evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: avena...

Evaluating the accuracy of mapping weedsin seedling crops using airborne digital imaging:Avena spp. in seedling triticale

D W LAMB*, M M WEEDON* & L J REW *Farrer Centre, Charles Sturt University, Locked Bag 588, Wagga Wagga, NSW, 2678, Australia,

and Tamworth Centre for Crop Improvement, Tamworth, NSW, 2340, Australia

Received 14 June 1999

Revised version accepted 30 September 1999

Summary

Airborne multispectral imaging has been used to map patches of Avena spp. (wild-oats) in a ®eld

of seedling triticale (X Triticosecale, Wittmack). Images of the target ®eld were acquired using a

four-camera airborne digital imaging system, recording in the infrared, red, green and blue wave-

bands. Spectral information derived from images of 0.5-, 1.0-, 1.5- and 2.0-m spatial resolution

were correlated with detailed on-ground weed density measurements to investigate the e�ect of

image resolution on mapping accuracy. Comparisons between normalized-di�erence vegetation

index (NDVI) or soil-adjusted vegetation index (SAVI) images and weed data achieved

correlations of up to 71%. The highest correlation was achieved with the 0.5-m-resolution images

and the lowest with the 2.0-m-resolution images. At 0.5-m resolution, NDVI images could not

reliably discriminate weed populations of less than 28 weeds m±2 from weed-free regions, while

SAVI images could not discriminate populations of less than 17 weeds m±2. At 1.0-, 1.5- and

2.0-m resolution, SAVI images could not discriminate populations of less than 23 weeds m±2,

while NDVI images again demonstrated a higher discrimination threshold. Results suggest that

airborne multispectral imaging could be used as part of a strati®ed weed sampling system.

Keywords: remote sensing, airborne imaging, weeds, precision farming, accuracy.

Introduction

The spatial distribution of weeds within arable ®elds has received considerable interest over the

past decade (Wilson & Brain, 1991; Mortensen et al., 1993; Johnson et al., 1996; Rew et al.,

1996; Gerhards et al., 1997). In particular, the emergence of accurate and a�ordable di�erential

global positioning systems (DGPS) has raised the possibility of patch-spraying weeds using

variable rate technology. While machinery is commercially available to apply herbicides variably,

wider scale commercial exploitation of patch-spraying will require the development of a rapid

and cost-e�ective technique for creating accurate treatment maps.

Correspondence: D W Lamb, Farrer Centre, Charles Sturt University, Locked Bag 588, Wagga Wagga, NSW, 2678,

Australia. Tel: (+61) 269332552; Fax: (+61) 269332737

Ó Blackwell Science Ltd Weed Research 1999 39, 481±492 481

Multispectral airborne imaging systems are capable of acquiring submetre-resolution images

of agricultural ®elds in visible and near infrared wavelengths (for example, Louis et al., 1995;

Anderson & Yang, 1996; Everitt et al., 1997; Sun et al., 1997; Escobar et al., 1998) and at mid-

infrared wavelengths (Everitt et al., 1987). Interest is now being shown in using this technology

as a rapid method of generating weed maps in crops and rangelands. The requirements of

mapping weeds in fallow ®elds and seedling crops from the air can often be reduced to spectrally

discriminating living vegetation against non-living vegetation (Lamb & Weedon, 1998) or bare

soil (for example, Thompson et al., 1990; Brown & Steckler, 1993; Christensen et al., 1994). In

most cases, there is a signi®cant di�erence in the spectral signature of each (for example,

Woebbecke et al., 1995). This approach is best suited to mapping weeds where there is a

predominance of one weed species within a ®eld, or where there is no requirement to distinguish

di�erent weed types within a single ®eld (Kondratyev & Fedchenko, 1979). Nevertheless, detailed

on-ground spectral measurements have demonstrated the potential of this technique in

discriminating between some weed species in addition to discriminating weeds from crop and

bare soil (Brown et al., 1990; Brown & Steckler, 1993).

