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Precision AgricultureAn International Journal on Advances inPrecision Agriculture ISSN 1385-2256 Precision AgricDOI 10.1007/s11119-012-9264-7

Spectral vegetation indices selectedfor quantifying Russian wheat aphid(Diuraphis noxia) feeding damage in wheat(Triticum aestivum L.)

M. Mirik, R. J. Ansley, G. J. Michels &N. C. Elliott

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Spectral vegetation indices selected for quantifyingRussian wheat aphid (Diuraphis noxia) feedingdamage in wheat (Triticum aestivum L.)

M. Mirik • R. J. Ansley • G. J. Michels Jr. • N. C. Elliott

� Springer Science+Business Media, LLC 2012

Abstract The effects of insect infestation in agricultural crops are of major economic

interest because of increased cost of pest control and reduced final yield. The Russian

wheat aphid (RWA: Diuraphis noxia) feeding damage (RWAFD), referred to as ‘‘hot

spots’’, can be traced, indentified, and isolated from uninfested areas for site specific RWA

control using remote sensing techniques. Our objectives were to (1) examine the use of

spectral reflectance characteristics and changes in selected spectral vegetation indices to

discern infested and uninfested wheat (Triticum aestivum L.) by RWA and (2) quantify the

relationship between spectral vegetation indices and RWAFD. The RWA infestations were

investigated in irrigated, dryland, and greenhouse growing wheat and spectral reflectance

was measured using a field radiometer with nine discrete spectral channels. Paired t test

comparisons of percent reflectance made for RWA-infested and uninfested wheat yielded

significant differences in the visible and near infrared parts of the spectrum. Values of

selected indices were significantly reduced due to RWAFD compared to uninfested wheat.

Simple linear regression analyses showed that there were robust relationships between

RWAFD and spectral vegetation indices with coefficients of determination (r2) ranging

from 0.62 to 0.90 for irrigated wheat, from 0.50 to 0.87 for dryland wheat, and from 0.84 to

0.87 for the greenhouse experiment. These results indicate that remotely sensed data have

high potential to identify and separate ‘‘hot spots’’ from uninfested areas for site specific

RWA control.

Keywords Remote sensing � Stress detection � Site-specific insect management �Insect infestation � Hot spots

M. Mirik (&) � R. J. Ansley (&)Texas AgriLife Research, 11708 Highway 70 S, Vernon, TX 76385, USAe-mail: mustafamirik@gmail.com

G. J. Michels Jr.Texas AgriLife Research, 6500 Amarillo Blvd. W, Amarillo, TX 79106, USA

N. C. ElliottUSDA-ARS, 1301 N. Western Road, Stillwater, OK 74075, USA

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Introduction

The Russian wheat aphid (RWA: Diuraphis noxia Mordvilko) an economically important

insect pest infesting wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) around

the world (Cooper et al. 2010; Randolph et al. 2009; Vandenberg et al. 2001; Weiland et al.

2009). While feeding, the RWA injects toxin into the plants manifesting into a variety of

stress symptoms. These symptoms are expressed with white, reddish-purple or yellow

longitudinal streaks on leaves and stems and rolling and stunting of either stems or leaves

(Burd and Burton 1992; Cooper et al. 2010; Kazemi et al. 2001; Unger and Quisenberry

1997). Stunting in heavily infested plants leads to reductions in plant and stem height, leaf

area, root development, chlorophyll concentration, grain mass, and vegetative biomass

(Burd et al. 1993; Cooper et al. 2010; Mirik et al. 2007a, 2009; Randolph et al. 2009;

Riedell and Blackmer 1999; Weiland et al. 2009). Plant stress from the RWA is a com-

bination of developmental, biochemical, physiological, and morphological responses. Plant

growth stages, time and duration of feeding, nutritional status of the host plants, aphid

abundance, and other environmental factors all can affect plant responses to RWA feeding

(Macedo et al. 2003). The RWA infestations in wheat usually are not uniform, but rather

occur in clusters referred to as ‘‘hot spots’’ (Fig. 1). Applying pesticides to only the

infested patches would reduce RWA control costs. These hot spots could be spatially

recognized within fields and site specific insect management plans could be implemented

to hot spots only.

While the RWA is widely distributed around the world, its economic impact on small

grains has been assessed only in Ethiopia, South Africa, Canada, and the United States

Fig. 1 Russian what aphid feeding symptoms and damage characteristics referring to as ‘‘hot spots’’in wheat fields at four scales. Images a–b was taken by Mustafa Mirik, c by G. Jerry Michels Jr., and d byTom Archer

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(US) (Archer and Bynum 1992; Archer et al. 1998; Gray et al. 1990; Lage et al. 2004;

Mirik et al. 2007a, 2009; Randolph et al. 2003). Studies indicate that economic injury

levels due to RWA feeding in small grains emerge to vary within a given region or among

regions (Archer and Bynum 1992; Archer et al. 1998; Gray et al. 1990; Mirik et al. 2007a,

2009; Randolph et al. 2003). It appears that in favorable conditions, RWA infestation can

result in heavy damage to wheat and barley in a short period of time (Mirik et al. 2007a,

2009). Wheat yield losses due to RWA infestation were estimated to be 37 % in the

Canadian Prairies (Butts et al. 1997), between 35 and 60 % in South African wheat fields

(Du Toit and Walter 1984), and between 41 and 79 % in barley fields in Ethiopia (Adulga

and Tadesse 1988). In the US, the RWA can reduce wheat grain yield up to 82.9 % and

vegetative biomass up to 76.5 % in Texas and Oklahoma Panhandles (Mirik et al. 2009).

Reflectance differences in the visible and NIR regions of the electromagnetic spectrum

led to the development of spectral vegetation indices (Genc et al. 2008; Hillnhutter et al.

