Assessing the crNorth China P

e

Jia. Liu, Zhongxin Chen, Limin Wang, Xiao Wang

Institute of Agricultural Resources and Regional Planning,

CAAS Beijing, China

Abstract - Image classifications in

analysis are often used to estimate directlwhile ground data collected during fiesecondary role. This type of crop areaimage classifications often leads to a biato non-representative selection of trsubjective a-priori knowledge. Instead reapproach combining remote sensing inrigorous ground sampling can resultassessment of crop acreage. In this stucrop statistics, the area frame sampling ato the strip-like cropping pattern on the NRemote sensing information is used toefficient stratification from which no-agrexcluded from ground survey. This inincluded in a later stage as an auxiregression analysis. The results showed tof remote sensing information as an auxiimprove the confidence of estimation variance of the estimates.

Keywords: crop acreage statistics; areregression estimator; image classification

I. INTRODUCTION

Much research has been conducted imapping and area assessment usingclassification. Most often using high such as LANDSAT TM/ETM+, IRS-P6series, each pixel is assigned to a specifior crop (pixel counting). More recentlysoft or sub-pixel classification was introthe geographic coverage and tempoanalysis [1].

However, this kind of estimationshown a large discrepancy with the offthe information collected during groudiscrepancy becomes pronounced whengeographically distant from the area training data were collected [2]. Thisdiscrepancy was already reported in oththe Activity B “Rapid Crop Area Chathe European MARS project [3]. It is athe agricultural statistics community sensing imagery has its special added vis used together with ground statistical s

rop acreage at county lePlain using an adapted restimator method

Qinghan Dong Flemish Institute for technological

Research Mol, Belgium

Email: qinghan.dong@vito.be

JJoiEur

cluding sub pixel ly the crop acreage, eld surveys play a a assessment using ased estimation due raining data and egression estimator nformation with a t in an accurate

udy to produce the pproach is adapted North China Plain. o perform a cost-ricultural areas are nformation is also liary estimator in that the integration iliary estimator can

by reducing the

ea frame sampling;

N in the field of crop g satellite image resolution images

6 AWiFS or DMC ic class of land use y, the approach of oduced to increase

oral resolution of

n has sometimes ficial statistics and und surveys. The n the target area is when the ground

s type of bias or her context such as ange Estimates” of also well known in

that the remote values only when it urveys.

In this study the informatsensing is used at two levels.were used to perform a cost-ewhich non-agricultural areas wsurvey. In a later stage the classwas included as a secondaryanalysis. On the other hand thadapted to the complex landscap

The study was carried out inthe Huaibei Plain, on the north(Fig. 1). The official statistics counts 1,545,908 inhabitants whectares according to the officcounty border vector generatedabout 214880 hectares. Like elsPlain, the predominant croppingrowth season with winter wdiversified summer crops incluand other vegetables. Althoupublishes the official crop areascaution should be paid to theareas outside of the administratbeds are not included in the offconstitute a non-negligible pa20%).

Fig. 1. Guoyang County (left) is lprovince and on the south edge of

evel on the regession

Javier F. Gallego int Research Center ropean Commission

Ispra, Italy

tion derived from remote . First the satellite images efficient stratification from

were excluded from ground sification of satellite images y estimator in regression he area frame sampling is pe of the Huaibei plain.

n the county of Guoyang on hern part of Anhui province

data show that the county within a surface of 210700 cial statistics. However, the d a figure for the total areas sewhere on the North China g system consists in a two-wheat followed by more ude soybean, maize, cotton ugh the local government s statistics each year, much

ese figures as the planting tive regulation such as river ficial statistics. These areas art of sowing area (up to

ocated on the north of Anhui f the North China Plain (right)

II. DATA AND METHO

A. Data The high resolution images used in

scenes of RapidEye at 5 meter resorespectively on August 5 and 13， 2013

The statistical data were acquiredstatistical Office of the Guoyang County

B. Area frame sampling (1): StratificatArea frame sampling is defined o

space. Using a reasonably gorepresentation of the target region as a enough to guarantee that the frame is there are no duplications; this means tbias can be kept into minimum. The unican be points, transects (lines of a certaiof territory, often named segments. Whframe are points, survey may be callesurvey”

• The area frame sampling in thisout in two stages: stratificatsurvey. Prior to the ground survstep is performed to separatestratum from other strata:

• A regular grid of 0.01° x 0.01°Google Earth in the study rcomposed by 2012 and 2013 im

• Stratifying the grid points loborder of Guoyang Couninterpretation, into 3 strata: 1) nagricultural, and 3) a thirdagricultural” in case that the available imagery is not sustratify properly the sample poin

• Agricultural stratum was subsapurpose. The number of pointsdetermined by the available rstudy, at least 10% of grid agricultural stratum were planne2).

