Evaluating the accuracy of mapping weeds in fallow fields using airborne digital imaging:...

Evaluating the accuracy of mapping weeds in fallow ®elds using

airborne digital imaging: Panicum effusum in oilseed rape stubble

D. W. LAMB* AND M. WEEDONFarrer Centre, Charles Sturt University, PO Box

588, Wagga Wagga, NSW 2678, Australia

Received 3 December 1997

Revised version accepted 28 May 1998

Summary

The potential accuracy of using airborne multi-spectral imaging to map weed patches rapidly in

a fallow ®eld has been evaluated. An image of a®eld of oilseed rape (Brassica napus L.) stubbleinterspersed with Panicum e�usum R. Br. was

acquired using a four-camera airborne digitalimaging system; recording in the infrared, red,green and blue wavebands. The image was con-

verted into georecti®ed weed maps using super-vised and unsupervised classi®cation procedures.Comparison of the airborne-derived maps with anaccurate weed map compiled from a detailed

ground survey demonstrated that weed:non-weedclassi®cation and mapping accuracies of better than87% are possible. The limitations of assessing the

accuracy of classi®ed imagery using ground-truthdata of similar spatial resolution are discussed.

Introduction

Early detection and mapping of weeds in cropsand fallow ®elds allows selective managementwith herbicides. Unlike the blanket applicationof herbicides, selective management (often re-

ferred to as patch spraying) increases the appli-cation e�ciency and can provide signi®cant®nancial savings to the user (Combellack, 1979;

Heisel et al., 1996; Sta�ord & Miller, 1996).

Patch spraying is based on either simultaneousweed monitoring and spraying (Felton et al.,

1991) or weed mapping before spraying (Nordboet al., 1995). Simultaneous weed monitoring andspraying based on surface re¯ectance analysis is

restricted to spraying weeds in fallow or recentlyploughed ®elds (Du�, 1993). The generation ofdetailed georeferenced weed maps allows an as-

sessment of the type of chemical and quantityrequired before application and monitoring ofthe e�ectiveness of current or past managementstrategies. Georeferenced weed maps are also

used to control computerized sprayers (Paiceet al., 1995; Sta�ord & Miller, 1996).

Weed maps are currently constructed using

on-ground techniques, including global posi-tioning system (GPS)-assisted ®eld walking(Sta�ord et al., 1996), vehicular-based manual

surveying (Rew et al., 1996), harvest-basedmonitoring (Colliver et al., 1996) and near-ground imaging (Thomson et al., 1990; Brivot &

Marchant, 1996). The applicability of thesetechniques in a real-time precision managementsituation is limited by the time and human re-sources required to acquire the weed data, the

suitability of the ®eld for foot or vehicular accessor the timeliness of the data acquisition relativeto strategic management opportunities.

Airborne digital imaging for generating weedmaps

Use of remote sensing to detect and map weedsin seedling crops or fallow ®elds requires aspectral or `colour' di�erence to exist between

the weed patches and the background ®eld. Thesigni®cant spectral di�erences between vegeta-tion and bare soil or dead plants can be used to

identify and map weeds by acquiring remotelysensed data at strategic times during crop de-velopment (Thomson et al., 1990).*Correspondence.

Weed Research, 1998, Volume 38, 443±451

Ó 1998 European Weed Research Society

Airborne digital imaging technologies are

capable of delivering high-resolution multispec-tral imagery of agricultural ®elds in a timely andcost-e�ective fashion (for example, Anderson &

Yang, 1996; Lamb, 1996; Sun et al., 1997). Re-cently, remote sensing has been used to detectand map a variety of weeds in rangelands andmixed-farming areas (Anderson et al., 1993;

Carson et al., 1995; Lass et al., 1996; Lass &Callihan, 1997). Remote sensing o�ers a non-invasive and rapid method of generating weed

maps required for computerized sprayers.As part of an overall programme of research

to investigate the opportunities and limitations

of airborne remote sensing for weed mapping,this project aimed to evaluate the accuracy ofmapping Panicum e�usum R. Br. in a fallow ®eldof oilseed rape (Brassica napus L.) stubble.

