Vegetation Measurement Using Image Processing

Methods

Mürvet Kırcı, Ece Olcay Güneş, Yüksel Çakır

Department of Electronics and Communication Engineering

Istanbul Technical University, Electrical and Electronics

Engineering Faculty, Maslak, Istanbul, Turkey

ucerm@itu.edu.tr, ece.gunes@itu.edu.tr, cakiryu@itu.edu.tr

Selver Şentürk

Center for Satellite Communication and Remote Sensing

(CSCRS)

Istanbul Technical University, Maslak, Istanbul Turkey

selver.senturk@tarbil.com

Abstract—Measurable value of the vegetation cover is an

important parameter widely used to estimate the yield, to

estimate the agricultural loss (because of illness, pests, freezing,

etc.), to be informed about land use, vegetation analysis,

desertification and degradation etc.. The current methods of

measuring vegetation cover are performed into two ways; field

measurements and remote sensing techniques. Traditionally, field

measurements are made subjectively by visual assessment and it

is very time consuming. Use of digital camera images for

measuring vegetation cover is flexible and low cost method for

monitoring the change of the land surface. In this work, the aim

is to determine the rate of vegetation cover of wheat fields by

benefiting from images captured at different phenological

periods of wheat using image processing techniques.

Keywords— vegetation cover measurement, image processing,

color indexes

I. INTRODUCTION

Measurable value of the vegetation cover is a parameter widely used to estimate the yield. Measuring vegetation accurately is also important to obtain information about vegetation analysis, land use, desertification and land degradation etc. This value can be measured in two ways; remote sensing and field measurements. These two methods has advantages and disadvantages relative to each other. Remote sensing is used to obtain measurable value of the vegetation cover of the large areas. However, the data is too large, expensive, and accuracy depends on too many parameters. To improve accuracy, high throughput is required. While field measurements give results at small spatial scales with high temporal data, usually require a lot of measuring and sampling. Additionally, this process is time consuming, may be less efficient, and depends on the person. Use of digital camera images for measuring vegetation cover is flexible and low cost method for monitoring the change of the land surface at high temporal and spatial resolution than satellite images. The digital camera images can be acquired quickly to obtain knowledge about the vegetation cover over time period and they can be easily archived. This method can provide more accurate and objective estimation about vegetation cover.

In the literature, in many works are emphasized on image analysis techniques for measuring the vegetation cover. Lukina et. al. gave a method to predict the ratio of the

vegetation coverage in wheat using digital camera images. This study is made to evaluate the biomass and the ratio of vegetation coverage using digital image processing techniques [1]. Olmstead et al. proposed a method for image capture and measurement of percent vegetative cover. This work was carried out to identify special cover crops in order to reduce wind erosion in vineyards [2]. A method was given for measuring the crop cover for wheat yield prediction by G. Pan et. al. The purpose of this work was to determine the feasibility of estimating crop cover by using digital camera images which were taken vertically 4m above the ground [3]. A method for detecting the phenological stages of cereal plants using digital image processing techniques was presented by Bağış and Üstündağ. State machine structure was used to detect phenological stages in time intervals [4]. Kakran and Mahajan suggested a method for deciding the age of the wheat crop using digital images. Image processing techniques were used for finding the green ratio of the vegetation by them [5]. Bauer and Strauss presented a quick and easy to handle field method for calculating the amount of different soil cover types i.e. dead and living biomass. They used an object based image analysis methodology to quantify the different cover types [6]. An accurate measuring of vegetation is required to identify plant biomass versus background (soil, residue etc). This process was done by using color indices in many works. Five color indices were tested by Woebbecke et. al. for bare, corn and wheat and ExcessGreen (ExG) index was found the most effective index in such process [7]. Other color indices have been proposed to separate plants from soil and residue background images. For example, the normalized difference vegetation index (NDI) was given by Perez et al. [8]. G. M. Meyer, J.C. Neto described an improved color vegetation index with an automatic threshold and to determine its accuracy using plant-soil-residue images [9].

TARBIL project (www.tarbil.org), aims to estimate crop yields and loss rates in Turkey. For this purpose, field measurements are made at stations installed in the field, as well as satellite images are used. In this work, the aim is to determine the rate of vegetation cover of wheat fields (one of the TARBIL station) by benefiting from near camera images captured at different phenological stages of wheat.

