Yunkai Li
email: yunkai@cau.edu.cn

Lingyan Wang

Hua Zheng

Hai Jin

Tingwu Xu

Peiling Yang

Xiaokai Tijiang

Zengcai Yan

Zhiheng Ji

Jianli Lu

Lingyan Wang

Zhanfeng Wang

Zhiyun Ouyang
email: zyouyang@rcees.ac.cn

Linyan Wang

Evolution Characteristics for Water Eco-Environment of Baiyangdian Lake with 3S Technologies in the Past 60 Years

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

mixed zone), the north zone was polluted most seriously. The main pollutants concentrations were fluctuated obviously over a year, and the pollution was more serious in summer of normal and dry years, while the pollution was serious at the beginning of summer flood in wet years, and then became less. At last, the evolution trend and driven mechanism of water eco-environment of Baiyangdian was analyzed from three aspects, including typical climate indexes (such as precipitation, evaporation, air temperature, and solar radiation), ecological/environmental hydrology regime in upstream watershed of Baiyangdian, and human activities. It would provide some references to the comprehensive treatment of water eco-environment in Baiyangdian Lakes.

Keywords: Baiyangdian Lake; Water Eco-environment; Evolution Trend; Remote Sensing;

Driven Mechanism

Introductions

Lakes, natural or artificial, are important for human development and for the preservation of sound ecosystems and biodiversity on our planet. They contain 90 percent of the liquid freshwater on the earth's surface and are critical elements of the water cycle; they sustain aquatic biodiversity and provide livelihoods and social, economic, and aesthetic benefits that are essential for the quality of life in Lake Basin communities. However, under the circumstances of global climatic changes, industrialization and urbanization, the lake-wetland presents a good many ecological and environmental problems, such as reduced water surface, deteriorated water quality and destroyed water ecosystem, etc. [START_REF] Wang | Study on creative ideas and key countermeasures of lake-wetland water ecological pollution treatment[END_REF]. The management and protection of lake water eco-environment are facing severe challenges. To let lakes recuperate and multiply by rationally developing and utilizing lake-wetland resource, strengthening management and protecting of lake-wetland, maintaining lake-wetland water eco-environmental health, has become a common view all over the world [START_REF]The 13 th World lake Conference[END_REF] .

During the past century, a lot of research results about the forming process of lake-wetland ecosystem and its hydrological mechanism (including ecological/environmental water requirement and water balance, dynamic changes of water level, water transfer, etc,) were obtained by all of the countries in the world [START_REF]The 13 th World lake Conference[END_REF] , ant it has been become the main basis of managing lake-wetland resources.

But on the whole, there are few researches about comprehensive analysis on evolution trend and driven mechanism of lake-wetland water eco-environment, which was centralized in the investigating research less than ten years, while the analytical results of evolution trend and driven mechanism of lake-wetland water eco-environment based on longtime locating monitoring are much less. Meanwhile, the complex relationship between lake-wetland ecological process and hydrological process, which not only relates to the development of wetland ecosystem itself, but also relates to ecological/environmental hydrological process of lakes upstream watershed, makes the evolving rule of each lake-wetland water eco-environment possess a certain common features as well as unique features.

Therefore, it is necessary to comprehensively analyze evolution trend and driven mechanism for the water eco-environment of every typical lake-wetland.

In this paper, Baiyangdian Lake was chose as the research object, which was the biggest grass-type lake-wetland in North China, to study systematically the evolution trend of lakes water eco-environment with long-term and field observations of water environmental quality and analysis for remote sensing images of water ecological landscape pattern. And the driven mechanism for water eco-environment from climatic changes and human activities was analyzed. It would be hoping for providing the references about the protection and ecological restoration of Baiyangdian Lake and even lake-wetland all over the world.