2011; Mirik et al. 2006a, b, 2007a). Spectral vegetation indices are mathematical terms of

reflectance values at different parts of the spectrum, aimed to increase the extraction of

optimal spectral information and intended to normalize measurements made in varied

environmental conditions (Mirik et al. 2007a, b; Subash et al. 2011; Yang et al. 2005,

2009). Varied environmental conditions may include differences in plant species, solar

angle, shadowing, illumination, canopy coverage, soil background, atmospheric condition,

and viewing geometry of the device over space and time (Govender et al. 2009; Mirik et al.

2011; Ortiz et al. 2011). Some of these indices were used to measure leaf nutrient con-

centration (Mirik et al. 2007a; Serrano et al. 2002) and chlorophyll, carotenoid, and

anthocyanin contents (Gitelson and Merzlyak 1996; Gitelson et al. 2001; Gitelson et al.

2002), whereas others were used to evaluate the variations in vegetative attributes (Jordan

1969; Mirik et al. 2005, 2006b; Tucker 1979) and stress due to biotic and abiotic factors

(Apan et al. 2004; Hunt et al. 2011; Liu et al. 2011; Prabhakar et al. 2011; Subash et al.

2011). Among these indices, perhaps the best known and most popular are the simple ratio

(SR) and normalized difference vegetation indices (NDVI) designed to diagnose the

changes in plant phenology, physiology, and stress level (Kim et al. 2011; Serrano et al.

2011).

There are numerous successful applications of stress and disease detection and quan-

tification in wheat and other vegetative canopy using a wide range of sensor systems

including aerial photographs, airborne and satellite multispectral and hyperspectral sensors,

ground based instruments, and other spatial information technologies (Backoulou et al.

2011a, 2011b; Bauriegel et al. 2011; Burling et al. 2011; Dammer et al. 2011; Elmetwalli

et al. 2012; Mewes et al. 2011; Moshou et al. 2011; Pacumbaba and Beyl 2011; Rosyara

et al. 2010; Zhang et al. 2011). Significant negative relationships between spectral vege-

tation indices and percent damage or disease severity have been reported (Elliott et al.

2007, 2009; Nutter and Littrell 1996; Pethybridge et al. 2007; Riedell and Blackmer 1999;

Steddom et al. 2005).

Several researchers argued that remote sensing is a better method to detect and quantify

the impact of plant disease pathogens and insect infestations in vegetation compared to

visual techniques because a vegetative unit can be repeatedly, objectively, and nonde-

structively examined in a fast, robust, accurate, and inexpensive way (Cammarano et al.

2011; Bauriegel et al. 2011; Elsayed et al. 2011; Pethybridge et al. 2008; Steddom et al.

2003). In addition, it removes human bias in visual interpretation that can be highly

variable among individuals. Despite spectral detection and quantification of foliar disease

and insect infestation have been successful in plant science, little research has been carried

out to quantify the relationship between spectral vegetation indices and RWA feeding

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damage in wheat. Our objective was twofold: (1) examine the use of spectral reflectance

characteristics and changes in selected spectral vegetation indices to discern infested and

uninfested wheat (Triticum aestivum L.) by RWA and (2) quantify the relationship between

spectral vegetation indices and RWA feeding damage (RWAFD).

Materials and methods

Field locations and greenhouse experiment

In the spring of 2006, a natural RWA infestation occurred in wheat fields near Boise City

in the Oklahoma Panhandle. This study investigated 10 commercial winter wheat fields

(fields 1–10 hereafter) consisting of four irrigated (fields 1–4) and six dryland (fields 5–10)

fields (Table 1). None of these fields were treated to control RWA infestation (personal

communication with field owners). In addition, a greenhouse experiment using wheat

grown in wooden flats was conducted at the Texas AgriLife Research facilities in Bush-

land, TX, during the summer of 2006. The greenhouse experiment was undertaken to check

whether there any difference occurred between reflectance characteristics of damaged and

undamaged wheat growing in controlled and field conditions.

Sampling procedures

In order to establish sample plots in the field, a ground survey was conducted to distinguish

between infested and uninfested areas. Infested plots were determined based on RWA

feeding symptoms as well as the physical presence of RWA, while uninfested plots were

identified by the absence of either. A total of 10 1-m2 plots of RWA-infested wheat in each

field were established. Another 10 1-m2 plots of uninfested wheat were also located as near

as possible to the infested samples. Visual inspection of these uninfested plots in all fields

verified that RWA was not present. Because of this clustering; there were distinct infested

and uninfested areas in the fields. Consequently, sample locations were placed systemat-

ically in each area, which effectively represented the full range of damage severity.

Table 1 Location of the fields infested by Russian wheat aphid and greenhouse experiment, sampling date,and wheat growth stage

Location Data type Latitude Longitude Wheat variety Sampling date Growth stage

Gr Exp Greenhouse 35.19 102.08 Tam 105 5-Sep-06 33

Field 1 Irrigated 36.76 102.44 Jagger 9-May-06 64

Field 2 Irrigated 36.74 102.54 Custer 10-May-06 64

Field 3 Irrigated 36.75 102.47 Jagalene 10-May-06 63

Field 4 Irrigated 36.67 102.61 Jagger 10-May-06 67

Field 5 Dryland 36.39 102.38 Karl 92 8-May-06 71

Field 6 Dryland 36.39 102.28 Karl 92 8-May-06 72

Field 7 Dryland 36.39 102.22 Jagalene 8-May-06 72

Field 8 Dryland 36.43 102.24 Karl 92 8-May-06 70

Field 9 Dryland 36.68 102.49 Unidentified 9-May-06 73

Field 10 Dryland 35.5 102.51 Unidentified 9-May-06 71

Gr Exp green house experiment

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The greenhouse experiment involved two treatments: (1) RWA-infested and, (2) uninfested

(control) wheat. There were 10 replications of each treatment. On 20 June 2006, 288 wheat

seeds with seed spaced at 2.5 9 3.2 cm per flat were planted in each of 20 wooden flats

(64 9 61 9 9 cm3) containing field soil as the growth medium. Ten-randomly-selected

flats were put in one greenhouse and the remaining 10 flats were kept in another green-

house separated by a breezeway. On 14 August 2005 when the wheat was tillering (GS24,

Zadoks et al. 1974), 10 wheat flats were artificially infested by releasing RWA obtained

from the preserved population at densities of about 75 aphids each in three flats, 150 aphids

each in two flats, 300 aphids each in three flats, and 500 aphids each in the remaining two

flats. The remaining 10 flats were not infested with RWA and served as a control. All flats

were watered three times per week. 22 days after infesting on 5 September 2006, flats of

both treatments were transported outside the greenhouse when the plants were at

approximately jointing stage (GS 33) to make spectral measurements in full sun.

