The Study of National Monitoring System of Arable Land Changing and Its Model in China

8/12/2019 The Study of National Monitoring System of Arable Land Changing and Its Model in China

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The Study of National Monitoring System of Arable Land Changing

and Its Model in China

Haiqi LIU

Dept. of Development Planning Ministryof Agriculture, China

Abstract

In this dissertation, a study of national operational monitoring system for arable land changing by remote

sensing is carried out according to the integration of remote sensing and stratified sampling technique under the

detail analysis of previous surveying and monitoring methods for natural resources and ecological environment.

Meanwhile the consideration of needs of agricultural practice and the productive level at present were taken

into in this project. Some problems such as how to divide stratum and how many stratum numbers is best and

how to control the error to minimum and so on are explained. The integration between the Remote Sensing

Technique and statistic method is a major characteristic in this project, which gives future applications of

remote sensing or statistics a better prospect. The operational monitoring system every year for national arable

land changing was thought to be established after completion of the study of this project for which the 4 years

were taken from 1993 to 1996.

1. Introduction

For the management of agricultural natural resources and studying of their potentiality, the project for the

study of monitoring system for cultivated land changing in China was carried out from 1993 to 1996 for whole

country because of decrease sharply of cultivated land since 1990. The main aim of this project is to establish a

operational monitoring system in whole country for monitoring of arable land changing every year. The model

for operational monitoring system in this project is key technique because the investigating population (wholeChina) is very large.

The operational monitoring system should meet four conditions at same time:

* Operational and feasible technical route;

* Under the capacity of financing support;

* The accuracy could meet users needs;

* The time consuming must be in the users requirements.

For a large population, the four conditions described above is difficulty very much to be need at same time

because they give us many limitations. Therefore, the method for the integration of the remote sensing and

sampling technique has to be accepted. The extrapolation model was focused on for the project because the

surveying region by Landsat TM image ( SAMPLING sites) is very small related to much large statistical

population (whole country of China).

2.Method

Every sampling sites include two scenery Landsat TM images which contain more than 20 counties

(sampling units) by which the sampling frame is made. Stratified freedom sampling technique is used as

extrapolation model. The particular steps described as following.

2.1The establishment of basic sampling unit and sampling estimator

In carrying out the monitoring of nationwide arable land changing, at the present stage in China, it is not

realistic economically or in practice whatsoever to follow the PSU (primary sampling unit) method used by the

U.S. Department of Agriculture or to follow the method of segment of 700m x 700m ) used by European


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Commonwealth. Therefore, monitoring of arable land changing can only be conducted under the existing

condition available in China. That is the annual statistical reporting delivered by every county and the

population can only be counted on the basis of the datum given by the counties. That is why at present, the

county level unit or county is taken as the basic sampling unit.

In order to obtain the total figures of arable land changing nationwide (total variation year by year for

whole country), it is obviously not feasible to take the total arable land area of each county, the ratio of total

arable land area and total land area as well as the ratio of the changing figures and the total arable land (or total

land area) as estimator to conduct the stratified sampling. Otherwise, it must widen the error range. The rational

and reasonable stratified sampling estimator should be based on the annual arable land changing figures of each

county. Even though the arable land changing figures of each county is counted as the statistical data reported,

it is still rational and reasonable to use the datum as the base of stratum to group the ranges or interval of

changing rate for county level. Therefore, the annual changing datum for arable land at county level has to be

chose to be as sampling estimator.

2.2 Data collection and processing

Due to the failure tocollect the arable land changing figures in county for years in row, the work programhas to base on the 1993 arable land changing figures in county nationwide in carrying out stratum sampling.

Before stratum sampling, the 1993 arable land changing figures in county level nationwide have been treated as

follows:

1) The 75 county-level units in Tibet Autonomous Region are exclusive for there are no datum available from

Tibet;

2) Some arable land-less municipal districts such as Beijings Eastern District and Western District and so on

are not included;

3)Delete 9 units that have absolute volume of above 100,000(1/15)haeach, of which 8 units have more than

100,000 (1/15)ha encroached up each. These 9 units have been arranged as separate sampling points, because if

these units were arranged for unified sampling in population, it would affect the accuracy of the sampling and thenumber of the sampling.

2. 3 Understanding of the population

After the processing of the datum, it was determined that a total of 2,550 units would take part in stratum

sampling. A straight square drawing with interval of 100 (1/15)hacan indicate the outlines of the population. (see

Fig.1)

Then the nationwide arable land changing frequency (indicated by letter f , according to interval of 100

(1/15)ha) in counties and its accumulation value could be calculated and listed (the list for f value omitted).

2.4Determining the number of stratum

As the annual arable land changing figures nationwide in counties involve quite a few random factors, for

stratum dividing, the less the number of stratum is, the more conformable to the characteristics of varied datum

very much between different years. However, in order to minimize the sampling rate, after passing the tests of 6

stratums, 8 stratums, 10 stratums and 12 stratums, 6 stratums were finally determined.


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Unit numbers

360

180

changing

Fig.1 Distribution of units with interval of 100 (1/15)ha for arable land changing nationwide.

