Hyperspectral indices to diagnose leaf biotic stress

International Journal of Remote Sensing Vol. 30, No. 8, 20 April 2009, 1887–1912

Downloaded By: [Rochester Institute of Technology] At: 20:42 19 May 2009

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 ISSN 0143-1161 print/ISSN 1366-5901 online # 2009 Taylor & Francis http://www.tandf.co.uk/journals DOI: 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


Hyperspectral indices to detect apple scab, considering leaf phenology

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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 noninfected 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. 2.1

Materials and methods 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 90 min 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 10 mm diameter using a plant probe containing a 100 W 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 doublebeam 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 stressinduced 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 , lNIR zlRED

ð2Þ

where lRED and lNIR refer to the spectral reflectance acquired in the red and nearinfrared regions of the electromagnetic spectrum respectively.

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Table 1. A synopsis of some existing ratio and standardized ratio vegetation indices from the literature used in this study. (a) Leaf level ratio indices


Index

Related to

R430/R680 R440/R690 R550/R680 R550/R800 R605/R760 R672/R550 R675/R700 R695/R420 R695/R670 R695/R760 R710/R760 R740/R720 R750/R550 R750/R705 R750/R710 R800/R550 R800/R635 R800/R680 R800/R1660 R900/R970 R1600/R820 R1660/R550 R1660/R680 D715/D705

Chlorophyll, stress Chlorophyll Water Chlorophyll Stress Chlorophyll b Chlorophyll a Stress Stress Stress Stress Chlorophyll Chlorophyll Chlorophyll, biomass Chlorophyll Chlorophyll, biomass Chlorophyll b Chlorophyll a Water Water Water Water Water Chlorophyll

Reference Penuelas et al. 1995 Lichtenthaler et al. 1996 Apan et al. 2004 Aoki et al. 1981 Carter 1994 Datt 1998 Chapelle et al. 1992 Carter 1994 Carter 1994 Carter 1994 Carter 1994 Vogelmann et al. 1993 Gitelson and Merzlyak 1997 Gitelson and Merzlyak 1994 Zarco-Tejada et al. 2001 Buschman and Nagel 1993 Blackburn 1998 Blackburn 1998 Apan et al. 2004 Penuelas et al. 1997 Hunt and Rock 1989 Apan et al. 2004 Apan et al. 2004 Vogelmann et al. 1993

(b) Leaf level standardized indices Index (R4152R435)/(R415 + R435) (R5282R567)/(R528 + R567) (R5312R570)/(R531 + R570) (R5702R539)/(R570 + R539) (R6802R430)/(R680 + R430) (R6802R430)/(R680 + R430) (R7502660)/(R750 + R660) (R7502705)/(R750 + R705) (R7502R445)/(R7052R445) (R8002635)/(R800 + R635) (R8002R680)/(R800 + R680) (R8192R1649)/(R819 + R1649) (R8602R1240)/(R860 + R1240) (R10702R1200)/(R1070 + R1200)

Related to Chlorophyll Xanthophyll Xanthophyll Xanthophyll Chlorophyll Pigments Chlorophyll Chlorophyll Chlorophyll Chlorophyll b Chlorophyll Water Water Water

Reference Barnes 1992 Gamon et al. 1992 Gamon et al. 1992 Gamon et al. 1992 Penuelas et al. 1994 Pen˜uelas et al. 1995 Gitelson and Merzlyak 1994 Sims and Gamon 2002 Blackburn 1998 Lichtenthaler et al. 1996 Hardinsky et al. 1983 Gao 1996 Ustin et al. 2002

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


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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 , li zlj

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 shaperelated indices. They have been proven to be highly correlated with crop physiological parameters such as chlorophyll content, nitrogen content, and water potential (Pen˜uelas 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 (DemetriadesShah et al. 1990, Carter 1991, Vogelmann et al. 1993, Filella and Pen˜uelas 1994, Filella et al. 1995, Gitelson and Merzlyak 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~

dSDVI~

l0i l0j

l0i {l0j l0i zl0j

ð4Þ

,

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. 3.1

Results and discussion 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 the logistic regression models with all possible two-band ratios as independent variables. Results for 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 resistant Rewena cultivar. The X- and Y-axes represent wavelength i and wavelength j respectively (see Equation (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 logistic regression models with all possible two-band ratios as independent variables for each measurement day. Results for the susceptible Braeburn cultivar are shown on the left, while results for the resistant Rewena cultivar are shown on the right. Different colours of the contour lines refer to different measurement days. The black dots represent the existing vegetation indices (Table 1). Areas including indices with high discriminatory performances (.0.8) at the last measurement day are shown in grey. The atmospheric water absorption regions 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 noninfected 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 disorganization 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 rededge 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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Figure 3. Representation of the reflectance spectra at a developed infection stage (20 days after inoculation). Light grey colours represent reflectance spectra of the non-infected plants of the susceptible cultivar, while black colours represent the spectra of the infected plants of the same cultivar.

