of data were taken.
One in March and the other in August 2009. As the graph below shows we were able to quantify a
deterioration in surface quality over this time period. Since March 2009 there was a significant
deterioration in the overall quality of the playing surface with the major issues being a 13%
decrease in the mean over this period and rather more worryingly a 67.2% decrease in the
minimum levels recorded thus indicating that the heavily worn areas were in fact showing further
deterioration.

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Figure 2. Graph showing change in turf quality/health from March to August 2009 on Skilled Stadium playing
surface.
When the images generated using GPS functionality are examined it can be seen that significant
variation existed in the March results, with even though the southern end of the ground showing
recovery from a disease incidence, the readings still being better than those gained in August.
0.586
0.67
0.167
0.511
0.804 0.792
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
August 2009 Mar-09
Average reading
Minimum value
Maximum value

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Colour Legend
0.511 to 0.546
0.546 to 0.581
0.581 to 0.616
0.616 to 0.651
0.651 to 0.687
0.687 to 0.722
0.722 to 0.757
0.757 to 0.792
Colour Legend
0.167 to 0.247
0.247 to 0.326
0.326 to 0.406
0.406 to 0.486
0.486 to 0.565
0.565 to 0.645
0.645 to 0.724
0.724 to 0.804

10. 0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
Figure 2. NDVI readings for a selection of sporting grounds throughout NSW, VIC and QLD
Minimum NDVI
Average NDVI
Maximum NDVI

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Moisture readings
The following work has all been done on existing swards over the last 18 months on a variety of
venues, soil and turf types. The table below summarises the results gained over this period.
Distribution Uniformity
Variability in irrigation coverage and its impact on soil moisture is vitally important if effective
irrigation is to occur to turf. The table below shows a classification for distribution uniformity.
Based on the above figures for distribution uniformity (DU) the minimum reading gained from the
monitored sites was categorised as being between poor to fair.
The average reading over all the sites was categorised as between very good to excellent and the
highest reading achieved was categorised as excellent.
The overall results gained are shown in Figure 3.
Minimum % Maximum % Average
Distribution Uniformity 59.97 84.48 73
Volumetric moisture content % 18.3 50.4 32.05
Type Excellent % Very Good % Good % Fair % Poor %
Rotor impact 80 70 65 60 50

12. Figure 4. Distribution Uniformity and mean volumetric moisture content for a selection of grounds throughout NSW, VIC, ACT and QLD
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
70.98
50.4
Distribution Uniformity Average Moisture

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Implications of results
A number of interesting implications have come from these initial results such as it immediately
becoming apparent that precision irrigation of many areas does not actually occur and if it does do
there appears to be little attempt to relate distribution uniformity with the soil moisture content.
A case in point with the results was Parliament House in Canberra where a distribution uniformity
categorised as very good existed in conjunction with a volumetric moisture content of 50.4%.
The latter figure was of particular interest as this indicated a huge potential for over watering until
it is realised that the site is predominantly cool season turf in contrast to every other site which
was composed predominantly of warm season couch or kikuyu.
This strongly implies that this benchmarking approach is currently far from perfect as familiarity
with specific sites is still a vital prerequisite in making any informed decision. However if all
relevant data is available some pretty useful conclusions relevant to turf quality, wear and water
use efficiency can be made specific to specific sites, and this can in turn can then be related to
other grounds.
The end result is the development of a database for grounds throughout Australia which
establishes the basis for benchmarking general grounds/turf areas vs existing grounds in the
database. This is not meant as a tool to enable direct comparisons between say a top NRL ground
such as Skilled Park on the Gold Coast, and a council ground in Sydney (although this is possible),
but instead enables turf managers to be able to interact and through the exchange of results and
ideas be better able to determine what strategies at say these respective venues are working and
which are not.
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lnst. 64:19-47.
Canaway PM 1992. The effect of two rootzone amendments on cover and playing quality of a sand
profile construction for football. J. Sports Turf Res. lnst. 68:50-61.

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