General Situation of Baiyangdian Lake-basin

Baiyangdian Lake, which is located between 38°43′-39°02′N and 115°38′-116°07′E and the heartland of three cities Beijing, Tianjin, and Shijiazhuang, is the most representative grass-type lake-wetland in North China [START_REF] Cui | Classification of hydrological conditions to assess water allocation schemes for Lake Baiyangdian in North China[END_REF] . In addition, Baiyangdian Lake is likely to play an important role in the South-to-North Water Division Project under construction [START_REF] Hu | Organo chlorine compounds and polycyclic aromatic hydrocarbons in surface sediment from Baiyangdian Lake, North China: concentrations, sources profiles and potential risk[END_REF] .Baiyangdian Lake is located at the middle reaches of Daqing River, Haihe River Basin. And it controls a total area of 31, 199 km 2 , which accounts for 69.1% of the total area of the watershed. Baiyangdian Lake can be divided into four basin units according to river systems, including South Branch mountain area, North Branch mountain area, South Branch plain, and North Branch plain. Fig. 1 showed that the distribution of Baiyangdian Lake and drainage basin water systems. Since ancient times, Baiyangdian Lake undertakes water from the south branch of Daqing-Zhaowang River (including Zhulong River, Xiaoyi River, Tang River, Fu River, Cao River, Baigouyin River, Pu River, and Ping River). The discharge is controlled by Zaolinzhuang regulation sluice and Shifangyuan Overflow Weir. It pours into sea via Zhaowangxin Canal, Duliujian River, and Beida Harbor [START_REF] Zhao | A study of the lowest ecological water level of Baiyangdian Lake[END_REF] .

Baiyangdian Lake watershed is located the monsoon climate of warm temperate area, which is hot and rainy in summer, cold and dry in winter. The lowest temperature is -30.6℃ and the highest is 43.5℃, and average annual temperature is 7.3-12.7℃, the average yearly accumulated temperature is 29.93-44.09℃. The average annual precipitation in the whole watershed is 546.2mm, and the average annual evaporation is 1000-1200mm. Baiyangdian Lake borders the dykes, which is 39.5km from east to west, 28.5km from north to south, and the total area is about 365km 2 . The main landscape of Baiyangdian Lake is water, the bottom of which is high in the west and low in the east, the altitude of it is 5.5-6.5m. The whole lake was divided to unequal and interrelated 143 sub-lakes with 3700 staggered channels. The tunable water storage capacity is 2.9×10 8 m 3 , water area accounts for about 50% [START_REF] Xu | The ecological degradation and restoration of Baiyangdian Lake, China[END_REF] .

Because of its special geographical position, Baiyangdian lake-wetland provides vital ecosystem service functions such as water conservation, water purification, regional climate regulation, species diversity maintenance, and so on. It plays an irreplaceable role in maintaining the ecological safety in North China, and is functioned as the "kidneys" of North China [5] (Zhao,Cui,Yang,2005).

With the affection of both climatic changes and human activities in the recent years, Baiyangdian Lake has been threatened by drought and pollution. The vegetation in upstream watershed becomes scarce and the function of water conservation is weak. Eco-environment problems appear, including frequent drying up, water pollution, the extinction of rare biological populations, etc. [START_REF] Gao | Changes in hydrological conditions and the eco-environmental problems in Baiyangdian watershed[END_REF] . With the rapid degeneration of ecosystems, Baiyangdian Lake has changed from an exuberant lake to a lake facing shrink or even disappearance. The existence or disappearance of Baiyangdian Lake not only affects people's life and production, but also affects economic development and environmental balance of the North China Plain. The environment problems of Baiyangdian Lake have led to concerns of all circles in society, and the researchers have carried on a large amount of researches on eco-environment problems of Baiyangdian Lake [START_REF] Li | Influence of hydrological characteristic change of Baiyangdian on the eco-environment in wetland[END_REF] , but few researches about comprehensive analysis on evolution trend and driving mechanism for water eco-environment of Baiyangdian Lake are reported.

Materials and Methods

Monitoring on Water Quality and Water Level in Baiyangdian Lake

The monitoring data about the water level of Baiyangdian Lake in the recent 60 years (1950-2008) were collected. The locations of the monitored spots were shown in Fig. 1.

