Xin Wang

Daoliang Li

Wenzhu Yang

Investigating image enhancement in pseudo-foreign fiber detection

Keywords: Image enhancement, Histogram Equalization, Wavelet Based Normalization, Homomorphic Filtering, Single Scale Retinex, Multiscale Retinex, Variational Retinex. 1.Introduction

The detection of pseudo-foreign fibers in cotton based on AVI(Automatic Visual Inspection) is crucial to improve the accuracy of statistics and classification of foreign fibers. To meet the requirement of textile factories, a new platform is introduced in which cotton bulks are floating with relative high speed of six meters per second, and the throughput of detected lint could be above 20kg per hour. However, images captured by the new platform are blurred and not clear enough for post processes such as segmentation, feature extraction, target identification and statistics. Because thickness of the moving cotton bulks are not uniform, a part of or the whole object of pseudo-foreign fibers are blocked. Thus image enhancement algorithms should be investigated and implemented. In this paper the characteristics of the images acquired by the new platform are analyzed, and several image enhance algorithms are studied and compared on effectiveness and efficiency, which include Histogram Equalization, Wavelet Based Normalization, Homomorphic Filtering, Single Scale Retinex(SSR), Multiscale Retinex(MSR) and Variational Retinex. Result indicated that the Variational Retinex has a better performance and should be implemented in on-line pseudo-foreign fibers detection.

cottonseed crumbs, straws, grass stems etc. which have the same color, size and shape as the foreign fibers. Because the quantity of pseudo-foreign fibers in cotton is much larger than that of the foreign fibers, in order to improve the accuracy of foreign fibers' detection and classification with AVI (Automatic Visual Inspection), pseudo-foreign fibers should be distinguished from the real foreign fibers clearly and efficiently. Image enhancement is an important preprocessing stage in machine vision. The aim of image enhancement is to remove the noise while retaining significant features of the image [START_REF] Tasmaz | Image enhancement via space-adaptive lifting scheme exploiting subband dependency[END_REF]. In the past few decades, many image enhancement methods such as histogram manipulation techniques, wavelet based image enhancement and PDE(Partial Differential Equations) based image enhancement have been active and open to researches. And there is no single image enhancement method applicable to all kind of images [START_REF] Tasmaz | Image enhancement via space-adaptive lifting scheme exploiting subband dependency[END_REF].Vicent Caselles et al. invented a novel approach for shape preserving contrast enhancement by means of a local histogram equalization algorithm which preserves the level-sets of the image [START_REF] Sengur | Color texture image segmentation based on neutrosophic set and wavelet transformation[END_REF]. Wang, X.; Liu, S.; Zhou, X. proposes a new algorithm called wavelet domain Inclusive-OR denoising algorithm (WDIDA), which distinguishes the wavelet coefficients belonging to image or noise by considering their phases and modulus maxima simultaneously and in order to enhance the edges of the image but not magnify noise, a contrast nonlinear enhancing algorithm is presented according to human visual properties [START_REF] Wang | New algorithm for infrared small target image enhancement based on wavelet transform and human visual properties[END_REF]. Farrahi Moghaddam, R.; Cheriet, M. propose a novel PDE-based method for the restoration of the degradations in single-sided document images [START_REF] Farrahi Moghaddam | RSLDI: Restoration of single-sided low-quality document images[END_REF]. Previous image enhancement methods work well in the foreign fiber detection platform of low throughput where the cotton layers are thinner and the image of foreign fibers are clearer and more prominent. Yang, W.Z., et al. proposed a piecewise transform model that splits the distribution range of image pixels into two or more pieces, and performs transformation to each piece respectively to enhance the region of interesting [START_REF] Yang | A new approach for image processing in foreign fiber detection[END_REF]. But, in order to improve the detection efficiency, a new platform is constructed in which the speed of cotton flow is above 6 meters per second, and throughput of cotton is more than 20 kg per hour, so the images taken by this new platform will have a background like clouds in the sky and with pseudo-foreign fibers, uneven color spots and shadows mixed into them. Thus the tasks of identification are more complicated, and the probability of misclassification will be greater if we use the previous image enhancement algorithm. The images acquired from the AVI system which contain pseudo-foreign fibers are usually not very clear because the clouds on of cotton layers. So the pseudoforeign fiber objects are imaged under uneven lighting conditions and should be enhanced before other processes such as segmentation etc.. Many methods are developed for the enhancement of images which have cloud-like background, dim and non-uniform illumination, some of them are very successful in foreign matters detection in wool or cotton, power line detection in urban area, and face recognition under uneven lighting conditions. For example, LU Xiang-Ju, et al. posed a novel pseudo-color approach which can effectively mark all different particulars while it has the ability to divide foreign pieces with indiscernible brightness in fiber masses [START_REF] Lu | A New Pseudo-color Transform for Fibre Masses Inspection of Industrial Images[END_REF]. Jing Sun, et al. put forward a new method to detect and remove shadow from solo images of natural scenes [START_REF] Sun | Shadow Detection and Removal from Solo Natural Image Based on Retinex TheoryIntelligent Robotics and Applications[END_REF]. Tang liang, et al. presented a fuzzy retinex to enhance the poor visibility of features in shadowed regions created by buildings in urban aerial images which prevents recognition of objects [START_REF] Liang | Removing Shadows from Urban Aerial Images Based on Fuzzy Retinex[END_REF]. Although the above techniques present good results, several disadvantages of the reviewed methods still exist. For example, the contradiction between algorithm speed and precision degree exist almost in every method. In order to find a better algorithm (relative) to use for the enhancement of images acquired by the new platform in pseudo-foreign fiber detection, several image enhancement methods are studied on principle and effectiveness, and experiment result are showed and analyzed. In summary, the novelties of this paper are the following:  A new pseudo-foreign fiber detection platform is introduced.  Several illumination improve algorithms are investigated and applied to the images produced by the new platform.  Experiment results are compared and analyzed, and best algorithm is chosen for further studies. The rest of the paper is organized as follows. Section 2 briefly introduce the new platform, and images samples collected by it. In Section 3, the proposed method is described in details. Section 4 describes the result and presents experimental evidence, while concluding remarks are drawn in Section 5.

Image acquisition and samples

Image acquisition

The images used in trial are taken from the foreign fiber detection platform constructed by the Agricultural Intelligence Research Laboratory of China Agricultural University and China Cotton Machinery & Equipment Co., Ltd. The schematic set-up of the computer vision system is presented in Fig. 1. The cotton processed by the Lint Opening Machine is passing through the Gas transmission pipeline then the computer triggers the flasher when an image is being grabbed by the Black-White CCD lighting by Ultraviolet lamps and the Three Color CCD lighting by Fluorescent lamps respectively. The image then will be processed by the algorithms installed in the computer, and both real foreign fibers and pseudo-foreign fibers would be detected. The algorithms should discern pseudo-foreign fibers from real foreign fibers, and the presence of real foreign fibers will trigger the subsequent cleaning processes. Cotton, foreign fiber, and pseudo-foreign fiber samples are provided by the China Cotton Machinery & Equipment Co., Ltd. which are picked out by human workers, sorted and classified by engineers.

Lint

Image samples

A typical image that contains pseudo-foreign fibers is presented in figure2 as an example. The size of the image is 4096*60 . Because the width of the image, the presentation in Fig. 2 is not very clear and we cannot discern the pseudo-foreign fiber in it, so a fraction of the image is cut out and presented as Fig. 3. In this image a typical pseudo-foreign fiber maybe a grass leave is buried into the cotton layers and compared to the whole background tiny and dim.

Image enhancement methods

By analyzing the images produced by the new platform, several features of them can be concluded:  The lighting of the image is not even because the bulks of cotton have different thickness and they absorb and reflect light differently;  The amount of cotton is not even in every image which make a complex background;  Most of the pseudo-foreign fibers are buried into the cotton, so the image can only capture a part of them, and the lighting of them is not even too;  Most of the objectives are small compared to the whole background, and have a low contrast. In order to detect foreign matters exist in cotton by AVI, and discriminate pseudo-foreign fibers from real foreign fibers, image enhance method should be implemented before other processes such as segmentation and pattern recognition. Thus several algorithms used in face recognition under uneven lighting conditions and thin line detection in cloudy weathers are studied and compared, which include Histogram equalization, Wavelet based normalization, Homomorphic filtering, SSR(Single scale Retinex), MSR(Multi-scale Retinex) and Variational Retinex.

Histogram equalization

Histograms are the basis for numerous spatial domain processing techniques. [10] Histogram equalization is a method that can usually increase the global contrast of many images, especially when the usable data of the image is represented by close contrast values. Through this adjustment, the intensities can be better distributed on the histogram. This allows for areas of lower local contrast to gain a higher contrast. HE is simple and effective in enhancing the low contrast image only if (a) it contains single object or (b) no apparent contrast change between object and background [START_REF] Cheng | A simple and effective histogram equalization approach to image enhancement[END_REF].

Suppose the gray probability density function) of image ( , )

f x y is () f pf ,if
the gray scale on point (x,y) is f in original image ( , ) f x y , then we do some kind of transform to it as ( , )

g x y , ( , ) 0 ( , ) [ ( ,
)] ( )

f x y f g x y T f x y p u du  
.So g is a probability distribution function and have attributes as follows:  The transform from g to f is single-valued, monotonic, and increasing;  01 g  ;

 g is a uniform random variable that distributed from 0 to 1.

As for digital image the histogram of it with intensity levels in the range [0,L-1] is a discrete function

() kk h r n  ,
1 0 [ ] ( ) ( ) k k k k k k l s T r p r s p r       
. Gray scales which have more pixels will be increased in differential with the previous scale after histogram equalization transform, and in general objective and background possess more pixels, hence the transform actually enhanced the contrast between the objective and the background. At the same time gray scales have little pixels will be decreased in differential with the previous scale after histogram transform, usually the number of pixels in the transition between border and background are small and they are incorporated either in border or in background, therefore the border will be more sharp after transform, so the contrast of image is enhanced.

Here we applied the transformation to all intensity levels, so the resulting intensities have a uniform PDF (probability density function). And all pixels in an image are ordered from the most negative to the most positive (from the one with the smallest intensity value to the one with the largest intensity value).There are other histogram equalization algorithms with different PDF and shape-preserving local histogram equalization, and we shall discuss them in other papers.