Remote sensing measurements

Spectral measurements at each sample plot and flat were made with a Cropscan Multi-

spectral Radiometer MSR16 (CROPSCAN Inc. Rochester, MN) on 8–10 May 2006. The

hand-held Cropscan Radiometer measures incoming solar light and canopy-reflected light

intensities simultaneously in nine fixed wavebands with a 28� field of view (Table 2). The

radiometer was held 2-m above the ground to record the canopy reflectance from the 1-m2

field plots. For the greenhouse study, it was held 0.7-m above flat level to record reflec-

tance from a 0.37 m2 flat. Spectral data acquisition occurred under clear sky conditions

between 11:00 and 15:30 to keep the effect of sun angle similar for all plots across the

fields. Percent RWAFD in each plot was determined visually by rating feeding symptoms

to undamaged wheat tillers or leaves by three observers. A mean value of these three

damage estimates was reported for each plot across the fields and the greenhouse

experiment.

Table 2 Band and spectral characteristics of the CropScan MSR16 Radiometer

Band center Waveband range (nm) Spectrum Properties

B460 456–463 Blue Absorbance by chlorophyll and atmospheric water

B510 506–514 Green Low absorbance by chlorophyll hence green colorof leaves

B559 555–564 Green Low absorbance by chlorophyll hence green colorof leaves

B610 607–617 Red High Absorbance by chlorophyll

B661 655–666 Red High Absorbance by chlorophyll

B710 702–714 Red High Absorbance by chlorophyll

B760 746–774 Near-infrared High reflectance by air–water interfaces in leafmesophyll

B810 797–829 Near-infrared High reflectance by air–water interfaces in leafmesophyll

B935 790–1,080 Near-infrared High reflectance by air–water interfaces in leafmesophyll

Modified from Steddom et al. (2005)

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Data analysis

Data analysis was made for each of the fields and the greenhouse experiment. Four indices:

NDVI (Rouse et al. 1973), green NDVI (GNDVI: Gitelson et al. 1996), modified NDVI

(MNDVI: Sims and Gamon 2002), and SR (Jordan 1969) were chosen and reported in this

paper (Table 3). SigmaStat (Systat Software Inc., Rochester, MN) was used to quantify the

relationships between spectral vegetation indices and ocular estimates of percent RWAFD.

Percent RWAFD was set as the independent variable and spectral vegetation indices were

set as the dependent variables. Paired t tests were conducted to compare reflectance and

indices from undamaged and RWA-damaged wheat for field and the greenhouse experi-

ments using SigmaStat. Statistical significance of regression models and paired t tests were

evaluated at a = 0.05 for all data sets.

Results

Representative average reflectance measured from the RWA-infested and uninfested wheat

canopies for irrigated, dryland, and greenhouse experiment is shown in Fig. 2. RWA-

infested wheat had higher visible reflectance from 460 nm to around 730–750 nm than

uninfested wheat. Beyond 730–750 nm, near infrared (NIR) reflectance from uninfested

wheat was higher when compared to RWA-infested wheat. Paired t test comparisons of

percent reflectance made for RWA-infested and uninfested wheat yielded significant dif-

ferences in the visible (460–710 nm) and in the NIR (760–935 nm) parts of the spectrum

except for the fields 6 and 9. Reflectance collected in fields 6 and 9 yielded insignificant

differences between infested and uninfested wheat in the NIR spectrum only.

Paired t tests showed that values of spectral vegetation indices between RWA-infested

and uninfested wheat significantly differed (Fig. 3). The RWA-infested wheat canopies

had lower index values than uninfested wheat. The RWA-infested dryland wheat had

higher index values than irrigated and greenhouse growing wheat. While not tested for

significance, index values derived from the RWA-infested irrigated wheat were little lower

than greenhouse growing wheat.

A wide range of percent RWAFD was evaluated in the experiment that ranged from 11

to 70 % for irrigated wheat from 6 to 58 % for dryland wheat, and from 27 to 71 % for

greenhouse-grown wheat. Simple linear regression confirmed that there were significant

relationships between percent RWAFD and spectral vegetation indices for all data sets

examined (Fig. 4; Table 4). The relationship between pooled data for irrigated fields and

Table 3 Spectral vegetation indices used to quantify percent Russian wheat aphid feeding damage in wheatgrowing in dryland, irrigated, and greenhouse conditions

Index Abbreviation Equation Reference

Simple ratio SR R800/R675 Jordan (1969)

Normalized difference vegetationindex

NDVI (R775 - R660)/(R775 ? R660) Rouse et al. (1973)

Green normalized differencevegetation index

GNDVI (R810 - R555)/(R810 ? R555) Gitelson et al.(1996)

Modified normalized differencevegetation index

MNDVI (R750 - R705)/(R750 ? R705 - 2*R445)

Sims and Gamon(2002)

R800 reflectance value of waveband centered at 800 nm

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GNDVI was the strongest, whereas the weakest relationship was found between NDVI and

dryland field 8 (Table 4). The relationships between pooled data from drylands and veg-

etation indices were weaker compared to pooled data from irrigated fields except for the

SR (Table 4). SR produced identical relationships with pooled data from irrigated and

Fig. 2 Spectral characteristics of uninfested and infested wheat by Russian wheat aphid in irrigated (a),dryland (b), and greenhouse growing wheat (c)

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dryland fields (Table 4). Pooled data for irrigated, dryland, and greenhouse experiment

exhibited good relationships (Table 4). The relationships across all data sets were negative,

indicating the value of an index decreased with increasing damage severity.

