rice identification and change detection using terrasar-x data

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This article was downloaded by: [Gazi University] On: 18 August 2014, At: 13:09 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Canadian Journal of Remote Sensing: Journal canadien de télédétection Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/ujrs20 Rice identification and change detection using TerraSAR-X data Zhiyuan Pei a , Songling Zhang a , Lin Guo a , Heather McNairn b , Jiali Shang b & Xianfeng Jiao b a Chinese Academy of Agriculture Engineering, Ministry of Agriculture of China, Beijing 100125, China b Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON KIA OC6, Canada Published online: 02 Jun 2014. To cite this article: Zhiyuan Pei, Songling Zhang, Lin Guo, Heather McNairn, Jiali Shang & Xianfeng Jiao (2011) Rice identification and change detection using TerraSAR-X data, Canadian Journal of Remote Sensing: Journal canadien de télédétection, 37:1, 151-156, DOI: 10.5589/m11-025 To link to this article: http://dx.doi.org/10.5589/m11-025 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

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Page 1: Rice identification and change detection using TerraSAR-X data

This article was downloaded by: [Gazi University]On: 18 August 2014, At: 13:09Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Canadian Journal of Remote Sensing: Journal canadiende télédétectionPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/ujrs20

Rice identification and change detection usingTerraSAR-X dataZhiyuan Peia, Songling Zhanga, Lin Guoa, Heather McNairnb, Jiali Shangb & Xianfeng Jiaob

a Chinese Academy of Agriculture Engineering, Ministry of Agriculture of China, Beijing100125, Chinab Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON KIA OC6, CanadaPublished online: 02 Jun 2014.

To cite this article: Zhiyuan Pei, Songling Zhang, Lin Guo, Heather McNairn, Jiali Shang & Xianfeng Jiao (2011) Riceidentification and change detection using TerraSAR-X data, Canadian Journal of Remote Sensing: Journal canadien detélédétection, 37:1, 151-156, DOI: 10.5589/m11-025

To link to this article: http://dx.doi.org/10.5589/m11-025

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: Rice identification and change detection using TerraSAR-X data

Rice identification and change detection usingTerraSAR-X data

Zhiyuan Pei, Songling Zhang, Lin Guo, Heather McNairn, Jiali Shang, and Xianfeng Jiao

Abstract. Rice is the staple grain in China and accounts for about 42% of the nation’s food production. Most of China’s

paddy rice production is located in the southern provinces of the country where cloud cover and frequent rain severely

limit opportunities for optical satellite acquisitions. The small field sizes, typical of paddy rice, also challenge the

exploitation of satellite data for monitoring rice production. Synthetic aperture radar (SAR) sensors are able to

successfully acquire data under most atmospheric conditions, and the change in backscatter, from rice emergence through

to crop maturity and harvest, permits the detection of rice fields using SAR imagery. Recently launched sensors, including

TerraSAR-X, can provide data at spatial resolutions suitable for rice monitoring in southern China. The objective of this

study was to assess TerraSAR-X imagery for identification of late rice and to develop a change detection methodology to

quantify changes in rice acreages. The lowlands of the Xuwen study site in Guangdong Province are dominated by rice

paddies. Results of this analysis revealed that the TerraSAR-X data were able to identify rice paddies with a 96% accuracy

and acreage change to an accuracy of 99%.

Resume. Le riz est la cereale de base en Chine et represente 42 % de la production alimentaire du pays. La majorite de la

production de riz est localisee dans les provinces du sud du pays, ou le couvert nuageux et les pluies frequentes limitent

considerablement les possibilites d’acquisition de donnees optiques satellitaires. La dimension reduite typique des rizieres

pose egalement un defi a l’exploitation des donnees satellite pour le suivi de la production du riz. Les capteurs radar

a synthese d’ouverture (RSO) permettent d’acquerir des donnees dans la majorite des conditions atmospheriques et le

changement dans la retrodiffusion du riz, du stade d’emergence jusqu’au stade de maturite et de la recolte, permet de

detecter les rizieres a l’aide des donnees radar. Les capteurs lances recemment, incluant TerraSAR-X, peuvent fournir des

donnees a des resolutions spatiales propices pour le suivi du riz dans le sud de la Chine. L’objectif de cette etude

etait d’evaluer les images de TerraSAR-X pour l’identification du riz tardif et de developper une methodologie de

detection du changement permettant de quantifier les changements dans les superficies rizicoles. Les terres basses du

site d’etude de Xuwen, dans la province de Guangdong, sont dominees par des rizieres. Les resultats de cette analyse ont

revele que les donnees de TerraSAR-X permettaient d’identifier les rizieres avec une precision de 96 % et le changement

dans les surfaces rizicoles avec une precision de 99 %.