Wavelet based normalization

A wavelet is a wave-like oscillation with amplitude that starts out at zero, increases, and then decreases back to zero. It can typically be visualized as a "brief oscillation" like one might see recorded by a seismograph or heart monitor. Generally, wavelets are purposefully crafted to have specific properties that make them useful for signal processing. The main advantage of wavelet analysis is that it can provide abilities of local analysis and refinement in both spatial and frequency domain. And as for high frequency signals it adopt a gradually meticulous steps in frequency or spatial domain, so wavelet can focus on and be used to analyze any detail of objects in a signal or image. The formula of Continuous wavelet transforms is as follows.

( , ) ( ) ( , ) c s p f t s p dt

     (1)
The results of Continuous wavelet transforms are many wavelet coefficients which are called scale factor s and translation function p respectively. If every possible scales or translation coefficients are calculated, the work will be too hard to accomplish, actually only scale factors and translation coefficients that are times of 2 j are chosen, thus the data for analysis would be decreased The algorithm proposed first transform the image into the logarithm and then into the frequency domain. Here, the high frequency components are emphasized and the low-frequency components are reduced. As a final step the image is transformed back into the spatial domain by applying the inverse Fourier transform and taking the exponential of the result [12].

In practice ideal low pass and high pass filter used in homomorphic filtering which suitable for all kinds of image are hard to find, so the homomorphic filter build on the bases of Fourier transform sometime can't achieve good results .

Single Scale Retinex and Multi Scale Retinex

SSR and MSR are based on the theory of Retinex proposed by Land in the year 1963 which is an image enhancement and illumination compensation model of the lightness and color perception of human vision [START_REF] Wang | New algorithm for infrared small target image enhancement based on wavelet transform and human visual properties[END_REF]. "Retinex" is a compound word came from Retina and Cortex. It is believed that our vision system is subjective when it comes to color perception. Our vision ensures that the perceived color of objects remains relatively constant under varying illumination conditions [START_REF] Almoussa | Variational Retinex and Shadow Removal[END_REF]. Because the principal of Retinex excludes the influences of variant lighting, a series of algorithm is developed based on this theory to detect objectives under uneven lighting or Remote Sensing images in cloudy day or other light lacking conditions. The formula of Retinex is showed in equation( 3).

( , ) ( , ) ( , ) S x y R x y L x y 

The digital image S can be separated into two images: the reflectance R and the illumination L. Then the problem is that L should be estimated from S, and R is the enhanced image needed. There are many ways to calculate L from S, SSR and MSR are two of them and the detail of other algorithms would be discussed in other papers. SSR and MSR are based on the principal of center/surround and the main idea of which is that the illumination component tends to change more smoothly than the surface reflectance, so this algorithm involves computing an average weighted by the distance from the point in question, and subtracting the logarithm of this average logarithm of the lightness [START_REF]INface: A Toolbox for Illumination Invariant Face Recognition Toolbox description[END_REF]. The formula of MSR is showed as below:

( , ) log[ ( , )] log log[ ( , )] log[ ( , ) ( , )] ( , ) S x y R x y S x y S x y G x y L x y     (4)
1 ( , ) {log[ ( , )] log[ ( , ) ( , )]} N i i i r x y S x y S x y G x y w      (7)
N represents the number of scales, normally N is equal to 3, is the weighting coefficient. Here is 1/3. The parameter c in equation ( 3) is denoted as the scale of the algorithm, and experiments indicate that the best multi scales in MSR are 15, 80 and 200 respectively.

Variational Retinex

Variational Retinex is shown to be able to unify previous methods, leading to a new illumination estimation algorithm. Although it performs well, the computational complexity of this model is relatively high, [START_REF] Enhancement | [END_REF] and quite a few algorithms are proposed to speed-up the method [START_REF]A fast algorithm for color image enhancement with total variation regularization[END_REF]. In this research calculus specifically Euler-Lagrange differential equation of variations are used, and the formulas is:

2 22 : [ ] ((| |) ( ) | ( ) | ) : , , 0
Minimize F l l l s l s dxdy Subject to l s and l n on

               (8)
The illumination estimation problem can be formulated as a Quadratic Programming (QP) optimization problem. Here represents the image, l denotes the illumination component, s is the original image, represents the edge of the image, a and are non-negative coefficients.

 

  0 ( ) Fl l l s l s l            (9)
To solve equation ( 6), the partial derivative of F on l should be zero, and there are various numerical solutions to calculate l from formula [START_REF] Lu | A New Pseudo-color Transform for Fibre Masses Inspection of Industrial Images[END_REF]. Here the idea of the steepest descent with an auxiliary variable is applied, and the initial value of l can be the image s. Through iterations of a given times we can get the estimation of the illumination image, and then the reflectance image or enhanced image can be calculated just as SSR .

Results and discussion

The effect of six algorithms and their histogram are showed as follows. By testing 40 images the average time cost of each algorithm is showed in Table1.The test images' size are 1000*60 pixels. The algorithms is implement in Matlab R2010a running on a PC with Intel(R) Core(TM) 2 Quad CPU Q6600 @ 2.4GHz 2.39G Hz , memory 3.25GB. If the algorithms is converted to C/C++ programs the speed of execution would be significantly increased, and if a DSP chip is designed and implemented the speed could be even faster than the C/C++ programs. The Homomorphic Filtering algorithm can smooth the edge between cotton bulk and the background, therefore make the foreign matters in the image more prominent. But verges of the foreign matter are blurred which are not conducive to object segmentation. And improvement of it should be investigated in the following works.

The SSR and MSR have the same enhancement effectiveness, but the speed of them are not suitable for online detection. The Variational Retinex's comprehensive performance is better in object enhancement, shadow removal and process speed.

Conclusion

In this paper, a new foreign fiber detection platform is introduced. Images collected by the platform are studied and analyzed. Several methods are investigated for image enhancement in pseudo-foreign fiber detection, their enhance effect and algorithm speed are compared. Results indicate that the Variational Retinex is suitable for on-line pseudo-foreign fiber detection.

Improvement on speed of it should be studied in further researches.

Fig. 1 .

 1 Fig. 1. Schematic set-up of the platform

Fig. 2 .

 2 Fig. 2. Image contains pseudo-foreign fibers

  dramatically. And this kind of calculates are called Discrete wavelet transforms, in general Discrete wavelet transforms are also named dual-scale wavelet. After the process of wavelet coefficients, the signal should be restored to its original form which is called wavelet restoration, wavelet synthesis or inverse wavelet transform. The fundamental principle for wavelet transform used in image enhancement is that the coefficients matrix result from wavelet transform represent different frequency traits of image, by multiply them with different enhance factors different traits are enhanced, then through the inverse wavelet transform some part of the images can be enhanced. Wavelet transfer has been used in many fields of image processing such as image enhancement, restoration, de-noising, compression and so on. All wavelet transforms may be considered forms of time-frequency representation for continuous-time (analog) signals and the method used in this tests equalizes the histogram of the approximation coefficients matrix and emphasizes (by scaling) the detailed wavelet coefficient in the three directions. As a final step it performs an inverse wavelet transform to recover the enhanced image. In general wavelet can enhance the detail information in an image while give rise to a lot of noises, thus reduce the visual effect.

3. 3 Fig. 4 .

 34 Fig. 4. Flow chart of Homomorphic filtering

Fig. 5 .Fig. 7 .Fig. 8 .Fig. 11 .

 57811 Fig. 5. The Original image and its Histogram

  where k r is the k th intensity value and k n is the number of pixels in the image with intensity k

							r . A normalized histogram is
	given by	( ) kk p r n MN / 	k		0,1,...,	L	1  where	() k pr is an estimate
	of the probability of intensity level k r in an image. So the gray scale on
	point(x,y) is

Table 1 .

 1 Algorithms' Average speedExperiment shows that the Histogram Equalization is much faster than other algorithms, but the enhanced image have more noises which came from the magnification of cotton shadows in the global enhance process. The Wavelet Normalization is fast too, although the global frequency transform enhance the noises and shadows just as the Histogram Equalization.

	Algorithm name	Average speed(s)
	Histogram Equalization	0.010004
	Wavelet Normalization	0.049369
	Homomorphic Filtering	0.036915
	Single Scale Retinex	0.634774
	Multi Scale Retinex	1.929681
	Variational Retinex	0.280129

Acknowledgements.The authors thank National Natural Science Foundation of China (30971693) and Ministry of Education of People's Republic of China (NCET-09-0731), for their financial support, and China Cotton Machinery & Equipment Co., Ltd. for providing foreign fiber samples and technical support.


Sijie Wu

Jiangxi Huang

Xingquan Liu

Jinlong Fan

Guannan Ma

Jinqiu Zou

Assimilating MODIS-LAI into crop growth model with EnKF to predict regional crop yield

Keywords: Multi-temporal, forest cover, decision tree Remote sensing, WOFOST, EnKF, Data Assimilation

Regional crop yield prediction is a vital component of national food security assessment. Data assimilation method which combines crop growth model and remotely sensed data has been proven the most potential method in regional crop production estimation. This paper takes Hengshui district as study area, WOFOST as crop model, MODIS-LAI as observation data to test and verify the efficiency of EnKF assimilation method. The results show that the precision of crop yield estimation are obviously improved with EnKF assimilation, in the WOFOST potential level the R 2 improved from 0.10 to 0.38 and RMSE was reduced from 2480 kg/ha to 880kg/ha. Our study indicates that EnKF assimilation method has great potential in regional crop production forecasting.

Introduction

Regional crop yield estimation plays an important role in the agriculture monitoring and food security. Traditional crop yield estimating methods such as investigation statistics, agro-meteorological model forecasting etc. These methods are hard to accurately predict the regional crop production due to costly and time-consuming. The satellite remote-sensing is characterized as fast, macroscopic, comprehensive, objective, dynamic, but restricted to spatio-temporal resolution, most of remotely sensed data only reflect the soil surface and crop canopy radiation information, cannot reveal the inherence mechanism of crop growth and yield formation. Crop growth models based on the crop photosynthesis, transpiration, respiration, nutrition are successfully applicable for yield forecasting in simple point scale. Because the model input parameters are high variable in the region scale, the simulated results are with high uncertainty. As a method that can integrate the crop growth model and remotely sensed data, data assimilation provides an approach to estimate the crop yield with high-accuracy and becomes the most promising method to predict the regional crop yield.

