1 Comparison of field measurements of canopy parameters, and BPMS-derived
values of the same parameters (‘validation’).
A large part of this thesis is concerned with the analysis of structural information
derived from the scattered radiation field simulated using 3D BPMS representations
of measured plant canopies. These canopies are constructed using the manually
measured key plant parameters described in XXX. In order that we may have faith in
any BPMS simulation results, and information derived thereof, it is vital that the
BPMS data are subjected to some form of validation, quality assessment and
sensitivity analysis. To make quantitative comparisons between field-measured1 and
BPMS-dervied values of key canopy indicators such as LAI and directional
reflectance it is imperative that any potential sources of bias are isolated. If this can be
done, these sources may either be removed, or at the least compensated for in some
way. The first stage of experimentation is therefore a direct comparison of BPMS
derived canopy parameters and their field-measured equivalents.
BPMS canopy representations
Validation is carried out using the BPMS data of barley, followed by sugar beet,
to compare canopy structural and radiometric parameters. 3D geometric barley
canopies have been created for a number of dates, and for three separate fields, with
the planting densities and row spacings taken from values measured in the field.
These simulated canopies are derived from the measurements of a number of
individual plants. The probability of occurrence of any particular plant within the
BPMS-generated canopy is random: this agrees with the measured canopies in terms
of the relative occurrences of one, two or three tillered plants. This does not take into
account the possibility of bias in measurement e.g. a favouring of selecting one and
two tillered plants over higher orders due to the difficulty of extracting the larger
plants cleanly from the ground.
1
Although both the BPMS plant parameters and other measurements such as those of directional
reflectance and LAI were made in the field, we will use ‘field’ or ‘field-measured’ to mean non-BPMS,
and vice-versa.
The measured plants are ‘cloned’ to create a virtual canopy. By this it is meant
that the plants are copied, rotated randomly about the vertical and distributed
according to a measured planting distribution. The plants are cloned over an area
sufficiently large to prevent ‘edge effects’ (anomalies caused by light rays escaping
the canopy unimpeded at the edges becoming significant in bi-directional reflectance
simulations). The linear dimensions of the model canopy are determined by the
requirement that the camera be positioned sufficiently high above the canopy to allow
a view zenith angle of 70o to be achieved without the viewing point falling below the
level of the top of the canopy.
To vary view zenith, the camera is rotated in the vertical plane about a point on
the ground at the centre of the canopy, as if on a boom of length L i.e.
L
h
cos
where ||  70o and h is canopy height (see Fig. XX). In this case, the canopy must be
a minimum of h tan() on a side. In actual fact, the plants were distributed over an
area of quadruple the size (twice the length and width) that is actually viewed by the
camera. In this way, edge effects are completely avoided. If an aperture of finite size
is used (i.e. not a pinhole camera) any off-nadir view will suffer from projection
effects (the projection of a square area onto the image plane will be tapezoidal). To
ensure the same area of surface is viewed from any view zenith angle an orthogonal
camera model is used.
view zenith
angle
L
h
Directional reflectance simulations
Spectral directional hemispherical reflectance was simulated for view zenith
angles from –70o to 70o in 10o steps in both the solar principle and cross-principle
planes, to match the PSII radiometer measurements. Sampling is increased to every 2o
for the 5o either side of the hotspot direction. The reflectances of the various canopy
elements such as stem, leaf and soil, are taken from measurements made in the field.
Transmittance values are taken from the PROSPECT leaf reflectance model
(Jacquemoud and Baret, 1992??). Simulations are carried out at the mean solar zenith
and azimuth angles of the time of PSII measurements made in the field. The sun
position is also calculated from the date, time, latitude and longitude of the
observations to provide a means of verifying the measured solar position. The
calculated values are likely to be more accurate than the measurements made in the
field, as they were made using a hand-held compass (azimuth) and clinometer
(zenith). A Zibbordi-Voss sky radiance model is used to create a map of direct and
diffuse solar illumination in order to make the simulations as accurate as possible to
the original conditions. The sky radiance is simulated for the location and time of the
measurements.
Reflectance is simulated at 45 wavelengths varying from 450 to 900nm in steps of
10nm using both direct and diffuse sampling. Bidirectional reflectance is defined as
the ratio of surface reflectance to a lambertian plane, so to obtain bidirectional
reflectance values, the reflectance of a lambertian plane is simulated under the same
viewing and illumination conditions as the vegetation canopy. The simulated canopy
reflectance is then divided by that of the lambertian surface to give bidirectional
reflectance in much the same way as with the radiometer data.
Simulated bidirectional reflectance is then compared to that of the PSII
measurements for the same dates and times and spectral bands, and any the similarity
(or difference) described and quantified.
Leaf Area Index (LAI)
The LAI of the modelled canopies is calculated by summing the total one sided
area of the stem and leaf canopy primitives over unit area within the canopy. These
values are compared with the values made in the field using the Licor LAI-2000
instrument. The LAI-2000 instrument has number of known limitations caused by its
design: LAI is understimated in the case of sparse canopies. Error can also be
introduced if there is a strong row element to the crop which is not sufficiently
sampled by taking measurements along transects at different directions relative to the
crop row orientation. To avoid this, LAI values are derived from 3 separate transects
at different locarions and orientations within the field, each containing 10 separate
LAI measurements. This is carried out several times in each field to arrive at a final
LAI measurement. In addition, conditions of strong direct sunlight are not ideal for
LAI measurement due to the model the LAI-2000 instrument uses. Masks are used to
mask out the operator and the suns direct rays in this case, but all these factors must
be considered when comparing the LAI values with the BPMS values. These effects
are all be much reduced in the denser canopies.
