University of Minnesota, December 9th 2013
Remote Sensing of Natural Resources and Environment
Forest Resources 5262
Class Project
Tavvs Micael Alves
Introduction
Rhizoctonia solani is a fungal pathogen patchy distributed within sugar beet fields. The
isolated clusters of infected plants are very important for spreading R. solani. The disease will be
widely disseminated in the sugar beet field if there is a late detection of the root or crown rot
symptoms. A proper spatial-temporal diagnose of hot spots infested by R. solani can prevent
outbreaks of economic importance. A localized management can also reduce the amount of
synthetic chemicals used for its control. Without methods for site-specific detection, fungicides
have been sprayed in the entire field. Therefore, sub-meter resolution is indicated to identify the
beginning of the infection at small areas.
Remotely sensed data have been proved to be an efficient technology for detection of
plants infected by R. solani (Laudien et al. 2004, Hillnhütter et al. 2011). The principle applied
for remotely access the disease infection is supported by the assumption that differences in the
plant reflectance can be associated with levels of injury prior economic losses. Combinations of
single spectral bands can be used to distinguish between healthy and infected plants. For
instance, it is expected that R. solani affects the plant reflectance on blue, green, red and infrared
wavelengths. Infected plant may show higher reflectance in the visible spectrum, but it has less
infrared reflectance than a vigorous plant.
The objective of this class project comprises the use of spectral canopy reflectance for
detection of sugar beet plants infected by R. solani.
Material and Methods
Two sugar beet fields on Crookston, MN, were randomly walked for four evaluators
seeking for visual cues of plants infected by R. solani. Three sampling sites of about 2 ha were
arbitrarily selected as representatives of highly infected spots for the entire field. Additionally,
Field A had two sampling sites representing healthy spots. Field B had three arbitrary sampling
sites to represent healthy spots. Evaluators rated twenty random plants inside each sampling site
according the level of wilting, yellowing or necrosis of leaves on a scale from 0 = plant healthy,
no symptoms on petioles, to 7 = plant dead, leaf brown and necrotic.
The sugar beet fields were flew on August 17, 2010 by an aircraft 3,000 feet above the
ground resulting in 0.5 m spatial resolution (AeroCam, UMAC). AeroCam is a multi-spectral
sensor that features 8-bit quantization and automatically georeferenced images. Spectrally,
AeroCam is similar to Landsat-5 TM sensor (bands 1–4) and commercial satellite sensors of
Ikonos, QuickBird, and GeoEye (Zhang et al. 2011). The AeroCam has provided imagery in the
NIR (765–825 nm), red (650–690 nm), and green (505–575 nm) spectral bands.
The pyramid layers of the tiff images were computed (Erdas Inc. 2013). The whole-field
image was clipped detaching subsets of each sampling site. Then, files were geo-rectified using
ground control points. A polynomial of first order was used for the georectification model.
Resampling was done using nearest-neighbor method to assign digital numbers to the new files.
Map layers were exported to img files (coordinate reference system: Lat/Lon, datum: WGS 84).
NDVI (Eq. 1) and AOKI (Eq. 2) spectral vegetation indices were computed using the
reflectance of the plants within the sampling sites (Rouse Jr et al. 1974, Aoki et al. 1981). These
indices result in a reduction of data dimension and may give a better understanding of the unique
spectral signatures of healthy and diseased plants. Spearman correlation test was used to
determine the strength of the relationship between the indices and the rating of the disease
symptoms (H0: Spearman’s coefficient equals zero, Rh0=0). The diseased and healthy sampling
sites was compared using T-test (α= 0.05).
NDVI = (ρNIR – ρR)/ (ρNIR + ρR)
Eq. (1)
AOKI = ρG/ρNIR
Eq. (2)
Where,
NDVI: Normalized Difference Vegetation Index (Rouse Jr et al. 1974).
AOKI: vegetation index reported to be highly correlated to the total chlorophyll content of
several plants (Aoki et al. 1981).
ρNIR: reflectance of the near-infrared band.
ρR: reflectance of the red band.