There are two category data assimilation algorithms: global assimilation such as variation algorithm and sequential assimilation like Kalman Filter. Evensen [START_REF] Bach | The use of radiative transfer models for remote sensing data assimilation in crop growth models[END_REF] firstly applied the EnKF data assimilation method in marine area. Vazifedoust [START_REF] Vazifedoust | Assimilation of satellite data into agrohydrological models to improve crop yield forecasts[END_REF] assimilated LAI and evapotranspiration (ET) retrieval from the remote sensing data into the SWAP model to explore whether data assimilation of LAI and ET can be used to forecast total wheat production as an indicator of agricultural drought, the results showed that predictions for in advance 1 month with assimilation at a regional scale were very promising with respect to the statistical data. However, longer-term predictions i.e. in advance 2 months, resulted in a higher bias between the simulated and statistical data. De Wit [START_REF] De Wit | Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts[END_REF] used an EnKF to assimilate coarse resolution satellite microwave sensor derived soil moisture estimates (SWI) for correcting errors in the water balance of WOFOST model, the results showed that assimilation of SWI has clearly improved the relationship with crop yield statistics for winter wheat for the majority of regions where a relationship could be established.

In the study, EnKF was used to assimilate MODIS-LAI and WOFOST model to forecast winter wheat yield and assess the accuracy and applicability of the method. Since the MODIS -LAI data of study area are lower than the actual values, we corrected the MODIS-LAI by combining the field measured LAI data of winter wheat and the MODIS-LAI trend data. Time-series LAI was assimilated through combined corrected MODIS-LAI and WOFOST simulated LAI. The assimilated optimal LAI was used to drive the WOFOST model in each pixel to obtain the regional yield.

Material and experiments

Study area

We chose Hengshui of Hebei province as the study area, since Hengshui is one of dominant winter wheat planting regions (Fig1). Hengshui is located in southeast Hebei and between 115° 10'-116° 34' E longitude and 37° 03'-38° 23' N latitude. Annual mean temperature in Hengshui is 9.0-15.3C and annual rainfall is 356.1-707.7mm. The winter wheat sow in the middle of October, seeding emerge 8-10 days later, grow into over-wintering stage in October, return green in late February, joint in late march, grow into anthesis stage in April, head out and fill in May every year. Winter wheat of Hengshui is usually harvested in early June. Because Hengshui is smaller, the growths of winter wheat of different areas are almost at the same time although growth stage in south is slightly earlier than in north.

Figure1 Study Area(red points represent the filed measured LAI sites)

Crop Model

We used the world food studies (WOFOST) crop model of Wageningen University. WOFOST is a mechanistic crop growth model that describes plant growth with light interception, takes CO 2 assimilation as growth driving processes and takes crop phonological development as growth controlling [START_REF] De Wit | Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts[END_REF].WOFOST model can be used in three different ways: a potential mode that crop growth is purely driven by temperature and solar radiation and no limiting factors are taken into count; a waterlimited mode that is characterized by the aforementioned factors as well as water availability derived from root characteristics, soil physical properties, rainfall and evapotranspiration; a nutrient-limited mode that nutrient availability depends on the supply of nutrients to the plant roots and is estimated from soil characteristics. The potential mode was used in this study.

To drive the WOFOST model, we collected regional meteorological, soil, crop data. Meteorological data include daily maximum and minimum temperature, radiation, rainfall, wind speed, water pressure between January 2007 and December 2008 in the study area were collected. These meteorological data were interpolated by distance inverse weight method. Regional crop data (including Temperature sum from sowing to emergence, temperature sum from emergence to anthesis, temperature sum from anthesis to maturity, sowing date) and soil data(including field moisture capacity, wilting point, saturated water of soil) were also got with the same method. In the study 1:1000000 China Soil Database were also used.

Remotely sensed data

In this study, we chose MODIS 250m 8-day LAI data which were downloaded from the Earth Observation System data (EOS) gateway (http://eos.nasa.gov) as observation data. The original data were converted to Albers Equal-area projections at 1 km resolution and with a size of 136 * 162 pixels by MRT tool. The pixels without the planted winter wheat were excluded by overlaying the land use data extracted from the 30m TM data and MODIS-LAI data in winter wheat growth stage of from October 2007 to June 2008.

Due to the influence of clouds, vapor and aerosol, MODIS-LAI data are contaminated and needed to eliminate the abnormal data. In this study we used the Savitzky-Golay(S-G) filter method to smooth the time series of MODIS-LAI, restored the spatial-temporal continuum LAI data of fertility period of winter wheat.

Assimilating MODIS-LAI into WOFOST

Ensemble Kalman Filter

The Ensemble Kalman Filter is an optional recursive data assimilation method. The basic principle of EnKF is to construct a Monte Carlo ensemble such that the mean of the ensemble is the best estimate, and the ensemble error covariance is the estimate of the forecast covariance [START_REF] Lin | An Ensemble Kalman Filter for severe dust storm data assimilation over China[END_REF]

B t = HA t +vt (1) A f t = MA a t-1 +wt (2) A a t = A f t +Kt(B-HA f t ) ( 3
)
where H is the observation operator that model states to the observations, M is the equation which transform state form time t to time t+1,vt is the noise of observation and wt the error which is produced when state transforms. Kt is the Kalman gain matrix; it is computed with following formulas:

1 ) (    t T f t T f t t R H HP H P K (4)
where f t P is the variance of forecast ensemble, R t is the variance of observation ensemble.

EnKF Assimilation

In our study, LAI is the state variable of assimilation, observation data is corrected MODIS-LAI, H=1(B t =A t +vt), M is defined as the LAI part of WOFOST -1 ] In this study, the EnKF assimilation was conducted for each pixel in the study area independently. WOFOST began running at sowing date. At first day of 2008 (DOY=1), a white Gaussian noise was added to shift the simulated LAI and generate the first ensemble of forecasted MLAI{mLAI 1 ,mLAI 2 ...mLAI N }( mean value is the simulated LAI). If there is observation data at time t, we add a Gaussian perturbationensemble to it and generate an observation ensemble OLAI{OLAI 1 ,OLAI 2 …OLAI N }, then assimilate the forecast ensemble and observation ensemble with EnKF, obtain the optimal estimate ensemble (MOLAI{LAI 1 ,LAI 2 …LAI N }) which will be put into WOFOST model to get the forecast ensemble at time t+1.If there isn't observation LAI, the forecast ensemble is put into the model directly. Repeat the process until crop mature. The average of MOLAI is the best estimate at time t.

Observation ensemble perturbation: after correction, the relations between MODIS-LAI data its size and actual values has been stable, in this research, the error of observation LAI is seted between 10% and 20%. EnKF Ensemble size: In EnKF assimilation, the ensemble size has great influence on the result and efficiency. In this study we test different size [START_REF] Komma | Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting[END_REF]30,50,90) respectively and analyzed the relationship between the ensemble size and the assimilation results. EnKF Assimilation step: The ensemble size has great influence on the result. We set different size [START_REF] De Wit | Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts[END_REF][START_REF] Doraiswamy | A: Crop condition and yield simulations using LANDSAT and MODIS[END_REF][START_REF] Gué | Adjustment procedures of a crop model to the site specific characteristics of soil and crop using remote sensing data assimilation[END_REF][START_REF] Vazifedoust | Assimilation of satellite data into agrohydrological models to improve crop yield forecasts[END_REF] to analyzed the impact and chose the best one to derive the finally result.

Finally, we put the optimal LAI into the WOFOST model and estimated the yield of each pixel. For getting convenient viewing of comparison results, we conducted spatial statistical analysis with the Geographic Information System (GIS) technology and calculated the forecast yield of each county and compared the estimated yield with statistical yield.

MODIS-LAI

Correct

OBS-LAI

Crop data

Results and discussion

Compared with the LAI simulated by WOFOST (WOFOST-LAI), LAI value with the assimilation (EnKF-LAI) are lower and indicates the trend that in over-wintering stage LAI decreased due to the death of leaves. Compared with the observation LAI, EnKF-LAI values are higher before 86th day and lower between 86th and 130th day. The EnKF adjust the time-series LAI trend and values effectively. In the research we found that at single point, when the EnKF ensemble size increases, the difference between the profiles of EnKF-LAI and observation LAI is become larger. For the whole study area, the bigger ensemble size, the better the result is obtained. The confidence coefficient(R 2 ) increased from 0.25 to 0.38 and the root mean square error (RMSE) decreased from 1020 kg/ha to 880 kg/ha as the size increased.

It was found that the bigger the assimilation step size, the greater the fluctuation of LAI profile is; the smaller the assimilation step size is, the more smoother the LAI profile. In terms of the yield forecast for each county, in the potential level, RMSE decreased from 1240kg/ha to 880kg/ha as the assimilation step size decreased.

Figure 4(a) show the estimated yield of WOFOST potential mode. In potential mode, without assimilation most of pixels' estimated yield are higher than 7000 kg/ha, the spatial distribution of the yield has no obvious differences, the reason is probably that in potential level WOFOST assuming that crop growth conditions are the best, in the condition of model parameters are similar, estimated yield are similar and lead to the higher estimated yield.

Conclusion

In this study we use EnKF to assimilate corrected MODIS-LAI into WOFOST model to estimate winter wheat yield and assess the accuracy and applicability of the method. The EnKF assimilation results indicate that, in potential mode, production forecast precision by EnKF data assimilation is significantly higher than without assimilation. Future work will focus on how to integrate the light,temperature and water parameters information retrieval from the multi-source remote sensing data (visible-near infrared, thermal infrared, microwave radar) using EnKF multiparameters algorithm to further improve the estimation accuracy.

  Fig shows LAI profile with S-G Filter.