Destructive LAI measurements were made in the field by removing all leaves
from plants contained within a fixed ground area, laying them flat, and photographing
them. The digitised photos were then classified to allow calculation of the total leaf
area. These measurements are also compared with the BPMS and LAI-2000
measurements to give a third estimate.
% cover
An estimate of % cover for each canopy can be made from scanned nadir
photographs of the various canopies taken in the field. By performing an HSI
transformation on the images and thresholding the hue channel, the proportions of leaf
and soil area within the image can be estimated. If the negative size and scanning
resolution are known, % cover can be simply calculated. To highlight the visible soil
areas an RGB to HSI transformation was performed on the images. The soil displayed
a far higher signal in the hue channel than the vegetation which enabled thresholds to
be applied to isolate the visible soil components. Figure XXa and XXb show an
example
BPMS values of % cover are calculated for each BPMS canopy by simulating the
reflectance at nadir using only diffuse illumination (to prevent shadowing within the
canopy). The material usage statistics give a direct values of % cover. These values
are compared with the values estimated from the nadir photography.
Joint gap probability
The gap probability of a canopy is the probability that a ray of light entering the
canopy at an angle i, will reach a level z within the canopy and then be scattered. It is
an important structural parameter, controlled by the density and clumpiness (or ‘gapiness’)of the canopy and the number density, orientation and scattering cross-section
of the canopy elements. P(z), the probability of reaching a depth z in the canopy from
the top of the canopy (z=0) is e(Ez/o) where E = NQ (N=number density,
Q=extinction efficiency, and o = cos(i) (Hapke, B. (1981) Reflectance spectroscopy
I, JGR, 86 B4, pp3039-3054). The joint gap probability is the probability that a ray of
light reaching a depth z within the canopy will experience a single scattering event
and then escape from the canopy along a separate path without being scattered further.
In most cases this probability will be of identical form as for the downward journey,
but with P(z’) = e(Ez/) , = cos(r). In the case where the phase angle is small, and
hence the exitant path is close to the incident direction, the light is bound to escape
preferentially, having already found a path to depth z in the canopy, hence P(z’) is
unity. This is known as the opposition, or hot-spot, effect.
Increased clumpiness in a canopy leads to an increase in the probability of a gap
through the canopy to a depth z. This in turn leads to both an increase in the amount
of scattering from vegetation layers lower in the canopy, and an increase in the
number of clumps of vegetation from which little radiation is transmitted (Goel, N. S.
(19XX) Inversion of canopy reflectance models for estimation of biophysical
parameters from reflectance data, Rem. Sens. Rev., pp205-251). This will tend to
increase the amount of radiation scattered back towards the observer. Hence the
importance of gap probabilty in controlling the observed vegetation structural
characteristics.
Joint gap probability can be estimated from BPMS simulations at nadir viewing
and illumination. It can also be determined from LAI-2000 measurements, and, where
possible, from hemispherical photography. If the focal length and FOV of the camera
are known, we can create an angle map of the camera view, and then map this onto
the hemispherical photographs. If we can then estimate where the gaps in the canopy
are through classification of the images, the gap probability can be estimated.
Sensitivity analysis
Analysis is carried out to examine the sensitivity of the BPMS models to small
variations in a number of measured parameters. Small errors or biases in measurement
may cause quite large discrepancies in LAI, % cover or joint gap probability. The
difficulty of accurately characterising tiller zenith angles once a plant has been
uprooted may cause the % cover and gap probability estimates to be inaccurate. The
tendency is to understimate the tiller zenith angles, and to assume the tillers lie almost
in a plane: uprooting the plants and laying them flat to assist measurement will tend to
maks azimuthal variations. These errors are likely to cause understimates of % cover
(particularly if the tiller zenith angles are understimated), and will introduce errors
into any simulation experiment.
Error in the characterisation of plant density in particular, and row width
(variability along row) to a lesser extent will cause both the %cover and LAI to bs
incorrect, This can be rectified by measuring plant density as far as possible from the
nadir photography. In addition there is the difficulty of assessing the probability of
occurrence of plants with different numbers of tillers within the simulated canopy.
Again, this can be estimated as far as possible from the nadir photography, but failure
to represent this accurately will not just affect the % cover and LAI, but it will affect
the clumpiness of the canopy quite considerably.
There are other factors to consider, such as the characterisation of leaf twist along
the leaf length as a linear function from the base to the tip, which is not necessarily
very realistic. This will affect % cover and the leaf projection function (G). However
it is more difficult to perform sensitivity analysis on this type of parameter due to the
difficulty of characterising the twist in the first place. It cannot be measured in the
same way as the the leaf and stem angles, and must be visually estimated.
As far as possible, comparisons between the BPMS representations and the
measurements made using the LAI-2000, the PSII radiometer and other methods
should come from the same dates. This is not always possible due to the restrictions of
weather over the summer: there are BPMS data for days when no radiometry and/or
LAI measurements could be made. In most cases, however, there are LAI and PSII
measurements data from within a few days either side of the BPMS data. Whilst not
ideal, this should be close enough for the purposes of comparison of structural
parameters.