Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology
Transcript of Hyperspectral indices to diagnose leaf biotic stress of apple plants, considering leaf phenology
Hyperspectral indices to diagnose leaf biotic stress of apple plants,
considering leaf phenology
S. DELALIEUX*{, B. SOMERS{, W. W. VERSTRAETEN{, J. A. N. VAN
AARDT{, W. KEULEMANS§ and P. COPPIN{
{M3-BIORES, Katholieke Universiteit Leuven, Celestijnenlaan 200 E, BE-3001 Leuven,
Belgium
{CSIR Natural Resources and the Environment, Ecosystems—Earth Observation, PO
Box 395, Pretoria, 0001, South Africa
§Crop Bio-engineering, Katholieke Universiteit Leuven, W. de Croylaan 42, BE-3001
Heverlee, Belgium
(Received 10 July 2007; in final form 15 January 2008 )
Novel and existing hyperspectral vegetation indices were evaluated in this study,
with the aim of assessing their utility for accurate tracking of leaf spectral
changes due to differences in biophysical indicators caused by apple scab. Novel
indices were extracted from spectral profiles by means of narrow-waveband
ratioing of all possible two-band combinations between 350 nm and 2500 nm at
nanometer intervals (2 311 250 combinations) and all possible two-band
derivative combinations. Narrow-waveband ratios consisting of wavelengths of
approximately 1500 nm and 2250 nm, associated with water content, have proven
to be the most appropriate for detecting apple scab at early developmental stages.
Logistic regression c-values ranged from 0.80 to 0.88. At a more developed
infection stage, vegetation indices such as R440/R690 and R695/R760 exhibited
superior distinction between non-infected and infected leaves. Identified
derivative indices were located in similar regions. It therefore was concluded
that the most appropriate indices at early stages of infection are ratios of
wavelengths situated at the water band slopes. The choice of appropriate indices
and their discriminatory performances, however, depended on the phenological
stage of the leaves. Hence, an undisturbed 20-day growth profile was examined to
assess the effect of physiological changes on spectral variations at consecutive
growth stages of leaves. Results suggested that an accurate distinction could be
made between different leaf developmental stages using the 570 nm, 1460 nm,
1940 nm and 2400 nm wavelengths, and the red-edge inflection point. These
results are useful to crop managers interested in an early warning system to aid
proactive system management and steering.
1. Introduction
The use of non-destructive methods to detect vegetation stress at an early stage of
development holds great promise for the optimization of the management of
commercially important agricultural crops. A more efficient application of fertilizers
and pesticides will become feasible and will in turn lower environmental and
ecosystem impact; this will significantly decrease the cost of plant production. Stress
*Corresponding author. Email: [email protected]
International Journal of Remote Sensing
Vol. 30, No. 8, 20 April 2009, 1887–1912
International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2009 Taylor & Francis
http://www.tandf.co.uk/journalsDOI: 10.1080/01431160802541556
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induction affects the physiological behaviour of plants, resulting in differences in
reflectance patterns and thus providing potential for remote sensing diagnosis of
vegetation stress (Rock et al. 1988, Martin and Aber 1997). Natural growth
processes (e.g. increase of biomass, development, maturation, senescence, plant
architecture and natural fluctuations in hydraulic properties) and the related
biochemical changes, for instance in the concentration of chlorophyll and other
pigments, also have an impact on the amount of solar energy that is reflected,
absorbed, and transmitted by plants (Lillesand et al. 2004, Ustin et al. 2001).
Research into vegetative spectral reflectance can help to gain a better understanding
of the physiological, chemical and physical processes in plants and to detect plant
stress when remedial action may still be effective.
Due to the potentially high spatial and spectral resolution and sensitivity of
hyperspectral data, this technique is an excellent tool for discerning between subtle
spectral reflectance differences that are indicative of early stress symptoms.
Moreover, due to the non-destructive nature of this technology, it can be exploited
for many applications. Hyperspectral research has consequently been expanding
over a number of years, leading to the need for advanced technologies for spectral
information extraction and for improved handling of computational demands
caused by enormous datasets. The development and optimization of band reduction
techniques and vegetation indices using only a limited amount of data are therefore
of utmost importance. Single wavebands are often good indicators of biochemical
constituents, but are subject to variability caused by environmental factors such as
solar angle and background scattering. Vegetation indices also lead to data
dimensionality reduction and therefore might be helpful in terms of data processing
and analysis since they are computationally efficient. Such indices are also able to
overcome the limitations of single band applications by minimizing external factors,
and therefore correlate more closely with vegetative biochemical constituents. A
vegetation index can be defined as a dimensionless, radiation based measurement
computed from the spectral combination of remotely sensed data. Numerous
vegetation indices, broadband as well as narrowband, have been developed to detect
plant stress (Carter 1994).
The potential long-term negative economic consequences of inappropriate
management practices make early detection of anomalies in the ‘normal’ plant
production process indispensable, particularly in intensive perennial cropping
systems. However, most studies explored the potential of hyperspectral sensors on
forests (Mohammed et al. 2000, Campbell et al. 2004, Dobrowsky et al. 2005), and
annual crop systems such as sorghum (Zhao et al. 2005), sugarcane (Apan et al.
2004), sugar beet (Laudien et al. 2003), rapes (Smith et al. 2005), tomatoes (Zhang
et al. 2003), and salt marsh plants (Wilson et al. 2004). To address biotic stress in
perennial production systems, this study focuses on apple trees with biotic stress
symptoms caused by the ascomycete Venturia inaequalis. V. inaequalis is responsible
for apple scab, the most economically important apple disease in temperate regions
(MacHardy 1996, Le Cam et al. 2002). Past hyperspectral research has focused on (i)
the assessment of the utility of hyperspectral data to differentiate healthy and
infected leaves, and (ii) the investigation at which developmental stage Venturia
inaequalis could be detected (Delalieux et al. 2007). Statistical data reduction
techniques (e.g. logistic regression, tree-based modelling, and partial least squares
logistic discriminant analysis) were used to select wavelengths or wavelength
domains that best differentiated between treatments. Identification of those spectral
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regions that are critical to the differentiation of scab infected and non-infected
leaves could potentially contribute to the development of robust stress indicators
based on hyperspectral imagery. The primary goal in this study therefore was to
generate novel and robust indices that enable effective detection and quantification
of anomalies in the ‘normal’ plant production process, e.g. apple scab.
Young leaves are most susceptible to apple scab and are important sites of
infection for the fungus, which complicates the study due to the rapid change of
pigment, water concentrations, and subsequently spectral behaviour during the early
developmental stages of these leaves. A distinction therefore has to be made between
spectral changes caused by phenological variability and those due to stress. Several
stress-related studies have shown that indices based on reflectance in the far-red can
precisely estimate chlorophyll concentration (Chappelle et al. 1992, McMurtrey et al.