Discussion

The presence of a disease, insect feeding or deficiency in growth limiting factors leads to

change in chemical-pigment concentrations, leaf area, and cell structure of the affected

tissue or plant (Franke and Menz 2007; Govender et al. 2009; Huang et al. 2012; Liu et al.

2011; Prabhakar et al. 2011). A significant increase in percent reflectance values from

infested-wheat canopies in the visible region provided evidence that RWA feeding

degraded the photosynthetic pigments and changed the leaf morphology in wheat canopies.

Since the leaf morphology has a strong influence on the leaf spectral properties (Lee et al.

2011; Yang et al. 2009), the change in leaf morphology because of RWA feeding resulted

in optical differences between RWA-infested and uninfested wheat. RWA feeding has

caused a reduction in chlorophyll a and b and carotenoids in infested plants (Deol et al.

2001; Heng-Moss et al. 2003; Matile 2000; Riedell and Blackmer 1999). Apparently, the

Fig. 3 Values of normalized difference vegetation index (NDVI), green NDVI (GNDVI), modified NDVI(MNDVI) (a); and simple ratio (SR) (b) for uninfested and infested wheat by Russian wheat aphid forpooled data from irrigated, dryland, and greenhouse growing wheat. IR UN irrigated uninfested, IR INirrigated infested, D UN dryland uninfested, D IN dryland infested, GH UN greenhouse uninfested, and GHIN greenhouse infested wheat

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Fig. 4 Scatter plots of therelationships between percentRussian wheat aphid feedingdamage for irrigated, dryland,and greenhouse growing wheatand (a) normalized differencevegetation index (NDVI),(b) green NDVI, (c) modifiedNDVI, and (d) simple ratio index

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reduction in pigment concentrations in addition to change in leaf morphology induced by

RWA feeding created difference in visible light reflectance between infested and unin-

fested wheat.

Symptoms from RWA infestation in addition to leaf senescence are often related to the

decrease in NIR reflectance (Huang et al. 2007; Huang et al. 2012; Mirik et al. 2007a; West

et al. 2003). In the present study, lower reflectance in the NIR region from infested wheat

indicates that the RWA feeding reduced green leaf area by rolling and stunting, and

increased lesion formation.

The spectral vegetation indices we used are known for their ability to discriminate

levels of biomass, cover, plant health, and other vegetative attributes. Some researchers

recommended the use of these indices not only as an indicator of early stages of leaf

senescence, aging, and stress responses to environmental extremes or herbicides, but also

for quantifying different phases of disease severity, pest damage, and density (Sims and

Gamon 2002; Gitelson et al. 1996; Gitelson and Merzlyak 1997; Genc et al. 2008; Eitel

et al. 2011; Rodriguez et al. 2006; Yang et al. 2009; Xue et al. 2004). The index values we

examined were significantly reduced in RWA-infested compared to uninfested wheat.

Reduced index values for vegetative cover under stress compared to healthy vegetation

have been observed by other researchers. For example, in a recent study by Yang et al.

(2009), SR values were between 3 and 5 for wheat damaged by RWA and greenbugs after

9 days of infestation. The SR values found by Yang et al. (2009) were slightly higher than

ours, probably due to lower damage in their study compared to ours.

All indices performed well and exhibited nearly similar relationships with percent

RWAFD in greenhouse growing wheat in addition to comparable standard errors of

Table 4 The coefficients of determination (r2) and standard error of estimate between percent Russianwheat aphid feeding damage and normalized difference vegetation index (NDVI), green NDVI (GNDVI),modified NDVI (MNDVI), and simple ratio (SR) for greenhouse, irrigated, and dryland growing wheat

Location Data type NDVI GNDVI MNDVI SR

r2 SEE r2 SEE r2 SEE r2 SEE

Gr Exp Greenhouse 0.85 0.05 0.84 0.04 0.86 0.03 0.87 0.20

Field 1 Irrigated 0.89 0.01 0.80 0.02 0.80 0.01 0.89 0.06

Field 2 Irrigated 0.85 0.07 0.88 0.05 0.87 0.04 0.71 0.79

Field 3 Irrigated 0.65 0.03 0.76 0.02 0.79 0.02 0.62 0.11

Field 4 Irrigated 0.86 0.05 0.88 0.03 0.90 0.03 0.8 0.24

Field 5 Dryland 0.77 0.03 0.82 0.02 0.81 0.02 0.79 0.25

Field 6 Dryland 0.78 0.06 0.82 0.05 0.84 0.05 0.87 0.60

Field 7 Dryland 0.80 0.05 0.85 0.04 0.86 0.03 0.83 0.35

Field 8 Dryland 0.50 0.06 0.63 0.04 0.64 0.04 0.55 0.69

Field 9 Dryland 0.68 0.04 0.68 0.03 0.65 0.03 0.65 0.26

Field 10 Dryland 0.55 0.05 0.78 0.07 0.79 0.07 0.56 0.3

Pooled data Fields 1–4 0.83 0.05 0.86 0.04 0.77 0.05 0.75 0.39

Pooled data Fields 5–10 0.73 0.06 0.71 0.06 0.67 0.05 0.75 0.38

Pooled data Fields 1–10, Gr Exp 0.75 0.06 0.70 0.07 0.68 0.06 0.72 0.44

Gr Exp greenhouse experiment, r2 coefficient of determination, SEE standard error of estimate

P \ 0.01 for all regression models

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estimates (SEE) except for SR. SR produced the highest SEE in comparison with other

indices for damaged-wheat canopies due to RWA infestation in the greenhouse experi-

ment. This trend of higher SEE by SR was consistent for irrigated and dryland growing

wheat as well. This implies that SR is not as sensitive as other indices to reduce SEE for

RWAFD in wheat and other environmental variations despite having comparable r2

values with other indices. There was a trend that the relationships between both NDVI

and GNDVI and percent RWAFD appears to be slightly stronger for irrigated and

greenhouse growing wheat than dryland growing wheat except for irrigated field 3 and

dryland field 7. Correspondingly, all indices had weaker relationships with RWAFD in

dryland fields 8–10. This might have been caused by differences in growth conditions.