Fig. 1 Water System of Baiyangdian Lake and Its Upstream Watershed Technical Specification for Investigating the National Wetland Resources [START_REF] China | Technical Specification for Investigating the National Wetland Resources,[END_REF] , and combining characteristics of Baiyangdian Lake landscape pattern and wetland evolving, Baiyangdian Lake landscape patterns can be divided into six kinds, which are: (

Image Disposal Methods Based on Support Vector Machine Support Vector Machine

(SVM) theory and method was used to classify the images. SVM is a new study method, which was developed based on Statistical Learning Theory put forward by Vapnik at AT&T Bell laboratory [START_REF] Vapink | The nature of statistical learning theory[END_REF] .

SVM, which can solve practical problems including small sample, nonlinearity, high dimension, local minimum point, has become a hot spot in machine learning area, and is successfully used in aspects such as classification and regression. According to the differences of the problems solved, SVM is called Support Vector Classification (SVC) and Support Vector Regression (SVR), respectively. The mechanism of SVC is to find an Optimal Hyperplane (OHP) which meets the demand of classification, in order to guarantee the accuracy of classification while not only classify the two kinds of data spots correctly as many as possible, but also make the blank area at the two sides of the Optimal Hyperplane the biggest -to make the classified two kinds of data spots farthest from classified plane [START_REF] Oded | The data mining and knowledge discovery handbook[END_REF] . SVC method was used to analyze the landscape classification of remote sensing images in this paper.

Given a training dataset

    m i i n i i i y R x y x X 1 1 , 1 , : ) , (      
, where each x i is labeled by y i.

Linear SVM finds a boundary that separates two different classes of feature vectors with a maximum margin. It leads to the following convex quadratic programming problem:

b w, min    m i i C w 1 2 2 1  (1) s.t. i i T i b x w y     1 ) ( (2) m i i , , 2 , 1 , 0     (3)
A nonlinear SVM projects feature vectors into a high dimensional feature space by using a kernel function such as a Gaussian kernel function ) , (

j i x x K
. The linear SVM procedure is then applied to the feature vectors in this feature space. The corresponding dual quadratic programming to (1)-( 3) for nonlinear SVM is as follows:

 min       m i i m i m j j i j i j i x x K y y 1 1 1 ) , ( 2
1    (4) s.t.    m i i i y 1 , 0  m i C i , , 2 , 1 , 0      (5)
where C is a constant controlling the trade-off between maximizing the margin and minimizing the errors. After the determination of the solution *  of ( 4)-( 5) and * b , we can get the decision function

          m i i i i b x Kx y x f 1 * * ) , sgn ) (  (6)
where the function sgn is a sign function. If x>0, sgn(x)=1; otherwise sgn(x)=-1. In the present work, the Gaussian kernel function

  2 2 2 / exp ) , (  j i j i x x x x K   
is used. Because of the need to classify the lake-wetland to six landscape patterns, we extended this bi-class classification problem studied by SVM to the field of multi-class classification, and used bi-class classification machine which constructed n(n-1)/2 to combine them together and realize n-class classification. In order to enhance classified accuracy of the research region, the optimal value of punishing factor C and slack variable ξ in SVM method were obtained by using LIBSVM process. The land use types of 516 points were investigated with GPS (The number of each landscape samples were almost the same), and it was divided two parts, which one half was used to form the training dataset, another one was used to calibrated the classification results combining the land use data in 1983. The results showed that the image classification was good, which the precision of image classification and Kappa index were over 0.92 and 0.88, respectively.

Analysis on Wetland Landscape Pattern and Its Dynamic Changes

According to the characteristics of Baiyangdian Lake and research need ,a set of landscape pattern indexes were chose to reflect the changes of landscape structure and landscape pattern. The calculating formulae were as follows:

(1) Landscape area index Patch type area (PA ): the total area of a certain landscape pattern.

Patch and landscape area ratio (PR): the percentage of a certain patch type accounts for the whole landscape area.

Patch number (PN): the total number of a certain patch type appears in the landscape.

(2) Landscape fragmentation index Patch density (PD): the ratio of patch number and the total patch type area.

The average patch fractal dimension (FRAC_MN): it reveals the relationship between shape and area of landscape constituted by patches, measures the complexity of the landscape shape and eco-process of the effect of landscape shape on the internal patches, and reflects the effect of human activity on landscape. The calculating formula is:

11 2 ln(0.25 ) ln( ) _= mn ij ij ij P a FRAC MN N       (7)
where ij a is patch's area, ij P is the perimeter of landscape type, N is the total number of patches.