2.5Determining the stratum interval based on accumulation value of squire root (f)

The nationwide arable land changing frequency (indicated by letter f , according to interval of 100

(1/15)ha) has been listed. The accumulating value of squire root (f) could be calculated out using the f list.

The national accumulation value in counties is 653.4 and counting on 6 stratums, the interval of

accumulation value of each stratum should be 653.4/6 = 108.9; take 108.9 as stratum interval, the border of 6

stratum interval will be defined as below.

stratum 1: ( - , - 11800]

stratum 2: (- 11800, - 3800]

stratum 3: (- 3800, - 900]

stratum 4: (- 900, + 300]

stratum 5: (+ 300, + 3600]stratum 6: (+ 3600, + )

When the 6 stratums interval borders has been determined, the numbers of units in every stratum could

be selected easily from the population. For this project, the sampling unit numbers in each of the 6 stratums

separately are listed below (see right column No.1):

stratum 1: ( - , - 11800] 115

stratum 2: (- 11800, - 3800] 223

stratum 3: (- 3800, - 900] 454

stratum 4: (- 900, + 300] 1187

stratum 5: (+ 300, + 3600] 419

stratum 6: (+ 3600, + ) 143

sum 2541*

+114100

+400

+300

+200

+100

0-100

-200

-300

-400

-191280


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*The 9 units were extracted from the population of 2550 units because the changing figures were very big. These

9 units should be investigated by inventory.

2.6 Determining of the accuracy and reliability

At the stage of the project plan, the accuracy and reliability are set at higher level with accuracy being 90%

and reliability 95%.Because:

The average value y of the population of 2550 which we have understood is - 1805.82 (1/15)ha

The total value of the 9 separate counties under sampling is 848233 (1/15)ha

After excluding the 9 counties, the population remains at 2541 sampling units.

So:

The sampling variance of the population V can be calculated on this base,:

V= square [(r*Y)/t]=square [0.1*2550*1805.82/2]=5.301*10E10

Where: r, error limitation;

Y, total variation of population in 1993 yearly (compared with last year);

t, the value of being equivalence to reliability of 95%;10E10 are equal to 10,000,000,000.

Due to the personal interference to the statistical datum, the actual arable land loss in 1993 was much

bigger than the statistical datum reported. Therefore, the datum only served as pre-assuming error limit which

should be meet users need.

2.7 The calculation of the sample size and its allocation

Based on the formula of optimum allocation method of sampling size in the stratified sampling technique,

the calculation formula for sampling size is as follows:

n0=square [ Nh*Sh]/V= 471 (sampling nuits)

Where: n0, primary sampling size;

Nh, the figures of sampling units in stratum No. h ;

Sh, the square roots of the real variance of stratum No. h;

V, the figures of pre-assuming variance.

Because:

fpc=471/2541=18.5%>5%,

So:

fpc(finite population correction) is needed and the final sample size is as follows:

n=n0/[1+ ]=238(sampling size)

It is concluded that of the population of 2550 county level units (excluding 75 units in Tibet and some

arable land-less cities and districts), the number of the counties under sampling is 238+9=247. It also means thesampling size is 247 basic units in which the 238 units should be allocated into every stratum of 6 while 9 units

should be inventoried.

Based on the optimum allocation method, nh= [(Nh * Sh) / Nh*Sh)] * n , the 238 counties can be

allocated into each stratum. The result is as shown in Table 1.

Table 1 The allocation of 238 samples in each stratum

Stratum No. 1 2 3 4 5 6 sum

Nh(Total units )

115 223 454 1187 419 143 2541

nh

(Sampling unit)

97 23 17 16 17 68 238


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2.8 Explanation on the number of the national population samples

The number of county level units in national land sampling statistics is 2843 (including some land-less

units). In this project, 2550 county level units were used. However, in estimating the national figures through

this project, the number of the population used was 2625 (plus Tibets 75 units) for calculating the nationwide

arable land changing.

2.9 Making of stratum map

In order to settle the sampling units, it is necessary to make national stratum map for county levelunits.

The map was made using the national countys administrative map of 1:4Million as well as geographic

information system. Each stratum is made with a specific color to represent, so that sampling unit can be

randomly sampled in stratum.

2.10 Determining the sampling units

In selecting the sampling units in each stratum, the principle of random sampling should be followed.

Considering the work stability, evenness of sampling units and cost budget, 8 surveying sites were set up in the

nation.

3. Experiment

The experiment was be conducted by agricultural remote sensing technology institutes in Chendu, Nanjing,

Harbin, Lanzhou, Nanchang and Taiyuan as well as the Remote Sensing Institute of former State Land

Administration. The American landsat TM images of 1:100,000 were interpreted by visual method. and following

the investigated sites chose with the above-mentioned stratum sampling program, the countys arable land

changing figures were investigated in 1994. The sampling unites of 202 counties (cities) under remote sensing

investigation are chosen from Sichuan, Jiangsu, Heilongjiang, Guangdong, Gansu, Jiangxi, Shanxi Provinces and

Xinjiang Autonomous Region. The total land area and total arable land area under remote sampling investigation

cover 370,000 square kilometers and 190 million (1/15)ha (accounting for 10.3% of the total arable land in thecountry) respectively. Plus 78 county level units (mainly distributed in stratum 1 and stratum 6) under other

investigation method, the total units under this investigation amounted to 280 counties.