For example, there was little to no background reflectance from the exposed soil in

greenhouse experiment, whereas dryland had the largest reflectance from exposed soil.

Additionally, irrigated and greenhouse wheat was probably in better condition and had

relatively clean leaves than dryland wheat. Overall, GNDVI and MNDVI demonstrated

somewhat higher prediction powers for variations in damage severity and wheat growth

conditions than those of NDVI and SR for individual fields. However, NDVI had slightly

higher relationships with RWAFD for pooled data sets from irrigated, dryland, and

greenhouse wheat than other indices except for GNDVI and SR. GNDVI and SR had the

highest relationships with RWAFD for pooled data from irrigated and dryland wheat,

respectively.

Based on the significant relationships between spectral vegetation indices and RWAFD,

reduced index values, and modified spectral characteristics of infested versus uninfested

wheat canopies demonstrate that RWA hot spots among fields and within a field can be

clearly and rapidly identified and separated from uninfested areas using remote sensing

techniques. Despite a large variation in dryland, irrigated, and greenhouse-grown wheat

due to different environments, and a gradient of damage severity levels, regression models

resulted in strong and significant negative relationships for all data sets analyzed. In

addition, the wavebands used to calculate spectral vegetation indices covered the full range

of the available wavelengths in the Cropscan Radiometer except for green at 510 nm and

red at 610 and 660 nm. This indicated that the spectral vegetation indices we used can be

applied to the full range of wheat varieties, agronomic conditions (irrigated, dryland), and

damage severity occurring in commercial wheat fields.

Moderate resolution satellite remote sensing (e.g., Landsat, Hyperion, Advance Land

Imager) provides sufficient data for large-scale, regional studies, high resolution satellite

imagery (e.g., GeoEye, QuickBird, IKONOS, SPOT) are sufficient for field level studies,

and air-borne systems may have higher resolution and time flexibility. Hand-held remote

sensing instruments are useful for small-scale operational field monitoring of biotic and

abiotic stress agents and novel research purposes (Jackson 1986). A limiting factor is

their applicability to small areas when compared with aircraft and other satellite sensors.

Using hand-held spectrometers to quantify the unknown spectral characteristics of

uninfested and infested plant canopies due to insect feeding at these smaller scales is

needed because hand-held remote sensing devices have better temporal, spectral, and

spatial resolutions. Reflectance data obtained by hand-held instruments over small areas

provides information to understand spectral interactions between insect pests and their

host plants, as well as fundamental ground-truth for interpretation of remote sensing data

measured from satellite and aircraft. Studies using image data acquired by satellite and

aircraft platforms for their ability to detect RWA infestations in wheat at broader spatial

scales are needed.

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Conclusion

Reflectance responses of the wheat canopy indicated that remote sensing can detect

damage caused by RWA. RWA-infestations significantly increased the visible reflectance

and decreased the NIR reflectance at the canopy level when compared to uninfested plants.

This indicates that the spectral properties of wheat plants are markedly degraded by RWA

infestation. This is also supported by significant reductions in spectral vegetation indices

compared to uninfested areas. The relationships between percent RWAFD and spectral

vegetation indices showed that remotely sensed data transformed into spectral vegetation

indices provides a method for detecting damage severity and discriminating damaged areas

from uninfested areas in commercial wheat fields. Results from the greenhouse experiment

showed robust relationships between spectral vegetation indices and percent RWAFD. In

addition to this, the value of vegetation indices and reflectance properties of wheat crop

(GS 33) significantly changed due to RWA feeding. These indicate that RWA-infestation is

detectible early enough to implement control measures for precision farming using a

remote sensing method.

The results of present study demonstrate that there exists high potential to distinguish

RWA infestation hot spots within wheat fields using remote sensing that can provide

managers and producers a quick and repeatable method for detecting and quantifying

RWA infestations in time and place. Once applied to a particular management unit or farm,

the georeferenced maps of infested and uninfested areas can then be used to facilitate more

efficient treatment of the disease with GIS-based precision farming spray equipment. Such

a practice for aphid management increases insecticide application efficiency and final

wheat yields, and decreases the potential for environmental contamination.

Acknowledgments Our special thanks to Karl Steddom, Robert Bowling, and Roxanne Bowling for theirhelp and beneficial discussion. We are thankful to Johnny Bible, Robert Villarreal, David Jones, JoyNewton, Sabina Mirik, Daniel Jiminez, and Timothy Johnson for technical assistance. This study wasfunded by the USDA-ARS Areawide Pest Management Program. Project Number: 500-44-012-00. We alsoexpress our thanks to the two anonymous reviewers and editors who made critical suggestions and com-ments to improve the manuscript.

References

Adulga, H., & Tadesse, G. M. (1988). Chemical control of the wheat aphid (Diuraphis noxia Mordw.) onbarley at Chacha, Ethiopia. Committee of Ethiopian Entomologist News Letter, 8, 13–16.

Apan, A., Held, A., Phinn, S., & Markley, J. (2004). Detecting sugarcane ‘orange rust’ disease using EO-1hyperion hyperspectral imagery. International Journal of Remote Sensing, 25, 489–498.

Archer, T. L., & Bynum, E. D., Jr. (1992). Economic injury level for the Russian wheat aphid (Homoptera:Aphididae) on dryland winter wheat. Journal of Economic Entomology, 85, 987–992.

Archer, T. L., Peairs, F. B., Pike, K. S., Johnson, G. D., & Kroening, M. (1998). Economic injury levels forthe Russian wheat aphid (Homoptera: Aphididae) on winter wheat in several climate zones. Journal ofEconomic Entomology, 91, 741–747.

Backoulou, G. F., Elliott, N. C., Giles, K., Phoofolo, M., & Catana, V. (2011a). Development of a methodusing multispectral imagery and spatial pattern metrics to quantify stress to wheat fields caused byDiuraphis noxia. Computers and Electronics in Agriculture, 75, 64–70.