Fig2

  Fig2(a) LAI profile with SG-Filter(Pixel[25,32])

Figure 4 (Figure 4

 44 Figure 4 Estimated yield with and without assimilation (in potential level)

  .At the current assimilation time t ,It assume that we have

	an ensemble of forecast A f t =(a f	1,t ,a f	2,t ,…,a f	N,t )∈R(n×N) (n: dimension, N:ensemble
	size) and an ensemble of observation B t =(b 1,t ,b 2,t …b N,t ) ∈ R(n×N).
	A a t =(a a the best estimate of time t. The relationship between A f 1,t ,a a 2,t ,…,a a N,t )∈R(n×N) is the optional estimate Ensemble, the mean of A a t is t , B t and A a t is shows in
	following equations:

Acknowledgements

This work is supported by the National Science Foundation of China (NSFC) project (NO.40901161), National High Technology Research and Development Program 863 of China (NO.2008AA10Z217) and Chinese Universities Scientific Fund(Project No. 2011JS142).

.References


Jiansheng Zhang

Jinglai Zhang
email: zhangjinglai@263.net

Shangwu Liu
email: liu_shangwu@sina.com

Jianning Mu

The reputation analysis based on the signal game theory model of the aquatic products of Logistics Company

Keywords: Aquatic product logistics, Signal game theory, Reputation

This article examines the reputation of the aquatic product logistics companies based on the signal model. We use the model to study these companies in separation equilibrium and pooling equilibrium situation and provide a static economic analysis. Suggestions are presented in the end to improve the reputation of these companies.

Introductions

In the operation process of logistics companies, the reputation problem is one of the critical factors that have an impact on their development in China. As with examples of reputation losses in our society, the reputation loss is also prevailing in logistics companies. One of the reasons is information asymmetry between logistics companies and their suppliers. The selection behaviors among those players actually become a strategy of game theory to pursue maximized benefits respectively. For example, logistics companies, who are aware of their own reputation status, tend to get larger benefits by frauds, while suppliers, without intelligence of logistics companies' reputation, are able to get the information to judge the situation only after products are delivered to destination. Thus the signal game method is a brilliant choice and will have a far-reaching influence on the information development.

Economists have long recognized the importance of trust in economic growth, judicial efficiency, government integrity, asymmetric information, and contractual enforcement. It is necessary to solve the asymmetry issue in the labor market in order to thoroughly use the labor market's information.

Analyzing effects of information asymmetry on labor market, which is the study of the reputation problems, has started from Spence since 1973 [START_REF] Spence | Market Signaling[END_REF] . ZhangWeiYing conducted a profound discussion and gave a modeling argument to reputation theory in 1997 [START_REF] Weiying | The game theory and information economics[END_REF] . He also explained currently realistic reputation problems in China through reputation theory in 2001 3 . WengJunYi 4 is an early researcher from this angle since 1996. In 1999, WangXiaoLong [5] explored reputation problems deeply from the view of business ethics . WangXinXin 6 introduced overseas reputation situation but failed to provide breakthroughs in 1998. Yet the reputation problem in his theory was merely a byproduct instead of a well-established systematic research. Other researchers, for instance, LiuBing, LuoYiMei(2000) 7 , ZhangLiZi (1994) 8 and LiuGuangMing (2001)  , LiBinYun(2005) [START_REF] Libinyun | The third logistics, the management of the supply line, reputation and law[END_REF] argued to enhance the power of the law in order to make a path for developing the reputation. Liu BinLian and LuYan(2007) [START_REF] Liubinlian | The reputation in the third logistics and the exploring of its' regulation[J][END_REF] papered the aspects of the logistics companies' reputation loss , and its cause and negative effects.

Wang HaiLan and WangLi (2008) [START_REF] Wanghailan | The reputation problems and the solution research in the process of the development of the logistics [M][END_REF] ,WangNing and LiuXiaoJin(2008) [START_REF] Wangning | The discussing for the logistic companies' reputation in China[END_REF] ,LiuHongYu(2009) [START_REF] Liuxiaojin | The research of the logistic companies' reputation [J][END_REF] and TaoGang(2010) [START_REF] Taogang | The meditation for the system building of China's logistic reputation[END_REF] surveyed the present situation of logistics companies' reputation and reasons and provided resolutions in China. Data from the report of Chinese companies` repution in 2006 shows that the economic cost in the loss of companies' reputation is as high as 600 billion [15] .

To sum up, domestic researches of logistics companies' reputation problem in China emphasize on unraveling the reasons and losses, leaving scopes for using practical model to solve the problem systematically and deeply. Nevertheless there are few articles discussing reputation problems in China from the perspective of information asymmetry.

Materials and Methods

The reputation problem is practical and puzzling in the transportation process for fish, where logistics companies and fish dealers can make an oral deal. However, due to the conflict of interest, fish dealers will design a considerable complicate contract to ensure their own interests, logistics companies will make a deviation to the fish dealers for their own interests. It is very difficult that for fish dealers to select a high reputation logistics companies. The paper tries to deal with the problem using the theory of complete information dynamic game model.

Assumptions

The transaction time sequence between fish dealers and logistics companies is as follows:

(1)Assume the reputation level of logistics companies' isӨ,which may be high(H) or low(L). Let P be the probability of "Ө = H" .

(2)Logistics companies realizes its reputation level and subsequently estimates a rough fresh fish rate as e (e≥0,e∈E).

(3) Fish dealers propose a pay requirement according to e based on their observation of how logistics companies operate.

(4)The fish dealer offers the logistic company a payment rate according to the last shipment.

For the logistics company, the average income is positively correlated to the proportion of fresh fish in transportation, which is a widely accepted fact that makes e a preferred variable.

Model contents

The model describes a signal game with two participants. One is fish dealers from the competitive market. The other one is a logistics company which sends the signal of the fresh fish rate level, e(e≥0,e ∈E). Assuming logistics companies are categorized into high(H) and low(L)according to their reputation level. The H company's prior probability is set for P..

Assume fish dealers pay  to logistics companies. ( , ) c e  is the cost of providing a fresh fish rate of e and ( , )

y e 
is the amount of fresh fish. When a logistics company is selected by a fish dealer, the logistics company's income is .

(

) 2
As a signal game, this model is discussed in two scenarios, namely separation equilibrium and pooling balanced equilibrium in certain conditions.

In separation equilibrium, low reputation companies can't imitate high reputation ones because the cost of high survival rate is not enough to compensate their own costs. Therefore we get:

)) ( , ( ) ( )) ( , ( ) ( * * * * H e L c H W L e L c L W    . ( 3
) See fig. 1

Fig. 1. Separation equilibrium condition

But in pooling equilibrium, low reputation companies can imitate high survival rate and therefore get larger benefits to compensate their own costs.

)] ( , [ ) ( )] ( , [ ) ( * * * * H e L C H W L e L C L W    . ( 4

Pooling equilibrium

The assumption in pooling equilibrium is that both two types of logistics companies choose the

Given fish dealer's given reaction, logistics companies that have a reputation of  will be forced to choose a e by the following condition:

)] , ( ) ( [ max e c e w e   . (8)
The equation( 8)is really simple, logistics companies with reputation of  choose a 6)and the dealers' strategy of () we in(7)combined to the perfect bayesian pooling equilibrium of the game.

Fig. 3. pooling equilibrium

Even for the indifference curves and the amount of products in fig. 3, there are also many other perfect poolings, some of which are chosen the different fresh fish rate and of other which are in the different disequilibrium signals.

Obviously, in fig. 3, as long as the fresh fish rate is between * e and ' e , it is easy to constitute more infinite pooling equilibriums.

It can be inferred that we can also get other pooling equilibriums by solely changing the path of information disequilibrium.

                    e e
) 12
Under the disequilibrium condition, the provisions are as follows:

        ) ( 1
) ( 0 ) | ( ~** H e e H e e e H P . ( 13
)
The optimal choice for fish dealers are: If we assume geometric relations in figure 2 instead of in figure. 1, we study the separation equilibrium. At this point, due to the tendency of being imitated, high reputation logistics companies need to take actions to resist it and require higher costs to obtain a higher fresh rate, thus making it difficult for imitation to continue. At last it appears a separation equilibrium. Fish dealers, who observe the costs of the high fresh fish rate, also consider these companies as high reputation ones and then offer them a higher wage rate as a reward.

        ) ( ) , ( ) ( ) , ( ) ( *
Thus, we know that the high reputation logistics companies need to choose ) ( (

) 16
The optimal choice of fish dealers is

Conclusions and Discussion

Based on current achievements in economics and realistic information asymmetry problems, the article discusses the reputation theory between the fish dealers and the logistics companies in aquatic logistics supply chain, and analyzes the reputation relations between them using Spence's models from the perspective of game theory.

Several measures can be taken to establish more stable relations with fish dealers and enhance the development of modern logistics industry, for instance:

(1)Offer a price high enough to high reputation logistics companies in order to make them feel worthwhile to keep their reputation, and to ensure that long-term revenues are not less than short-term income .

(2)For low reputation logistics companies, effective regulations and punishment mechanism should be founded. Once frauds are found in such companies, fish dealers can fire them and discard them afterwards (3) Use the "collectivism" punishment mechanism. If a logistics company's reputation is low, all dealers in one transaction region will participate in this mechanism and ensure that any company in blacklist will never be hired again.

(4) For the companies in the pooling equilibrium situation, namely low reputation ones that imitate high ones, government should on one hand establish stricter laws for prevention and punishment, and on the other hand launch favorable policies to promote the logistics industry. Try to turn the pooling equilibrium into the separation equilibrium, thus eliminating low reputation companies or helping them convert to high reputation companies.

Applications of strict measures to control dynamic information and to monitor the logistics companies' reputation can to some extent lead logistics industry to a better stage. However in face of many different equilibriums, it is crucial to understand why certain equilibrium is eventually chosen and its subsequent development. This article does not explore how to gradually squeeze out low reputation companies from the industry, or change into the high reputation companies, which, hopefully, I will be able to supplement in later researches.

Fig. 2 .

 2 Fig. 2. Pooling equilibrium condition

  fresh fish rate and equal to the low reputation logistics companies' * () eL . In fig.3, the former choice is optimal for both types of companies that the low reputation companies' indifference curves through the point * * [ ( ), ( )] e L w L are below the point ( , ) p p ew ' s indifference curves and the high ones' indifference curves through the point ( , ) p p ew are above the payment function of ( , ) w y L e  . The outcome is that, according to the given indifference curves in fig. 3, the amount of products and the value in graph, the companies' strategy of e * (L) e * (H)

  fish dealers' strategy is :

Fig. 4 .