• A printout of Google Earth witwas used for guiding the survey

C. Area frame sampling (2): ground suThe selected (202 points in this stud

expanded to segments with natural field3). The survey is performed through GPassess the percentages of different cropThe approach is adapted according to ththe landscape in the region, which is stthe example of Figure 3. Fields of a divided in thin stripes and cultivahouseholds. The adapted sampling propoints. The point generates by expansThe segment is thus conceived as a setthat does not straddle any permanent li

ODS

this study are two olution, registered 3.

d from the local y government.

tion on the geographic ood cartographic

sampling frame is complete and that

that the sources of ts of an area frame in length) or pieces hen the units of the ed a “point frame

s study was carried tion and segment vey, a stratification e the agricultural

° is overlaid to the region most often

magery (Fig. 2)

ocated within the nty by photo-

non-agricultural, 2) d stratum “other

resolution of the fficiently fine to nts.

ampled for survey to be surveyed is resources. In this points within the ed for survey (Fig.

th a scale 1:10000 .

urvey dy) grid points are d boundaries (Fig.

PS measurement to ps in the segment. e characteristics of tripe-shaped, as in

few hectares are ated by different ocedure starts with sion a “segment”. t of parallel stripes inear element (dirt

road, hedge etc.). The proportiosegment is assessed by the prop

Fig. 2. A grid of 0.01° was overlaid(above). There are 2074 grid pGuoyang County. The sub-sampwas carried out in a systematic wselected for field survey to asssample/grid point (below).

Fig. 3. The crop proportion of a p

assessed by expanding the poinphysical boundaries of the fieldpoint. The proportion of eachmeasuring on the border of the pl

on of the targeted crop in portion along the transect.

d on the Google Earth imagery points within the boarder of the pling of the agricultural stratum

way. 202 grid points plot were thus sess the crop proportions in this

particular grid/sample point was nt to a segment according to the d plot harbouring the grid/sample h crop was assessed by GPS lot.

The geographical localization was performed using a Trimble GeoXT in stand-alone mode.

D. Image classification The RapidEye scenes were interpreted using the

Maximum Likelihood Classifier [4]. The ground truth data derived from the survey segments were split into two sets according to a ratio of 1:7, in a systematic sampling way:

• according to the field numbering in a set of 8 plots, first seven plots are used to train the classifier;

• the eighth plot of the set was assigned to the validation dataset to assess the accuracy of the classifications.

E. Regression estimator The regression estimator improves the accuracy of

area estimates by adjusting the estimate of mean y and reducing the variance [5]. In other words, the introduction of the remote sensing information (here the output of image classification) as an auxiliary variable, enabled to reduce the amount of ground samples to be collected, if the accuracy of estimation remains constant. On the other hand if the ground sample size is a constant, introduction of remote sensing allow improvement of estimation accuracy.

In this study: y y b p p (1) (1)

where y is the regression estimate for a target crop area mean; y the crop area mean derived from ground survey; p is the proportion of pixels classified as the target crop in the arable land stratum of the county; p is the average proportion of pixel classified as the target crop in the surveyed segments (in the arable land stratum). b is the slope of the regression p (crop proportion in the segment according to ground survey) and y (crop proportion in the segments according to the image classification).

For large random samples (n>50), the variance of the regression estimator can be approximated by: var y var y 1 R var y 1 R (2)

where R is the coefficient of determination for the regression.

III. RESULTS AND DISCUSSION

A. Stratification and ground survey As described above, three strata were defined:

• agriculture (arable land)

• non agriculture (urban, artificial, water)

• other agricultural land (in case of geometric uncertainty in the stratification step because of resolution of satellite imagery for example). In

this study case, all grid points could be exactly photo-interpreted with a priori knowledge

Each of 2074 grid points in total was assigned to one of 2 strata. 1502 points or 72.42 % (155616 hectares) are identified as belonging to the stratum “arable land” and 572 points or 27.58% (59264 hectares) are interpreted as belonging to the non-agricultural stratum. In the stage of field survey, 202 points were selected in the agricultural stratum, and expanded to the segments according to their natural field boundaries. They were surveyed in August, 2013.

TABLE I. STATISTICS ON SEGMENT/FIELD SURVEY (202)

Maize Soybean Other crops

Non agriculture

Average (%) 6.21% 91.58% 2.21% 0.74%

Std dev (%) 1.72% 0.35% 7.70% 2.25%

Total Area (ha)

57. 27 867. 34 18.70 3.51

Average segment Size

(ha) 4.6877

B. Image classification Fig. 4 shows the results of classifications using 2

registrations of the RapidEye imagery. The results from the classification showed that crops cover 87% of the classified image, with soybean and maize sharing 73% and 12% of the classified area respectively. The area of each class was obtained by considering a total area of 214,880 hectares in Guoyang County.

Fig. 4 also reveals that maize appears to be under-cultivated in this county in comparison with the neighbouring counties and is more commonly found on the south eastern part of the county, especially near the border with Mengcheng County [6].

The estimates of crop areas generated by image classification are usually higher in this region than those obtained by photo-interpretation, 85% from image classification against 72% from photo-interpretation.

TABLE II. RATIO AND AREA OF EACH CLASS DERIVED FROM THE IMAGE CLASSIFICATION

% area of the county

Hectare

Maize 12.37 26083.56 Soybean 72.07 151965.96

Other crops 0.06 127.93 Woodlands 5.70 12028.98

Built-up 9.61 20254.85 Water bodies 0.19 399.12

Total 100.00 210860.4

The validation results of the classifications are displayed as confusion matrix in Tab. 3. The overall accuracy was found high around 95% contributed mostly by the highly dominant crop soybean in the county. Not surprisingly, the level of confusion between maize and soybean is relatively high.

