MONITORING OF MAIZE DAMAGE CAUSED BY WESTERN CORN ROOTWORM BY
REMOTE SENSING
Gizella Nádor(1), Diána Fényes(1), László Vasas(2) and György Surek(3)
(1)
Institute of Geodesy, Cartography and Remote Sensing, Bosnyák tér 5, H-1149 Budapest, Hungary,
Email: nador.gizella@fomi.hu, fenyes.diana@fomi.hu
(2)
Agricultural Office of County Békés, Szarvasi út 79/1, H-5600 Békéscsaba, Hungary,
Email: Vasas.Laszlo@bekes.ontsz.hu
(3)
MLOG Ltd., Klebersberg Kunó u. 36, H-1158 Budapest, Hungary, Email: surek@mlog.hu
ABSTRACT
The gradual dispersion of western corn rootworm
(WCR) is becoming a serious maize pest in Europe, and
all over the world. In 2008 using remote sensing data,
the Remote Sensing Centre of Institute of Geodesy,
Cartography and Remote Sensing (FÖMI RSC) carried
out this project to identify WCR larval damage. Our
goal with the present project is to assess and identify the
disorder and structural changes caused by WCR larvae
using optical (IRS-P6 AWiFS, IRS-P6 LISS, SPOT4
and SPOT5) and polarimetic radar (ALOS PALSAR)
satellite images. We used 3 different individual features
(Mono-maize feature, Optical feature, Radar feature)
derived from remote sensing data to accomplish this
goal. Findings were tested against on-the-spot ground
assessments. Using radar polarimetry increased the
accuracy significantly. The final results have
implications for plant protection strategy, farming
practices, pesticide producers, state authorities and
research institutes.
1.
INTRODUCTION
More than 300 man-years R&D investment during the
Hungarian Agricultural Remote Sensing Project
(HARSP 1980-) led to the National Crop Monitoring
and Production Forecast Program (CROPMON 19972003) at FÖMI RSC [4]. The CROPMON provided
nationwide and regional crop yield forecast to the
Ministry of Agriculture and Rural Development
(MARD) during the growing period. WCR damage
identification is one of the applications, which has
developed on the basis of CROPMON technology.
The WCR was introduced to Europe from the USA.
First it was detected in Europe near Beograd in 1992.
The WCR has spread from its initial infestation point to
a range of several hundred kilometers, affecting many
countries in the region including Hungary first in 1995.
A massive WCR population was grown up during the
last 15 years in the Carpathian Basin. WCR is found
almost every corn producer country in Europe. The
average corn yield loss is 30% in Europe [6].
There are more than 100 000 ha WCR damaged areas in
Hungary. The estimated corn yield loss is around
5%/year [6]. It means approximately 30 million
euro/year without expenses of chemicals. The estimated
total loss is around 45 million euro/year. It is very
important to have the data of the WCR damage location
and the damage scale. The remote sensing is a very
accurate, objective and reliable method. It can
effectively support the WCR monitoring.
The structure of a healthy, WCR free maize field shows
straight rows in a clear order and upstanding maize
stalks. The WCR infection causes wilted broken cornstalks lying randomly on the ground. That means the
damage itself causes physical and visible disorder in the
maize field. Our goal is to assess and identify the
disorder and structural changes caused by WCR using
polarimetic radar images (ALOS PALSAR).
We used 3 different individual features (Monoculturefeature, Optical-feature, Radar-feature) derived from
remote sensing data to identify WCR larval damage.
The integrated assessment of the 3 features gave more
accurate WCR damage identification than to assess
features separately one-by-one. We carried out
retrospective examinations for 2007 and operative
regional assessment in 2008 to test the efficiency of our
remote sensing methodology.
As a result of the retrospective examinations the
accuracy of damage identification was 61-70% with the
one-by-one evaluation of the remote sensing features.
The accuracy of damage identification is higher than
80% with the integrated evaluation of the 3 features.
This result is 10 % better than with the one-by-one
evaluation.