 4 Fig. 4. Sending signals costs in separation equilibrium

  When it is incomplete information, if the fish dealers observe the signals for e and then get a .Assuming the risk for fish dealers is neutral, then we can get a expectation output of the logistics company as follows:

	posteriori probability of H company for	) ~e H | ( P
		) ~e L , ( )) | ( 1 ( ) , ( ) | ( y e H P e H y e H P  	.	(1)
	Due to competition, fish dealers would take the [4] step.
	W	( e		( ) H P	|	) e	y	(	H	,	) e		[		( 1 H P	|	) e	y	(	, L	)] e
					( , ) c e 												c 	e L  , (	e )		c 	e H  (	,	e )



and the fish dealer's income is

( , )

y e   

. One basic assumption is that if a low reputation logistics company wants to get the same fresh fish rate, it has to cost more compared with a high reputation company. "e"s are

Ackonwledgemants

The authors would like to thank Project supported by the public welfare project of the agriculture industry of the Ministry of Agriculture, (200903009)&Chinese Universities Scientific Fund (2010JS027).


Yanqing Chen

Jianyu Yang
email: ycjyyang@126.com

Chao Zhang

Zhouting Sun

Dongping Ming
email: mingdp@sohu.com

Research on Change Monitoring Method of Cultivated Lands Level Based on Volatile Indicators

Keywords: Cultivated Lands Level, Change Monitoring Method, volatile indicators, Grading Unit, Change analysis

With the second national land survey carried out, land grading method is nearly mature, but change monitoring of cultivated lands level has not yet been proposed an effective method. The paper makes the second national land survey as research basis for change monitoring. According to the differences of indicator monitoring periods, authors introduce the concept of volatile indicators. Change monitoring indicators are divided into volatile and non-volatile indicators. This way updates non-volatile indicators only when monitor can improve the efficiency of monitoring. For the region data is missing, this paper suggests filling the missing data through spatial correlation. In order to unify monitoring units, authors propose a new method-Be postgrading units as monitoring units, convert pre-grading values to post-grading units by area-weighted. Finally, we analyze the monitoring result by calculating the quantity, quality, layout, production levels and other aspects. The analysis will play a role in early warning for the quality of cultivated land.

Introduction

Quality of cultivated land means the sum of natural factors and environmental conditions what constitute the land, which shows the level of production capacity and product quality and the merits of environmental conditions of arable land [START_REF] Liu You Zhao; Ma Xin | Preliminary Study on the Early Warning of Cultivated Land Quality[END_REF] . The level of cultivated lands is one of the critical factors which reflect its quality. Change Yanqing Chen 1, Jianyu Yang 1,*, Chao Zhang 1, Zhouting Sun 1, Dongping Ming2 monitoring of cultivated lands can accumulate the relevant information and improve the content of the land resource management and also achieve the changes from the number of management to the quality and quantity management. Through long-term monitoring, with other relevant knowledge, analyze the monitoring results in all aspects and study the trends in land levels. The analysis can support land management science to decision-making.

In the second national land survey process, it has formed a set of relatively mature methods of Agricultural Land Classification system, but how to monitor the change of cultivated lands has not yet made a systematic approach. This paper combine he knowledge and previous research about grading for dynamic monitoring of Quality of cultivated land, and propose a new set of change monitoring method of cultivated lands level. Finally, we carry out a systematic analysis to achieve the quality of cultivated lands early warning role.

Current Situation of the Research

The methods of cultivated lands level's change monitor commonly use time-series model analysis combined with analysis of influencing factors. By comparing land grading indicators of different periods, reveal the variation of cultivated lands level and characteristics, combined with the variation of the factors and trends, forecasting the direction of the evolution of land level [START_REF] Guoqiang | Research of Dynamic Monitor of Cultivated Lands Quality[END_REF] . Chinese cultivated land soil survey started in 1985, but rapid progress. With development of computer and spatial technology, dynamic monitoring method of cultivated lands quality based on 3s technology has been used widely. This method completes land quality monitor using automatic discovery of computers and technology of overlay analysis with the RS, GIS, GPS and other software tools, and the data basis are remote sensing images, soil images and other data [START_REF] Tian Yan | Research of Dynamic Monitor of Cultivated Lands Quality Based on 3S Technology[END_REF] . The second national land survey has done a lot of works in evaluation of land level, which provide a theoretical basis for monitor and data protection for future monitor in land levels. Peng Ru-yan and Zhang Xiao-pei [START_REF] Pengru-Yan | Designing Dynamic Monitoring System of National Plow Land Quality Based on Graduation[END_REF] make the agricultural land classification rules as a basis, from the principle of the monitoring points' layout, monitoring index system, monitoring data collection, evaluation of monitoring results and spatial information technology, five-pronged approach, initially built national quality of cultivated land dynamic monitoring system which provide basis for China's cultivated land resources conservation and management decision for the future.

Research of Change Monitoring Method of Cultivated Lands Level

The assessment of cultivated lands level is premise of change monitor, "Agricultural Land Classification Regulations" [START_REF]Agricultural Land Classification rules" People's Republic of land and resources industry standards:TD[END_REF] (Later referred to as "Regulations") clearly defines agricultural land classification process, as figure 1 shown, the whole process can be briefly summarized as: Target areas identified ->Grading unit Indicators 3 identified->Grading factors identified->Index calculation->Determine the level->Summary acceptance.

Volatile index and non-volatile index

Level of agricultural land is affected by natural factors and socio-economic factors. Natural factors include climate, topography, soil, water, etc. Socio-economic factors include the socio-economic environment and geographic conditions, etc. In the "Regulations", all these factors are taken into account. All of the above factors will change with time. However, due to the intensity of driving force which makes them change is different, some factors change slowly and some are not. On this point, this paper introduces the concept of volatile index and non-volatile index. Volatile index is the indicators which are significant changes in agricultural land quality when driven forcefully in a short time, such as the status of land use. Non-volatile index refers to which can maintain a relatively stable index over an extended period in natural state [START_REF] Baisong | Indices system and methods of monitoring gradation of agricultural land quality[END_REF] , such as soil type. Different indicators have different change periods, and the same indicators at different index areas, the periods probably are different. Based on the differences of change monitoring scope and extent in land quality levels, monitoring cycle is divided into three categories: Regular monitoring: monitor and evaluate various indicators those cause cultivated land quality level changes, monitor once every three years. Real -time monitoring: monitor and evaluate various changed indicators caused by various types of engineering measures in the "increase, reduce, establish" process, monitor once every one year. Immediate monitoring: monitor and evaluate various changed indicators those caused by unpredictable natural and manmade destruction. It is measured only when the land level monitoring database changed .In practical application, it should set the change cycle based on the difference of driving forces which promote indexes changed in target area. Change monitoring gets the change information by comparing two cultivated grades. It also has a certain periodicity. If this cycle is less than the change cycle of the evaluation, these indicators can be seen as non-volatile. Others are volatile indexes which should be graded by the updated data. This rapid detection method can greatly reduce the workload of data collection and improve the efficiency of grading and monitoring. However, this method can not apply to mutated region, such as level evaluation and monitoring of land reclamation and development region. As figure 2 shown, this article will be divided into two kinds of areas-mutative and gradient regions-to monitor.

Mutated region (

The changes monitoring process based on spatial correlation

For the areas whose evaluations almost are known, it is desirable that combines with the concepts of volatile indicator and non-volatile indicator to grad. But if some areas are not able to acquire such these evaluation indexes or the data are missing, this method will be out of function.. For such problems, this paper proposes a level estimation method based on spatial correlation. We can use neighboring cells whose levels are known to estimate the levels value of the unknown elements through the spatial interpolation. In practice, on the basis of the characteristics of the data, we can select different interpolation methods for interpolating. It is worth to note that the level of mutations in the region can't be estimated by this method because the level does not have the continuity with the adjacent areas' level. For change monitoring, there are two different estimation methods: The first, according to the two period levels' difference of adjacent units, we can directly estimate the level of change in the value of the unit. This method does not involve any period level of the unit, so this method is applicable to this condition that two period levels of adjacent units are known in the two period levels. The second, according to the second period level of adjacent units, we can estimate the unknown value of the after-changed unit, and make use of the difference between the estimated level value and the before-changed level value to acquire the value of grade changes. This method is suitable for the first period level of the estimated unit which is known. Due to the change monitoring is carried out on the basis of the second national land survey, the first period level of all units is known. In theory, the two methods are reasonable and practical. But in order to take full advantage of the known conditions to reduce the estimation error, this paper introduces the second method to estimate the unknown value of the second period level of the unit. When only a few indicators are missing, interpolation for index data is also feasible. We can specify a threshold value "n". When the number of missing data is greater than the index n, it is considered to directly operate on the level. On the contrary, it is considered to interpolate for these missing indicators. General process is shown as Figure 3.