ρG: reflectance of the green band.
A hybrid classification method was used to classify the reflectance of the plants in the
whole-field image. Firstly, an unsupervised classification was performed using K means method
to create ten classes. The process of assigning pixels to the classes was broken either with a
maximum of fifty iterations or achieving the convergence threshold of 0.95. The principal axis
was used for initializing class means. Scaling range mode was set to automatically compute
initial class means. The resulting classes were grouped and recoded in agreement with the
arbitrary sampling sites of the visual maps. The recoded classes were 1) high-intensity disease
infection, 2) intermediate-intensity disease infection, 3) healthy plants, and 4) sparse canopy
showing soil. Then, the resulting classes were used to guide training samples for a supervised
classification. The training pixels were selected drawing circles in the patterns recognized by the
unsupervised classification and in accordance with the arbitraty map of symptoms. Pixels were
grouped into classes using the Maximum Likelihood classifier. The ground-truthing data
distinguished between healthy and highly infected plants, but it did not show intermediateintensity disease infection. Therefore, the final product of the supervised classification did not
included intermediate-intensity class.
Results and Discussion
There were significant differences between the rating of diseased and healthy plants (tvalue= 2.79, p= 0.02). Differences between diseased and healthy plants were also found in the
red (t-value= 3.1, p= 0.001) and NIR (t-value= -3.0, p= 0.001) wavelengths. In other words, R.
solani increased in 16% the reflectance of sugar beet canopy in the red range and decreased in
15% the reflectance of sugar beet canopy in the NIR range. However, the disease did not affect
the reflectance in the green range (t-value= 1.26, p= 0.24). The NDVI index (𝑥̅ ±SD) inside the
diseased sampling sites (0.62±0.03) was lower than inside the healthy sampling sites (0.71±0.02)
(t-value= -6.72, p= 0.001). The disease also affected leaf chlorophyll concentration estimated by
AOKI index (t-value= 6.14, p= 0.001). Indeed, Spearman’s rank correlation showed a strong
relationship between ground-truthing rating data and the NIR wavelength, NDVI and AOKI
indices (Table 1). Curiously, disease rating was not correlated to red wavelength (Table 1).
Table 1. Spearman’s correlation coefficients (r) testing the strength of the relationship between
ratings for R. solani and spectral responses.
Wavelength
Index
Ground-truthing variable
GREEN
RED
NIR
NDVI
AOKI
r= -0.229
r= 0.159
r= -0.774
r= -0.602
r= 0.658
Disease rating
p= 0.499 ns p= 0.642 ns p= 0.005 *
p= 0.05*
p=0.028*
* Spearman’s coefficient (r) is significantly different from zero (p<0.05, t-test). ns Denotes the
acceptance of the null hypothesis that Spearman’s coefficient is equal zero.
The classes in the unsupervised classification apparently matched to the arbitrary maps of
symptoms in the Fields A and B. The low-intensity (healthy) sampling sites have fallen in the
same pixels with more NIR and lower reflectance in the green and red bands (i.e., green pixels of
Fig. 1c and Fig. 2c). Indeed, high-intensity (diseased) sampling sites were also accordingly
associated to less NIR and more reflectance in the green and red bands (i.e., red pixels of Fig. 1c
and Fig. 2c). An intermediate class seems to bridge low- and high-intensity classes (Fig. 1b and
Fig. 2b). That is expected once R. solani spread from infected plants to adjacent spots. However,
training samples could discriminate between low- and high-intensity classes, but most of
intermediate-density pixels were taken by the low-density class in the supervised classification
(Fig. 1c and Fig. 2c).
There were evidences to believe that there is a border effect in the disease infection.
Images of supervised classification of Field A (Fig. 1c) showed high-intensity values (i.e., red
pixels) in the borders and low-intensity values (i.e., green pixels) in the central part of the field.
However, temporal shifts in development of disease symptom should be taken into diagnose of
the progress of disease infection. Temporal variation in onset of symptoms is important to
determine the beginning and cumulative plant stress due to the disease infection.