[Traduit par la Redaction]

Introduction

Although rice has accounted for less than 30% of the

grain-planted acreage in China over the past decade, this

crop has contributed to more than 40% of grain production.

Rice has the highest production and acreage of all grain

crops (available (in Chinese) from http://www.zzys.gov.cn).

Rice acreages fluctuate annually because of agricultural

policies, rice markets, and environmental impacts such as

water resource limitations. Accurate annual statistics on rice

acreages are vital for sound agricultural decisions and

environmental protection.

Multispectral optical satellite imagery is capable of provi-

ding rice acreage information. Landsat Thematic Mapper

(TM) and Satellite pour l’Observation de la Terre (SPOT)

data have classified rice crops at accuracies above 80%

(Fang, 1998; Turner and Congalton, 1998). But with 85%

of rice production located in southern China, the high

frequency of cloud cover and heavy precipitation impede

the use of optical imagery for annual monitoring of rice

production. In an attempt to overcome these challenges,

some studies have assessed sensors, such as Advanced Very

High Resolution Radiometer (AVHRR), Moderate Resolu-

tion Imaging Spectroradiometer (MODIS), and SPOT-

VEGETATION, that provide daily observations (Huang,

1998; Kamthonkiat et al., 2005; Xiao et al., 2005). However,

the coarse resolution of this class of sensors negatively

impacts accuracies given the small dimensions of rice paddies

typical of southern China.With their ability to acquire data regardless of cloud

cover, synthetic aperture radar (SAR) sensors are well

suited for rice mapping in regions such as southern China.

Received 21 June 2010. Accepted 6 October 2010. Published on the Web at http://pubs.casi.ca/journal/cjrs on 4 October 2011.

Z. Pei1, S. Zhang, and L. Guo. Chinese Academy of Agriculture Engineering, Ministry of Agriculture of China, Beijing 100125, China.

H. McNairn, J. Shang, and X. Jiao. Research Branch, Agriculture and Agri-Food Canada, Ottawa, ON KIA OC6, Canada.

1Corresponding author (e-mail: [email protected]).

Can. J. Remote Sensing, Vol. 37, No. 1, pp. 151�156, 2011

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The sensitivity of radar backscatter to the phenology of

rice growth has been well documented, in particular with

C-band SAR sensors. Le Toan et al. (1997) established that

rice fields can be accurately mapped based on the temporal

variation in radar backscatter using ERS-1 data. Ribbes

(1999) demonstrated similar results using RADARSAT-1

data. Shao et al. (2001) used multitemporal RADARSAT-1

data and a neural network classifier to map rice in China.

The accuracy of the rice classification was 91% and then

was 97% after postclassification filtering. Li et al. (2003)

successfully mapped land cover over a site in China with

a supervised classification of a RADARSAT-1 ScanSAR

image. The average accuracy for identifying rice fields

was 90%. Choudhury and Chakraborty (2006), used multi-

temporal RADARSAT ScanSAR (SCNB) data and a

knowledge-based decision rule classifier to achieve �98%

accuracy in mapping rice. Chen and McNairn (2006) used

multitemporal RADARSAT-1 fine mode images, with an

integrated change detection and neural network approach, to

delineate rice production areas. A minimum mapping

accuracy of 96% was achieved.

Other recently launched SAR sensors provide data with

polarization diversity. Chen et al. (2007) used Envisat

Advanced Synthetic Aperture Radar (ASAR) HH and HV

polarization data to map rice fields in southern China with

an accuracy of 81%. Yang et al. (2008) chose ASAR dual

like-polarization (VV/HH) data with a threshold classifica-

tion method to achieve an accuracy of 84%

for rice identification. While most research has evaluated

C-band satellite data, some results have been reported

with X- and L-band data. Inoue et al. (2002), using a

ground-based scatterometer, found that lower frequency

(L-band) microwaves provided deeper penetration into the

rice canopy. High frequency (X-band) data were signifi-

cantly correlated with the weight of rice heads and were

sensitive enough to detect thin rice seedlings just after

transplanting. Kim et al. (2008) also analyzed scatterometer

data and reported that biomass was correlated with L-HH

backscatter at a large incident angle. Leaf area index was

highly correlated with C-band HH and HV polarizations,

while grain weight was correlated with X-VV backscatter.