The method research for the unity of change monitoring units

According to "Regulations", the division of agricultural land grading unit includes four ways: Overlay method: Land use map, topographic maps and soil maps in the same scale are superimposed, through this way it can be formed a new layer, the new layer can be seen as grading units layer; Land block method: Directly using polygons of the land use map as the grading units; Grid method: grids with certain sizes are used as grading units; Polygon method: All grading factor maps are superimposed. In these four methods, the second one-land block method-is common to use, but there is a problem for change monitoring: Assuming that we monitor the land level change between 2007 and 2010, grading units in 2007 are based on 2007 land use map, and grading units in 2010 are based on the 2010's, but the two land use maps can't be exactly the same, there is the case that the two period grading units do not correspond. So these units cannot be directly used in change monitoring. We have to make the two grading results correspond to each other in grading units. To facilitate the description, the grading result of an earlier phase is called pre-grading result. The later result of the grading is called post-grading result. No matter what technology is used to convert the arable land grading results, the law of natural grad ,using grad and economy grad after conversion must reflect the actual of regional resources and must match the distribution of regional characteristics about the light, temperature, soil and water [START_REF] Kong Xiangbin | Inspiration of foreign experience on arable land protection of Beijing[END_REF][START_REF] Qianwu | Field quality limit to grain potential productivity based on farmland classification[END_REF][START_REF] Qiang | Verification of productive capacity of farmland graduation[END_REF][START_REF] Yuling | Empirical analysis in the quality of the farmland in front and back land consolidation--the case of basic farmland demonstrate area land consolidation project in Tianhu town song county[END_REF] . The approach is as follows: post-grading units are seen as basic units of change monitoring. Making the area as converted weight, pre-grading result is transformed into post-grading unit. As figure 3 shown, shaded area is one of post-grading units which steps over the four pre-grading units. The numbers in the figure are the levels

Research on Change Monitoring Method of Cultivated Lands Level Based on Volatile

Indicators 7 of pre-grading units. In addition to cultivated land, other land types' levels assign to "0".We calculate pre-grading level of the shaded unit by the ratio between area of intersection and the pre-grading level. This method can get two different time (Pregrading and post-grading) levels at the same grading unit. The two values can be seen as two property value fields of change monitoring unit data. There are three levels in grading: natural grad, using grad and economy grad. In attribute table, we name them as "The value of pre-natural grad", "The value of post-natural grad", "The value of pre-using grad", "The value of post-using grad", "The value of pre-using grad" and "The value of post-economy grad", then post-grading value subtract pre-grading value, we can get the changed grading value. Add three fields to attribute table: "Changed natural level", "Changed using level" and "Changed economy level". When change in level is equal to 0, it means no change in grading unit level; when change in level is greater than 0, the land levels increased, indicating quality of arable land improved; when change in level is less than 0, the land levels decreased, indicating quality of arable land declined. About the judgment for new arable land, noncultivated land's polygon of pre-data and cultivated land's polygon of post-data seek common ground by ARCGIS software. The common ground is the new arable land. About the judgment for disappeared arable land, non-cultivated land's polygon of post-data and cultivated land's polygon of pre-data seek common ground by ARCGIS software. By calculating the new land and the disappearance of arable land area, we monitor changes in the amount of cultivated land. By monitoring changes in levels, we can monitor changes in land quality, so as to achieve double control-the quantity and quality of arable land.

The Analysis of Monitoring Results

Analysis for monitoring result is also very important. According to "Pilot monitoring land quality grade Technical Manual" [START_REF]Level of Quality of cultivated land monitoring pilot technical manual[END_REF] , as shown in figure 5, the main Yanqing Chen 1, Jianyu Yang 1,*, Chao Zhang 1, Zhouting Sun 1, Dongping Ming2 change analysis can be divided into four parts: change analysis on arable land level, mutation analysis on arable land quantity, changes in the layout analysis and analysis of changes in the level of production capacity. The changes of number and grade have been analyzed in Section 3.3, this section will mainly analyze changes in the layout and production capacity.

Fig. 5. Process of Change analysis of land level

Spatial layout can be measured by plaque fragmentation index of regional cultivated land "FN". "FN" can be calculated by "Np" and "Nc", "Np" is the total plaques of regional cultivated land and "Nc" is the ratio of cultivated land's total area and the smallest plaque area of the region. Specific calculation is as shown formula 1:

(1)

The change of spatial layout can be reflected in the intensity "SFN" and the margin "EFN" of regional cultivated land's plaque fragmentation. "SFN" can be calculated by plaque fragmentation index of regional cultivated area as formula 2 shown:

(2)

In formula 2, "FN n " means regional cultivated land plaque fragmentation index of year n, "FN n-x " is regional cultivated land plaque fragmentation index of year n-x. "x" is the discrepancy between two monitoring periods. "EFN" can be calculated by formula 3:

(3) SFN> 1, which indicates the fragmentation degree of arable land is increased; SFN=1, which means there is no change in fragmentation; SFN<1, indicating the degree of fragmentation of arable land is decreased. The elevation of fragmentation, is not conducive to field management and mechanization, and indicates that the adjacent Indicators 9 land probably has been destructed. From a regional point of view, the overall quality of arable land is decreased.

Like the spatial layout, change analysis of arable land capacity levels is also based on regions. The change intensity of the arable land capacity is calculated by formula 4:

(4)

In formula 4, "SWi" is the change intensity of arable land capacity of region i. "Wi,n" is the arable land capacity level of region i, year n. "x" is the monitoring period.

The Magnitude of arable land capacity change is calculated by formula 5:

(5)

Obviously, when SWi>1 or EWi>0, it means the capacity was improved; when SWi=1 or EWi>0, it means the capacity has no change; and when SWi<1 or EWi<0, it means that the capacity was decreased. Zhang Qingpu et al [START_REF] Qingpu | National agricultural land grading distribution law based on conversion from provincial level to national level in Chongqing[J][END_REF] in the study "National agricultural land grading distribution law based on conversion from provincial level to national level in Chongqing" proposed that natural level indices, utilization index and economic index of Chongqing municipal agricultural land have linear correlation with the actual standard grain yield. That means, the capacity and quality of arable land has a direct relationship, so we can forecast trends in land quality through the change of capacity.

Conclusion

(1) Based on monitoring period, the concept of volatility index and Non-volatile index can be pointed out, which can greatly reduce the workload of data collection and improve the efficiency of grading and monitoring.

(2)This paper proposes to interpolate evaluation or rating value in order to obtain the unknown data by the spatial correlation of the data, and chooses different methods to operate on the basic of the characteristics of the data.

(3) It is the prerequisite for change monitoring that integrates the two periods' grading units. Pre-grading result is transformed into post-grading unit with the area for weight, and regards post-grading unit as basic unit of change monitoring. Through this method, the prerequisite can be achieved. Theoretically, both ensuring the uniform of two periods' grading units, and avoiding the situation that monitoring units and the latest land use map do not correspond.

(4) Analysis of monitoring results is very important for quality of cultivated land. Through analyze the quantity, quality, layout, production capacity and other changes, analysis of monitoring results can be used to forecast trends in cultivated land quality, which play a role of early warning for land quality.

Fig. 3 .

 3 Fig. 3. Change monitoring process based on spatial correlation

Fig. 4 .

 4 Fig. 4. Conversion figure of pre-grading result

  Differences between mutations and gradual region in gradingResearch on Change Monitoring Method of Cultivated Lands Level Based on Volatile Indicators 5

		Gradient region
	Land reclamation	(County land)
	and Development)
		Determine monitoring
		indicators According to the
		monitoring period
	All monitoring indicators data update	Volatility Over period	Non-volatile Not exceed period
		index	indicator
	Grading based on	Update data	Using the original data
	grading procedures	Grading based on
		grading procedures
	The regional level Update	The County regional level Update
	Change Monitor of
	Cultivated Lands Level
	Fig. 2.

Yanqing Chen 1, Jianyu Yang 1,*, Chao Zhang 1, Zhouting Sun 1, Dongping Ming2

Funded project: The development of the information management system for Farmland grading monitoring(201011006-5) ; Ministry of Land and Resources Industry research special funds for public welfare projects: Study on the fast recognition on Farmland grading monitoring information and data integration(201011006-4). This work was also supported by the National Natural Science Foundation of China (41001259)


Hengbin Li

Jinxing Wang
email: jingxingw@163.com

Wenzhu Yang

Shuangxi Liu
email: shuangxl@163.com

Zhenbo Li

Daoliang Li
email: daoliangli@163.com

Feature Selection for Cotton Foreign Fiber Objects Based on PSO Algorithm

Keywords: Cotton, Foreign fiber, Feature selection , Particle swarm optimization

Due to large amount of calculation and slow speed of the feature selection for cotton fiber, a fast feature selection algorithm based on PSO was developed. It is searched by particle swarm optimization algorithm. Though search features by using PSO, it is reduced the number of classifier training and reduced the computational complexity. Experimental results indicate that, in the case of no loss of the classification performances, the method accelerates feature selection.

Introduction

The foreign fibers in cotton is non-cotton fiber and non-nature color cotton fiber, such as chemical fiber, hair, hemp rope and so on, which are missing with the cotton and have serious influence on the quality of cotton and its product. Foreign fiber has serious impact on textile products even the quantity is very small. Once the fiber is mixed into cotton fiber, it will not only affect the textile spinning capacity, but also form color spots in dyed fabric which may seriously affect the appearance of the fabric and make a great damage to the cotton textile industry. In China, a number of cotton processing enterprises mainly rely on large numbers of workers employed to pick the foreign fiber. It takes a great deal of manpower and material resources. It's very necessary for improving efficiency and reducing costs to online monitoring technology in cotton foreign fiber based on machine vision.

Feature extraction of cotton foreign fiber can obtain the initial characteristics set, which can describe the feature. By the premise of he classification accuracy is not reducing, select the optimal feature subset with powerful ability of classification from the initial feature set. It can minimize complexity of the classifier design and increase classification speed, which is the premise and guarantee to real-time classification.

Particle swarm optimization (abbreviation: PSO) algorithm is a new global optimization algorithm. It searches optimal solution by the group of each particle kept moving and the motion of each particle is determined by current local optimal solution and global optimal solution. PSO as a heuristic search algorithm is concise, fast convergence, little parameters, etc., have been used in feature selection, classification and many other aspects. It can help the feature optimization and be able to extract optimal feature set of image by using the PSO in characteristics of cotton foreign fiber. The optimal feature subset can shorten the time of classification; even can be improve the classification accuracy in a certain extent.

Cotton Foreign Fiber Objects

It has many different foreign fibers in cotton. It is almost impossible to accurately classify in using certain features for the preprocessing foreign cotton fiber's target. Many different types of foreign fibers in cotton, the fiber pretreated to be the target of the opposite sex alone is almost impossible to use certain features on the accurate classification of foreign fibers of cotton, combined with the color, shape and texture features, you can improve the classification accuracy of the opposite sex fibers. So combing color, shape and texture features can improve the accuracy of foreign fibers' classification.

The characteristic of cotton foreign fibers can be expressed by 75dimensional feature vectors, and the 75 characters include 24 color features, 43 texture features and 8 shape features.

It has 234 sample images, containing foreign fiber with 4000*500 24-bit true color images, in this sample image. These images were collected from real-time images of 4000election.

The sample image was processing segment and the small target was divided to six types, which are plastic, cloth, hemp rope, hair, polypropylene fiber silk and feather. Selected 135 small targets for each type. And obtained 810 samples of 75-dimensional feature data set according feature extracting to small target. The data set of sample characteristics randomly equal divided into 10 parts, each time taking one of them as a test set and the other 9 parts being merged into the training set.