(3) Landscape diversity index Shannon diversity index (SHDI): it reflects landscape heterogeneity and is sensitive to the Disequilibrium distribution condition of each patch type, which emphasizes on the contribution of rare patch type to information. The formula is:

1 ln( ) m ij ij i SHDI P P      (8)
where, i P is the percentage of a certain patch type accounts for the whole landscape area.

K= (U b -U a ) /U a ×1/T×100% ( 10
)
where K is dynamic degree of a certain landscape type in the research period of time, U a and U b are the number of a certain landscape type at the beginning and in the end, respectively; T is the length of the research time.

Transfer matrix of landscape is the area matrix and probability matrix of the transfer between different landscape types in a sequence unit, which not only reflects landscape type structures of different periods, but also reflects the condition of transfer between different land types. Transfer probability matrix can be calculated as follows:

P ij =A ij /A ( 11
)
where

1 0   ij P and    N i ij P 1 1 ( , 1, 2, , ), i j n  P ij
is the probability of transferring from type i to type j (from the beginning of the research to the end), A ij is the area transferring from type i to type j (from the beginning of the research to the end), A is the area of type i at the beginning of the research; n is the number of landscape types.

Meteorological Data

The yearly and seasonal sequence observation data (including air temperature, solar radiation, evaporation) from 1956 to 2008 in Anxin county of Baoding City were collected, which is the location of Baiyangdian Lake. Yearly precipitation materials in Baiyangdian lake-wetland from 1956 to 2008 were also collected. The distribution of precipitation and weather stations were shown in Fig. 2.

3 Evolution Trend for Water Eco-environment in the past 60 Years

Dynamic Water Level in Lakes

The ecological service functions of lake-wetland were closely correlated with the water level and water surface area, and the biodiversity and even the whole ecological function had great difference under different water lever conditions [START_REF] Hofmann | Temporal scales of water-level fluctuations in lakes and their ecological implications[END_REF] . In the recent decades, the water level of Baiyangdian wetland has fluctuated greatly, and Fig. 3 showed the annual average water level of Baiyangdian Lake observed by Shifangyuan in the recent 59 years (1950-2008). According to the result of Mann-Kendall significance testing, U=-4.96<0 , /2 U =4.96>U 2.32   ( =0.01), which meant that water level showed obvious decreasing tendency. For the recent 60 years, the water level in Baiyangdian fluctuated between drying up level (Lake drying up was defined as the observed water level by Shifangyuan Monitoring Site was lower than 6.5m) and 10.03m. From 1950s to the middle 1960s, the water level was high, and the lowest water level was 8.12m. Between 1965 and 1983, water level was between 6.37m and 8.69m, and Baiyangdian Lake was dried up from 1984 to 1987(the gap in Fig. 3). From landscape pattern evolving indexes listed in Tab.1, PN and PD had decreasing tendencies as a whole, which meant that heterogeneity of different landscape types decreased [START_REF] Chen | Analysis of impact of human activity on landscape structure in Yellow River Delta -a case study of Dongying Region[END_REF] . FRAC_MN showed a decreasing tendency, which indicated that landscape fragmentation degree increased and was more complex, suffered a tremendous influence from human factor, but had no significant difference [START_REF] Li | Dynamic change of landscape and its driving forces in midstream of Heihe mainstream basin after water redistribution[END_REF] . In 1974, SHDI and SHEI in the research region were the biggest, which showed that landscape diversity degree was high in this period, heterogeneity was high, and dominance degree of patches in landscape was small; in 1987, SHDI and SHEI were the lowest, which meant that heterogeneity of different landscape types decreased, dominance degree increased, being in conformity with the fact of the sharp decrease of water area and the significant increase of emergent plants and cultivated land; from 1996 to 2007, SHDI and SHEI showed an increasing tendency, which indicated that landscape developed to diversity and uniformity, and embodied the tremendous effect of human activity in this period.

Tab.