4. Results & discussions

4.1 Result

Based on 2616 (2625 9) basic units, the average volume of national population, its total volume and its

standard error can be figured out.

Estimation of average for sample: y=(1/N)*( Nh*yh)

Where: y, estimated value of population average;

N, total unites of population;yh, average value of stratum No. h.

V(y)=[1/(N*N)]*[ Nh*(Nh-nh)*Sh*Sh/nh]

Where: V(y), the variance value of population average;

Sh, variance value of stratum h;

nh, real sampling unites in stratum h.

The population total value Y=2612*y

The sample data are managed by database of FOXPRO2.5. The calculation formula for sample average

volume and standard error are worked out through computer programming. The following results obtained are

shown in Table 2.


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Table 2 The calculation result of estimator in different stratum

stratum Nh Real nh Sh Average forsample y

Y(2616)population

StandardError of Y

1 115 97 17941.97

2 223 26 4578.773 454 25 3534.51

4 1187 39 2446.92

5 419 19 3474.61

6 143 74 18679.89

-2812.74 -7358127.80 708410.92

sum 2541 280

The 9 separate counties under investigation by inventory decrease a total of 56457 (1/15)haof arable land

in 1994. Based on this figure, decreasing quantity of arable land in 1994 in the country amounted to 7.415 million(1/15)haand the standard error was 708,000(1/15)ha.

4.2 Discussions

The errors occurred in this project came from two sources. One was the sampling error, which is S1=70.8

(sampling standard error); the other was the error in visual interpretation of satellite imagery (TM) . The visual

interpretation work has been carried out for 3 years in row, thats why the accuracy is high and here the error in

visual interpretation set at only 5% , that is S2 = 741.5* 5% = 371,000 (1/15)ha.

So, the general error (S) is calculated as below according to the law of error delivery:

S = square root (37.1*37.1+70.8*70.8) = 800,000 (1/15)ha

For the accuracy in this project, some discussions according to formula of calculation of reliable interval (Y+S*t)

are below:

4.2.1. If assuming the reliable interval is 90% (t = 1.64),

then, the limiting error is t*S = 1.64*80= 1.31 million (1/15)ha;

So, the accuracy of this investigation is (741.4 - 131) / 741.5 = 82.5%.


then the limiting error is t*s = 1.46*80= 1.17 million (1/15)ha;

So, the accuracy of this investigation is (741.4 - 117) / 741.5 = 84.2%.


then the limiting error is t*S = 1.00*80=800,000(1/15)ha;So, the accuracy of this investigation is (741.4 - 80 ) / 741.5 = 89.0%.

Summarizing the above, it can be accepted that this sampling investigation has reached a accuracy level of

about 85%. It is especially emphasized that thanks to the wide distribution of sampling sites nationwide this time,

the sampling was highly represented and therefore, it is also reasonable to say that the accuracy of this sampling

investigation is about 90%.


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4.3 Analysis

The accuracy of 1994 remote sensing monitoring for the national cultivated land changing was relatively

down compared with the original feasibility plan. The major reason was that the original data were statistical data

that could not reflect the real situation. taking Dongguan City of Guangdong Province for an example, the city

decrease several dozens thousands (1/15)haof arable land annually in recent years, but the decrease information

was not reflected in its statistical datum. Some of the counties in Gansu Province reported their statistical datumwhich were of wide difference compared with the datum from remote sensing investigation and moreover, the

absolute value was same but the symbol was just opposite. Such problems can be only solved gradually with the

deepening understanding of the population in the work and continuing improvement of stratum program. This

conforms to the epistemology rule that is knowledge - practice - knowledge again and the understanding will be

deepened continuously.

5. Conclusion

5.1 The professional technology model combined by stratum sampling mathematical model with remote sensing

technology and area sampling technology, are the most effective method for implementing the monitoring of the

nationwide arable land resources changing. This monitoring system could be put into operational practice becauseIt is economic, fast, accurate and simple.

5.2 The monitoring technology model formed by the method, plus the different stratum sampling mathematical

model, can also monitor other utilization status of the land, such as the planting area of main crops.

5.3 This method is also applicable to macro investigation, but it is not possible mapping for the population.

References

1) William G. Cochran: Sampling Techniques, Third Edition, John Wiley & Sons,USA.(1977)

2) Wigton W., Area frame sampling, documents for training course, USDA. (1979).

3) Gallego,F.J., Delince,J., Area estimation by segment sampling. In Euro-Courses: Remote Sensing applied to

Agricultural Statistics. JRC-ISPRA,ITALY,(1993)

4) Meyor-Roux J., The ten years research and development plan for application of remote Sensing in Agricultural

Statistics. Joint Research Center, EEC. publication No: JRC SP 1.87.39, p.24.

5) 1991 campaign final report, MARS project. JRC, ISPRA, ITALY. (1991)

Documents

The Study of National Monitoring System of Arable Land Changing and Its Model in China