Using Airborne Remote Sensing in GM Risk Assessment
L. J. Elliott1*, D. C. Mason1, M. J. Wilkinson2 and J. Allainguillaume2.
1. NERC Environmental Systems Science Centre, The University of Reading, Earley Gate,
Whiteknights, RG6 6AL, UK (Email: lje@mail.nerc-essc.ac.uk, dcm@mail.nercessc.ac.uk) .
2. School of Plant Sciences, The University of Reading, RG6 6AS, UK (Email:
m.j.wilkinson@reading.ac.uk, j.allainguillaume@reading.ac.uk) .
Abstract
The potential environmental risks due to gene flow from genetically modified (GM)
crops into wild plant populations are currently of great concern. Of particular interest is gene
flow from GM oilseed rape to its wild parental species Brassica rapa. The paper describes an
ongoing study using remotely sensed data to quantify annual gene flow rates between oilseed
rape and B. rapa across the whole of the UK. Initial work involved developing a detailed model
of gene flow that used satellite remote sensing to predict the amount of oilseed rape growing
near to wild B. rapa on riverbanks. On the basis of this study the number of B. rapa hybrids
formed across the whole of the UK each year was estimated, together with an error on this
number. A large component of the significant error was shown to be due to the uncertainty
regarding the distribution of waterside B. rapa. Airborne remote sensing employed in a stratified
sampling approach provides a potential tool for surveying B. rapa populations along riverbanks
at the necessary high spatial resolution. Preliminary results of the analysis of data acquired from
a feasibility flight in May 2003 are described. To date methods have been developed for the
detection of large B. rapa populations in images for which no ground reference data are
available, and for the detection of medium-sized populations and mixed pixels. In the future
these results may be used to improve the prediction of the number of hybrids produced annually
across the whole of the UK.
1 Introduction
The overall aim of this research is to use remotely sensed data to aid quantification of annual
gene flow rates between oilseed rape and one of its wild relatives, Brassica rapa, across the UK.
If genetically modified (GM) crops are to be approved for release on a commercial basis in the
UK it is important to quantify exposure to the risks and if possible, to develop effective strategies
for risk management. B. rapa can be subdivided into three ecotypes; wild, weedy and ruderal.
The wild ecotype grows almost exclusively on riverbanks, the weedy form grows within
broadleaf crop fields (typically rapeseed) and the ruderal is an inhabitant of disturbed land such
as building sites. The focus of this research has been on the former two ecotypes since the
ruderals only occur occasionally in generally short-lived populations. The initial work involved
developing a detailed model of gene flow that used satellite remote sensing to predict the amount
of oilseed rape growing within 30 metres of waterside wild B. rapa for every 2km square of the
UK (Wilkinson et al., 2003; Elliott et al., in press). The distribution of oilseed rape fields at
further distances from waterside populations and the frequency of weedy B. rapa infestations
were also investigated.
On the basis of this study the number of B. rapa hybrids formed across the whole of the
UK each year was estimated as 48,000 ± 27,000. These findings set targets for strategies to
eliminate hybridisation and represent the first step towards quantitative risk assessment on a
national scale. However, the large size of the error term indicates that the number of hybrids per
annum can effectively only be estimated to the nearest order of magnitude at present. A large
component of the error is due to the uncertainty regarding the distribution of waterside B. rapa,
since complete distribution data is currently unavailable and it is infeasible to study the whole of
the UK manually. Historical surveys of B. rapa are not extensive and do not separate the wild B.
rapa from its weedy and ruderal forms. In addition, various assumptions are made throughout
this procedure regarding the distribution of B. rapa (see below) and require verification in order
to improve the estimation accuracy. Thus, it is of high importance to enhance the general
knowledge of B. rapa distribution in the UK.
Airborne remote sensing provides a potential tool for surveying specific areas with a high
spatial resolution and thus gives the possibility of identifying B. rapa populations over a large
area at a faster rate than fieldwork. Nevertheless, a sampling strategy will have to be adopted,
since it would be infeasible to cover the entire country with aircraft images. The data could
potentially be useful for refining the previous predictions of hybrid locations and numbers.
Assumptions will still have to be made about the areas that are not flown over but the error will
be smaller due to a larger data resource. The campaign will focus on improving the distribution
knowledge for wild / waterside B. rapa populations.
2 Assumptions and hypotheses to test
Waterside B. rapa occurs on tributaries of a river in the same frequency as on the main river
There are approximately 260,000km of river within Britain, giving a vast number of possible B.
rapa habitats to survey. It is important to survey a random sample of all riverbank types in order
to study its distribution. However, the only rivers that can be surveyed from the ground are those
with a public right of way running alongside and those that are navigable by boat, which are
generally the main rivers. Consequently, current knowledge is restricted to the distribution of B.
rapa along the main rivers and it is assumed that the distribution of B. rapa populations on these
accessible riverbanks is representative of all other riverbanks that have been recorded to contain
B. rapa historically. However, tributaries form a substantial proportion of the total river length
and a fault in this assumption could lead to enormous error. To test this hypothesis, a river that is
known to contain B. rapa (from previous ground surveys) together with a few of its tributaries
need to be surveyed and any variation in the B. rapa distribution analysed.