1994, Gitelson and Merzlyak 1994, 1996, Lichtenthaler et al. 1996, Datt 1998,
Carter and Knapp 2001), but phenological variability has never been taken into
account. Each leaf displays a characteristic temporal spectral evolution, which
permits tracking of different developmental stages, such as young and mature
leaves. A single date that provides maximum contrast between infected and non-
infected leaves can be selected from such an evolutionary spectral profile. However,
the goal of this study was to detect stress independent of the age of the plant leaves.
A second focus of this paper was therefore on assessing leaf phenology implications.
The study objectives are (i) to investigate physiologically-based hyperspectral
indices for the early detection of leaf spectral changes due to apple scab by means of
narrow-waveband ratioing involving all possible two-band combinations, and
narrow-waveband ratioing involving all possible two-band derivative combinations;
(ii) to evaluate the influence of leaf developmental stage (leaf phenology) on
hyperspectral data; and (iii) to identify a stable stress indicator in the sense that
apple scab can be detected independently of the age of the leaves.
2. Materials and methods
2.1 Plant material
A greenhouse experiment was set up in order to establish a controlled environment
for scab infection and development. Two apple cultivars were selected based on their
susceptibility to V. inaequalis infection: Braeburn was chosen as a model for cultivars
susceptible to apple scab, while Rewena represented the resistant cultivars, with
resistance based on the Vf gene (Parisi et al. 1993). Apple plants were chip budded
on M9 rootstock and grown in 2.5 l pots. Plants were actively growing at the time of
inoculation (¡25 cm high), with more than four unfolded leaves. All plants were
irrigated using a computer-based drip system to maintain favourable soil moisture
conditions. In 2004, 30 plants were used for each apple cultivar: 10 infected, 10
mock-infected and 10 untreated control plants. In 2006, 75 plants, chip budded on
M9 rootstock, were used for each apple cultivar: 25 infected, 25 mock-infected and
25 control plants. For infection studies, plants were inoculated with a suspension of
150 000 conidiospores per millilitre and placed in the dark at 100% relative humidity
conditions for 48 hours at 20uC. Afterwards, the plants were replaced in a
greenhouse environment with a relative humidity of approximately 60% and a
temperature of 20uC. Mock-infected plants were treated the same way as infected
plants, i.e. they were put in 100% relative humidity, but sprayed with water only.
Photographs were taken to link later analyses to the degree of infection. Leaf
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infection symptoms were visually classified according to the scale of Chevalier
(Chevalier et al. 1991).
2.2 Spectral data collection
Hyperspectral reflectance data were obtained with a plant probe attached to a
FieldSpec Pro JR spectroradiometer (ASD 1999) (Analytical Spectral Devices Inc.,
Boulder, USA) with a spectral range of 350–2500 nm. The sampling interval across
the 350–1050 nm range is 1.4 nm with a spectral resolution of 3 nm (full width at half
maximum). The sampling interval and the spectral resolution are approximately
2 nm and 30 nm, respectively, for the 1050–2500 nm range. Resultant data
afterwards were interpolated by the ASD software to produce values at each
nanometre interval. The instrument was warmed up for 90min prior to
measurement in order to avoid spectral steps at the detector overlap wavelength
regions, which occur due to different warm-up rates for the three spectroradiometer
arrays. A Spectralon (ASD, Boulder, USA) white reference panel was used to adjust
the sensitivity of the instrument detector according to the specific illumination
conditions. Once the instrument was optimized, the Spectralon was used to collect
white reference scans. Every surface measured by the fibre optic is processed as a
ratio between the digital number (DN) of the surface relative to the DN of the
Spectralon white reference. Leaf reflectance spectra were collected by measuring
spots of 10mm diameter using a plant probe containing a 100W halogen
reflectorized lamp. This provides the versatility of an internal light source for the
spectroradiometer and allows spectral data collection regardless of weather and time
of day. Each scan represented an average of 10 reflectance spectra. Measurements in
2004 were initiated 10 days after inoculation and were repeated regularly until scab
symptoms were clearly visible to the naked eye (14, 18, 21, 26 and 31 days after
inoculation). Measurements in 2006 started from the second day after inoculation
and were repeated at shorter intervals than in 2004 (1, 2, 4, 5, 8, 10, 12, 15, 18, 22
and 26 days after inoculation). The two youngest and most susceptible leaves from
each plant were marked during inoculation. Each time, two measurement points at
different positions were taken from each of these marked leaves in order to obtain
an adequate representation of the whole leaf. This paper focuses mainly on
representation of the discrimination between infected and mock-infected leaves.
Two additional well-established datasets, namely LOPEX (Hosgood et al. 1994)
and HYPERPEACH (Delalieux et al. 2006) were used to evaluate the suitability of
the indices developed to detect spectral changes due to infection while also
considering leaf phenology. The LOPEX data were acquired during an experiment
conducted by the Joint Research Centre of the European Commission (Ispra, Italy)
and were made available by the JRC through the FTP site ftp://ftp-gvm.jrc.it/
verstmi/lopex/. Plant species with different types of leaves were collected during
two separate periods in the summer of 1993. Approximately 70 leaf samples
representative of more than 50 species were obtained from trees, crops and plants in
the neighbourhood of the JRC, Ispra, Italy. More than two thousand reflectance
and transmittance spectra were acquired with a Perkin Elmer Lambda 19 double-
beam spectrophotometer equipped with a BaSO4 (barium sulphate) integrating
sphere. The spectral resolution varied from 1 to 2 nm in the visible/near infrared
(400–1000 nm) and from 4 to 5 nm in the middle infrared (1000–2500 nm). The
HYPERPEACH data were obtained during an experimental field campaign in a
peach orchard in Zaragoza, Spain in July 2005. Leaf spectral reflectance and
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transmittance values were gathered for 716 peach leaves with a FieldSpec Pro JR
spectroradiometer, as described above.
2.3 Vegetation indices
Most of the vegetation indices developed to detect stress in plants are based on
chlorophyll and water content. Zarco-Tejada et al. (2001) describe chlorophyll
content as a potential indicator of vegetation stress because of its direct role in the
photosynthetic processes of light harvesting and initiation of electron transport and
its responsiveness to a range of stresses. Knipling (1970) also stated that stress-
induced alterations of spectral reflectance in the visible spectrum result from the
sensitivity of leaf chlorophyll concentrations to metabolic disturbance.
Table 1 gives a synoptic overview of stress-related spectral reflectance indices as
developed and tested by different authors (table 1).