Zhang et al. (2009) used Advanced Land Observa-

tion Satellite (ALOS) L-band synthetic aperture radar

(PALSAR) images and a support vector machine classifier

to map rice and other land uses at user’s and producer’s

accuracies of 90% and 76%, respectively.

The TerraSAR-X satellite, launched in 2007, provides

data at spatial resolutions suitable for monitoring China’s

southern rice-producing regions. This satellite acquires data

at X-band (3.1 cm) in one of three modes including Spot-

Light (1 m pixel spacing), StripMap (3 m spacing) and

ScanSAR (18 m spacing). Scatterometer studies have

demonstrated the sensitivity of X-band data to rice even at

this crop’s early developmental stages. The objective of this

study was to assess TerraSAR-X imagery for identification

of late rice and to develop a change detection methodology

to quantify changes in rice acreages. This approach, which

quantifies annual changes in rice acreage, would support the

activities of China’s Ministry of Agriculture for operational

monitoring of rice production.

Site description

The Xuwen study site is located close to Leizhou City,

Guangdong Province, China (20.888N, 110.78E). The re-

search study site covered approximately 15 km�15 km,

and could be imaged using a single TerraSAR-X stripmap

acquisition. Topographic relief in this region is low with

an elevation of B25 m (above mean sea level). Xuwen falls

in the tropical ocean monsoon climate zone with a mean

annual temperature of 238C and annual precipitation of1400�1700 mm. The rainy season spans from May to

October with heavy storms occurring mainly in September.

The climate of the Xuwen region supports two rice

growing seasons per year. Although rice production dom-

inates, other crops are also present including sugar cane,

corn, legume, vegetables, and some aquatic plants. The early

rice season begins in late March and ends in mid June. For

late rice, seeding takes place in early August with harvestingapproximately 3 months later in early November. During

the rainy season, rice paddies experience periodic flooding

followed by periods of drying. Seeding methods vary among

paddies and include machine sowing as well as manual

approaches such as direct hand planting and manual broad-

casting of seeds. Nonrice crops are often planted along

with rice in the same field. The availability of water and

nutrients will result in variable rice growth among fields. Ricepaddies also vary in size, but within this study site the field

dimensions were on average approximately 30 m�100 m.

This field size limits the suitability of some SAR sensors for

rice mapping in this region of China.

Data acquisition

Two TerraSAR-X acquisitions were programmed for

the late rice growing season, the first acquisition on

21 September 2008 and the second on 19 September 2009.

Timing of these acquisitions coincided with the period ofpeak growth, with rice crops at the heading and flowering

stage. Data were collected in stripmap dual-polarization

mode with VV�VH polarizations. The acquisitions were

planned as exact repeats with an incidence angle of 358in an ascending orbit. Nominal spatial resolution of the

stripmap product is 6 m. This resolution is well suited to the

dimensions of the paddy fields typical of this study site.

Approximately 15 000 ha of rice paddies are found with-in the Xuwen study site. Ground surveys were conducted

close to the satellite acquisition dates. Sixty-one fields

were surveyed in 2008, and 65 fields were surveyed in

2009. Details of the field distributions between rice and

nonrice crops are listed in Table 1. The location of each

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field was recorded using a differential GPS with submeteraccuracy. These ground control points were required to

support ortho-correction of the imagery. Crop type was

recorded for each field surveyed. For rice paddies, a

measurement of crop height was taken and the phenological

growth stage was also recorded.

The field survey information was used to train image

interpreters to visually classify crop types from satellite

imagery. Visual interpretation of optical imagery is astandard approach of The Ministry of Agriculture of China

to support annual reporting of crop acreages. For the 2008

and 2009 late rice season, no cloud-free optical imagery was

available over the Xuwen study site. Therefore TerraSAR-X

images were used and the result of this visual interpretation

was a 2008 and 2009 thematic map of rice paddies for the

entire study site. These maps were used as reference for

assessing the accuracy of the rice acreage change analysisfrom the TerraSAR-X data.