B. rapa seeds are carried in the waterways and dispersed onto the banks during flooding events
At present the method by which B. rapa is spread is unknown. It is suspected that B. rapa seeds
are transported along rivers and canals and deposited on the banks during a flooding event. After
this deposition the seeds could germinate and populations develop. It is important to understand
the mechanism by which this species is distributed since this will determine how far GM hybrids
could possibly spread.
The results from the ground surveys of various rivers and canals show that on average
there is one B. rapa population every 88 metres of river, whereas there is one population every
22 kilometres of canal (151km of river banks surveyed with 1717 populations found; 330 km of
canal banks surveyed with 15 populations found). These data imply that B. rapa tends to be rare
along canals and dykes. Canals and dykes rarely flood, due to flood control methods, and this
could offer a possible explanation as to why B. rapa is uncommon on their banks. However,
further evidence is required to prove or disprove this hypothesised method of dispersal. Ground
survey work was carried out to look for B. rapa populations on the riverbanks and at greater
distances away from the river bank. However, this survey highlighted the difficulty in studying
sufficient areas outside of the flood plain (due to minimal public access away from the river) to
generate robust conclusions about the frequency of B. rapa inside and outside of the flood extent.
To test this hypothesised method of B. rapa seed distribution, a river and canal whose courses
are very close and interlinked at points could be surveyed. Analysis of the frequency of
populations would indicate whether the density on canal banks is lower, the same or higher than
that of the riverbanks. Additionally it would be useful to survey areas of a river that are within
and outside of the predicted 1 in 100 year flood extents. This would enable a comparison of
species density to be made between non-flooding canals, flooding rivers and non-flooding rivers.
3 Data acquired
A feasibility aircraft campaign was carried out on 30th May 2003. Airborne remote sensing data
was acquired in seven strips that covered a total of approximately 85km (by 1.2km wide for the
ATM and 0.6km for the CASI) of land around the River Cherwell (a tributary of the Thames)
and the Oxford canal (two parallel flights of 18km to cover the canal and the wide meanders of
the river course plus two flights of the tributary, totalling 5km), the River Nene and the GrandUnion canal (two flights, 18km plus 10km), and the River Tove (16km, the aircraft followed the
river course rather than the usual straight line method). Both ATM and CASI images were
acquired and therefore a comparison of spectral versus spatial resolution could be carried out.
4 Methods
The primary objective with the aircraft data was to develop a methodology for detecting B. rapa
plants in the images. Classification and masking procedures needed to be carried out to achieve
this. In order to create a mask from the classified image, ground reference information is
required, either prior to the classification (supervised) or subsequently (unsupervised). However,
ground reference information is not available for all of the aircraft swaths, and since the B. rapa
populations are often small and scattered it is not possible to use photo-interpretation to identify
the training pixels. Thus, a method needed to be developed for classifying those images for
which no ground surveys had been carried out. The aim is to obtain spectral data from an image
that contains ground reference information and classify all other images using this information.
In order to use spectral information from one image to classify another image, the images
need to be calibrated to one another. The reflectance value (digital number) that is recorded by
the aircraft sensor for a given point on the ground varies according to a number of factors,
including atmospheric conditions, sun illumination direction and sensor viewing direction, and
thus is only a relative value rather than the actual ground reflectance. The relative reflectance
values recorded by the sensor from a given land cover type can vary across the swath and need to
be normalized. Across the swath the sensor viewing angle varies and the pixels on the edges are
subject to different scattering and absorption effects than the centre pixels. This variation was
corrected for by carrying the “cross-track illumination correction” process in the ENVI software
package.
Once each image had been corrected for the differences in sensor viewing angle across
the swath, the differences in atmospheric conditions between the images needed to be corrected
for. One method for carrying out relative atmospheric normalisation is known as flat-field or
dark-object calibration. Flat-field calibration can be used to correct for differences in flight
direction, flight time and weather conditions between images. The darkest pixels in an image are
used to represent the ‘flat-field’ pixels with the assumption that they should have a zero
reflectance and any measured reflectance must be due to scattering and absorption effects
between the sun, the ground and the sensor. The images were normalized using the flat field
correction method. A supervised maximum likelihood classification approach was then used to
facilitate the classification of one image using training data from an independent image. This
enabled the detection of the larger B. rapa populations (at least 2m x 2m in size).
In order to detect the smaller populations the multiple cover types within single pixels
needed to be analysed. A partial spectral mixture analysis known as matched filtering was
carried out. This enabled detection of populations greater than 1m x 1m in size and identification
of the edge pixels of the larger populations.
5 Summary
At this stage methods have been developed for the detection of large B. rapa populations in
images for which no ground reference data is available and for the detection of medium
populations and edge pixels. These methods need to be applied to all of the data that were
acquired during the campaign in order to determine the overall distribution on main riverbanks,
tributaries and canal banks and in relation to the flood plain. It is only when all images have been
classified that the hypotheses set out above can be tested. These results will then be applied to
the whole of the UK and the predicted hybrid numbers and locations updated accordingly. To
reduce the error in the assumption that the data acquired to date are representative of all other
river systems, more aircraft images could be acquired that cover a wider area of the UK. A
stratified sampling approach would need to be employed in selecting the locations to image.
6 Acknowledgements
This work was carried out under UK BBSRC/NERC Grant 45/GM114202 (Brassica as a model
to describe the pattern and consequences of transgene movement into the environment).
References
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