Backoulou, G. F., Elliott, N. C., Giles, K. L., Phoofolo, M., Catana, V., Mirik, M., et al. (2011b). Spatiallydiscriminating Russian wheat aphid induced plant stress from other wheat stressing factors. Computersand Electronics in Agriculture, 78, 123–129.

Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., & Herppich, W. B. (2011). Early detection of Fusariuminfection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture, 75,304–312.

Precision Agric

123

Author's personal copy

Burd, J. D., & Burton, R. L. (1992). Characterization of plant damage caused by Russian wheat aphid(Homoptera: Aphididae). Journal of Economic Entomology, 85, 2017–2022.

Burd, J. D., Burton, R. L., & Webster, J. A. (1993). Evaluation of Russian wheat aphid (Homoptera:Aphididae) damage on resistant and susceptible hosts with comparisons of damage ratings to quan-titative plant measurements. Journal of Economic Entomology, 86, 974–980.

Burling, K., Hunsche, M., & Noga, G. (2011). Use of blue-green and chlorophyll fluorescence measure-ments for differentiation between nitrogen deficiency and pathogen infection in winter wheat. Journalof Plant Physiology, 168, 1641–1648.

Butts, R. A., Thomas, J. B., Lukow, O., & Hill, B. D. (1997). Effect of fall infestations of Russian wheataphid (Homoptera: Aphididae) on winter wheat yield and quality on the Canadian prairies. Journal ofEconomic Entomology, 90, 1005–1009.

Cammarano, D., Fitzgerald, G., Basso, B., O’Leary, G., Chen, D., Grace, P., et al. (2011). Use of the canopychlorophyll content index (CCCI) for remote estimation of wheat nitrogen content in rainfed envi-ronments. Agronomy Journal, 103, 1597–1603.

Cooper, W. R., Dillwith, J. W., & Puterka, G. J. (2010). Salivary proteins of Russian wheat aphid(Hemiptera: Aphididae). Environmental Entomology, 39, 223–231.

Dammer, K. H., Moller, B., Rodemann, B., & Heppner, D. (2011). Detection of head blight (Fusarium ssp.)in winter wheat by color and multispectral image analyses. Crop Protection, 30, 420–428.

Deol, G. S., Reese, J. C., Gill, B. S., Wilde, G. E., & Campbell, L. R. (2001). Comparative chlorophylllosses in susceptible wheat leaves fed upon by Russian wheat aphids or greenbugs (Homoptera:Aphididae). Journal of the Kansas Entomological Society, 74, 192–198.

Du Toit, F., & Walters, M. C. (1984). Damage assessment and economic threshold values for the chemicalcontrol of the Russian wheat aphid, Diuraphis noxia, (Mordvilko) on winter wheat. In M. C. Walters(Ed.), Progress in Russian wheat aphid (Diuraphis noxia Mordw.) research in the Republic of SouthAfrica, Technical Communication 191 (pp. 58–62). Johannesburg, South Africa: Department ofAgriculture, Republic of South Africa.

Eitel, J. U. H., Vierling, L. A., Litvak, M. E., Long, D. S., Schulthess, U., Ager, A. A., et al. (2011).Broadband, red-edge information from satellites improves early stress detection in a New Mexicoconifer woodland. Remote Sensing of Environment, 115, 3640–3646.

Elliott, N., Mirik, M., Yang, Z., Dvorak, T., Rao, M., Michels, J., et al. (2007). Airborne multi-spectralremote sensing of Russian wheat aphid injury to wheat. Southwestern Entomologist, 32, 213–219.

Elliott, N., Mirik, M., Yang, Z., Jones, D., Phoofolo, M., Catana, V., et al. (2009). Airborne remote sensingto detect greenbug stress to wheat. Southwestern Entomologist, 34, 205–211.

Elmetwalli, A. M. H., Tyler, A. N., Hunter, P. D., & Salt, A. C. (2012). Detecting and distinguishingmoisture- and salinity-induced stress in wheat and maize through in situ spectroradiometry measure-ments. Remote Sensing Letters, 3, 363–372.

Elsayed, S., Mistele, B., & Schmidhalter, U. (2011). Can changes in leaf water potential be assessedspectrally? Functional Plant Biology, 38, 523–533.

Franke, J., & Menz, G. (2007). Multi-temporal wheat disease detection by multi-spectral remote sensing.Precision Agriculture, 8, 161–172.

Genc, H., Genc, L., Turhan, H., Smith, S. E., & Nation, J. L. (2008). Vegetation indices as indicatorsof damage by the sunn pest (Hemiptera: Scutelleridae) to field grown wheat. African Journal ofBiotechnology, 7, 173–180.

Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing ofglobal vegetation from EOS-MODIS. Remote Sensing of Environment, 58, 289–298.

Gitelson, A. A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithmdevelopment for remote sensing of chlorophyll. Journal of Plant Physiology, 148, 494–500.

Gitelson, A. A., & Merzlyak, M. N. (1997). Remote estimation of chlorophyll content in higher plant leaves.International Journal of Remote Sensing, 18, 2691–2697.

Gitelson, A. A., Merzlyak, M. N., & Chivkunova, O. B. (2001). Optical properties and nondestructiveestimation of anthocyanin content in plant leaves. Photochemistry and Photobiology, 74, 38–45.

Gitelson, A. A., Zur, Y., Chivkunova, O. B., & Merzlyak, M. N. (2002). Assessing carotenoid content inplant leaves with reflectance spectroscopy. Photochemistry and Photobiology, 75, 272–281.

Govender, M., Dye, P. J., Weiersbye, I. M., Witkowski, E. T. F., & Ahmed, F. (2009). Review of commonlyused remote sensing and ground-based technologies to measure plant water stress. Water SA, 35,741–752.

Gray, M. E., Hein, G. L., Walgenbach, D. D., & Elliott, N. C. (1990). Effects of Russian wheat aphid(Homoptera: Aphididae) on winter and spring wheat infested during different plant growth stagesunder greenhouse conditions. Journal of Economic Entomology, 83, 2434–2442.