Methods

In this study, the change detection methodology was aided

by the acquisition of exact repeats of TerraSAR-X, almost

exactly 1 year apart. This approach minimized variances in

crop phenology between these two image acquisitions and

any sensor related differences. Image processing details are

illustrated in Figure 1.

Speckle is an inherent phenomenon for coherent systemssuch as SARs. To suppress speckle, the imagery was filtered

using a 3�3 Lee filter. TerraSAR-X data were then converted

to ß8 (dB). Ortho-correction was performed on both images

using ground control points, DEM, and satellite ephemeris

information. A root mean square error under 1 pixel was

achieved.

The change detection approach used a visual interpreta-

tion of the backscatter response from both the 2008 and2009 images, as well as the difference in backscatter responses

from one year to the next. Such an approach requires the

representation of the maximum amount of information in

a three-channel display. To accomplish this, a principal

component transformation (PCA) was applied to each

image. The first PCA channel (PC-Y1 for 2008 and PC-Y2

for 2009) contained most of the information for both

polarizations (VV and VH). The third channel containedthe difference between the response of the first principal

component of the first (2008) and second (2009) TerraSAR-X

images. For ease of visual display, the PCA images were

scaled to 8-bit. This three-channel composite is displayed in

Figure 2; orange and cyan denote fields where rice was

grown in only one of the two years. Orange-coloured fields

were planted with late rice in 2008 but not in 2009.

Conversely cyan identifies fields where late rice was planted

in 2009 but not in 2008.

An object-based approach was adopted to aid with visual

image classification and to assist with quantitative deter-

mination of acreage change (Benz et al., 2004; Geneletti and

Gorte, 2003). Image segmentation was applied to the three-

channel image in order to clearly demarcate parcels (objects)

of homogeneity. Segmentation was completed using eCogni-

tion (Definiens Imaging, GmbH, Munich, Germany). Next,

within eCognition, decision rules were set to delineate areas

where change took place between 2008 and 2009 and areas

where no change occurred (Figure 3). No change may be

due to the existence of rice in either both years or in neither

year. Where change did occur, decision rules were set to

identify where rice was planted in one year but not the

other. Decision rules (Table 2) consisted of two compo-

nents: feature selection and threshold determination of each

feature. First, spectral feature images (such as brightness,

variance, and hue) and shape feature images (such as

area, length and width, and ellipticity similarity) were

obtained using eCognition. Brightness, variance, maximum

brightness difference, ellipticity similarity, and hue were

selected as classification features through visual comparative

Figure 1. TerraSAR-X data processing flow chart.

Table 1. Field survey distribution among crops in 2008 and 2009

seasons over Xuwen.

Year

No. of

rice fields

Fields of

other crops

Total no. of

fields surveyed

2008 60 1 61

2009 65 3 68

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Page 5: Rice identification and change detection using TerraSAR-X data

analysis. Next, based on field survey data, thresholds of

each feature were extracted and decision rules were set

to classify the image. Classification results and field sur-

vey data were analyzed and compared through human�computer interaction.

Results and discussion

The resultant rice change map between 2008 and 2009 is

shown in Figure 4. Accuracies of the change analysis were

tested using two approaches.

Accuracy assessment of rice identification

The crop maps derived from visual interpretation were

validated using field survey data (Table 1). In 2008, a total

of 61 fields were used as test fields. An overall accuracy

of 98.8% was achieved (Table 3). For the 2009 late

rice identification, a total of 68 fields were used as

validation. An overall accuracy of 96.2% was reached

(Table 3). As reported by Inoue et al. (2002), high frequency

X-band microwaves are very sensitive to the rice heads. This

sensitivity may explain the success at identifying rice crops

in Xuwen, at a time when the rice crops were in their head-

ing and flowering growth stage. The high accuracies of

Figure 3. Flow chart of decision rules.

Table 2. Image classification decision rules.

Classes Decision Rules

Area with change

in rice planting

Rice planted in

2008 No rice

planted in 2009

Rule no. 1:

Brightness: PC-Y1 B150,

jPY1-PY2j B80

Variance: 0.7�3.0

Ellipticity similarity:

0.2�1.0

Rice planted in

2009

No rice

planted in

2008

Rule no. 2:

Brightness: PC-Y2 �54,

150 BjPY1-PY2j B239

Variance: 0.38�1.0

Ellipticity similarity:

0.2�1.0

Area with

no change

Rice planted in

both years

Rule no. 3:

Difference: 0�0.66

Hue: 0.39�0.62

No rice planted

in either year

Other

Figure 4. Changes in the regions of late rice grown between the

2008 and 2009 seasons.