T.R. Ministry of Food, Agriculture and Livestock I.T.U. TARBIL Agro-informatics Research Center (sponsors).

II. MATERIALS AND METHODS

A. Site description and image acquisition

To measure vegetation cover, the images were taken from

the south-eastern region of Turkey, (Urfa, Ceylanpinar

TARBIL station 9). One of the views of the field was shown

in Fig.1. The climate properties of this area are continental

climate properties influenced by Mediterranean climate.

Coldest month temperature average varies between 1.5°C to

6°C, average of the highest temperature month at 2012 is

25,1°C, and average daylight period is 6.5 hours. There are 2

observation cameras with respect to cultivation plan of the

surrounding fields in TARIT station 9. They have different

zoom rates at x1 to x10 level and still image resolution is 4

Mega pixels. SANYO-4000 digital cameras held at a height of

10 m were used. Length of view of cameras was 18m and

pointed directly downward. In the station the images in RGB

format are taken 30 minutes intervals in every day in day time.

These images were taken from wheat (Triticum aestivum)

fields at some phenological phase periods. The images were

taken sowing until harvest (from December 2011 until June

2012). In this study, images of early phenological phase

periods were used only. These periods are determined

according to the growing time of wheat. Data were collected

close to solar noon (11.30–13.30 hours) when changes in solar

zenith are minimal. The images have 2288X1712 pixel size

within the visible spectrum and in JPEG format. This data has

been processed by MATLAB functions in a personal

computer. Firstly, 1000x600pixel sized pieces of images were

subtracted from original images by cropping (Fig.2).

Fig. 1. View of the wheat field (TARBIL station 9)

B. Methods

RGB matrices show color space matrices obtained from the images in JPEG format. R, G, and B represent the value of red, green, and blue in RGB color space, respectively. Each element of this matrix gives us the intensities of red, yellow and green colors of a pixel in the range of 0-255. But, to make the more accurate comments, the use of different transform methods based on the color matrices is very helpful. Here, some transform methods based on color conversion have been applied such as HS, difference index, ratio index, normalization index and modified difference index to green.

The definitions of these indexes are given as;

a) The difference index

GRDrg

BRDrb

b) The HS index

)]B,G,R[min(BGR

31S

BGR2

)BG(3arctg

2

3

BGR2

)BG(3arctg

2H

c) The ratio index

R

GRrg

B

GRrb

d) The normalization index

BGR

Rr

BGR

Gg

e) The modified difference index to green

mBmGmR

dGGR r

r

3

mBmGmR

dGGR b

b

3

Where

mRmGdGr , mBmGdGb

mR, mG and mB are mean values of R, G, and B respectively.

The scatter plots of those 2D perspective color spaces were drawn as vegetation and background classes for only early periods of growing time of wheat as shown in Fig. 4.

(a) (b)

( c) (d)

Fig. 2. Images of the field taken at (a) Dec.25th, (b) Jan.15th, (c) Feb.13rd, and

(d) Mar. 4th .

As an example four of the images which belong to determined period were shown in Fig.2. Box plots of these images on the color space plots were obtained as shown in Fig.4. As shown in Fig. 4, green levels have increased starting from December, as expected. And also, for all the pictures had been obtained histogram curve, RGB color intensity changes were examined. Histograms were shown in Fig.6. The different threshold values determined according to indexes, box plots and histograms were used to find the best greenness value of the images.

III. RESULTS

The block diagram of the applied method was given in Fig.3. The method were explained below.

Fig. 3. The block diagram of the method

(a)

(b)

(c)

(d)

(e)

Fig. 4. The scatter plots of indexes: a) normalization index, b) ratio index,

c) H-S index, d) difference index, and e) modified difference index to green

Preprocessing

Threshold for greenness by using

indexes and

histograms

Gray scale

image

Black&White

image

Rectangular

filter

Σ(white pixels)

(a)

(b)

(c)

(d)

Fig. 5. The box plots of RGB spaces of images in Fig.2 historically in order

(a, b, c, and d)

(a)

(b)

(c)

(d )

Fig. 6. The histogram plots of RGB spaces of images in Fig.2 historically in

order (a,b,c, and d)