Spatiotemporal Variation of Water Environmental Quality

From 1960s, because of self-purification capability decreased for water source deficiency and the effect of human activity, large amount of wastewater had flown into Baiyangdian Lake, which made the water environmental quality worse day by day [START_REF] Zhong | Eco-environmental water demands for the Baiyangdian Wetland[END_REF] times the water quality standard of Grade V (15mg/L). The concentrations of BOD 5 increased obviously from 1999 to 2005, worse than water quality standard of Grade V (10mg/L). The concentrations of TP were mostly between 0.2 mg/L and 1.8 mg/L, which were higher than surface water quality standard of Grade V (0.2mg/L). After the largest concentration in 1999, it showed a decreasing tendency. The concentrations of TN fluctuated obviously, which were 2 to 18 times the water quality standard of Grade V (2.0mg/L) each year, but generally showed a decreasing tendency.

On the whole, the water quality of north pollution zone was at a worse than Grade V.

The pollution degree of south pollution zone was lower than that of north pollution zone, except that the TP and TN concentrations were comparatively higher, and the TP concentrations met the water quality standard of Grade V from 1999 to 2008, and the TN concentrations were worse than water quality standard of Grade V. The NH 4 + -N concentrations were mostly between 0.15 mg/L and 1.0 mg/L, which met the water quality standard of Grade Ⅲ. The COD Mn concentrations were mostly between 2 mg/L and 10 mg/L, which were better than water quality standard of Grade Ⅳ. The BOD 5 concentration were within water quality standard of Grade Ⅳ(6mg/L) before 1999, and increased to the concentrations between the water quality standard of Grade Ⅳ-Ⅴ. The NH 4 + -N concentration in mixed pollution zone was affected by the pollution of both north and south pollution zone. But due to the affect of water body dilution in Baiyangdian Lake and the removal function of plants, which was decreased along the water movement path, the NH 4 + -N concentration was lower than both of the other two pollution zones. Meanwhile, the main pollutants concentrations fluctuated obviously in a year. In normal and dry years, the pollution was more serious in summer, while the pollution was serious at the beginning of summer flood in wet years and then became less. It was the reason that upper regions of rivers were almost no flow and large amounts of pollutants stored in the rivers flowed into Baiyangdian Lake with the initial flood. For instance, a great flood occurred in 1996, the flood brought a large amount of pollutants in rivers at the beginning which caused the pollutants (such as NH 4 + -N, COD Mn , and BOD 5 ) concentrations increased, later with the increasing of water flowing into Baiyangdian Lake, the dilution function of river water emerged, which caused the pollutants concentrations decreased. 7 and Fig. 8 showed the results of evaporation in Baiyangdian Lake. The annual average evaporation in Baiyangdian was 1303.6 mm, this was 2.5 times the annual average precipitation. In the early 1970s, annual average evaporation with 10-year running means was 1289.0 mm, and decreased continuously. The lowest evaporation value was in the early 1990s, and then increased gradually to 1347.1mm in 2004. We can see that, in the recent 30 years, water evaporation in Baiyangdian Lake has showed a increasing tendency which decreased at first and increased later. The significance test in Tab.6 also verified that the evaporation of Baiyangdian Lake showed a increasing tendency.

According to Fig. 7, Fig. 8, and the Mann-Kendall significance test in Tab.3, evaporation of different seasons except autumn in Baiyangdian showed unobvious increasing tendency, which passed the confidence level of 99%. The evaporation in summer increased the largest, which was 31.40 mm more than that of 1970s, and then was spring more 17.36mm than that of 1970s. The increase of air temperature would enhance evaporation capacity, so the increasing continuously temperature was the main impact factor caused the water surface shrinked [START_REF] Gao | Changes in hydrological conditions and the eco-environmental problems in Baiyangdian watershed[END_REF] . showed that annual average temperatures with 10-year running means in Baiyangdian Lake were increased and then decreased tendency in the past over 30 years. The annual average temperature in the 1980s was 0.13℃ higher than that of the 1970s. From the 1990s, the air temperature increased greatly, and the annual average temperature was 0.38℃ higher than that of 1970s. After 2000, the air temperature decreased, and the annual average temperature equal to the temperature of 1970s.