2.4 Ratio indices (RI)
Ratio indices are defined as quotients between measurements of reflectance in
separate portions of the spectrum and are known to be effective in enhancing
relevant information when there is an inverse relationship between two spectral
responses to the same biophysical phenomenon. However, an inherent drawback of
these indices is the loss of uniqueness in information due to the fact that different
leaves can have different spectral responses, but have band ratio values that are
similar.
All possible ratio indices at the different measurement periods were calculated in
this study using equation (1):
RI~li
lj, ð1Þ
where li and lj are the spectral reflectance values at wavelength i and j, respectively,
with i and j ranging from 400 to 2500 nm.
Derived ratio indices were used as independent variables in a logistic regression
analysis (Delalieux et al. 2007) and the c-index was calculated and used as a
statistical measure for the evaluation and comparison of the discriminatory
performance of the different ratio indices. Values of c-index above 0.8 are
considered to represent significant discriminative models (Boyd 2005).
2.5 Standardized difference vegetation indices
The Normalized Difference Vegetation Index (NDVI) (equation (2)) is probably the
most studied and implemented vegetation index ever (Tucker 1979). The NDVI
makes use of the characteristic features of vegetative reflectance spectra, namely low
reflectance in the red region of the spectrum due to chlorophyll absorption and high
reflectance values in the near infrared domain due to scattering caused by internal
leaf structure:
NDVI~lNIR{lRED
lNIRzlRED
, ð2Þ
where lRED and lNIR refer to the spectral reflectance acquired in the red and near-
infrared regions of the electromagnetic spectrum respectively.
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The optimal information on the physiological status of a plant is, however, not
necessarily related to those two regions. Moreover, the NDVI is often not a good
indicator of stress as it is only accurate for Leaf Area Index (LAI), biomass, and
chlorophyll determination at relatively low factor levels, due to a saturation effect at
higher levels of those factors (Gamon et al. 1995, Lichtenthaler et al. 1996). The
theory that underpins this vegetation index is however promising. Standardized
indices have the potential of estimating biophysical parameters in a manner more
Table 1. A synopsis of some existing ratio and standardized ratio vegetation indices from theliterature used in this study.
(a) Leaf level ratio indices
Index Related to Reference
R430/R680 Chlorophyll, stress Penuelas et al. 1995R440/R690 Chlorophyll Lichtenthaler et al. 1996R550/R680 Water Apan et al. 2004R550/R800 Chlorophyll Aoki et al. 1981R605/R760 Stress Carter 1994R672/R550 Chlorophyll b Datt 1998R675/R700 Chlorophyll a Chapelle et al. 1992R695/R420 Stress Carter 1994R695/R670 Stress Carter 1994R695/R760 Stress Carter 1994R710/R760 Stress Carter 1994R740/R720 Chlorophyll Vogelmann et al. 1993R750/R550 Chlorophyll Gitelson and Merzlyak 1997R750/R705 Chlorophyll, biomass Gitelson and Merzlyak 1994R750/R710 Chlorophyll Zarco-Tejada et al. 2001R800/R550 Chlorophyll, biomass Buschman and Nagel 1993R800/R635 Chlorophyll b Blackburn 1998R800/R680 Chlorophyll a Blackburn 1998R800/R1660 Water Apan et al. 2004R900/R970 Water Penuelas et al. 1997R1600/R820 Water Hunt and Rock 1989R1660/R550 Water Apan et al. 2004R1660/R680 Water Apan et al. 2004D715/D705 Chlorophyll Vogelmann et al. 1993
(b) Leaf level standardized indices
Index Related to Reference
(R4152R435)/(R415 +R435) Chlorophyll Barnes 1992(R5282R567)/(R528 +R567) Xanthophyll Gamon et al. 1992(R5312R570)/(R531 +R570) Xanthophyll Gamon et al. 1992(R5702R539)/(R570 +R539) Xanthophyll Gamon et al. 1992(R6802R430)/(R680 +R430) Chlorophyll Penuelas et al. 1994(R6802R430)/(R680 +R430) Pigments Penuelas et al. 1995(R7502660)/(R750+R660) Chlorophyll(R7502705)/(R750+R705) Chlorophyll Gitelson and Merzlyak 1994(R7502R445)/(R7052R445) Chlorophyll Sims and Gamon 2002(R8002635)/(R800+R635) Chlorophyll b Blackburn 1998(R8002R680)/(R800 +R680) Chlorophyll Lichtenthaler et al. 1996(R8192R1649)/(R819+R1649) Water Hardinsky et al. 1983(R8602R1240)/(R860+R1240) Water Gao 1996(R10702R1200)/(R1070+R1200) Water Ustin et al. 2002
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meaningful than simple ratio indices due to their inherent characteristic of reducing
the effects of spectral variations caused by surface topography (Holben and Justice
1981) and sun elevation for different parts of an image. In line with this assumption,
the standardized difference of the measured spectral reflectance values was
calculated for each possible combination of two different wavelengths (equation (3)).
This standardized difference was then also used as independent variable in a logistic
regression analysis. This approach allows the selection of an optimal standardized
difference vegetation index, as well as a tool to test existing vegetation indices.
SDVI~li{lj
lizlj, ð3Þ
where li and lj are the spectral reflectance at wavelength i and wavelength j,
respectively, with i and j ranging from 400 to 2500 nm.
2.6 Derivative ratio indices (dRI) and derivative standardized difference vegetation
indices (dSDVI)
Derivatives act as high-pass filters, thereby enhancing minute fluctuations in the
spectral behaviour of steep slopes in the raw reflectance spectrum. Typical derivative
features are found around 750 nm, 1350 nm, 1550 nm, 1850 nm, 2050 nm and
2350 nm. Derivative indices can therefore also be referred to as spectrally shape-
related indices. They have been proven to be highly correlated with crop
physiological parameters such as chlorophyll content, nitrogen content, and water
potential (Penuelas et al. 1994). These indices also seemed to be better correlated
with per cent leaf area infected by the fungus Botrytis fabae than the original
spectral reflectance data in the study of Malthus and Madeira (1993). Moreover,
many stress-related studies focused on the behaviour of the red edge feature
characterized by the relationship between the high red chlorophyll absorption
(,680 nm) and the high spectral reflectance around 800 nm associated with leaf
internal structure. A blue-ward shift can be observed as a function of chlorophyll
content or environmentally stressful conditions in the wavelength position of this
far-red shoulder, often referred to as the ‘blue shift’ of the red edge (Demetriades-
Shah et al. 1990, Carter 1991, Vogelmann et al. 1993, Filella and Penuelas 1994,
Filella et al. 1995, Gitelson andMerzlyak 1996, Smith et al. 2004, Zarco-Tejada et al.