_____________________________________________________
Proc. of ‘4th Int. Workshop on Science and Applications of SAR Polarimetry and Polarimetric
Interferometry – PolInSAR 2009’, 26–30 January 2009, Frascati, Italy (ESA SP-668, April 2009)
The preliminary results were presented at 28th EARSeL
Symposium in June, 2008 [3]. The results of the
retrospective examinations for 2007 and the first results
of the operative regional assessment for 2008 were
presented at ALOS Symposium in November, 2008 [5].
In this paper the focus is mainly on the detailed
presentation of the methodology and the operational
regional assessment carried out by FÖMI RSC in 2008.
This project was implemented in 2008 in the framework
of our approved tender called by the Hungarian Space
Office and with the support of the Ministry of
Environment and Water. This project was submitted to
ESA and the contract for the “Utilization of ESA Data
under Category-1 scheme” was signed in April, 2008
(ESA EO CAT-1 5162). The contract specifies that 74
ALOS PALSAR and 30 ENVISAT ASAR radar
satellite image data will be provided for this project at
reproduction cost.
2.
DATA AND METHODS
2.1. The WCR damage
The structure of a healthy, WCR free maize field
appears straight rows in a clear order and upstanding
corn stalks. In injured cornfields, WCR larval damage
results in disordered rows and corn stalks laying on top
of each other randomly on the ground. Rootworm
damage also can result in extensive "gooseneck"
lodging and harvest difficulties. Most of the damage in
corn is caused by WCR larval feeding. WCR larvae feed
on and destroy maize roots in different scale.
It is appropriate to describe the degree of the root injury
by using the Iowa corn root rating scale. It is a 1-6 root
damage scale depending on the root injury intensity.
The important point of the remote sensing analysis is
that ratings from and over 4 indicate “gooseneck” prone
lodging corn stalks. Although sometimes root rating 3
combined with extreme weather condition can also
show the same result.
2.2. Ground assessments
The Central Agricultural Office of Békés County
(CAO) provided the GPS recording of the WCR
damaged field, the related data and the underlying basic
ground assessment. CAO determined and classified the
WCR larval damage degree by 1-6 Iowa corn root rating
scale created by Hills-Peters. The damages recorded
ranged between 1 and 6 on Iowa root rating scale.
We used the reference ground assessment reports from
the year 2008 recorded in Békés County, Hungary in
cooperation with the local agency of CAO. Tab. 1
shows the main features of the reference parcels. Fig. 1
gives information about their spatial location.
In 2008, we created the guidelines to carry out the
ground assessments. These detailed guidelines help to
standardize the assessments and calibrate the remote
sensing methodology. First we identified the WCR
damaged cornfields (spots) by remote sensing method
(detailed in 2.3.) and then CAO experts implemented
the ground assessment to verify our methodology.
We received the CAO-reports which included among
others photos of the tested cornfields, GPS coordinates,
representative IOWA rating scale of the root damage,
the, agricultural information of the nearby fields
(forecrops, plant protection done, etc.) and the
description of the damage.
In 2008, a rainy summer created very good conditions
for corn plants and in which the damaged roots could
quickly regenerate. The mid-scale damage (Iowa rating
scale 3-4) was typical. The reference ground
assessments, which we implemented in Békés County
2008, show the described statement. Tab. 1 presents the
results 2008. Fig. 1 shows the location of the tested
fields. Fig. 2 shows the process of reference ground
assessment carried out in Békés County, 2008.
Table 1. Characterisation of the reference cornfields
based on WCR damage, Békés County, 2008
Area
(ha)
Area
percentage
(%)
Average
area of
the fields
(ha)
8
38,7
22,9
4,8
2-3
5
55,7
33,0
11,1
3-4
1
4,4
2,6
4,4
4-5
2
3,3
1,9
1,6
>=5
3
66,8
17,5
29,5
Totals
19
168.8
100,1
8,9
IOWA root
rating scale
No. of
fields
1 - no damage
(control)
The optical-feature measures the relative difference rate
of the limit value.