Temperature changes of different seasons also showed obvious increasing tendency, which was more obvious in spring and winter. The average temperature in spring after the year 2000 was 0.94℃ higher than that of 1970s, and the average temperature in winter after the year 2000 was 0.21℃ higher than that of 1970s. The increase of temperature would cause all kinds of hydrological variables (such as the decreasing of precipitation, the increasing of soil temperature, the increasing of agricultural water consumption, and so on) changed, directly or indirectly caused the degeneration of Baiyangdian lake-wetland. ﹡﹡Correlation is significant at the 0.01 level(2-tailed).

Relationship between the Local Climate and Water

Level in Lake Water level and meteor -ological factors of each year were carried on correlation analysis, the results of which were shown in Tab.4. The result showed that there was obvious positive correlation between water level and precipitation, the relativity of which was 0.483. And the water level showed negative correlation with temperature and evaporation, the relativity of which were 0.326 and 0.314, respectively. Water level and solar radiation were little related. Therefore, precipitation had a vital effect on the changes of Baiyangdian Lake eco-environment.

Tab. and close relationship between nature and society. At present, most of the researches on lakes and watersheds studied them respectively, which ignored the integrality of watershed hydrological process and ecosystem. The emphasis of some researches on lake water environment had already transferred to complicated lake-watershed macro-system, from watershed scale to carry through pollution prevention and cure, ecologic recovery, and ecosystem management. The changed eco-hydrological process in upstream watershed had significant effect on Baiyangdian lake-wetland.

The water storage of Baiyangdian Lake mainly relied on runoff formed by the precipitation in upstream watershed, which flowed into Baiyangdian through rivers. Runoff into Baiyangdian Lake in 1950s was abundant, but after the year 1958, 6 large reservoirs (Hengshanling, Koutou, Wangkuai, Xidayang, Longmen, and Angezhuang), 12 medium-sized reservoirs, and 116 small reservoirs were constructed in the upstream watershed in succession. The reservoirs controlled 54.93% of the watershed area in mountainous area, with a total storage of 3.619 billion m 3 (Zhang,Tian, Li,2007) , and had vital effect on intercepting the runoff from the upstream watershed. The building of water conservancy projects caused water flowing into Baiyangdian Lake decreased. In wet year, the runoff into Baiyangdian was abundant; in normal year, the control coefficient of reservoirs to runoff was 60%;

while in dry year, the water in the reservoirs was mostly used for agricultural irrigation and little water was left, control coefficient to runoff was 96%.

Tab. The serious problems of Baiyangdian Lake, including resources water shortage, water quality-induced water shortage, unbalanced ecosystem, had attracted much attention from state council, ministry of water resources, Haihe River Commission, leaders and experts from departments in Hebei province. Deficiency of water was gradually acknowledged as the bottleneck of the Baiyangdian Lake eco-environmental management [START_REF] Zhang | Ecological problems and restoration measures of Baiyangdian Wetland[END_REF] . In order to ease this situation, water was refilled to Baiyangdian from upper region reservoirs for 28 times, with a total release of 2.35 billion m 3 and a total refill of 1.09 billion m 3 water to Baiyangdian Lake, and Tab.5 showed the results. From it, we can find that Baiyangdian Lake had changed from a natural controlled lake-wetland to a totally artificial regulated lake.

Baiyangdian watershed is one of the primary grain-produce areas in north China, and the ratio of agricultural water is high. For instance, in 2006, the total water used in Baiyangdian watershed was hectares. The increasing of agricultural water in the upstream watershed would necessarily cause the runoff into Baiyangdian Lake decreased, especially in drought year, the conflict between agricultural water and water required in Baiyangdian Lake was more obvious. It would be aggravate rapidly the deterioration trend of water eco-environment in Baiyangdian Lake.