2004). This shift is most often quantified by the ‘red edge inflection point’ (REIP),
where the first derivative of the spectral reflectance spectrum is at a maximum
(Kumar et al. 2001). Consequently, the above-mentioned techniques were also
evaluated with derivative spectra. A ratio and a standardized difference of the first
derivative of the spectral reflectance values was calculated for each possible
combination of two different wavelengths (see equations (4) and (5), respectively).
dRI~l0i
l0j
ð4Þ
dSDVI~l0i{l0j
l0izl0j
, ð5Þ
where l9i is the spectral reflectance derivative value at wavelength i and l9j the
spectral reflectance derivative value at wavelength j, with i and j ranging from 400 to
2500 nm.
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Derivatives are useful in detecting spectral subtleties, but are also susceptible to
noise. Spectral smoothing preprocessing is required prior to derivative analysis to
attenuate the noise impacts. Smoothing algorithms used in spectroscopy include the
Savitzky-Golay algorithm (Savitzky and Golay 1964), the Kawata–Minami linear
least mean square (Kawata and Minami 1984), and the mean filter (Tsai and Philpot
1998). These three methods differ only slightly when dealing with high frequency
noise with respect to the spectral features of interest, as in this study (Zhang et al.
2004). Hence, the widely used Savitzky-Golay filter (Demetriades-Shah et al. 1990,
Zarco-Tejada et al. 2001) was chosen in this study. This method, based on a
simplified polynomial least squares procedure, tends to smooth the spectral curve
while maintaining its underlying shape and resolution.
2.7 Leaf phenology
An appropriate index with high discriminatory performance between infected and
non-infected leaves can be selected for each single measurement in time, i.e. for
leaves of different ages (phenology). However, the goal is to determine stress
irrespective of the age of the plant leaves. A preliminary leaf phenological
experiment is therefore performed. Considering the facts that leaves are fully
expanded from 13 days after unfurling and that scab stress manifests mostly in
young developing leaves (Schwabe 1979), it is important to distinguish old, stressed
leaves from young healthy leaves. The assumption was made in this study that leaves
unfurling at inoculation time were certainly fully expanded from 20 days after
inoculation.
Moreover, as a first step for further research at canopy level, results of this study
could be used to take into account the complexity of the upscaling of indices for
stress detection. The combination of old and young leaves will have a definite
impact on the canopy reflectance spectra, and the proportion of young and old
leaves in a canopy can provide more information about the plant’s physiological and
phenological states. Firstly, averaged spectra were used for each cultivar and for
each measurement day to obtain an idea of the temporal changes. However, it has to
be considered that the data are not normally distributed with more divergence in the
data at the end of the measurement period, i.e. not all infected leaves displayed
similar infection symptoms at the same time as visually confirmed during the
experiment. Tree based modelling (TBM) (Delalieux et al. 2007) was therefore used
to distinguish between the different leaf developmental stages, since the normality
assumption was not required. Standardized difference vegetation indices were
calculated, as in the previous stress-related experiment, for each possible
combination of two different wavelengths and for each possible combination of
two different measurement days. This was done to detect different developmental
stages in plant leaves.
2.8 Discriminatory performance (c-index)
C-index values were calculated and used as a statistical measure for the evaluation
and comparison of the discriminatory performance of the indices. This c-index is
identical to a widely used measure of diagnostic discrimination, the area under the
‘receiver operating characteristic’ (ROC) (Harrell 2001). ROC plots are created
by plotting the ‘sensitivity’ values, the true positive fraction (i.e. infected leaf
correctly classified as infected) against ‘1-specificity’, the false-positive fraction (i.e.
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non-infected leaf classified as infected). A curve that maximizes the sensitivity for
low values of the false-positive fraction is considered a good model and is quantified
by the area under the curve (c-index). The c-index has values usually ranging from
0.5 (random) to 1.0 (perfect discrimination) but can exhibit values below this range,
which indicates that a model is worse than random class assignment. Values above
0.8 are generally accepted to represent significant discriminative models (Boyd
2005). For the measurements obtained in 2006, 100 reflectance values of infected
plants were compared with 100 values of non-infected plants for each wavelength
and each measurement day. For those in 2004, only 40 spectral values were available
for each treatment, wavelength and measurement day.
3. Results and discussion
3.1 Ratio indices and standardized difference vegetation indices
Similar discriminatory performances (c-values) were obtained to distinguish between
infected and non-infected leaves using ratio and standardized ratio indices, with only
minor differences (maximum 0.10) for very narrow band combinations, which are
not useful in sensor applications. Therefore, the difference between both index types
could be neglected at leaf level, and the choice of RI versus SDVI appears to be
Figure 1. The greyscale coding symbolizes the discriminatory performance (c-index) of thelogistic regression models with all possible two-band ratios as independent variables. Resultsfor the susceptible Braeburn cultivar are shown for the second day (upper left) and 15th day(upper right) after inoculation. Underneath, the same results are shown for the resistantRewena cultivar. The X- and Y-axes represent wavelength i and wavelength j respectively (seeEquation (1)).
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arbitrary at leaf level research. Consequently, this section describes both the results
of RI and SDVI together. The c-index values of the logistic regression models with
all possible two-band ratios as independent variables are represented by a three
dimensional graph per measurement day. Figure 1 shows the results obtained from
the measurements taken on the second day and on the 15th day after inoculation.
These days were chosen as examples to compare the results immediately after
inoculation and those at the time that the first lesions developed (usually from 12 to
15 days after inoculation, as stated by MacHardy (1996) and visually confirmed
during the measurements). The X- and Y-axes represent wavelength i and
wavelength j, respectively (equation (1)). The third dimension represents the
discriminatory performance (c-index) of the logistic regression analysis and is
visualized via greyscale coding.
It is evident from figure 1 that the infected resistant cultivar can be distinguished
from the non-infected plants by combining all possible NIR (750–1350 nm) and
SWIR (1350–2500 nm) bands immediately after inoculation. As stated by
MacHardy (1996), the infected Rewena cultivar tends to absorb more water than
the non-infected plants, probably due to the activation of the resistance mechanism.
MacHardy (1996) found that host responses in cultivars containing the Vf gene
encompass changes in the cell wall and the collapse and dying of epidermal cells
underneath the infection site, without affecting the visible appearance of the leaf.