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.


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Table 2. The appropriateness (c-index) of the classic indices to detect apple scab at different developmental stages (1, 2, 4, 5, 8, 10, 12, 15, 18, 22, 26 and 31 days after inoculation). (a) Leaf level ratio indices Days after infection


Index R430/R680 R440/R690 R550/R680 R550/R800 R605/R760 R672/R550 R675/R700 R695/R420 R695/R670 R695/R760 R710/R760 R740/R720 R750/R550 R750/R705 R750/R710 R800/R1660 R800/R550 R800/R635 R800/R680 R900/R970 R1600/ R820 R1660/R550 R1660/R680

1

2

4

5

8

10

12

15

18

22

26

31

0.74 0.73 0.54 0.63 0.60 0.56 0.56 0.70 0.62 0.62 0.61 0.60 0.62 0.62 0.61 0.59 0.63 0.59 0.52 0.60 0.59 0.62 0.55

0.57 0.68 0.70 0.78 0.72 0.69 0.71 0.73 0.77 0.77 0.80 0.79 0.78 0.80 0.80 0.67 0.78 0.69 0.58 0.66 0.67 0.78 0.60

0.75 0.68 0.50 0.62 0.60 0.50 0.54 0.67 0.56 0.66 0.69 0.67 0.62 0.69 0.68 0.52 0.62 0.60 0.66 0.51 0.52 0.65 0.62

0.67 0.47 0.62 0.53 0.50 0.62 0.56 0.51 0.54 0.53 0.54 0.51 0.54 0.54 0.53 0.60 0.53 0.49 0.62 0.59 0.58 0.56 0.57

0.63 0.54 0.63 0.60 0.60 0.63 0.61 0.54 0.61 0.58 0.59 0.59 0.60 0.59 0.59 0.50 0.60 0.58 0.60 0.51 0.49 0.58 0.61

0.66 0.48 0.77 0.59 0.47 0.76 0.72 0.59 0.51 0.51 0.55 0.56 0.59 0.55 0.55 0.62 0.59 0.52 0.72 0.67 0.64 0.62 0.70

0.64 0.50 0.77 0.52 0.57 0.76 0.70 0.53 0.68 0.63 0.57 0.56 0.51 0.56 0.56 0.80 0.52 0.62 0.78 0.75 0.79 0.60 0.69

0.71 0.58 0.77 0.73 0.80 0.78 0.73 0.50 0.73 0.81 0.76 0.76 0.73 0.77 0.77 0.90 0.73 0.82 0.85 0.85 0.88 0.56 0.74

0.64 0.53 0.69 0.77 0.79 0.72 0.68 0.55 0.71 0.80 0.81 0.82 0.78 0.80 0.81 0.91 0.77 0.82 0.86 0.89 0.92 0.53 0.67

0.77 0.68 0.73 0.79 0.80 0.74 0.64 0.70 0.63 0.86 0.87 0.88 0.79 0.86 0.86 0.91 0.79 0.81 0.85 0.91 0.91 0.54 0.73

0.90 0.96 0.72 0.77 0.79 0.72 0.83 0.90 0.86 0.84 0.88 0.88 0.77 0.87 0.88 0.61 0.77 0.79 0.85 0.78 0.57 0.76 0.51

0.92 0.96 0.62 0.92 0.91 0.58 0.89 0.92 0.91 0.96 0.93 0.93 0.92 0.93 0.93 0.77 0.92 0.89 0.88 0.83 0.74 0.70 0.62

18

22

26

31

(b) Leaf level standardized indices Days after infection Index (R4152R435)/ (R415 + R435) (R5282R567)/ (R528 + R567) (R5312R570)/ (R531 + R570) (R5702R539)/ (R570 + R539) (R6802R430)/ (R680 + R430) (R6802R430)/ (R680 + R430) (R7502660)/ (R750 + R660) (R7502705)/ (R750 + R705) (R7502R445)/ (R7052R445) (R8002635)/ (R800 + R635) (R8002R680)/ (R800 + R680)