Materials and methods

The project was completed using a fallow ®eld ofoilseed rape stubble interspersed with patches of

Panicum e�usum. The target ®eld containedthree primary land cover classes: Panicum e�u-sum, red±brown soil and oilseed rape stubble. A

detailed re¯ectance spectrum of each land coverclass was acquired using a PSII ®eld radiometer(Analytical Spectral Devices, Boulder, CO,USA) (Fig. 1).

High-resolution images of the target ®eldwere acquired using Charles Sturt University'sfour-camera airborne video system (ABVS)

(Louis et al., 1995). Each camera contains a

740 ´ 576 pixel array and is ®tted with a 12-mm-

focal length lens. When ¯own at an altitude of1167 m above the ground, the cameras producean image resolution of 1 m2 and a ground cov-

erage of 50 ha. Each camera acquires informa-tion in a preset spectral band governed by aninterchangeable interference ®lter (25 nm band-pass). An on-board IBM-compatible 486 com-

puter, comprising a four-channel framegrabberboard, captures and digitizes four-band com-posite images from the cameras. In the present

work, images were acquired using the generalvegetation 440-nm blue, 550-nm green, 650-nmred and 770-nm near-infrared ®lters.

An image of the target ®eld was acquired atnoon (AEST) on 14 January 1997. The imagewas corrected for geometric and radiometricdistortion, and recti®ed to map coordinates

using the image processing software ER Mapper(Earth Resource Mapping, San Diego, CA,USA). Image recti®cation used ®ve reference

points located with a Trimble ProXL di�erentialglobal positioning system (DGPS) (Trimble,Sunnyvale, CA, USA). The recti®ed image was

classi®ed into `weed' and `non-weed' regions bydiscriminating between living vegetation andnon-living vegetation stubble or soil. This was

achieved using an unsupervised classi®cation ofone-dimensional normalized di�erence vegeta-tion index (NDVI) data and a supervised classi-®cation of the multiband image data.

Normalized di�erence vegetation index (NDVI)

Each pixel of the four-band composite imagewas transformed into a single NDVI accordingto the relation (Rouse et al., 1973):

NDVI � (near infrared� ÿ (red)

(near infrared)� (red)�1�

Eqn (1) assigns a value between ± 1.0 and + 1.0to each image pixel. Classifying each pixel of theNDVI image as vegetation or non-vegetation

required the selection of an appropriate thresh-old NDVI value (NDVIthresh). According to thespectral signatures of each land cover class

present in the target (Fig. 1), vegetation pixelsare expected to have higher NDVI values thanthe bare soil or oilseed rape stubble. Conse-quently, pixels with an NDVI value greater than

Fig. 1. Detailed re¯ectance spectra of Panicum e�usum, red±brown soil and oilseed rape stubble.

444 D. W. Lamb and M. Weedon

Ó 1998 European Weed Research Society, Weed Research 38, 443±451

NDVIthresh were classed as vegetation, and those

pixels with a value equal to or less thanNDVIthresh were classed as non-vegetation.

Selection of the NDVIthresh was based on an

iterative process of measuring the accuracy ofweed maps generated for all possible values ofNDVIthresh between ±1.00 and +1.00. Theoverall classi®cation accuracy of each weed map,

generated at each of the 201 NDVIthresh valuesbetween ±1.00 and +1.00 (intervals of 0.01), wascalculated by comparing each map directly with

the ground-truth map and counting the numberof correctly and incorrectly classi®ed vegetationand non-vegetation pixels (Congalton, 1991).

The optimum NDVIthresh was then selected asthe one yielding the highest overall accuracy,de®ned here as the maximum attainable accuracy(MAA), and the corresponding weed map was

used in the subsequent accuracy analysis.