Conversely, non-infected plants of the susceptible cultivar had similar reflectance
spectra as the infected susceptible plants immediately after inoculation, but showed
larger variations in later measurements, as shown in figure 2. This figure was
constructed to limit the amount of space needed to plot the results of all
measurement days. The coloured lines represent contour lines delineating spectral
space within which c-index values above 0.8 were obtained. These regions could be
considered as consisting of only discriminative band ratios. The grey colour is used
Figure 2. Synoptic overview of delineated regions having c-index values . 0.8 of the logisticregression models with all possible two-band ratios as independent variables for eachmeasurement day. Results for the susceptible Braeburn cultivar are shown on the left, whileresults for the resistant Rewena cultivar are shown on the right. Different colours of thecontour lines refer to different measurement days. The black dots represent the existingvegetation indices (Table 1). Areas including indices with high discriminatory performances(.0.8) at the last measurement day are shown in grey. The atmospheric water absorptionregions are removed from the analysis to reduce computing requirements.
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to stress the band ratios that were able to distinguish between infected and non-
infected leaves 31 days after inoculation. The vegetation ratio indices, as mentioned
in table 1, were also plotted on the graphs as black dots. The atmospheric water
absorption regions were removed from the analysis to reduce computing
requirements. Indices in these regions will also not be appropriate in the larger
framework of the research, i.e. scaling from leaf level indices to canopy level indices.
It can be observed from the right image of figure 2, showing the c-values for the
susceptible cultivar, that it is nearly impossible to discriminate between infected and
non-infected leaves of the resistant cultivar using ratio and standardized ratio
indices from the fifth day after inoculation onwards. The discriminatory
performances of the indices differ according to the duration of infection. The
indices with the best discriminatory performances immediately after inoculation are
found to be ratios consisting of wavelengths in the water absorption dominant
region (SWIR). It was supposed that the water holding capacity in the plant
changed at this stage since infected leaves tend to lose more water (MacHardy 1996).
In case of airborne and satellite studies, indices containing the main water
absorption bands around 1400 nm and 1900 nm are not appropriate due to
atmospheric water absorption in these regions. The most appropriate and broadly
applicable ratio index to detect stress in an early infection stage therefore was found
to consist of wavelengths at approximately 1500 nm combined with wavelengths
around 2250 nm, with c-values ranging from 0.80 to 0.88 in the first week after
inoculation. All possible combinations of NIR and SWIR regions seemed to give
good results from 12 days after inoculation onwards. Most spectral changes in the
NIR region in this stage can be attributed to internal structural malformations in the
leaves due to a subcuticular growing mycelium. The spectral differences caused by
infection can thus be enhanced by ratioing NIR bands showing more light
absorption, with SWIR bands having less light absorption due to water. Ratio
indices containing visible wavelengths and red-edge wavelengths also became more
appropriate from 15 days onwards. At this stage, the scab lesions appear due to a
decrease in chlorophyll and carotenoid content. These results corroborated the
findings of MacHardy (1996) who noted that reductions in chlorophyll and
carotenoid content were regarded as direct responses to an irreversible disorganiza-
tion of chloroplasts. The degradation of chlorophyll causes an increase in reflectance
in the visible part of the spectrum. The red-edge region experiences a blue shift due
to this chlorophyll degradation. Spectral changes in this stage can thus again be
enhanced by combining higher spectral reflectance values in the visible and the red-
edge region of the spectrum with lower spectral reflectance values at the NIR region.
Conversely, SWIR/NIR ratios seem to be unsuitable at the end of the study period,
due to an increase in NIR reflectance in dead/ dry leaves and a decrease in NIR
reflectance in highly infected, but not yet totally dry/dead leaves (figure 3).
These two opposite reactions resulted in a low c-index value, which could
highlight the need for c-index thresholds. Hence every index value below and above
a threshold value could be seen as stressed plant material.
Apart from the suitability of the SDVIs, figure 2 also includes the representation
of the appropriateness of vegetation indices. Not all tested vegetation indices, as
mentioned in table 1, are equally applicable, as shown in table 2. However, it is
shown in table 2 and figure 2 that most of the tested chlorophyll and stress-related
indices can be used for apple scab stress detection from 15 days after inoculation,
with R440/R690 and R695/R760 being the most appropriate indices at a progressed
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infection stage with a c-index of 0.96. These indices are closely related (c-index
values ranging from 0.8 to 1) to the differences in reflectance patterns of developing
leaves, as will be discussed in the next section. It is important to make a distinction
between young, healthy and older, infected leaves, especially since apple scab mainly
affects young leaves. Therefore, it appears to be useful to take into account other
indices than those described in literature to discriminate infected from non-infected
leaves at an early infection stage, or to link the reflectance spectra to the
phenological stage of the leaf.
3.2 First derivative standardized vegetation indices
Figure 4 represents a synoptic overview of the c-index values of the logistic
regression models with all possible derivative two-band ratios as independent
variables for all measurement days. The illustration of all derivative indices with
c-index values .0.8 provides an image that is too complex. Therefore, we opted to
present only those indices having a c-index value above 0.8 within a sensor shift of
30 nm. Moreover, this 30 nm is in accordance with the coarsest spectral resolution of
the ASD spectroradiometer, i.e. 30 nm in the SWIR region.
Results of the logistic regression (figure 4) show that the most appropriate indices
consist of wavelengths located on the slopes of the spectrum, thereby confirming
that derivatives are highly sensitive to a minimal distortion in the regions with steep
slopes, as stated above. In the very early stage of the experiment, the infection is
mostly visible in the bands positioned at the slopes defining the water absorption
regions. At a more developed infection stage, indices including wavelengths at the
slope mostly affected by the amount of pigments, i.e. the red edge, are found to be
most discriminative. These findings are in concurrence with the conclusions of the
raw spectra as described in previous paragraph.
Figure 3. Representation of the reflectance spectra at a developed infection stage (20 daysafter inoculation). Light grey colours represent reflectance spectra of the non-infected plantsof the susceptible cultivar, while black colours represent the spectra of the infected plants ofthe same cultivar.
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Table 2. The appropriateness (c-index) of the classic indices to detect apple scab at differentdevelopmental stages (1, 2, 4, 5, 8, 10, 12, 15, 18, 22, 26 and 31 days after inoculation).