In a fallow ®eld, any living weeds can be identi®ed and mapped against a stubble or soil

background. Senescent weeds may be more di�cult to identify, as the spectral signature can be

modi®ed to the point that it is indistinguishable from the background residue. In a

vegetation:non-vegetation classi®cation exercise involving both supervised and unsupervised

classi®cation procedures, Lamb & Weedon (1998) used a metre-resolution airborne image to

map Panicum e�usum R. Br. in a ®eld of oilseed rape Brassica napus L. stubble. Panicum e�usum

was observed to occur in discrete patches, and the mapping accuracy was quanti®ed by an error

matrix (errors of omission and commission), calculated using a detailed on-ground weed map.

Mapping errors ranged from 19% to 37%, depending on the classi®cation procedure used.

In seedling crops, the vegetation:non-vegetation approach to weed mapping relies on

acquiring images at an altitude at which the seedling crop is indistinguishable from the

background soil or residue. If the weed populations develop in the inter-row spacings or in

relatively large intra-row patches, only the weed patches will be detected. Brown & Steckler

(1993) mapped Elymus repens L. (common couch), Setaria spp. (foxtail), Taraxacum o�cinale

Weber (dandelion) and Chenopodium album L. (fat hen) in a seedling crop of no-tillage maize

(Zea mays L.), comprising a background of bare soil and some stubble. In this work, individual

weed species were classi®ed separately using a supervised classi®cation procedure. All weed

species could be discriminated from the background, but the grass weed species were easier to

discriminate from each other as a result of their patchiness. Again, the accuracy was measured by

comparing the presence or absence of weeds in the classi®ed image and the ground data. Errors

of omission and commission in the weed classi®cation were less than 25%.

To date, the majority of accuracy investigations have relied on the use of error matrices to

estimate the reliability of weed discrimination. However, as this technique involves the counting

of correctly and incorrectly classi®ed image pixels based on the presence or absence of weeds on

the ground, it is applicable to discrete rather than to continuous weed populations. Furthermore,

little data are available concerning the e�ect of image spatial resolution on the ability to map

weeds. This paper reports on a project to quantify the ability of airborne multispectral imaging

to map continuous weed populations of predominantly Avena spp. (consisting of A. fatua L. and

A. ludoviciana, Durieu) in a seedling triticale (X Triticosecale, Wittmack) crop undersown to

subterranean clover (Trifolium subterranean L.) and lucerne (Medicago sativa L.). The e�ect of

image resolution on the ability to map the weeds is also reported.

482 D W Lamb et al.

Ó Blackwell Science Ltd Weed Research 1999 39, 481±492

Materials and methods

Acquisition of on-ground weed data

The ®eld site was located north-east of Wagga Wagga, NSW, Australia (Lat. 34°9¢48¢¢S, Long147°26¢10¢¢E). The ®eld (52 ha) was sown to triticale, undersown to pasture legumes and infested

predominantly with Avena spp. The triticale and Avena spp. were at the two- to ®ve-leaf stage,

with a mean triticale density of 36 plants m±2. The clover and lucerne seedlings were small and at

very low densities.

A subsection of the ®eld (126 m ´ 98 m), incorporating the centres and edges of Avena spp.

patches with varying densities, was sampled using a 7 m ´ 7 m grid. The grid was marked out

using 100 m of tape, and each intersection was marked with a ¯exicane (Permex, Sydney,

Australia). AllAvena spp. plants were counted within 0.25-m2, 0.5-m2 and 1.0-m2-square quadrats

at each grid intersection. The location of the corners of the selected region, each marked with a

square metal sheet visible from the air, was logged using a submetre accuracy Trimble ProXL

di�erential global positioning system (DGPS) (Trimble, Sunnyvale, CA, USA). The location of

each point was averaged over a period of �1 min, providing a spatial accuracy of �25 cm. The

location of each grid intersection was subsequently determined by interpolation. The position of

additional ground control points (GCPs), namely other metal sheets, fence lines and trees, were

also recorded with the DGPS to ensure accurate georecti®cation of the airborne imagery.

Detailed re¯ectance spectra of the soil, stubble, crop (triticale and pasture legumes) and

Avena spp. at di�erent densities were obtained using a PSII ®eld radiometer (Analytical Spectral

Devices, Boulder, CO, USA).