Supervised classi®cation

Vegetation and non-vegetation classes weregenerated using the maximum likelihood method

of supervised classi®cation (for example, Camp-bell, 1996). Three composite images were gener-ated by combining the four bands, combining

the green, red and near-infrared bands andcombining the red and near-infrared bands. Ineach case, appropriate vegetation and non-veg-etation training regions were selected from the

target image. The training regions used for eachsupervised classi®cation comprised approxi-mately 1% of the target image, and these pixels

were excluded from the accuracy assessment.The accuracy of these weed maps was calculatedby comparing them directly with the ground-

truth map and counting correctly and incorrectlyclassi®ed pixels as per the classi®ed NDVI image.

Ground-truth map and accuracy assessment

A detailed ground-truth map of vegetation andnon-vegetation areas in the target ®eld was

constructed using a Trimble TDC1 data loggerand an all-terrain vehicle (DGPS-ATV) equip-ped with the Trimble DGPS described earlier.

Because P. e�usum occurred in clearly de®nedpatches within the short stubble, it was possibleto identify patch boundaries and map them using

the perimeter method (Colliver et al., 1996).Patch boundaries were located visually based ona threshold of approximately 25% ground

coverage, as seen from directly overhead. In the

®eld, this threshold was identi®ed on the basisthat a single weed plant was observed to occupyapproximately 500 cm2, and a clump of ®ve

plants therefore constituted the minimum patchsize. Percentage ground coverage was used in-stead of plant density, as this is directly related tothe presence or absence of weeds as perceived by

a remote-sensing instrument. The performancecharacteristics of the DGPS limited the posi-tional accuracy of the ground-truth data to

between �0.8 and �1.2 m.The ground-truth data were imported into the

GENAMAP geographic information systems soft-

ware package (Genasys II, Fort Collins, CO,USA), corrected for topological errors and con-verted into a raster-format weed map. The mapwas then imported into ER Mapper to allow

direct comparison to be made with the weedmaps generated from the classi®ed imagery.

Results

An airborne image of a portion of the target ®eldis displayed as a grey-scale NDVI image (Fig. 2).The vegetation (P. e�usum) appears pale grey±

white against the non-vegetative stubble and soilbackground (dark grey±black). The detailedground-truth map acquired by the DGPS-ATV,showing vegetation and non-vegetation areas in

the target ®eld, is also depicted (Fig. 3).The result of the iterative calculation to de-

termine NDVIthresh is graphed in Fig. 4. The

overall classi®cation accuracy is graphed as afunction of possible NDVIthresh values between)1.00 and +1.00. An overall classi®cation ac-

curacy of 29% exists for NDVIthresh values be-tween ±1.00 and ±0.10 and is associated withevery pixel in the target being classi®ed as weeds.Similarly, an overall classi®cation accuracy of

71% exists for NDVIthresh values between +0.10and +1.00, where the entire target is classi®ed asnon-weed. AMAA of 87.2% appears as the peak

in the curve and occurs here at an optimumNDVIthresh of 0.00.

The accuracy statistics of each of the classi-

®ed NDVI and multiband images are summa-rized in Table 1. The overall accuracy of theclassi®ed images was approximately 86%,

regardless of the method of classi®cation(Table 1). The highest overall accuracy resultedfrom the classi®cation of the NDVI image

Mapping of weeds with airborne digital imaging 445


based on the optimum NDVIthresh selected fromFig. 4. The images classi®ed by the supervisedmethod showed a small decrease in overall ac-curacy with a reduction in the wavebands used.

The P. e�usum class of the classi®ed NDVIimage had an omissional error of 22%, but theimages classi®ed by the supervised method had

omissional errors ranging from 33% to 37%.The class not containing P. e�usum had a 6%omissional error when the supervised method

was used. The classi®ed NDVI image had aslightly higher omissional error for the non-P. e�usum class.