(a) Leaf level ratio indices
Index
Days after infection
1 2 4 5 8 10 12 15 18 22 26 31
R430/R680 0.74 0.57 0.75 0.67 0.63 0.66 0.64 0.71 0.64 0.77 0.90 0.92R440/R690 0.73 0.68 0.68 0.47 0.54 0.48 0.50 0.58 0.53 0.68 0.96 0.96R550/R680 0.54 0.70 0.50 0.62 0.63 0.77 0.77 0.77 0.69 0.73 0.72 0.62R550/R800 0.63 0.78 0.62 0.53 0.60 0.59 0.52 0.73 0.77 0.79 0.77 0.92R605/R760 0.60 0.72 0.60 0.50 0.60 0.47 0.57 0.80 0.79 0.80 0.79 0.91R672/R550 0.56 0.69 0.50 0.62 0.63 0.76 0.76 0.78 0.72 0.74 0.72 0.58R675/R700 0.56 0.71 0.54 0.56 0.61 0.72 0.70 0.73 0.68 0.64 0.83 0.89R695/R420 0.70 0.73 0.67 0.51 0.54 0.59 0.53 0.50 0.55 0.70 0.90 0.92R695/R670 0.62 0.77 0.56 0.54 0.61 0.51 0.68 0.73 0.71 0.63 0.86 0.91R695/R760 0.62 0.77 0.66 0.53 0.58 0.51 0.63 0.81 0.80 0.86 0.84 0.96R710/R760 0.61 0.80 0.69 0.54 0.59 0.55 0.57 0.76 0.81 0.87 0.88 0.93R740/R720 0.60 0.79 0.67 0.51 0.59 0.56 0.56 0.76 0.82 0.88 0.88 0.93R750/R550 0.62 0.78 0.62 0.54 0.60 0.59 0.51 0.73 0.78 0.79 0.77 0.92R750/R705 0.62 0.80 0.69 0.54 0.59 0.55 0.56 0.77 0.80 0.86 0.87 0.93R750/R710 0.61 0.80 0.68 0.53 0.59 0.55 0.56 0.77 0.81 0.86 0.88 0.93R800/R1660 0.59 0.67 0.52 0.60 0.50 0.62 0.80 0.90 0.91 0.91 0.61 0.77R800/R550 0.63 0.78 0.62 0.53 0.60 0.59 0.52 0.73 0.77 0.79 0.77 0.92R800/R635 0.59 0.69 0.60 0.49 0.58 0.52 0.62 0.82 0.82 0.81 0.79 0.89R800/R680 0.52 0.58 0.66 0.62 0.60 0.72 0.78 0.85 0.86 0.85 0.85 0.88R900/R970 0.60 0.66 0.51 0.59 0.51 0.67 0.75 0.85 0.89 0.91 0.78 0.83R1600/ R820 0.59 0.67 0.52 0.58 0.49 0.64 0.79 0.88 0.92 0.91 0.57 0.74R1660/R550 0.62 0.78 0.65 0.56 0.58 0.62 0.60 0.56 0.53 0.54 0.76 0.70R1660/R680 0.55 0.60 0.62 0.57 0.61 0.70 0.69 0.74 0.67 0.73 0.51 0.62
(b) Leaf level standardized indices
Days after infection
Index 1 2 4 5 8 10 12 15 18 22 26 31
(R4152R435)/(R415 +R435)
0.59 0.80 0.58 0.63 0.64 0.67 0.62 0.73 0.71 0.80 0.70 0.74
(R5282R567)/(R528 +R567)
0.63 0.71 0.75 0.66 0.50 0.46 0.53 0.67 0.64 0.73 0.96 0.99
(R5312R570)/(R531 +R570)
0.53 0.58 0.70 0.59 0.56 0.57 0.59 0.79 0.67 0.93 0.89 0.94
(R5702R539)/(R570 +R539)
0.51 0.53 0.60 0.53 0.56 0.63 0.64 0.81 0.74 0.74 0.76 0.81
(R6802R430)/(R680 +R430)
0.74 0.60 0.75 0.67 0.63 0.66 0.64 0.71 0.64 0.70 0.85 0.92
(R6802R430)/(R680 +R430)
0.74 0.50 0.75 0.67 0.63 0.66 0.64 0.71 0.64 0.70 0.85 0.92
(R7502660)/(R750 +R660)
0.53 0.72 0.59 0.55 0.50 0.65 0.71 0.84 0.85 0.81 0.84 0.84
(R7502705)/(R750 +R705)
0.62 0.59 0.69 0.53 0.59 0.55 0.56 0.77 0.80 0.86 0.92 0.93
(R7502R445)/(R7052R445)
0.55 0.65 0.51 0.56 0.55 0.63 0.70 0.80 0.82 0.80 0.74 0.65
(R8002635)/(R800 +R635)
0.58 0.59 0.60 0.49 0.58 0.52 0.62 0.82 0.82 0.80 0.96 0.89
(R8002R680)/(R800 +R680)
0.52 0.56 0.66 0.62 0.60 0.72 0.78 0.85 0.86 0.84 0.85 0.88
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3.3 Leaf phenology
The goal in this part of the study is to gain a better understanding of the processes
occurring during the first days after leaf unfolding, because that is the most critical
period for apple scab infection (MacHardy 1996), and visible symptoms only appear
after two weeks, i.e. in mature leaves. Temporal changes in the spectral behaviour of
leaves are therefore visualized in figure 5 for each wavelength. The amount of
proportional decrease/increase of the spectral reflectance value for each wavelength
is coded via greyscaling. Black colours refer to a decrease of spectral reflectance of
more than 50% with respect to the spectral reflectance at the first measurement day.
Light grey colours indicate no changes with respect to the first day. These results in
other words have to be interpreted as being normalized differences, i.e. the
(a) Leaf level ratio indices
Index
Days after infection
1 2 4 5 8 10 12 15 18 22 26 31
(R8192R1649)/(R819 +R1649)
0.59 0.57 0.51 0.59 0.51 0.62 0.79 0.90 0.90 0.93 0.77 0.76
(R8602R1240)/(R860 +R1240)
0.66 0.59 0.54 0.53 0.52 0.55 0.75 0.83 0.93 0.84 0.80 0.92
(R10702R1200)/(R1070 +R1200)
0.60 0.60 0.53 0.57 0.57 0.60 0.71 0.84 0.85 0.93 0.79 0.77
Table 2. (Continued.)
(b) Leaf level standardized indices
Figure 4. Synoptic overview of the delineated regions having c-index values . 0.8 of thelogistic regression models with all possible derivative two-band ratios as independentvariables for each measurement day. Only indices having c-values . 0.8 within a sensor shiftof 30 nm are shown. Results are illustrated for the susceptible Braeburn cultivar. Differentcolours refer to different measurement days. The atmospheric water absorption regions areremoved from the analysis to reduce computing requirements.
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difference in normalized spectral reflectance values is divided by the spectral
reflectance values at day one.
Based on this figure and a statistical test (TBM), it may be concluded that
wavelengths 570 nm, 725 nm, 1460 nm, 1940 nm and 2400 nm are important
discriminating wavelengths for distinguishing between different levels of leaf
development. The visualization obtained by extraction and enhancement of specific
spectral domains from the spectral curve is shown in figure 6.