Fig. 4. Classification using two scenes of RapidE

The class “other crops” which inclusuch as cotton, peanuts are cultivateproportion in this county. Howeclassification provides still smaller areais marginally represented.

C. Regression Estimator Fig. 5 illustrates the regressio

percentages of crops in the 202 samplfield survey and those from image clacrops soybean and maize.

Tab. 4 shows the proportions of mother crops in the arable land stratum aground survey, from remote sensing (imand from the combination of the grremote sensing analysis through the regFollowing the regression estimator, the asoybean and maize are respectively 8within the arable land stratum (155,138,809 hectares and 16,558 hectares res

TABLE III. CONFU

Class Maize

Cla

ssifi

catio

n

Unclassified 0

Maize 74.47

Other crops

Soybean 25.30

Woodland \ Trees

Water Body

Artificial Surface

Total 100

Eye imagery

udes various crops ed in very small ever the image

a for this class as it

ons between the led segments from assification for the

maize, soybean and s derived from the

mage classification) round survey and gression estimator. area proportions of 89.2% and 10.6% ,616 hectares), or spectively.

Fig. 5. Regressions of crop proportiomaize (below) derived from 202 derived from the classification fo

The R of the regresproportions (from the 202 seground survey and from the cla0.78, and 0.59 for the classes “sstandard deviations (SD) of ththe regression estimator are thwhen the image classificationvariable.

USION MATRIX IN TERMS OF PERCENTAGE FOR THE CLASSIFICATION

Ground Truth

Other crops Soybean Woodland \ Trees

Water Body

0 0 0 0

8.31

91.64

100.00

100 100 100 100

ons for the soybean (above) and surveyed segments against those

r the same segments

sions between the crop gments) derived from the assification are respectively soybean” and “maize”. The he estimates obtained with hus reduced by 37 to 50% n is used as an auxiliary

(2013)

Artificial Surface Total

0 0

0.15 6.63

0.00 49.37

0.00 25.87

99.85 18.13

100 100

TABLE IV. TAB. 10: CROP AREA PROPORTIONS DERIVED FROM GROUND SURVEY, FROM IMAGE CLASSIFICATION AND FROM THE

REGRESSION ESTIMATOR

Maize Soybean

Area mean from ground survey (202 segments) and (SD)

7.64% (1.72%)

89.24% (0.35%)

Regression slope (b) and coefficient of determination (R )

0.59 (0.56)

0.78 (0.58)

Area mean from image classification in terms of arable land points 13.62% 85.80%

Area mean within the 202 (arable) segments 8.57% 86.26%

Regression estimator and (SD) in the arable stratum

10.6% (0.85 %)

89.2% (0.22%)

Relative efficiency of RS & equivalent sampling size

2.5 412

1.7 246

Number of ha in the county (assuming 155616 ha arable area) 16558 ha 138809 ha

In conclusion, this study demonstrated the use of

remote sensing images for improving agricultural statistics derived from ground survey for the Guoyang County on the North China Plain. The contribution of remote sensing was made on two levels: (1) it helped in area frame stratification, therefore in optimising the sampling design prior to the ground survey; (2) remote sensing, by means of image classification, was incorporated as an auxiliary variable in the analysis of regression estimator, and therefore improved the precision of estimates by reducing the error of variance.

Moreover in our study, the crop area proportions in the surveyed segments were regressed against those derived from the image classification. With coefficients of determination of 0.58 and 0.56 for soybean and maize respectively, the incorporation of remote sensing information allowed to adjust the crop area estimates while reducing the variance of these estimates, by a factor of about 2.5 in the case of maize.

According to the official statistics, the arable land area in the county in 2012 was about 144000 hectares with 50% of this area planted with soybean and 30% cultivated with maize. The regression estimates for soybean and maize from our study in 2013 showed rather different figures.

Acknowledgment This study was supported by the 7th EU framework

programme (FP7) projects E-AGRI.

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[2] R. VAN HOOSLT, Q. DONG AND H. KERDILES, “Area estimations for winter wheat over the North China Plain using a sub-pixel classification approach,” unpulished.

[3] F.J. Gallego, “Review of the main remote sensing methods for crop area estimates” In Proceedings of the Workshop Remote Sensing Support to Crop Yield Forecast ISPRS vol 36, pp. 1-6. 2007

[4] R. Duda, , P. Hart, and D. Stork, “Pattern classification” Second Edition, Wiley-Interscience, New Jersey, US, 2000.

[5] E. Carfagna and F.J. Gallego, “Using Remote Sensing for Agricultural Statistics,” International Statistical Review vol. 73 (3): pp. 389-404, 2005.

[6] H. Kerdiles, Q. Dong, S. Spyratos, F.J. Gallego,: Use of high resolution imagery and ground survey data for estimating crop areas in Mengcheng county, China. In Proceedings of 35th International Symposium on Remote Sensing of Environment (ISRSE35), 2013, in press.