The total omissional error for the images withP. e�usum is shown in black for the weed andwhite for the non-weed areas in Fig. 5. Theslightly higher omissional error of the non-weed

class in the classi®ed NDVI image is re¯ected inthe increase in the amount of white pixels. Sev-eral of these errors appear to be as isolated pixels

in the centre right of the NDVI ®gure withinlarge areas of the non-weed class. It is visuallydi�cult to identify the 33% di�erence in theweed omissional error (in black) in the four-band

classi®ed image of P. e�usum when comparedwith the classi®ed NDVI image. Most of theerror occurred in the boundary of the weedy

areas.

Discussion

Part of the success of the project is based on the

spectral di�erence between soil, stubble andvegetation. The red±brown soil had spectralvalues similar to the living vegetation in the redband and lower in the blue, green and near-in-

frared bands (Fig. 1). Spectral values of theoilseed rape stubble were higher in the blue,green and red wavebands than the living

Fig. 2. Grey-scale NDVI image of the target ®eld showing oilseed rape stubble (dark grey±black) and patches of Panicum e�usum(pale grey±white). The area of this target is 27.2 ha.



vegetation because of the increased re¯ectance ofthe straw. In the near-infrared band, the valuesof living vegetation were signi®cantly higherthan those of the bare soil or stubble. The lower

omissional error of weeds in the classi®ed NDVIimage may be caused by the greater interactionof the red and near-infrared when separating

living vegetation from soil and dead plant stub-ble. The higher omissional error in the non-P.e�usum class of the NDVI image may have re-

sulted from patches of ¯attened stubble with ahigher near-infrared re¯ectance. Such patches

would invariably have been covered in thetraining regions of the supervised classi®cationimages. Derived NDVI values within the imagemay be in¯uenced by the changing nature of soil

background, particularly for a low proportion ofvegetation cover relative to the pixel size (Hueteet al., 1985; Elvidge & Lyon, 1985). However,

this e�ect can be ruled out because of the uni-form soil pro®le of this particular target.

The other images using conventional super-

vised classi®cation with red and near-infraredbands also had high overall accuracy, with anincrease from 33% to 37% in omissional error in

going from a procedure involving four bands toone using only the near-infrared and red wave-bands. This suggests that a simpler two-bandimaging system (red and near-infrared wave-

bands) may have been adequate for this mappingtask.

The other part of the success of the project is

based on the phenological timing of the weedemergence and growth habit of the seedlingweeds. Panicum e�usum tends to emerge as dense

colonies because most of the seed is droppedwithin a few metres of the parent plant. Detec-tion and mapping of weeds with high-resolution

Fig. 4. Overall classi®cation accuracy as a function of(NDVIthresh) in the range )1.00 to +1.00, showing the opti-mum NDVIthresh at a value of 0.00.

Table 1. Accuracy of classi®cation of weed (Panicum e�usum)and non-weed using classi®ed NDVI image (NDVIthreshdetermined from Fig. 4) and supervised classi®cation offour-band, three-band and two-band images

Ground truthOverallclassi®-cation

Non-weed Weed accuracy(%) (%) (%)

NDVIArea classi®ed as non-weed 62.8 6.9 87.2Area classi®ed as weed 6.0 24.4Omission error 8.7 22.1Commission error 9.9 19.6

Four-band supervisedArea classi®ed as non-weed 66.6 9.7 85.8Area classi®ed as weed 4.7 19.2Omission error 6.6 33.1Commission error 12.5 19.7

Three-band supervisedArea classi®ed as non-weed 66.9 9.9 85.6Area classi®ed as weed 4.5 18.7Omission error 6.3 34.7Commission error 12.9 19.3

Two-band supervisedArea classi®ed as non-weed 66.8 10.7 84.8Area classi®ed as weed 4.5 18.0Omission error 6.3 37.3Commission error 13.8 20.1

In total, 27.2 ha of the target ®eld was mapped.

Fig. 3. Ground-truth map generated using the DGPS-ATV,showing recorded vegetation (black � weed: Panicum e�u-sum) and non-vegetation areas (grey � non-weed).



multispectral imagery may, therefore, be limitedto species with similar dispersion characteristics.