Wavelengths 1460 nm and 1940 nm are known as main water absorption bands in
the reflectance spectrum. The decrease in spectral reflectance in the water absorption
bands could be caused by an increase in the leaf water content (Gausman 1985). In
the visible region, the spectral reflectance values around 570 nm decreased distinctly
throughout time. This is a logical consequence, since younger leaves contain fewer
pigments than older leaves, resulting in a reduced absorption in the visible region at
the early stage of leaf development.
The red-edge also played an important role according to the results obtained from
tree-basedmodelling using first derivative spectra. Very young, unfurling leaves have a
distinctly steeper slope in their reflectance spectrumaround 715nm in comparisonwith
20-day-old, totally expanded (mature) leaves. The first derivative ratiol9715/l9740varies
remarkably from the first until the lastmeasurement day (figure 7(a)).These results lead
to the investigation of the red-edge inflection point (REIP), which represents the point
of maximum slope in the reflectance spectra. Figure 7(b) shows a shift of the REIP to
longer wavelengths during leaf development.
The resulting REIP shift is further confirmed when the scientific literature is
reviewed, among them Mutanga et al. (2003) who attributed the movement of the
REIP to longer wavelengths to an increase in chlorophyll concentration, which
broadens the absorption feature around 670 nm. Chlorophyll related indices, such as
Figure 5. (a) Representation of the averaged spectral behaviour of healthy, developingleaves from the first day of unfolding until full expansion (20 days). The grey-scale symbolizesthe normalized differences of the reflectance pattern with respect to the reflectance spectrumat the first day. The spectral regions with the largest changes in reflectance values during leafdevelopment are indicated with arrows and enhanced in Figure 6. (b) The averaged spectrumof healthy leaves is shown on the second day (grey) of the measurements and at fullydeveloped stage (20-days) (black). (c) The proportional differences for each wavelength areshown between the second (grey) and the 20th (black) day of the leaf development.
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Figure 6. The patterns for the TBM-extracted wavelengths, namely 570 nm, 1460 nm, 1940nm and 2400 nm (see also Figure 5) are represented in (a), (b), (c), and (d), respectively. Fiveconsecutive stages are considered, with 1 representing a very young leaf; 2, a five-day-old leaf;3, a ten-day-old leaf; 4, a fifteen-day-old leaf and 5 representing a twenty-day-old leaf.
Figure 7. (a) The change of the first derivative ratio of wavelength 715 nm and 740 nmthroughout time is presented for successive measurement days (day 1–20) showing a decreasein ratio values during the development of a leaf due to the changes in spectral behaviour asenhanced in figure 7(b); (b) A shift in the red-edge inflection point is shown over time forconsecutive stages with 1 being a very young leaf; 2, a five-day-old leaf; 3, a ten-day-old leaf;4, a fifteen-day-old leaf and 5 representing a twenty-day-old leaf.
1902 S. Delalieux et al.
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l750/l550 and l750/l710 (Zarco-Tejada et al. 2000) were used to confirm these
findings. Their index values for different developmental stages of the leaves resulted
in a steady increase through time (figure 8), indicating that chlorophyll content
increased as function of time, which is consistent with the above findings.
Vegetation indices related to chlorophyll and water content can be used to detect
stress in a well-developed scab infection stage, but these are also the most
appropriate to detect the temporal changes in reflectance spectra of developing
leaves. Hence, it is difficult to interpret the chlorophyll and water-related indices in
terms of an effect of infection since phenology-related spectral changes are also
involved, as described in the next section.
3.4 Phenology infection
The differences in spectral reflectance due to scab stress and phenological variability
are shown in figure 9, relative to the reflectance spectrum at the first day of
measurement.
The only indices that were found to be invariable based on logistic regression,
throughout the development of the healthy leaves from unfurling to full expansion,
were those based on wavelengths around 1250 nm and 1050 nm with a constant
value around 0.9. However, the ratio of 1250 nm and 1050 nm increased from 15
days after inoculation in the stress-related experiment described in the previous
paragraph, which makes this index ideally suited to detect apple scab stress
independently of the age of the leaves. The index has not been tested at canopy level,
but problems are expected due to the large impact of structure in these spectral
regions (Kumar et al. 2001, Mutanga et al. 2003). The index is closely related to the
NDWI-hyp index suggested by Ustin (2002), which was found to be useful in remote
sensing studies to predict water content. Considering the invariability of the index
throughout time, it is improbable that the index is only related to water content.
Spectral changes due to water content modifications are supposed to be
Figure 8. The representation of two classic chlorophyll-related indices (l750/1710 and l750/1550), showing an increase of chlorophyll concentration throughout time (1–20 days).
Hyperspectral indices to detect apple scab, considering leaf phenology 1903
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compensated by spectral changes due to structural alterations. Further analyses to
investigate the appropriateness of this index to predict water content were
performed on the LOPEX dataset including fresh and dried leaves and showed a
very good linear correlation (R250.79) when using only healthy and well-developed
leaf reflectance spectra (figure 10). However, this linear relationship did not hold
true for stressed leaves (dry leaves), as shown by grey squares in figure 10.
Deviation of the index from a threshold interval (l1250/l105050.90 to l1250/l10505
0.97) as represented in figure 11 could potentially predict stress, where a value of less
than 0.90 indicates excessive water content in the leaves, and an index value above
0.97 indicates a change in structure/lack of water in leaves. This index is tested on
data of the LOPEX experiment, as well as on spectral reflectance data of scab
infected and mock infected apple (2006) leaves and peach (2005) leaves, as can be
seen in figure 11.
The upper (l1250/l105050.97) and lower (l1250/l105050.90) limits were chosen in
such way that 95% of the fresh, non dried LOPEX leaf data fell within this range. It
can be seen in figure 11 that all index values of the apple scab experiment located
above the threshold are attributed to leaves of the susceptible Braeburn cultivar in a
well-developed infection stage (10–19 days after inoculation). So, based on the ratio
index l1250/l1050, a first distinction can be made between healthy and infected leaves.
This index is not directly related to chlorophyll content, the physiological parameter
affected most by stress. It is difficult to assign chlorophyll content changes to stress
or phenological variability, due to the rapid change of chlorophyll during the
development of a leaf. A phenological calendar is recommended to make possible a
correct interpretation of results from existing chlorophyll indices.
4. Conclusions
Present results indicate that hyperspectral vegetation indices are able to accurately
track spectral changes due to differences in biophysical indicators of apple scab,
Figure 9. Phenological and scab induced spectral behaviour of the susceptible cultivar over20 days. The grey-scale symbolizes the proportional differences of the reflectance pattern withrespect to the reflectance spectrum at the first day.
1904 S. Delalieux et al.
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Figure 10. The close relationship of 1250/1050 with water content is shown for healthy leafspectra of the LOPEX dataset. Dried leaves, represented by squares, are identified andexcluded from the linear analysis.
