Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena...
Transcript of 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.
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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
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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.
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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.
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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
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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).
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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)
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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.
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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
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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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