In the case of automated herbicide applica-tion, it may be bene®cial for the weed class to

have a high commissional error and a lowomissional error when mapping weeds. It isbetter to spray an area without weeds (com-

missional error) than miss a weed patch (omiss-ional error). In all of the classi®cationprocedures, the majority of the commissional

and omissional errors in the P. e�usum classwere created by misclassi®cation of pixels alongthe weed patch boundaries.

Mixed pixels account for a large fraction ofclassi®cation error when standard classi®ers areused (Campbell, 1996). In the present work, se-lection of the 25% plant coverage threshold as a

means of identifying weed patches in the ®eldwas based on `feel' rather than on a detailedmathematical analysis of the spectral mixing

problem. Nevertheless, weed patches of less than1 m2 are a source of mixed pixels. A variation inweed vigour from plant to plant would also have

contributed to a variation in the spectral signa-ture and may have a�ected its subsequent pixelclassi®cation.

Fig. 5. (a) Error map, generated from classi®ed NDVI image, depicting regions of correct classi®cation (weed � pale grey; non-weed � dark grey) and omissional error (weed � black; non-weed � white).



Camera misalignment may also contribute tothe mixed pixel problem. At the boundary of aweed patch, each camera may record the spectralcontribution of a di�erent class. Before acquiring

imagery, all four cameras were manually alignedto within approximately half a pixel of each other(Louis et al., 1995). Camera misalignment could,

therefore, in¯uence the classi®cation of a 2-pixel-wide strip around the perimeter of any feature.

Assuming the image classi®cation itself to be

correct, discrepancies in overlaying the ground-truth map and image class boundaries will arisefrom a combination of a rated GPS error, here

�1.2 m, and possible distortions introduced inthe image when rectifying it to map coordinates.Increasing the number of ground registrationpoints will reduce the error associated with im-

age recti®cation. A further contribution to theerror in locating weed patch boundaries mayalso have arisen from observer inconsistencies in

recognizing the 25% weed coverage thresholdsurrounding weed patches.

The contributions of image recti®cation,

DGPS error and ground mapping thresholds tothe accuracy of weed maps are the subject offurther investigation.

Fig. 5. (b) Error map generated from the four-band supervised classi®cation procedure (same colour coding as Fig. 5a).



The inclusion of an appropriate bu�er region

around mapped weed patches is an obviousmeans of reducing the e�ects of the errors thatoccur at patch boundaries. The long-term cost-

e�ectiveness of adding bu�ers of up to 4 maround weed patches has been studied in orderto address navigational errors associated withground-based weed mapping and possible sea-

son-to-season seed migration (Rew et al., 1997).The e�ect on mapping accuracy of introducingbu�er regions around weed patches is also the

subject of further investigation.

Conclusion

A preliminary evaluation has demonstrated thatmetre-resolution airborne digital imaging is ca-

pable of mapping patches of P. e�usum in afallow ®eld of oilseed rape stubble with an ac-curacy in excess of 87%.

The accuracy determination was limited bythe similar spatial resolution characteristics ofthe ground-truthing equipment and the airborne

imaging system. Nevertheless, the application ofairborne digital imaging to mapping weeds incrops potentially o�ers a timely, rapid and cost-

e�ective method for farm managers and scien-tists alike.

Acknowledgements

The authors gratefully acknowledge support re-

ceived for this project in the form of partialfunding from the Cooperative Research Centrefor Weed Management Systems Vacation and

Honours Scholarships Programs (M.W.) andCharles Sturt University Farrer Centre SeedFunding (D.L.). The assistance provided by MrJ. Medway and Mr J. Lucas (Farrer Centre) is

also gratefully acknowledged, as is the continuedsupport from members of Charles Sturt Uni-versity's Spatial Analysis Unit (CSU-SPAN).

The authors acknowledge the contribution of theanonymous referees in the revision of themanuscript.

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