Data were taken every week after the start of the

germination. Some of these pictures, because of the rain and

wind to be seen in a horizontal position of the plant due to

give erroneous results were extracted. In the emergence and

tillering phase, plants as well as straw residue remaining from

the previous period and the soil are seen in the images. To

measure the vegetation cover, these parts except plants must

be remove from the images. To do this process plants (1st

class) and soil, straw, puddle (2nd

class) should be classified in

the images. With the emergence phase green parts start to be

seen in the images. To separate these two classes is possible to

benefit from the various color indexes. In this study, the

difference index, HS index, the ratio index, the normalization

index, and the modified difference index were tested. Some of

these indexes (the difference index, the ratio index, the

normalization index and modified difference index) have been

redefined according to the green color. In Fig. 4, when the

images divided into two classes as plant (1st class) and soil,

others (2nd

class) scattering plots are shown. The period

December until March (the period starting from the

germination until the stem elongation), decomposition studies

were conducted using these thresholds. Before, processed

RGB images were obtained by filtration. This will highlight

plant areas in the image by calculation of greenness. 0-255

level green images were transformed to gray-scale images.

Then the black and white image was obtained using an

appropriate threshold. In the image the regions of the plant are

found to consist of elongate shapes, the image was filtered by

a rectangular filter. For obtained images that application was

made and without made, vegetation cover measurement has

given good results in some images, but the plant in different

directions as the germination period and stem elongation

period give erroneous results were observed. Therefore,

filtering was applied only to interim. For measuring vegetation

cover in the images, white portions of images were counted

pixel by pixel and this value is the measurable value of the

vegetation cover. One of the parameters used in the

calculation of vegetation cover known as NDVI values.

Calculated NDVI values for this station and obtained

vegetation cover values from image analysis were normalized

and regression relationship between them was examined

(Fig. 7).

Fig. 7. The regression relationship between vegetation cover and NDVI

It is seen that from Fig.7, measured vegetation cover by the

proposed method and real NDVI values are agreement with

each other.

IV. CONCLUSION

In this study, an automated image processing based vegetation

cover measurement method has been presented. Images have

been taken from one of the TARBIL station established in the

field. The images have been taken different phenological

phases of wheat. For measuring the vegetation cover, the other

parts (background) except the green parts of the images

(correspond to plant) must have to remove. For this, different

color based methods applied to images such as color indexes

and color histograms. Different color indexes have been used

and also some of them have been redefined according to green

color. Measured values of vegetation cover by the presented

method have been compared with the real NDVI values of

corresponding region. Results are in agreement with each

other.

Acknowledgment This research was funded by T.R. Ministry of Food,

Agriculture and Livestock and I.T.U. TARBIL Agro-informatics Research Center.

References [1] E. V. Lukinaa, M. L. Stonea, and W. R. Rauna “Estimating

vegetation coverage in wheat using digital images”, Journal of Plant Nutrition, Volume 22, pp. 341-350, Issue 2, 1999

[2] M. A. Olmstead, R. Wample, S. Greene, and j. Tarara, “Nondestructive measurement of vegetation cover using digital image analysis”, Hort Science, 39 (1), pp.44-59, 2004

[3] G. Pan, F. -M. Li, G. –J. Sun, Digital camera based measurement of crop cover for wheat yield prediction” IGRASS 2007,.proceeding pp. 797-800, Barselona, Spain 2007

[4] S. Bağış, B. B. Üstündağ, “Image based auotomated phenological stage detection of cereal plants”, Agro-geoinformatics 2012, Shangai, China, 2012

[5] A. Kakran and R. Mahajan, “Monitoring Growth of Wheat Crop using Digital Image Processing”, International Journal of Computer Applications, 50, (10), pp.18-22, July 2012.

[6] T. Bauer, P. Strauss, “A rule-based image analysis approach for calculating residues and vegetation cover under field conditions”, Catena, 113, pp.363-369, 2014

[7] D. M. Woebbecke, G. E. Meyer, B.K., V., D. A. Mortensen, “Color indices for weed identification under various soil, residue and lighting conditions”, Transactions of the ASAE 38, pp.259–269, 1995

[8] A. J. Perez, F. Lopez, J. V. Benlloch, S. Christensen, “Color and shape analysis techniques for weed detection in cereal fields”, Computer and Electronics in Agriculture 25, 197–212, 2000

[9] G. M. Meyer, J.C. Neto, “Verification of color vegetation indices for automated crop imaging applications”, Computers and Electronics in Agriculture, 63, pp.282–293, 2008