University of Minnesota, December 9th 2013Remote 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 intermediate-

intensity 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 (t-

value= 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 (x±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. Ground-truthing variable

Wavelength IndexGREEN RED NIR NDVI AOKI

Disease ratingr= -0.229 r= 0.159 r= -0.774 r= -0.602 r= 0.658p= 0.499

nsp= 0.642

nsp= 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 5th-

7th 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

Aoki, M., K. Yabuki, and T. Totsuka. 1981. An evaluation of chlorophyll content of leaves based on the spectral reflectivity in several plants. Research Reports of the National Institute of Environmental Studies of Japan 66: 125-130.

Erdas Inc. 2013. Erdas Field GuideTM, Norcross, GA.Hillnhütter, C., A.-K. Mahlein, R. Sikora, and E.-C. Oerke. 2011. Remote sensing to detect

plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crops Research 122: 70-77.

Laudien, R., G. Bareth, and R. Doluschitz. 2004. Comparison of remote sensing based analysis of crop diseases by using high resolution multispectral and hyperspectral data–case study: Rhizoctonia solani in sugar beet. Geoinformatics: 670-676.

Rouse Jr, J., R. Haas, J. Schell, and D. Deering. 1974. Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication 351: 309.

Rush, C., and S. Winter. 1990. Influence of previous crops on Rhizoctonia root and crown rot of sugar beet. Plant Disease 74: 421-425.

Weinhold, A. 1977. Population of Rhizoctonia solani in agricultural soils determined by a screening procedure. Phytopathology 67: 566-569.

Zhang, X., H. J. Kim, C. Streeter, D. A. Claypool, R. Sivanpillai, and S. Seelan. 2011. Near real-time high-resolution airborne camera, AEROCam, for precision agriculture. Geocarto International 26: 537-551.