Acquisition and analysis of multispectral imagery

High-resolution images of the target ®eld were acquired using a four-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. Image pixel size is governed by the camera altitude above

ground. For example, at an altitude of 1524 m, the cameras produce an image resolution of 1 m

(1 m ´ 1 m pixel). Each camera acquires information in a preset spectral band governed by an

interchangeable interference ®lter (25 nm bandpass). An on-board IBM-compatible 486

computer, ®tted with a four-channel frame-grabber board, captured and digitized four-band

composite images from the cameras. In this study, images were acquired using the general

vegetation ®lters: 440 nm blue, 550 nm green, 650 nm red and 770 nm near infrared.

Images of the target ®eld were acquired at noon AEST (Australian Eastern Standard Time)

on 1 July 1998, at altitudes of 3048 m, 2286 m, 1524 m and 762 m above ground. Image

resolution, coverage at each altitude and number of GCPs used to georectify the imagery are

summarized in Table 1.

Table 1 Spatial characteristics for each image of the target ®eld

Altitude above ground (m) Image resolution (m) Image coverage (ha) Number of ground control points

3048 2.0 197 16

2286 1.5 111 16

1524 1.0 49 17

762 0.5 12 8

Mapping weeds in seedling crops 483


Each image was corrected for geometric and radiometric distortion, and recti®ed to map

co-ordinates using the image processing software ER Mapper (Earth Resource Mapping, San

Diego, CA, USA). Image recti®cation used a minimum of eight GCPs (Table 1). The

multispectral images were transformed into re¯ectance images, using two known re¯ectance

zones in each image to adjust for camera gain and o�set (Stow et al., 1996), and subsequently

converted into normalized-di�erence vegetation index (NDVI) images by transforming each

image pixel according to the relation described by Rouse et al. (1973):

NDVI � �near infrared� ÿ �red��near infrared� � �red� �1�

The NDVI values of each image pixel assumed to correspond to the location of a grid

intersection were initially correlated with the weed counts by a least squares analysis to check the

accuracy of the overlay between the georecti®ed imagery and the grid of ground-truth data

points. This process was repeated after displacing the image by single pixel steps, up to 20 pixels,

in each of the two cartesian directions (approximately N±S and E±W) relative to the grid of weed

data. The image/ground data overlay that returned the highest linear correlation value was set as

the ®nal image/ground overlay, and this was used for further detailed analyses of weed detection

accuracy. This procedure was completed for all images and ground-based data. Once properly

overlaid, the weed data were log-transformed, before a detailed comparison with the

corresponding NDVI data (eqn 1), and an additional soil-adjusted vegetation index (SAVI)

was calculated of the form;

SAVI � �1� n� � �near infrared� ÿ �red��near infrared� � �red� � n

�Huete; 1988� �2�

The SAVI adjustment factor n is used to compensate for the in¯uence of varying soil

backgrounds on the measured plant index and is typically assigned a value of n � 0.5 (Huete,

1988). Note, for n � 0, eqn 2 reduces to the NDVI (eqn 1). Here, rather than soil variations,

eqn 2 was used in order to test the e�ect of a varying soil/stubble background.

In each image, the best ®t between NDVI, or SAVI and log-transformed weed data, was

recorded by a polynomial model: y � b + c1x + c2 x2 + c3x

3, where b and c are constants.

Results

The spectral re¯ectance characteristics of the target ®eld could be divided into two distinct land

cover classes: Avena spp. and background cover types. Background cover types consisted of crop

with soil or crop with soil and stubble. Detailed re¯ectance spectra of the background cover types

show that relatively small di�erences exist between them compared with areas of high-density

Avena spp. (Fig. 1). At all image resolutions, the target pixels e�ectively comprised a mix of

Avena spp. and background cover types.