Fig. 1. Digital numbers associated with the canopy reflectance of sugar beet plants in the Field
A, Crookston, MN, 2010. (A): composited image using false color (CIR image) as background
and an overlay image with the NDVI values of the healthy sampling sites and diseased sampling
sites. (B): unsupervised classification using K means and encoded to show four classes (gray:
sparced canopy showing soil, green: low intensity infection, yellow: intermediate intensity
infection, and red: high intensity infection). (C): supervised classification using training samples
agreeing with the unsupervised classification and arbitrary map of symptoms (gray: sparced
canopy showing soil, green: healthy plants, and red: high intensity infection).
Fig. 2. Digital numbers associated with the canopy reflectance of sugar beet plants in the Field
B, Crookston, MN, 2010. (A): composited image using false color (CIR image) as background
and an overlay image with the NDVI values of the healthy sampling sites and diseased sampling
sites. (B): unsupervised classification using K means and encoded to show four classes (gray:
sparced canopy showing soil, green: low intensity infection, yellow: intermediate intensity
infection, and red: high intensity infection). (C): supervised classification using training samples
agreeing with the unsupervised classification and arbitrary map of symptoms (gray: sparced
canopy showing soil, green: healthy plants, and red: high intensity infection).
In conclusion, aerial images of sub-meter resolution can be used to detect the symptoms
of Rhizoctonia solani in sugar beet fields. Using a scouting method of adequate precision, remote
sensing techniques can contribute reducing the waste of fungicides, determining the timing and
location for disease control accurately, and minimizing the chances of economic losses for sugar
beet worldwide. Here, I could correlate the reflectance changes with low or high intensity of
disease infection. Detailed studies should focus in the correlation of canopy reflectance and
diversified levels of plant infection occurring naturally.
I have assumed that all differences in the canopy reflectance were resulted of disease
infection, which does not necessarily happen even in high infected fields. Other confounding
factors (e.g., soil, nutritional status of plants, insect herbivore, and moisture) should be evaluated
to isolate the reflectance changes due to the infection by R. solani. Therefore, future researches
should record co-variables that may play a role as confounding factors of the disease infection. I
also point out the need of ground spectrometers to strengthen the conclusions about the
relationship between disease and canopy reflectance. If possible, hyperspectral can be used to
detect spectral changes in narrow bands associated with the intensities of disease infection.
In the way that the ground reference data was collected, the sampling sites could be
biased by the interpretation of each evaluator about symptomatic and healthy plants. A lacking in
the ground-truthing plan was made disregarding classes of intermediate-density of R. solani. The
small number of degrees of freedom caused problems to test statistical assumptions and
precluded more sophisticate statistical analyses. Actually, the identification of sampling sites
with different levels of disease infection will be unviable or extremely slow if plant symptoms
are used exclusively. Quantitative determination of the population of R. solani can use soil
samples, genetic screening of leaves, and the history of crops grew in the area (Weinhold 1977,
Rush and Winter 1990).
For future studies, I suggest sampling using a fixed grid with regularly spaced samples
covering the entire area. Alternatively, an image could be taken previously to the sampling. A
pre-process could be performed to equally partition the range of sugar beet canopy reflectance
into fourteen classes. This is twice the number of classes necessary for assigning the disease
rating adopted here. A random stratified sampling pattern could be established accessing the
disease rating inside each pre-processing class. The number of samples for ground-truthing will
be assigned proportionally to the variability of the classes in the pre-processing image. Having
more samples distributed in narrow ranges of canopy reflectance, the rating levels could be
associated with more than 1/14 of the canopy reflectance range. A hypothetical example of that
is: the scale zero (plant healthy) taking 1st-4th parts of the reflectance range, scale one taking 5th7th parts, scale two taking 8th-9th parts, scale three taking the 10th part, scale four taking the 11st
part, scale five taking the 12nd part, scale six taking the 13rd part, and scale seven (plant dead)
taking the 14th part. Spearman correlation test can provide the relationship of each part of the
spectrum with the disease rating scale.
References cited
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