Precision Agric

123

Author's personal copy

Heng-Moss, T. M., Ni, X., Macedo, T., Markwell, J. P., Baxendale, F. P., Quisenberry, S. S., et al. (2003).Comparison of chlorophyll and carotenoid concentrations among Russian wheat aphid (Homoptera:Aphididae)-infested wheat isolines. Journal of Economic Entomology, 96, 475–481.

Hillnhutter, C., Mahlein, A. K., Sikora, R. A., & Oerke, E. C. (2011). Remote sensing to detect plant stressinduced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crops Research,122, 70–77.

Huang, W., Lamb, D. W., Niu, Z., Zhang, Y., Liu, L., & Wang, J. (2007). Identification of yellow rust inwheat using in situ spectral reflectance measurements and airborne hyperspectral imaging. PrecisionAgriculture, 8, 187–197.

Huang, J., Liao, H., Zhu, Y., Sun, J., Sun, Q., & Liu, X. (2012). Hyperspectral detection of rice damaged byrice leaf folder (Cnaphalocrocis medinalis). Computers and Electronics in Agriculture, 82, 100–107.

Hunt, E. R., Li, L., Yilmaz, M. T., & Jackson, T. J. (2011). Comparison of vegetation water contents derivedfrom shortwave-infrared and passive-microwave sensors over central Iowa. Remote Sensing of Envi-ronment, 115, 2376–2383.

Jackson, R. D. (1986). Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology,24, 265–287.

Jordan, C. F. (1969). Derivation of leaf area index from quality of light on the forest floor. Ecology, 50,663–666.

Kazemi, M. H., Talebi-Chaichi, P., Shakiba, M. R., & Jafarloo, M. M. (2001). Biological responses ofRussian wheat aphid, Diuraphis noxia (Mordvilko) (Homoptera: Aphididae) to different wheat vari-eties. Journal of Agricultural Science and Technology, 3, 249–255.

Kim, Y., Glenn, D. M., Park, J., Ngugi, H. K., & Lehman, B. L. (2011). Hyperspectral image analysis forwater stress detection of apple trees. Computers and Electronics in Agriculture, 77, 155–160.

Lage, J., Skovmand, B., & Andersen, S. B. (2004). Resistance categories of synthetic hexaploid wheatsresistant to the Russian wheat aphid (Diuraphis noxia). Euphytica, 136, 291–296.

Lee, Y., Yang, C., Chang, K., & Shen, Y. (2011). Effects of nitrogen status on leaf anatomy, chlorophyllcontent and canopy reflectance of paddy rice. Botanical Studies, 52, 295–303.

Liu, M., Liu, X., Wu, L., Duan, L., & Zhong, B. (2011). Wavelet-based detection of crop zinc stressassessment using hyperspectral reflectance. Computers & Geosciences, 37, 1254–1263.

Macedo, T. B., Higley, L. G., Ni, X., & Quisenberry, S. S. (2003). Light activation of Russian wheat aphid-elicited physiological responses in susceptible wheat. Journal of Economic Entomology, 96, 194–201.

Matile, P. (2000). Biochemistry of Indian summer: Physiology of autumnal leaf coloration. ExperimentalGerontology, 35, 145–158.

Mewes, T., Franke, J., & Menz, G. (2011). Spectral requirements on airborne hyperspectral remote sensingdata for wheat disease detection. Precision Agriculture, 12, 795–812.

Mirik, M., Ansley, J., Michels, J., & Elliott, N. (2009). Grain and vegetative biomass reduction by theRussian wheat aphid in winter wheat. Southwestern Entomologist, 34, 131–157.

Mirik, M., Jones, D. C., Price, J. A., Workneh, F., Ansley, R. J., & Rush, C. M. (2011). Satellite remotesensing of wheat infected by wheat streak mosaic virus. Plant Disease, 95, 4–12.

Mirik, M., Michels, G. J., Jr, Kassymzhanova-Mirik, S., & Elliott, N. C. (2007a). Reflectance characteristicsof Russian wheat aphid (Hemiptera: Aphididae) stress and abundance in winter wheat. Computers andElectronics in Agriculture, 57, 123–134.

Mirik, M., Michels, G. J., Jr, Kassymzhanova-Mirik, S., Elliott, N. C., & Bowling, R. (2006a). Hyperspectralspectrometry as a means to differentiate uninfested and infested winter wheat by greenbug (Hemiptera:Aphididae). Journal of Economic Entomology, 99, 1682–1690.

Mirik, M., Norland, J. E., Biondini, M. E., Crabtree, R. L., & Michels, G. J. (2007b). Relationships betweenremotely sensed data and biomass components in a big sagebrush (Artemisia tridentata) dominatedarea in Yellowstone National Park. Turkish Journal of Agriculture and Forestry, 31, 135–145.

Mirik, M., Norland, J. E., Crabtree, R. L., & Biondini, M. E. (2005). Hyperspectral one-meter-resolutionremote sensing in Yellowstone National Park, Wyoming: II. Biomass. Rangeland Ecology & Man-agement, 58, 459–465.

Mirik, M., Steddom, K., & Michels, G. J., Jr. (2006b). Estimating biophysical characteristics of musk thistle(Carduus nutans) with three remote sensing instruments. Rangeland Ecology and Management, 59,44–54.

Moshou, D., Bravo, C., Oberti, R., West, J. S., Ramon, H., Vougioukas, S., et al. (2011). Intelligent multi-sensor system for the detection and treatment of fungal diseases in arable crops. Biosystems Engi-neering, 108, 311–321.

Nutter, F. W., Jr, & Littrell, R. H. (1996). Relationships between defoliation, canopy reflectance and podyield in the peanut-late leafspot pathosystem. Crop Protection, 15, 135–142.

Precision Agric

123

Author's personal copy

Ortiz, B. V., Thomson, S. J., Huang, Y., Reddy, K. N., & Ding, W. (2011). Determination of differences incrop injury from aerial application of glyphosate using vegetation indices. Computers and Electronicsin Agriculture, 77, 204–213.