Figure 1. Spatial location of WCR larva damage and
control reference data, Békés County (Source: CAO of
County Békés), 2008
Figure 2. Process of reference ground assessment,
Békés County, 2008
2.3. Remote sensing method
We used 3 different individual features (Monoculturefeature, Optical-feature, Radar-feature) derived from
remote sensing data to identify larval damage (Fig.6).
Monoculture-feature
Monoculture maize cultivation increases the risk of
WCR infection (Fig.3). Satellite data time series 19972007 (during 10 years, before 2008) are available in
FÖMI which we analysed and determined the frequency
of maize cultivation in the tested parcels. This became
the value of monoculture-feature.
Optical- feature
The WCR larval damage results in stress in corn plants.
It leads to the vegetation index decreasing (Fig. 4). If
the vegetation index curve of the cornfield is lower in a
certain period than a given limit value it means that the
parcel is probably damaged by WCR otherwise it is not.
Radar-feature
The WCR damage itself causes physical and visible
disorder in the cornfield. This effect can be
characterized by the followings. The polarization status
of the transmitted and received pulses is known. The
target affects the degree of polarization. It proves [2] the
degree of the polarization of the backscattered beam, in
the case of a healthy, regularly cultivated cornfield,
depends on the reflective-grid (the corn stalks act like a
regular grid) position as well as the polarization and
direction of the incident beam. The WCR larval infected
corn-stalks which are randomly lying on the ground.
These infected stalks decrease the degree of polarization
in any case of incident beam. The so-called Shannon
entropy (SE) can describe the ratio of this “polarization
scattering”. Shannon entropy [1] consists of two
components:
SE=SEi + SEp, (1)
where:
SEi: intrinsic degrees of coherence,
SEp: degrees of polarization.
Based on the analysis of the reference parcels this kind
of disorder can be characterized most effectively by SEp
component of SE.
Considering the above mentioned theory we decided to
apply L-band (wavelength: 23 cm) polarimetric radar
images in our remote sensing methodology. We derived
the Shannon entropy of the corn field as described [1]
by analysing polarimetric radar images. This value
describes the disorder of the parcel. Figure 5 introduces
a WCR damaged (delimited by bourdon line) and a
control (delimited by yellow line) cornfield. The
difference on the radar satellite image is well
recognizable. According to ground assessment the corn
plants were completely lodged and broken corn stalks
were randomly lying on the ground, the Iowa rating
shown 4,7 root damage in 55% of the tested cornfield.
The measured entropy of the root damaged maize field
(light tone) is different than the control (dark tone) field
in the entropy map based on radar data.
Conditions of remote sensing method
First, the methodology was set up on the basis of
retrospective WCR damage data from 2007. In 2008, we
tested and developed (refined) our methodology. The
findings of this project can be useful for agriculture in
several direct ways so we have tested the methodology
in more than one agricultural year.
2007 and 2008 were good examples of two different
agricultural years. In 2007, both the spring and the
summer, which is the key period of WCR larval rootfeed and damage, were extremely dry without any
remarkable precipitation. The corn plants were stressed
and could hardly grow in the drought. The corn
roots/plants could hardly or could not at all regenerate
after larval feed. The WCR larvae could result in clear
and visible damage.
Figure 5. WCR damaged (bourdon) and control
(yellow) maize fields on Shannon entropy map derived
from ALOS PALSAR (R: SE, G:SEi, B: SEp)
Table 2. Features of optical satellite data (a) and radar
image (b) in connection with the remote sensing
assessment of WCR larval damage in 2008
a.
Figure 3. Monoculture maize map shows the riskiest
areas of WCR infection
Spatial
resolution
(ha)
Acquisition
date
Type
Spectral
resolution
2008.02.08
IRS-P6 AWiFS
0,3
4
2008.04.21
SPOT5
0,01
4
2008.05.30
IRS-P6 AWiFS
0,3
4
2008.06.23
IRS-P6 AWiFS
0,3
4
2006.07.03
IRS-P6 AWiFS
0,3
4
2008.07.15
IRS-P6 LISS
0,1
4
2008.08.01
SPOT4
0,1
4
2008.08.20
IRS-P6 AWiFS
0,3
4
2008.09.03
IRS-P6 AWiFS
0,3
4
2008.09.07
IRS-P6 AWiFS
0,3
4
b.
a.