Feature selection based on PSO algorithm 2.1 feature selection

Feature selection can be described like that, selecting a feature subset which can make the categorizer have the best classification performance in the d-dimensional feature set. There are two problems which need to be solved, one is the criteria of selection, namely the evaluation criterion which is used to evaluate the properties of the different feature subsets; the other one is the searching strategy, if d has been defined, there are d D C kinds of possible combinations. If all of the possible combinations are calculated in one pass, the amount of calculation is very large. Thus a effective searching method which can searched out a group of characteristics in the allowed time is needed.

The method of feature selection can be divided into two kinds: one is Filter method, the other is Wrapper method. Filter method is one kind of high efficient method which can eliminate the big kinds noise, it generally choose information entropy, correlation and class separation distance as the evaluation criterions. When the correlation between the categorizer and features is large, the method does not necessarily selectee a good feature subset. Although the sped of Wrapper method is slower than Filter method, the method is closely associated with the corresponding study arithmetic, then avoids the situation of selecting the wrong feature subsets. Nowadays,more and more researchers choose Wrapper method to selecte features.

The binary basic principle of particle swarm optimization algorithm

The basic principle of particle swarm optimization algorithm

PSO algorithm is the earliest be by Eberhart and Kennedy put forward 1995. Like other swarm algorithm, pso searches optimal solution by the continual moving of each individual. The motion direction of each particle is determined by local optimal solution Pbest and global optimal solution Gbest.

Each particle represents a D-dimensional solution space of a point, and its next position is determined by its current position and speed.

The location of i-th particle in the k-th iteration can be expreessed by id 1 2

X ( ) ( ( 1), (2),..., ( ))

i i iD k x x x k  . And the speed i 1 2
( ) ( ( 1), (2),..., ( )) be global optimal solution. So in the iteration of K + 1 times, the entire particle positions and speeds is updated as following formula:

i i iD k v v v k  V is a vector in D-dimensional space. Let i i1 i2 iD ( ) (
1 1 2 2 ( 1) ( ) ( )[ ( ) ( )] ( )[ ( ) ( )] id id id id d id v k wv k c r k pBest k x k c r k gBest k x k       (1)
( 1) ( ) ( 1)

id id id x k x k v k      (2)
In the formula, 1 c , 2 c is learning factor, usually the value of its in the span 0 to 2.

The 1 r and 2 r are random number , its value in the span 0 to 1. The w is weighted coefficients,and the  is constraint factor. d = 1, 2… D.

It can be used to deal with continuous space search problem by PSO, but can not be used to solve optimal combination problem. Therefore, Binary Particle Swarm Optimization (abbreviation: BPSO) is be by Eberhart and Kennedy put forward 1997 to solve discrete problem.

In BPSO, the value of id X is 0 or 1, and id V is the probability of id X taking 1, limited by the transfer function to the range of 0 to 1. Usually Sigmond function is be used, which is

id id (k) sig(v (k)) =1/[1+exp(-v )] (3)
In BPSO, the formula of particle position update as follow:

id id id r (k+1) otherwise 1, sig(v (k+1)) 0, x      (4)
It is an important research direction to improve the speed of Wrapper by heuristic search method in order to overcome the shortcomings of slow speed of feature selection with Wrapper. As a heuristic search method, PSO has the characteristics of simple, few parameters, and fast convergence and so on. Therefore, BPSO was be used to be a search algorithm of feature selection in this article.

Fitness function

Define particle's evaluation standard of evaluation standard performance as follows:

( )

( ) / (1 ( )) F i p i n i    (5)
In the formula, () Fi is fitness value of generating solution of particle i . And () pi is the mean rate of correct classification using this subset.

()
ni is the number of selected features,

1 2 d ( ) ... i i i n i x x x     (6)
 is weight parameter of the number of feature, and  generally is 0.01. The larger the value of F , the better indicate that the selected subset has better performance, obtaining a higher classification accuracy with fewer features.

classification and recognition based on support vector machine

A new optimum design criterion to linear classifier was put forward by Vapnik and others on the basis of year's research of statistical learning theory. The principle of support vector machine (abbreviation: SVM) is based on linearly separable, extended to linearly inseparable problem, even extended to using nonlinear function.

In this system, take optimal feature subset of cotton foreign fiber objects selected by BPSO algorithm as input vector of svm. And use radial basis function as kernel factions, which can obtain relatively superior classification effect. Classification learning of dataset, verify the quality of feature selection by the SVM classifier.

Algorithm steps

The process of feature selection for cotton foreign fiber objects based on PSO algorithm: 1) Initializing the group of particle, obtain SVM classifier s according training samples set x, taking i=0; 2) For each particle ii px  ,calculate the characteristics of that subset of the new sample j x ( ) Y k ;

3) If the new moderate value is greater than moderate local optimal solution, the particle j X local optimal solution update value:

i pBest i x  ;
4) If the new moderate value is greater than global optimal solution, the global optimal solution update value:

i i x  gBest ; 5) Calculating the particle's new speed:

1 1 2 2 ( 1) ( ) ( )[ ( ) ( )] ( )[ ( ) ( )] id id id id d id v k wv k c r k pBest k x k c r k gBest k x k       (7)
6) Calculating the particle's new location:

2 ( 1) ( ) ( 1)c id id id x k x k v k      (8) 7
) Self-increasing i, if I is less than the maximum the number of iterations, then turn step 2), otherwise the algorithm terminates.

Results of the experiment and discussion

The algorithm used java into programming, using K-fold cross-validation in this experiment, in which K is 10. The data set of sample characteristics randomly equal divided into 10 parts, each time taking one of them as a test set and the other 9 parts being merged into the training set. In this study, some parameters were set as: 1 c = 2 c = 2, PN (particle number) = 20, (the maximum iteration number) = 1000.

In this experiment, performance of feature selection was compared between pso algorithm and ant colony algorithm [5] (abbreviation: IACA). Each algorithm run 10 times and takes the average. The iteration number of PSO is 718, and running time is 3120s ; The iteration number of IATA is 1472, and running time is 5152s. The iteration number of PSO is less 52% than IACA and running time is less 40%, which can prove PSO has higher searching efficiency. To show the accuracy of PSO algorithm for improving the effectiveness of classification, compare the sets chose by two algorithms ( show in Table 1) and its classification performance ( show in Table 2). As can be seen from Table 1, optimal feature subset is smaller and more conducive to classifier design after feature selected by PSO. Table 2 shows that PSO can improve the classification accuracy of classifier after comparing. The above results indicate that PSO for feature selection methods to some extent improve the classification accuracy is valid.

conclusions

A fast feature selection for cotton foreign fiber objects based on PSO algorithm is be proposed in this paper. The time of feature selection is greatly reduced in the case of lossless of classification accuracy in this method. But the further research needs to improve the algorithm and its classification accuracy.

Table 1 .

 1 Comparison of the number of original feature and selected feature set selection

		The original	The number of features	The number of features
	Types	number of	after feature select of	after feature select of
		features	IACA	PSO
	Color characters	24	10	11

Table 2 .

 2 Comparison of classification performance of original feature set and selected future set

	Types	Accuracy of	Accuracy of	Accuracy of
		original	selected by IACA	selected by PSO
	Plastic	92	99	99
	Cloth	82	94	95
	Hemp rope	78	88	89
	Hair	82	89	89
	Polypropylene	94	100	100
	fiber silk
	Feather	74	85

Acknowledgements

The authors would like to thank the Project supported by the National Natural Science Foundation of China (Grant No. 30971693) and supported by Program for New Century Excellent Talents in University (NCET-09-0731).


Yanfang Diao

Chengming Zhang

Jiping Liu

Yong Liang

Xuelian Hou

Xiaomin Gong

Optimization Model to Estimate Mount Tai Forest Biomass Based on Remote Sensing 1

Keywords: forecast biomass, remote sensing, support vector machine (SVM)

The development of low-carbon economy and the promotion of energy conservation are becoming a basic consensus of all countries. Therefore, global carbon cycle becomes a widespread concern research topic in scientific community. About 77% of the vegetation carbon stores in forest biomass in terrestrial ecosystems. So forest biomass is the most important parameter in terrestrial ecosystem carbon cycle. In this paper, for estimating the forest biomass of Mount Tai, a support vector machine (SVM) optimization model based on remote sensing is proposed. The meteorological data, terrain data, remote sensing data are taken into account in this model. In comparison the results of SVM with that of regressive analysis method, both the training accuracy and testing accuracy of regressive analysis method are lower than those of SVM, so SVM could obtain higher accuracy.

Introduction

Because forest ecosystem is the most important part of terrestrial ecosystems, its research occupies a pivotal position in the global. Forest biomass is the energy-based of the entire forest ecosystem running and the sources of nutrients, which is the basis of studying biological productivity, net primary productivity (NPP), carbon cycle and global change research. Forest biomass includes tree layer biomass, living ground cover layer (shrub layer, herb layer and moss, lichen layer) biomass and animal and microbial biomass. There are three basic traditional research approaches to estimate biomass: by measuring photosynthesis; by measuring the carbon dioxide produced by respiration change; and by measuring the stock of organism. But above mentioned methods are difficult to estimate the biomass and NPP of large scale of forecast. Spectral information of remote sensing images with good comprehensive and current trend has a correlation with forest biomass [START_REF] Spencer | Integrating eucalypt forest inventory and GIS in western Australia[END_REF]. In our country, in spite of a large number of survey of biomass sample plots are carried out [START_REF] Fang | Estimation biomass carbon of China's Forecast: Supplementary Notes on Report Published in Science(291[END_REF][START_REF] Fang | Biomass and net production of forest vegetation in China[END_REF], an abundant of data are accumulated, and the local site biomass data are gotten, but these data are difficult to estimate the biomass of large scale of forecast. How to combine these biomass plots survey data, topographical and meteorological data with remote sensing data, explore the establishment of remote sensing to study the biomass estimation model, is a worthy of study. In this paper, Mount Tai, for example, combining forest biomass with meteorological data, topographical data and remote sensing data, a support vector machine optimization model based on remote sensing information to estimate forest biomass in Mount tai is established.