Pacumbaba, R. O., Jr, & Beyl, C. A. (2011). Changes in hyperspectral reflectance signatures of lettuceleaves in response to macronutrient deficiencies. Advances in Space Research, 48, 32–42.

Pethybridge, S. J., Hay, F., Esker, P., Groom, T., Wilson, C., & Nutter, F. W., Jr. (2008). Visual andradiometric assessments for yield losses caused by ray blight in pyrethrum. Crop Science, 48, 343–352.

Pethybridge, S. J., Hay, F., Esker, P., Wilson, C., & Nutter, F. W., Jr. (2007). Use of a multispectralradiometer for noninvasive assessments of foliar disease caused by ray blight in pyrethrum. PlantDisease, 91, 1397–1406.

Prabhakar, M., Prasad, Y. G., Thirupathi, M., Sreedevi, G., Dharajothi, B., & Venkateswarlu, B. (2011). Useof ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper(Hemiptera: Cicadellidae). Computers and Electronics in Agriculture, 79, 189–198.

Randolph, T. L., Peairs, F. B., Kroening, M. K., Armstrong, J. S., Hammon, R. W., Walker, C. B., et al.(2003). Plant damage and yield response to the Russian wheat aphid (Homoptera: Aphididae) onsusceptible and resistant winter wheats in Colorado. Journal of Economic Entomology, 96, 352–360.

Randolph, T. L., Peairs, F., Weiland, A., Rudolph, J. B., & Puterka, G. J. (2009). Plant responses to sevenRussian wheat aphid (hemiptera: Aphididae) biotypes found in the United States. Journal of EconomicEntomology, 102, 1954–1959.

Riedell, W. E., & Blackmer, T. M. (1999). Leaf reflectance spectra of cereal aphid-damaged wheat. CropScience, 39, 1835–1840.

Rodriguez, D., Fitzgerald, G. J., Belford, R., & Christensen, L. K. (2006). Detection of nitrogen deficiencyin wheat from spectral reflectance indices and basic crop eco-physiological concepts. AustralianJournal of Agricultural Research, 57, 781–789.

Rosyara, U., Subedi, S., Duveiller, E., & Sharma, R. C. (2010). Photochemical Efficiency and SPAD valueas indirect selection criteria for combined selection of spot blotch and terminal heat stress in wheat.Journal of Phytopathology, 158, 813–821.

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in theGreat Plains with ERTS. Third ERTS Symposium, 1, 309–317.

Serrano, L., Gonzalez-Flor, C., & Gorchs, G. (2011). Assessment of grape yield and composition using thereflectance based water index in mediterranean rainfed vineyards. Remote Sensing of Environment,118, 249–258.

Serrano, L., Penuelas, J., & Ustin, S. L. (2002). Remote sensing of nitrogen and lignin in mediterraneanvegetation from AVIRIS data: Decomposing biochemical from structural signals. Remote Sensing ofEnvironment, 81, 355–364.

Sims, D. A., & Gamon, J. A. (2002). Relationships between leaf pigments content and spectral reflectanceacross a wide range of species, leaf structures and developmental stages. Remote Sensing of Envi-ronment, 81, 337–354.

Steddom, K., Bredehoeft, M. W., Khan, M., & Rush, C. M. (2005). Comparison of visual and multispectralradiometric disease evaluations of cercospora leaf spot of sugar beet. Plant Disease, 89, 153–158.

Steddom, K., Heidel, G., Jones, D., & Rush, C. M. (2003). Remote detection of rhizomania in sugar beets.Phytopathology, 93, 720–726.

Subash, N., Ram Mohan, H. S., & Banukumar, K. (2011). Comparing water-vegetative indices for rice(Oryza sativa L.)-wheat (Triticum aestivum L.) drought assessment. Computers and Electronics inAgriculture, 77, 175–187.

Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. RemoteSensing of Environment, 8, 127–150.

Unger, L. M., & Quisenberry, S. S. (1997). Categorization of six wheat plant introduction lines for resistanceto the Russian wheat aphid (Homoptera: Aphididae). Journal of Economic Entomology, 90,1408–1413.

Vandenberg, J. D., Sandvol, L. E., Jaronski, S. T., Jackson, M. A., Souza, E. J., & Halbert, S. E. (2001).Efficacy of fungi for control of Russian wheat aphid (Homoptera: Aphididae) in irrigated wheat.Southwestern Entomologist, 26, 73–85.

Weiland, A. A., Peairs, F. B., Randolph, T. L., & Kerzicnik, L. M. (2009). Seasonal presence of the Russianwheat aphid (Hemiptera: Aphididae) on alternate hosts in Colorado. Southwestern Entomologist, 34,121–129.

West, J. S., Bravo, C., Oberti, R., Lemaire, D., Moshou, D., & McCartney, H. A. (2003). The potential ofoptical canopy measurement for targeted control of field crop diseases. Annual Review of Phytopa-thology, 41, 593–614.

Precision Agric

123

Author's personal copy

Xue, L., Cao, W., Luo, W., Dai, T., & Zhu, Y. (2004). Monitoring leaf nitrogen status in rice with canopyspectral reflectance. Agronomy Journal, 96, 135–142.

Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., & Popham, T. W. (2005). Using ground-basedmultispectral radiometry to detect stress in wheat caused by greenbug (Homoptera: Aphididae)infestation. Computers and Electronics in Agriculture, 47, 121–135.

Yang, Z., Rao, M. N., Elliott, N. C., Kindler, S. D., & Popham, T. W. (2009). Differentiating stress inducedby greenbugs and Russian wheat aphids in wheat using remote sensing. Computers and Electronics inAgriculture, 67, 64–70.

Zadoks, J. C., Chang, T. T., & Konzak, C. F. (1974). A decimal code for the growth stages of cereals. WeedResearch, 14, 415–421.

Zhang, J., Huang, W., Li, J., Yang, G., Luo, J., Gu, X., et al. (2011). Development, evaluation andapplication of a spectral knowledge base to detect yellow rust in winter wheat. Precision Agriculture,12, 716–731.

Precision Agric

123

Author's personal copy