Grey-scale NDVI images of the target ®eld at 2.0-, 1.5-, 1.0- and 0.5-m resolution,

respectively, are shown in Fig. 2. Avena spp. had the highest NDVI values (shown as white), and

this is expected from the spectral characteristics depicted in Fig. 1. The majority of the Avena

spp. were in the south and south-west portions of the ®eld (Fig. 2), which was where the ground-

truth measurements were taken (outlined by a white rectangle in each image). The neighbouring

®eld was sown to pasture, but also had high densities of Avena spp.

484 D W Lamb et al.


A contour weed map, generated from the grid-based weed counts (1 m2 quadrat), is depicted in

Fig. 3. Simple correlation coe�cients between the weed counts recorded at each grid intersection

using the 0.25, 0.5 and 1.0 m2 quadrats are summarized in Table 2.

An example of the correlation surface resulting from the progressive displacement of an image

over the grid-sampled weed map is given in Fig. 4. The location of the expected peak correlation

between the 1.5-m resolution image and the grid-based 1 m2 quadrat data is shown (X).

However, the maximum linear correlation (R2 � 0.61) is achieved by displacing the image one

pixel to the west and south relative to this location. The additional displacement of the image

Fig. 2 Georeferenced grey-scale

NDVI images of the target ®eld

acquired at altitudes of (a) 3048 m

(2 m pixels); (b) 2286 m (1.5 m

pixels); (c) 1524 m (1.0 m pixels);

and (d) 762 m (0.5 m pixels) above

ground level (North ). Thevegetation associated with weeds,

trees and pasture (neighbouring

®elds) appears white (higher NDVI

values), while soil, stubble, crop and

shadow appear black-grey (low

NDVI values). The 126 m ´ 98 m

weed sampling area is marked as a

rectangle in each image.

Fig. 1 Re¯ectance spectra of

the three primary ®eld cover

types; Avena spp. and two

background cover types, using

the ®eld radiometer. The aerial

re¯ectance bands are

superimposed.



from the expected overlay position was found to be necessary for all images in order to achieve

the best correspondence of image and ground data. Example scatterplots of log-transformed

weed data and corresponding image pixel NDVI and SAVI values are depicted in Figs 5 and 6.

A full summary of Pearson's correlation coe�cients achieved for each NDVI image is given in

Table 3. The NDVI images and Avena spp. data accounted for �58±71% of the variation, with

highest levels of explanation achieved with the highest image resolution and largest quadrat size

(Table 3). The highest level of explanation between each SAVI image and the Avena spp. data

varied with the chosen adjustment factor (Table 4), with the highest values achieved for n � 0.2.

In each case, the SAVI provided an increase in R2 of only 1±2% over the NDVI. The SAVI

(n � 0.2) regression equations extracted for each of the 0.5, 1.0, 1.5 and 2.0 m images and 1.0 m2

quadrat weed data are summarized in Table 5.

Fig. 3 Georeferenced contour map (North ) of weed counts in the 126 m ´ 98 m weed sampling area (7 m ´ 7 m

grid, 1 m2 quadrat).

Compared quadrat sizes R 2

0.25 m2 and 0.5 m2 0.98

0.25 m2 and 1.0 m2 0.95

0.5 m2 and 1.0 m2 0.98

Table 2 Comparison between weed

counts recorded using 0.25 m2,

0.5 m2 and 1.0 m2 quadrats

486 D W Lamb et al.


At densities of less than 28 Avena spp. plants m±2, the 0.5-m resolution NDVI values were

similar to weed-free values; above 28 plants m±2, the NDVI values tended to be higher, thus

providing scope for discrimination and detection (Fig. 7). The 0.5-m resolution SAVI (n � 0.2)

values, however, could discriminate and detect weeds plants above 17 plants m±2. For the

Fig. 4 Correlation surface resulting from the progressive displacement of an NDVI image (1.5 m resolution)

over the grid-based weed data (1 m2 quadrat). Note the di�erent positions of the expected overlay location and

actual best correlation point.

Fig. 5 Polynomial regression of

0.5-m resolution NDVI values vs.

co-located 1 m2 quadrat Avena spp.

density (ln transformed).