Figure 11. l1250/l1050 index values for the LOPEX dataset, a peach dataset, the scabinfected Braeburn dataset, and the mock infected Braeburn dataset, respectively from left toright. Theupper (l1250/l10505 0.97) and lower (l1250/l10505 0.90) limitswere chosen in suchaway that 95%of the fresh, nondriedLOPEX leaf data fellwithin this range.Measurementswithindex values beyond the limits are identified and named. For example, ‘BI ld’ refers to data takenfrom the infected Braeburn dataset measured at the first day after inoculation.
Hyperspectral indices to detect apple scab, considering leaf phenology 1905
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such as chlorophyll and water content, but the indices and their discriminatory
performances are dependent on the phenological stage of the leaves. A distinction
could be made between infected and non-infected leaves in the susceptible cultivar,
Braeburn, with c-index values of more than 0.80 over the entire time period.
However, in the resistant cultivar, Rewena, it was more difficult to accurately
distinguish infected from non-infected leaves. Narrow-waveband ratios consisting of
wavelengths around 1500 nm and 2250 nm, which are known as being dominated by
water, were found to be the most appropriate to detect apple scab in the first week
after inoculation, with c-values ranging from 0.80 to 0.88. At a more developed
infection stage, vegetation indices such as R440/R690 and R695/R760 were preferred to
distinguish between non-infected and infected leaves. Derivative indices gave similar
results, with ratios consisting of wavelengths at the water band slopes being the most
appropriate at the beginning of the infection period. Indices including the red-edge
feature, which was found to be an important feature in chlorophyll related studies,
seemed to become more important at later infection stages when pigments degraded
and visual symptoms appeared.
Moreover, a basic understanding of the phenological impact on leaf reflectance
patterns was obtained in this study. The results have proven that the effects of
physiological processes could be detected using hyperspectral reflectance data
combined with established physiological knowledge. Results suggested that an
accurate distinction could be made between different leaf developmental stages
using the 570 nm, 1460 nm, 1940 nm and 2400 nm wavelengths, and the red-edge
inflection point. The selected wavelengths and indices suggested a strong increase in
chlorophyll content during the first 20 days. This result corroborated established
research on underlying physiological processes. The shift of the REIP also was
attributed to an increase in chlorophyll content.
These results might indicate that the detection of plant physiological processes
using remote sensing has significant potential for diagnosing biotic stress in an early
and well-developed infection stage. This is particularly important to the agricultural
market, e.g. where an early warning system, based on spectral inputs, would be an
ideal solution to the enforced reduction of pesticides. Farmers can apply treatments
more efficiently at early stress stages, before stresses become visible to the naked eye.
This study therefore has important implications for biotic and abiotic stress
detection and management in agricultural applications. It should however be noted
that knowledge of the spectral behaviour of individual leaves is important for
understanding the spectral characteristics of vegetation canopies, but cannot
completely explain reflectance at canopy and orchard levels. This is due to the fact
that various factors will have a considerable and specific impact on the vegetation
spectrum, thereby making it difficult to use vegetation indices at all sensing levels
(i.e. leaf, canopy, airborne and space-borne). These factors include (i) the complexity
of the canopy structure; (ii) soil background/understorey vegetation; (iii) atmo-
spheric variation; and (iv) illumination variations due to sensor–sun geometry. Each
of these aspects will be discussed briefly in the next paragraphs.
(i) Canopy structure is defined by the leaf area, the number of leaf layers, the
leaf angle distributions within the canopy, and the distribution of different
canopy elements such as stems, green foliage and litter (Rencz 1999).
Numerous spectral models have been developed to quantify the interaction
of solar energy with canopies by using leaf reflectance as the major input
parameter (e.g. Verhoef 1984, Kuusk 1994, North 1996). Hence, further
1906 S. Delalieux et al.
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research is required to test the developed indices with regards to
their robustness relative to varying structural parameters used in these
models.
(ii) The second problem that hinders immediate application of laboratory-
developed algorithms to remote sensing image analysis is the relatively coarse
spatial resolution of aerial and satellite images. In most cases the spatial
resolution exceeds the size of the object of interest. As a consequence, the
sub-pixel spatial contribution of the background, e.g. soils and shadows,
hampers the accurate site-specific monitoring of agricultural crop character-
istics. It is therefore essential to significantly reduce the sub-pixel spectral
contribution of bare soils and shadows in order to facilitate information
extraction from aerial or satellite imagery. Work in this domain is
continuing, with Somers et al. (2007) recently proposing an algorithm to
extract pure vegetation signals from soil–vegetation mixtures and illustrated
a successful up-scaling of vegetation indices from leaf and canopy level to
airborne and satellite level.
(iii) All radiation that is captured by the sensor of the remote platform has to
pass through the atmosphere, resulting in relatively minor (low-flying
aircrafts) or considerable (satellites) impact on the spectral data captured by
sensors. Spectral information from regions dominated by atmospheric water
absorption bands, e.g. 1350–1500 nm, 1800–1950 nm, and 2450–2500 nm,
might prove useful at leaf level but will be lost at higher sensing levels. Indices
containing those wavelengths will therefore not be suitable as robust remote
sensing tools.
(iv) Knowledge of the optical behaviour of a surface with respect to illumination-
observation geometry constitutes another factor to consider. Research on the
mathematical description of this optical behaviour has resulted in many
bidirectional reflectance distribution function (BRDF) studies (e.g. Biliouris
et al., 2007). Most airborne campaigns gather near-nadir information at a
specific time of the day and include atmospheric correction as part of the pre-
processing steps in order to minimize this problem.
The present research has to be considered as a baseline study which emphasizes
the importance of incorporating phenological variability in vegetation monitoring
studies. Phenological knowledge has proven to be very important when analysing
stresses that primarily affect the most vulnerable, young leaves. The ratio of 1250 nm
and 1050 nm was found to be ideally suited to detect apple scab stress independent
of the age of the leaves. This information might be important in discriminating
between different stresses, e.g. nutrient stress, fungal stress, water stress. The
knowledge of stress effects and phenology at more detailed (leaf) scales enhances our
understanding and may offer essential information for the detection of biotic
stresses of vegetation at higher (airborne or space-borne) remote sensing scales
where more complex interactions are involved. Future research efforts should
however validate this statement.
Acknowledgements
This project was funded by the Katholieke Universiteit Leuven, Department of
Biosystems. The authors further would like to acknowledge funding for preliminary
research (Hypercrunch and Hyperpeach project) provided by the Belgian Science
Hyperspectral indices to detect apple scab, considering leaf phenology 1907
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Policy Office. We extend our gratitude to the Joint Research Centre of the European
Commission (Ispra, Italy) for providing free access to their LOPEX database.
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