Acquisition
date
Type
Polarization
Wavelength
(cm)
Spatial
resolution
(ha)
2008.07.24
ALOS
PALSAR
dual
(HHHV)
L-band
23
0,1
This fact is reflected in vegetation index curve shown in
Fig. 3.a. There is a great relative difference (10-15%)
between the damaged and control corn field’s curve in
July-August period. In 2007, we identified the WCR
damaged cornfields using all three features
(monoculture-, optical- and radar-).
b.
Figure 4. Vegetation index curves of a damaged (red)
and a control(green) cornfield derived from optical
satellite image data in 2007 (a) and in 2008 (b)
In 2008, precipitation reached or exceeded the average
during spring and early summer. This weather created
good condition for the corn plants and roots. The WCR
larvae root-feeding could not result in a clear visible
damage because the corn plants and roots were stronger
and healthier than in 2007.
Figure 6. The sketch of the methodology for identification of WCR damaged cornfields by remote sensing
Figure 7. Decision rule for WCR map generation
Figure 8. Location of WCR damaged cornfields
identified by remote sensing (brown) in the monoculture
cornfield map 2008, in Békés County
Figure 9. Results of the ground assessment. The two
new damaged cornfields were found during assessment
(14th and 15th spot) are blue.
Figure 10. WCR damaged spot (yellow) identified by
remote sensing near Kunágota (the 15th spot) and a
control field(white) in SPOT5 28/05/2008 (a), in IRS-P6
LISS 20/07/2008 (b), in SPOT5 20/07/2008 (c), in IRSP6 AWiFS 20/08/2008 (d) optical satellite images,
in Shannon entropy map derived from ALOS PALSAR
radar satellite image 24/07/2008 (R: SE, G:SEi, B: SEp)
(e) and the photo of the15th spot 22/09/2008 (f)
This fact is reflected in vegetation index curve shown in
Fig. 3.b. There is a small relative difference (less than
5%) between the damaged and control corn field’s curve
in July-August period.
Table 3. Results of the ground assessment, Békés
County, 2008
The two new damaged cornfields were found during
assessment (14th and 15th spot) are blue.
Spot
Damage
Identifier
Description
1
high
The owner reported lots of broken
corn-stalks and “gooseneck” lodging.
The cornfield was harvested before the
ground assessment.
2
low
Low-scale WCR damage affected
1-2 % of the whole cornfield.
3
5
6
7
8
9
There was higher-scale damage than
“spot No 2”. The cornfield was
middle
harvested
before
the
ground
assessment.
Sowing problems were identified
without
which did not affect the whole
damage
cornfield.
The cornfield as a whole looked
healthy and the structure was orderly
low
but small WCR damage spots were
identified.
There were some WCR damage spots
in the north and north-east of the field.
low
The damage affected 5% of the
cornfield.
The cornfield as a whole looked
healthy and the structure was orderly
low
but small WCR damage spots were
identified.
Large areas of broken corn-stalks and
“gooseneck” lodging (stalks laying
randomly on the ground) were
high
identified. The damages appeared in
spots but the spots were relatively big
and very close to each other.
12
high
Wilted and broken corn-stalks were
everywhere in the cornfield.
13
low
Higher-scale damages were identified
only in the north, north-east of the
field.
14
low
WCR damage appeared in spots but
not in the whole cornfield as a whole.
15
high
Serious WCR damage (Iowa: 5-6).
The damage was clearly identified by
remote sensing.
The damage did not appear in spots but wilted broken
corn-stalks were spread all over in the cornfields. In
2008, the corn plants were not stressed by heat-weaves
and drought so the optical-feature could hardly or could
not contribute to the WCR damage identification. In
2008, we carried out the assessments and the
methodology using only two features: monoculture- and
radar-feature. Based on the results presented in Chapter
3 these two features gave surprisingly accurate result.
The decision rule of classification
We defined the above detailed three distinct features for
all pixels by evaluation of the optical and radar images.