Description of Mount Tai

Mount Tai is located in the central of Shandong province, 36°5'N~36°15'N, 117°5'E~117°24'E, and the highest peak is Yuhuangding, which altitude is 1545m. Mount Tai is a warm-humid and semi-humid monsoon climate and the vegetation type is the warm temperate zone deciduous broad-leaved forest. At present, the forest coverage rate reached 81.57%, and the vegetation coverage rate more than 90%. Vegetation types could be roughly classified into 19 types, including 11 types of forest, 2 types of shrub meadow, 3 types of meadow and 3 types of others. In the main forest types, Pinus tabulaeformis, Platycladus orientalis, Robinia pseudoacacia, Quercus variabilis account for about 92.9% [START_REF] Guo | Landscape Pattern of Taishan Mountain and Its Spatio Temporal Variation[END_REF].

The source of data

Air temperature, precipitation, humidity, sunshine, wind direction, wind speed and evaporation etc. are important ecological factors, which are also important factor that affect the forest ecosystem biomass. Meanwhile, the elevation and the types of vegetation are the very important factors. In order to determine above-mentioned factors' influence on the Mount Tai biomass, meteorological data, terrain data, remote sensing data are used to estimate forest biomass.

Meteorological data include temperature data and rainfall data. The temperature data includes annual mean temperature data (TA) and greater than 0 ℃ accumulated temperature data (T0), and the rainfall data is the annual mean rainfall data (PA).

Topographic data is primarily the 1:250 000 digital topographic map data. The contour lines could be obtained by topographic maps from digital data, and the digital elevation model (DEM) and slope data (ASP) could be obtained by ARC / INFO. Remote sensing data uses Landsat-7 image data. Landsat-7 inherits technical parameters of the previous Landsat series, and the most important feature of it is to carry an Enhanced Thematic Mapper Plus ETM+.

ETM+ is in developed on the basis of the Landsat-4, 5 satellite thematic mapper TM. Its band data cover larger scope from visible to infrared region, contain an abundant of information, and each band have different characteristics. At the same time, it is obvious that surface features in different spectral band are differences, so the band data of ETM+ have high practical value [5] . As the Landsat-7 ETM+ sensor has an advantage in spatial resolution, spectral resolution, and performance price ratio and other aspects. Therefore, ETM+ data has become one of the most commonly used remote sensing data at present, which is widely used in agriculture, forest and grassland resources survey, land use and mapping, geology, hydrology and other fields [START_REF] Xu | Study on Data Fusion and Classification of Landsat 7 ETM + Imagery[END_REF].

A series derived data of vegetation indexes are produced from Landsat-7 ETM+ data, for example normalized differential vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI) and so on. The computational methods of above vegetation indexes are as follows. where TM3 and TM4 are red light and near infrared wave bands respectively. A and B equal 0.96916 and 0.084726 respectively.

From a sample survey data of forest resources to calculate biomass data

According to the types of forest of Mount Tai, the biomass estimation models of various organs are selected (such as Pinus tabulaeformis biomass estimation models [START_REF] Fang | Biomass and net production of forest vegetation in China[END_REF][START_REF] Wu | Study on the social characteristics and biomass of the pinus tabulaeformis forest systems in China[END_REF], Quercus variabilis and other broad-leaved biomass estimation models [START_REF] Wu | The biomass and net primary productivity of Pinus kesiya var langbianensis stands in Chang-Nin district, Yunnan[END_REF]). Employing the breast-height diameter of per plant to calculate biomasses of every organs; the various biomasses of every organs are summed to be the biomasses of per plant; and the biomasses of every per plant are summed to be the biomasses of forest.

3 Optimization Model to estimate Mount Tai Forest Biomass Based on Remote Sensing

Principle of Support Vector Machine (SVM)

The support vector machine (SVM) [START_REF] Christianini | An Introduction to Support Vector Machines[END_REF][START_REF] Vapnik | The nature of statistical learning theory[END_REF][START_REF] Vapnik | Statistical learning theory[END_REF] proposed by Vapnik et al. is a novel approach for solving pattern recognition (binary) problems. SVM holds many advantages compared with other approaches. It is a quadratic programming (QP) problem, which assures that its solution is the global optimal solution. And its sparsity assures a better generalization. It implements the structural risk minimization principle, which minimizes the upper bound of the generalization error, instead of the empirical risk minimization principle. Moreover, it has a clear geometric intuition on the classification task. Due to the above merits, SVM has been successfully used in many fields and extended to regression problems [START_REF] Vapnik | The nature of statistical learning theory[END_REF][START_REF] Vapnik | Statistical learning theory[END_REF][START_REF] Mukherjee | Nonlinear prediction of chaotic time series using support vector machines[END_REF][START_REF] Jeng | Support vector interval regression networks for interval regression analysis[END_REF].

SVM based on statistic learning theory is a novel learning machine, which balances learning ability and structural complexity of the model based on small samples, so this model can avoid effectively local optimization. And it has a small parameters set by people in advance which make it have better generalization and popularization ability [START_REF] Zhang | Introduction to statistical learning theory and support vector machines[END_REF][START_REF] Burges | A tutorial on support vector machines for pattern recognition[END_REF]. The basic strategy of SVM is to seek decision rules with popularization ability.

The analysis process of SVM is described as follows.

Firstly, suppose there is a sample aggregate named   1

,

N i i i xy  , m xR  ,   1 i
y  , and then an SVM can be defined by possible mappings :

m n R R   ,
where n R are lower dimensional spaces aggregate and where n R are higher dimensional spaces aggregate. The largest interval classification hyperplane can be shown as

  A x b        (1)
where

  1 N i i i x      
is obtained in the higher dimensional spaces, and where i  and b are Lagrangian multipliers and constant, respectively.

According to the theory of Reproduce Kernel Hilbert Space (RKHS), the kernel function

      , i j i j K x x x x       
which satisfies the condition of Mercer can be obtained in lower dimensional spaces.

Then the problem of seeking the optimal classification hyperplane using Lagrangian optimization method is conversed to the solution of its dual problem, which can be described as

    1 , 1 1 max , 2 N N i i j i j i j i i j Q a y y K x x          (2)
where i  should subject to the constraint and where  

, i j K x x is kernel function 1 0, 0, 1, , N i i i i y i N        (3)
Kernel functions usually adopt the follow three ones: Linear function, polynomial function, and the radial base function, which are shown as follows.

Linear Kernel function:

  , i j i j K x x x x  Polynomial function:     , d i j i j K x x s x x c       Radial basis function:   2 2
, exp

i j i j xx K x x            
Only a small number of nonzero solutions i  are obtained from Eq.( 2), these vectors corresponding to nonzero solution i  are looked on as support vectors. Then the optimization decision function can be written as

        1 sgn sgn , N i i i j i f x x b y K x x b                    (4)
If these data can not be classified with error-free in higher dimensional spaces, a relax quantum  

0 i i  
which is used to balance the maximum classification interval and the minimum misclassification samples is introduced to support vector machine. Then the optimum equation can be described as

      2 ' 0 1 1 min 1 , 1, , 2 N i i i i i C y x i N                 (5)
where C is penalty coefficient.

The optimum classification plane function can be written as

    1 1 1 1 1 max , 2 0 0 N N N i i j i j i j i i j N i i i i y y K x x C y                    (6)
Then the decision function f (x) can be obtained using the Karush-Kutn-Tuker rules.

Estimate Mount Tai Forest Biomass

Fifty sample plots in the same size are selected in Mount Tai, in which forty sample plots are regarded as training samples and ten are testing samples. Ten independent variables TA, T0, PA, DEM, ASP, NDVI, PVI, RVI, SARVI, TSAVT are taken as input variables in the SVM and regressive analysis method. Then the SVM and regressive analysis method are applied to train the forty samples and test the ten samples. In this paper, the forest biomass calculated by the SVM and regressive analysis method are considered as predicted values, and those calculated by the method in the section 2.3 are considered as actual values. The accuracy function can be written as 1 [START_REF] Spencer | Integrating eucalypt forest inventory and GIS in western Australia[END_REF] 1 100%

n i i i i a p Accuracy abs n a                    
where i is the sequence number of samples, n is the amount of samples, a is actual value and p is predicted value.

The training and testing accuracy of the SVM and regressive analysis method are shown in Table 1.

Conclusion

(1) Because the training accuracy and testing accuracy of SVM and the regressive analysis method are high in Table 1, these are a close correlation between meteorological data, terrain data, remote sensing data and forest biomass. It is also shown that the independent variables selected in this paper could reflect the information of the Mount Tai forest biomass.

(2) From the Table 1, we could see that both the training accuracy and testing accuracy of regressive analysis method are lower than those of SVM, so SVM could obtain higher accuracy and it is a suitable method to estimate the forest biomass.

(3) In the current conditions, the proposed method to estimate Mount Tai forest biomass using remote sensing could replace the field survey methods. Its advantages are to reduce the amount of the field survey work, save the survey funds, reduce the labor intensity and have great social and economic benefits.

(4) The proposed method is relatively simple which is suitable to estimate biomass in larger area.

  Difference vegetation index: DVI=ETM4-A×ETM3 Normalized differential vegetation index: NDVI=(ETM4-ETM3)/(ETM4+ETM3) Perpendicular vegetation index: PVI=(ETM4-A×ETM3-B)/SQR(1+A 2 ) Ratio vegetation index: RVI=ETM4/ETM3 Soil adjusted ratio vegetation index: SARVI=ETM4/(ETM3+B/A) Transformative Soil adjusted ratio vegetation index: TSAVI=A×(ETM4-A×ETM3-B)/(ETM3+A×ETM4-A×B) Transformed vegetation index: TVI= (NDVI+0.5) 0.5

Table 1

 1 The accuracy of SVM and regressive analysis method

	Accuracy	Method	SVM	Regressive analysis
	Training accuracy	86.09%	84.06%
		Testing accuracy	84.62%	82.93%

* corresponding author. Email: chming@sdau.edu.cn.

Acknowledgements

This study is funded by: ( 1) Basic Research Fund of Chinese Academy of Surveying and Mapping under Grant (Contract Number: 7771109); (2) Award Fund of Outstanding Young Scientist of Shandong Province (No. BS2011DX031).