Table 4 Pearson's correlation coe�cients obtained by comparing aerial image SAVI data and ground-based

Avena spp. counts using a 1 m2 quadrat

SAVI adjustment factor (n)

SAVI image resolution n = 0.0 n = 0.1 n = 0.2 n = 0.3 n = 0.5 n = 0.75 n = 1.0

2 m 0.593 0.603 0.604 0.604 0.602 0.600 0.599

0.5 m 0.706 0.711 0.712 0.711 0.709 0.707 0.706

SAVI adjustment factors (n) are varied between 0.1 and 1.0. Note SAVI (n = 0) is equivalent to NDVI. Values in

bold indicate strongest correlations between images and ground-based weed counts.

NDVI image resolution (m)

Quadrat size (m2) 2 1.5 1.0 0.5

0.25 0.575 0.625 0.668 0.677

0.5 0.581 0.637 0.679 0.698

1 0.593 0.648 0.687 0.706

Values in bold represent the strongest correlations between images and

respective quadrat data.

Table 3 Pearson's correlation

coe�cients obtained by comparing

aerial image NDVI data and ground-

based Avena spp. counts using

di�erent quadrat sizes

Fig. 6 Polynomial regression of

0.5-m resolution SAVI (n � 0.2)

values vs. co-located 1 m2 quadrat

Avena spp. density (ln transformed).

Table 5 Polynomial regression equations generated from the SAVI (n = 0.2) images and 1 m2 quadrat weed data

Image resolution

SAVI (n = 0.2) regression equation

x = weed density (m)2) R 2 Equation no.

0.5 m 0.00080x3 + 0.0025x2 ) 0.0041x + 0.1126 0.712 (3)

1.0 m 0.00030x3 + 0.0053x2 ) 0.0109x + 0.3064 0.692 (4)

1.5 m 0.00020x3 + 0.0056x2 ) 0.0124x + 0.2700 0.657 (5)

2.0 m )0.00002x3 + 0.0061x2 ) 0.0109x + 0.3587 0.604 (6)

488 D W Lamb et al.


1.0-, 1.5- and 2.0-m resolution NDVI and SAVI images, the minimum detection thresholds were

54 plants m±2 and 23 plants m±2 respectively.

Discussion

On this occasion, good correlation was observed between the weed counts acquired with di�erent

quadrat sizes (Table 2). Similar correlations have been recorded at other Avena spp. sites

(L J Rew, unpubl. obs.) and suggest that there may be no loss of data, at the spatial resolution we

are interested in, using smaller quadrats. This may not be true for other species with di�erent

spatial distributions. In future ground-truthing of high-resolution imagery of Avena spp., use of

the smaller 0.25-m2 quadrat will provide a signi®cant time advantage over the 1-m2 quadrat,

while retaining the accuracy of mapping.

Accurate mapping of weeds using aerial imagery requires the combination of a good image-

to-weed map transformation algorithm and accurate spatial registration of the image to ground

co-ordinates. In earlier work, Lamb & Weedon (1998) highlighted the limitations of using metre-

resolution GPS units to georectify and then ground-truth similar spatial resolution imagery. In

this work, the location of each GCP was measured with greater accuracy, yet an error of �1 pixelwas still encountered in overlaying the images with the grid-based weed data (Fig. 4). It can only

be concluded that this is a result of a residual geometric distortion in the imagery associated with

the cameras and a non-horizontal image plane. Furthermore, although the DGPS unit provided

submetre resolution of the grid sampling points and GCPs, these objects were di�cult to locate

accurately even in submetre-resolution imagery. It is not surprising that better recti®cation of

ground and image data was achieved with the highest resolution imagery (Table 3). Obviously,

improvements would result from using a greater number of more highly visible GCPs and

arti®cial targets exhibiting di�use rather than specular re¯ection characteristics. As also shown in

the present work, it is important to overlay images and ground data as accurately as possible

before conducting any rigorous error analysis.