Figure 2. Colour composite constructed using 2008 and 2009

PCA channels where red represents PC-Y2, green represents PC-

Y1, and blue represents the difference channel (PCY1 � PCY2).

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the two reference maps provided reliable bases for evaluating

the late rice acreage change detection between the 2 years.

Accuracy assessment of rice change analysis

The visually interpreted theme maps were used as refer-

ence datasets; rice acreage and acreage change information

were extracted (Table 4). Rice acreage change statistics were

also calculated from the change detection map (Figure 4)

derived directly from TerraSAR-X images using the inte-

grated PCA and object-oriented approach. These statistics

were compared and accuracies are reported in Table 5.

Areas of change in rice between 2008 and 2009 are

clearly identified in Figure 4. Areas in red grew rice in 2008

but not in 2009. Green areas had rice in 2009 and no rice

in 2008. Rice was planted in the vast majority of the region

in both years, and this area is delineated in dark grey in

Figure 4.

Table 5 reveals that rice change analysis using the 2-year

PCA approach achieved an overall accuracy of 99.0%. For

rice acreage change detection, a user’s accuracy of 95.6%

was achieved. This analysis revealed that of the almost

15 000 ha of rice in the Xuwen study site about 10% of this

land experienced change. Rice acreage change in this area

has been interpreted as driven mainly by market demand.

When market prices of other cash crops such as peanut

and watermelon are high some of the rice paddies are

drained to plant cash crops. Conversely, when the price

of rice increases, some nonrice crop land is converted to

rice paddies. The annual fluctuation of rice acreage has a

significant impact on local and regional food security.

Ongoing monitoring and reporting of rice acreage change

are required at both regional and national levels. The Xuwen

site was used as a pilot study to develop a methodology

for rice change detection using SAR data as the lowland

areas are dominated by rice production. This method can

be applied to other rice growing regions in southern China.

Conclusions

This study demonstrated that TerraSAR-X dual-

polarization (VV�VH) stripmap data are capable of provid-

ing accurate late rice acreage change statistics. Using

TerraSAR-X data acquired in late September 2008 and

2009, a three-channel data set was constructed using the

first principal component channel from each year of Terra-

SAR-X data. The third channel represented the difference

in the response of the first principal component of each

year. With these three channels of information, rice acreage

change between the 2 years was visually apparent. This

approach is a simple and effective method to identify changes

in rice acreages. In China’s southern rice growing regions,

where rice paddies are small in dimension, TerraSAR-X

clearly provides a viable option to support operational

requirements to report changes in rice acreages. This paper

presented research findings based upon a limited data set.

Thus, more extensive validation of this methodology and

assessment of the repeatability of these classification accura-

cies are required, particularly in regions with more varied

land cover. As well, acquisition and integration of a more

comprehensive data set, which would include multiple dates

of TerraSAR-X, will likely permit the identification of other

land covers and monitoring of minor crops in the Xuwen

region. Acquisition and integration of multiple dates of

TerraSAR-X data will likely permit the identification of

other land covers and monitoring of minor crops in the

Xuwen region.

Acknowledgement

The authors would like to acknowledge the support of the

German Space Agency (DLR). The TerraSAR-X data were

provided by DLR under scientific project LAN0337.

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Table 3. Xuwen study site classification accuracies (%).

YearRice Other crops

Producer’s

accuracy

User’s

accuracy

Producer’s

accuracy

User’s

accuracy

Overall

accuracy

2008 98.4 100.0 100.0 100.0 98.8

2009 98.5 95.6 92.5 97.4 96.2

Table 4. Xuwen study site rice acreage change between 2008 and

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Area with

change (ha)

Area without

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Acreage derived from reference

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Acreage derived from integrated

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approach

1472.8 14357.1

Table 5. Xuwen study site rice acreage change analysis accuracies

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Producer’s accuracy User’s accuracy

Area with

change

Area

without

change

Area with

change

Area

without

change

Overall

accuracy

94.7 99.6 95.6 99.3 99.0

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