Classification was a two-step process. In the first step
pixels were classified into three “in-class” categories:
“damaged”, “no-damaged” or “ambiguous”. Pixels
showing high entropy and high stress were classified as
“damaged”. Pixels showing low entropy and low stress
were classified as “no-damaged”. If the radar and
optical features of a pixel show contradictory damage
findings we classified it as “ambiguous”. In the second
step pixels were classified in two “out-class” categories:
“damaged” and “no-damaged” by the following way:
pixels classified in the first step as “ambiguous” were
classified by monoculture-feature, while classification
of the other pixels was not changed. The sketch of the
decision rule applied for WCR map generation is shown
on Fig. 7.
3.
RESULTS
We carried out this project in South-east Hungary, in
Békés County (Fig.1). This is one of the biggest corn
producers area (near 100 000 ha). 30% of corn
production is monoculture. The relative risk of WCR
infection is high.
First, we created the corn map 2008 of Békés County
test area based on the quantitative evaluation of high
resolution optical satellite data time series (Tab. 2.a).
Secondly, we determined which fields were
monoculture cornfields based on corn maps 2007 and
2008. This map was the monoculture cornfield map.
After analyzing the monoculture cornfield map, we
clearly concluded that of 29.462 ha cornfields in 2008
8.979 ha (22% of 2008) were also cornfields in 2007.
We could identify WCR larval damaged monoculture
cornfields based on the entropy map derived from radar
image (Tab. 2.b) and the monoculture cornfield map.
The WCR damaged cornfields showed high entropy.
Based on this observation, we could create the WCR
larval damaged map. We eliminated areas (spots) less
than 2 ha (Fig. 8).
We could identify 1085 ha (175 spots) WCR damaged
cornfields by the remote sensing methodology described
in Chapter 2.3. We double-checked 5,7% (10 spots) of
the identified spots on the ground. The Fig. 9 shows the
result of ground assessment.
Tab. 3 shows the detailed description of the surveyed
spots. The damage scale was identified by the “decision
rule” shown in Fig. 7. The degree of damage and the
description of the spot were based on ground
assessment. Only 1 spot of the 10 selected spots was
WCR free (this is the 5th spot). It was selected as a
damaged spot due to a seed sowing problem. It caused
the high entropy in the radar image. 5 spots showed low
WCR infection which appeared in smaller damage
spots. 1 spot presented mid-scale and 3 spots high-scale
damage. During the ground assessment, 2 additional
clearly infected cornfields were found and recorded (the
14th and 15th spots in Fig. 7 and Tab. 3 assigned blue).
One of these new cornfields (the 15th spot) was
identified by remote sensing methodology as well (Fig.
10).
4. CONCLUSIONS
To sum up the results, the operational regional
assessments verified the goal of our project. We vividly
demonstrated a methodology which clearly identifies
WCR larval damage efficiently by remote sensing.
Based on the achieved objectives of the project there is
potential in the integrated analysis of optical and radar
images to assess and identify disorders and structural
changes caused by WCR larvae.
WCR is quarantine pest in the European Union. It is
necessary to detect WCR damage in order to comply
with the statutory management requirements and good
agricultural and environmental conditions which are the
basic requirements to receive agricultural subsidies.
This project is unique in the European Union. The
outcome of this project can be used directly to create a
nationwide and regional maize damage risk map. The
important outcome of our mission is that polarimetric
radar data can add a competitive advantage in remote
sensing vegetation research and detecting structural
changes.
Further development of this technology and the use of
additional radar images will create the possibility to
accomplish a more accurate damage identification
system. With the on-going development, this project can
effectively contribute to WCR identification, spread-
monitoring and control in Hungary as well as in WCR
affected EU Member States.
ACKNOWLEDGEMENTS
The project was carried out with the support of the
Hungarian Space Office and Ministry of Environment
and Water. The ALOS PALSAR data were provided by
ESA (ESA EO CAT-1 5162) and FÖMI. The reference
data were collected by the experts of the Central
Agricultural Office of Békés County.
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