Use of remote sensing on
turfgrass
Soil 4213 course presentation
Xi Xiong
April 18, 2003
Why is it necessary to use sensor
on turfgrass management?
Stress lead to reduced turfgrass
quality.
Traditional turfgrass management is
based on visible observation.
The use of remote sensing may allow
the turf manager to see stress before
it becomes visible as damage to the
turf.
Background
Stress causes an increase in reflectance
in the red and blue portions of the
spectrum and decreased reflectance in the
near infrared (NIR) region.
methods of assessing plant reflectance
properties include infrared thermography,
multispectral radiometry, and near infrared
spectroscopy.
Background
(continued)
Indices such as the Leaf Area Index (LAI) (IR
reflectance/Red reflectance) and Normalized
Difference Vegetative Index (NDVI) [(IRR)/(IR+R)] have been correlated with the
presence of green biomass and provide a
quantitative estimate of general stress on a
plant.
All of these technologies and knowledge make it
possible of detecting turf quality by remote
sensing.
Use of remote sensing
to detect disease on turf
Use of remote sensing
to detect disease on turf
Use of remote sensing
to detect disease on turf
Use infrared aerial
photographs to detect
Dollar Spot and
Brown Patch on
turfgrass.
Data was analyzed by
multivariate
discriminant analysis
as using software
provided by Infrasoft
International.
Use of remote sensing
to detect disease on turf
Use of remote sensing
to detect disease on turf
From the Dollar Spot experiment, 20 out of 193
samples (10.3%) were classified incorrectly.
The data from Brown Patch study showed 29 out
of 337 samples (8.6%) were classified
incorrectly.
These results indicate that VIS-NIRS is a viable
method for assessing brown patch and dollar
spot severity. However, enough data should be
collected before to build the threshold levels of
disease which is need to determine the proper
fungicide treatment.
Use of remote sensing
to detect white grub on turf
Use of remote sensing
to detect white grub on turf
Using GER 1500 field spectrometer (hand
held type) to determine the damage
severity.
using satellite remote sensing and geoinformation technologies to predict when
and where pest populations are likely to
develop over large geographic regions.
Using Micro Air Vehicle (MAV) and
Unmanned Air Vehicle ( UAV) to detect
pest population within field.
Use of remote sensing
to detect white grub on turf
A small UAV that carries
a multispectral imaging
system with a wingspan
of just 6 inches, and the
cost is $300 a piece.
It will be supposed to use
as detecting insect
infestations, nematodes,
water stress and plant
pathogens.
Use of remote sensing
to detect soil compaction on turf
A small hand-held
unit in this study
analyses 507nm,
559nm,661nm,706n
m,760nm,935nm to
separate soil
compaction.
Use of remote sensing
to detect soil compaction on turf
The result showed that the VIR (visible
range) had significant positive correlations
to soil penetrometer readings, while
readings in most of the NIR (near infrared)
portion of the spectrum did not correlate
with soil strength.
One issue needed to concerned is when
the turf was overseeded with other
turfgrass species, the strong relationship
will decrease.
Use of remote sensing
to detect nitrogen content on turf
Digital image analysis
used to determine if it can
quantify nitrogen levels in
grasses .
The technology will use
the color analysis
software to detect
different amounts of
chlorophyll in the turf,
eventually aimed to
reduce fertilizer inputs.
Use of remote sensing
to detect nitrogen content on turf
using vehiclemounted optical
sensors to map turf
area received
different N fertilizer
rate.
The NDVI map
provide early warning
of plant decline,
indicate areas in need
of N fertilization and
the amount of
fertilizer required.
Use of remote sensing
to detect drainage patterns on Golf course
A project under development in Clemson
University is trying to build a threedimensional digital elevation model (DEM)
to identify drainage patterns and possible
areas of runoff problems.
The project will use GIS to help locating
aerial maps, and eventually optimize
chemical application rates and irrigation
rates in golf courses.
Use of remote sensing
to detect turfgrass quality
The relationship between
normalized difference
vegetation index (NDVI)
and tall fescue turf color
on a 1 to 9 scale with 9
the deepest green and
percentage live cover
(PLC) on a 0 to 100%
scale. Model: NDVI =
0.258 + 0.4867 x log10
turf color + 1.053 x 10-7 x
PLC3, R2 = 0.80, P <
0.0001.
Use of remote sensing
to detect turfgrass quality
The relationship between
normalized difference
vegetation index (NDVI)
and creeping bentgrass
turf color on a 1 to 9 scale
with 9 the deepest green
and percentage live cover
(PLC) on a 0 to 100%
scale. Model: NDVI =
0.305 + 0.3072 x log10 turf
color + 1.1757 x 10-7 x
PLC3, R2 = 0.50, P <
0.0001.
The use of Greenseeker on turf
The concept of
precision turfgrass management
The combination of GPS,
GIS, sensors and VRT
(variable rate technology)
will allow turfgrass
managers to maintain
their turf according to site
specific needs, thereby
reducing excessive and
potentially unnecessary
application of pesticides
and nutrients.
The concept of
precision turfgrass management
First, accurately scouting to
develop zones of
management.
Second, use GIS software
decision-support system and
data collected from the site to
give a appropriate
management operation for
each area.
Third, use application
hardware to precisely deliver
management operations to
each selected area in the
same time frame as normal
maintenance operations.
Questions?
Thank you!