1

2

4

5

8

10

12

15

0.59 0.80 0.58 0.63 0.64 0.67 0.62 0.73 0.71 0.80 0.70 0.74 0.63 0.71 0.75 0.66 0.50 0.46 0.53 0.67 0.64 0.73 0.96 0.99 0.53 0.58 0.70 0.59 0.56 0.57 0.59 0.79 0.67 0.93 0.89 0.94 0.51 0.53 0.60 0.53 0.56 0.63 0.64 0.81 0.74 0.74 0.76 0.81 0.74 0.60 0.75 0.67 0.63 0.66 0.64 0.71 0.64 0.70 0.85 0.92 0.74 0.50 0.75 0.67 0.63 0.66 0.64 0.71 0.64 0.70 0.85 0.92 0.53 0.72 0.59 0.55 0.50 0.65 0.71 0.84 0.85 0.81 0.84 0.84 0.62 0.59 0.69 0.53 0.59 0.55 0.56 0.77 0.80 0.86 0.92 0.93 0.55 0.65 0.51 0.56 0.55 0.63 0.70 0.80 0.82 0.80 0.74 0.65 0.58 0.59 0.60 0.49 0.58 0.52 0.62 0.82 0.82 0.80 0.96 0.89 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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S. Delalieux et al. Table 2. (Continued.) (a) Leaf ratio indices (b) Leaf levellevel standardized indices Days after infection

Index

1

2

4

5

8

10

12

15

18

22

26

31


(R8192R1649)/ 0.59 0.57 0.51 0.59 0.51 0.62 0.79 0.90 0.90 0.93 0.77 0.76 (R819 + R1649) (R8602R1240)/ 0.66 0.59 0.54 0.53 0.52 0.55 0.75 0.83 0.93 0.84 0.80 0.92 (R860 + R1240) (R10702R1200)/ 0.60 0.60 0.53 0.57 0.57 0.60 0.71 0.84 0.85 0.93 0.79 0.77 (R1070 + R1200)

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

Figure 4. Synoptic overview of the delineated regions having c-index values . 0.8 of the logistic regression models with all possible derivative two-band ratios as independent variables for each measurement day. Only indices having c-values . 0.8 within a sensor shift of 30 nm are shown. Results are illustrated for the susceptible Braeburn cultivar. Different colours refer to different measurement days. The atmospheric water absorption regions are removed from the analysis to reduce computing requirements.



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Figure 5. (a) Representation of the averaged spectral behaviour of healthy, developing leaves from the first day of unfolding until full expansion (20 days). The grey-scale symbolizes the normalized differences of the reflectance pattern with respect to the reflectance spectrum at the first day. The spectral regions with the largest changes in reflectance values during leaf development are indicated with arrows and enhanced in Figure 6. (b) The averaged spectrum of healthy leaves is shown on the second day (grey) of the measurements and at fully developed stage (20-days) (black). (c) The proportional differences for each wavelength are shown between the second (grey) and the 20th (black) day of the leaf development.

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-based modelling using first derivative spectra. Very young, unfurling leaves have a distinctly steeper slope in their reflectance spectrum around 715 nm in comparison with 20-day-old, totally expanded (mature) leaves. The first derivative ratio l9715/l9740 varies remarkably from the first until the last measurement 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


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Figure 6. The patterns for the TBM-extracted wavelengths, namely 570 nm, 1460 nm, 1940 nm and 2400 nm (see also Figure 5) are represented in (a), (b), (c), and (d), respectively. Five consecutive 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 nm throughout time is presented for successive measurement days (day 1–20) showing a decrease in ratio values during the development of a leaf due to the changes in spectral behaviour as enhanced in figure 7(b); (b) A shift in the red-edge inflection point is shown over time for consecutive 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.


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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).


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Figure 9. Phenological and scab induced spectral behaviour of the susceptible cultivar over 20 days. The grey-scale symbolizes the proportional differences of the reflectance pattern with respect to the reflectance spectrum at the first day.

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,



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Figure 10. The close relationship of 1250/1050 with water content is shown for healthy leaf spectra of the LOPEX dataset. Dried leaves, represented by squares, are identified and excluded from the linear analysis.

Figure 11. l1250/l1050 index values for the LOPEX dataset, a peach dataset, the scab infected Braeburn dataset, and the mock infected Braeburn dataset, respectively from left to right. The upper (l1250/l1050 5 0.97) and lower (l1250/l1050 5 0.90) limits were chosen in such a way that 95% of the fresh, non dried LOPEX leaf data fell within this range. Measurements with index values beyond the limits are identified and named. For example, ‘BI ld’ refers to data taken from the infected Braeburn dataset measured at the first day after inoculation.


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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) atmospheric 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



1907

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 laboratorydeveloped 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 characteristics. 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 illuminationobservation 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 preprocessing 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

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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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