For each image, the use of a SAVI provided only a small increase in the accuracy of the

regression equations compared with an NDVI (Table 4), although a signi®cant improvement in

the weed detection threshold was achieved (Fig. 7). In this work, an adjustment factor of n �0.2 yielded the highest correlation between the image-derived SAVIs and the grid-based weed

data. An adjustment factor of 0.2 is expected to be `optimal' for leaf area indices (LAIs) in excess

Fig. 7 Mean NDVI (d) and SAVI

(n � 0.2) (m) values, with standard

errors, plotted as a function of weed

density from the 1 m2 quadrat data.

Image resolution � 0.5 m.



of 2 (Huete, 1988), while in this work, the LAI of weeds was estimated to range from 0 to less

than 1. This demonstrates the relatively small in¯uence of variations in background spectral

signature on the SAVI transformations.

The minimum weed density for discrimination and detection should not change in response to

changing image resolution, provided the weed patches are comparable in size to the spatial

resolution of the image. In a situation in which weed patches are smaller than the image pixels,

the e�ect of increasing image pixel size would be to increase the apparent detection threshold as a

result of the relative increase in the weed-free proportion of each pixel. While such an e�ect was

observed in going from the 0.5-m to 1.0-m resolution imagery, it was not the case between the

1.0- to 1.5- and 1.5- to 2.0-m resolution imageries. Here, the lack of a signi®cant increase in

detection threshold is more likely to result from not having su�cient population classes and

having to group populations of >35 plants m±2 than from the e�ects of changing imaging

resolution. A larger distribution of weed populations in excess of 35 plants m±2 would doubtless

improve the characterization of this trend.

It is apparent from this analysis that it is possible to detect densely infested areas of Avena

spp. in a seedling triticale crop using aerial multispectral imaging. However, areas with fewer

than 17 Avena spp. plants m±2 could not be consistently distinguished from weed-free ones.

There was a reasonable relationship between the aerial images and Avena spp. data, which

accounted for a majority of variance. The resulting R2 values clearly indicate that the

relationship improved with the higher resolution aerial images, with a measured improvement

in the discrimination thresholds at the highest image resolution. Consequently, a submetre

image resolution is recommended for the production of weed maps, even for higher density

infestations.

The ultimate aim of this work is to develop a system for detecting and mapping weeds and

convert this into a treatment map for precision spraying. In the current trial, the 0.5-m resolution

multispectral image provided the best correlation with ground-truth data. Low densities of wild

oats were not reliably distinguished from weed-free areas, but further work on resolution,

georecti®cation and spectral composition may improve this. In the future, with an improved

understanding of weed spatial dynamics and errors involved with the imaging and

georecti®cation, it may be possible to apply bu�ers to the weed data (e.g. Rew et al., 1997) to

create useable and accurate treatment maps.

Nevertheless, at present, aerial imagery is unlikely to provide a stand-alone technique for

creating treatment maps for the precision spraying of weeds at low density. Rather, the

imagery has immediate potential as a time- and cost-e�ective means of supporting strati®ed

sampling. Studies have shown that often only a small number of weed species will occur in

considerable densities throughout a single ®eld (for example, Rew et al., 1997). Multispectral

images could be used to identify the highly infested areas of a ®eld, and a DGPS would

then be used on the ground to locate the patches in order to identify the species and their

density.

Conclusion

Airborne multispectral imaging has been used to map patches of Avena spp. in a ®eld of seedling

triticale. Correlation coe�cients of up to 0.71 were achieved by comparing images of varying

spatial resolution with detailed on-ground weed population measurements. The minimum weed

detection threshold increased in going from using 0.5-m resolution imagery to 1.0-m resolution

490 D W Lamb et al.


imagery, but was invariant to changes in image resolution in the range of 1.0 m to 2.0 m. In each

case, SAVI images provided higher correlations with measured weed populations than

corresponding NDVI images.

Acknowledgements

The authors gratefully acknowledge Mr R McLaren for the use of his ®eld, Dr D Lemerle,

Messrs Y Alemseged, J Broster, S Cormack, R Early, J Gavin, J Lucas, D McMahon,

D Pickering and A Taylor for their help with the ground surveying, S Harden for biometric

assistance, and the ongoing support of members of Charles Sturt University's Spatial Analysis

Unit (CSU-SPAN).

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