Environmental Hydrology Situation Changed a lot in Upstream Watersheds, and

Wastewater Discharge Quantity in the Watersheds Increased Rapidly Except Baigouyin River of the 8 rivers whose water quality was good, water qualities of other rivers were worse than the water quality standard of Grade V. Fu River mainly undertook the domestic wastewater of Baoding City [START_REF] Hu | Concentrations and accumulation features[END_REF] , Xiaoyi River mainly undertook the industrial wastewater from Li and Gaoyang counties, Cao River and Bao River mainly undertook the domestic and industrial wastewater from Mancheng and Xushui counties, respectively. Meanwhile, the increasing of agricultural land area in the upstream watersheds caused the application of chemical fertilizer and pesticide increased sharply, so the non-point pollution from agriculture was also one of the main pollution sources. The pollutants discharged from Fu River and Cao River were the most of the 8 rivers. Fu River flows through the center of Baoding City, Cao River flows through the northwestern part of Baoding City. In the past 40 years, the daily wastewater discharge in Baoding City increased continuously. The daily wastewater discharge of Baoding City in 1962 was 0.43×10 5 tons, which was 1.6×10 5 tons in 1974 and 2.1×10 5 tons in 1987. The daily wastewater discharge of Baoding City was 2.5×10 5 tons in 2005, and it was still increasing. However, at present there are only two wastewater treatment plants with a total daily treatment capacity of 1.6×10 5 tons, which results in 0.9×10 5 tons wastewater flowing into Fu River without being treated, and becomes the biggest pollution source in Baiyangdian Lake. Cao River was not polluted before the middle of 1980s. After the middle of 1980s, 156 paper mills were constructed in the upstream watershed of Cao River, and the daily wastewater increased to 0.8×10 5 tons, which was discharged into Cao River only after simple treatment except 0.3×10 5 tons wastewater was treated by Daceying Town wastewater treatment plant. In addition, with the domestic wastewater discharged from Mancheng County, Cao River had become a Sewage River. Fig. 13 showed the monitoring results of the five main pollutants concentration (NH 4 + -N, COD Mn , BOD 5 , TP, TN) in the estuary of Fu From Fig. 13 (a), it could be found that the water quality of Fu River was worse than the standard of Grade V, the five pollutants concentrations were far beyond the standards. The average concentrations of NH 4 + -N, COD mn and BOD 5 were 19.88mg/L, 20.83mg/L and 34.29 mg/L, respectively, which were 10 times, 1.4 times and 3.5 times the water quality standard of Grade V. The annual average concentrations of TP and TN were 1.44mg/L and 24.34mg/L, which were 7 times and 12 times more than the standard of Grade V. From Fig. 13 (b), it could be found that Cao River mainly discharged oxygen consumed pollutants, and the BOD 5 and COD Mn concentrations were high, which were 3.6 times and 2.2 times the standard of Grade V. The average concentration of NH 4 + -N was 21.14mg/L, which was 10 times the standard of Grade V. The concentration of TP was 0.27mg/L, which was similar to the standard of Grade V. The concentration of TN was 8 times the standard of Grade V.

The Sharp Increasing of Human Activity Directly Caused the Water Requirement in

Baiyangdian Lake Increase Greatly In the recent years, the population in Baiyangdian Lake increased greatly, which increased from 364,800 in 1989 to 387,800 by now, and the results of remote sensing interpretation also showed the residential land increased from 820.4hm 2 in 1987 to 1490.0 hm 2 in 2007. The increasing of population in Baiyangdian Lake directly caused the discharge of domestic wastewater increased.

After the water refilling with Baiyangdian Lake in 1988, the tourism developed quickly. As recorded, it received the tourists 3,704,000 from 1988 to 2000, and the peak of daily received tourists was 6833 [18] . The water consumption and domestic pollutants of tourists, and the pollutants discharged by the motorboats accelerated the artificial pollution in Baiyangdian Lake.

From the 1950s, the counties and cities around Baiyangdian Lake continually developed agricultural irrigation. The agricultural water around lake increased from 2×10 7 m 3 per year in the early 1950s to2×10 8 m 3 now. The increasing of water diversion decreased the regulating storage volume of Baiyangdian Lake, which brought a series of environmental problems.

Results

Four categories of conclusions have been achieved based on the research in this paper:

(1) Monitoring with remote sensing on the water ecological landscape pattern in Baiyangdian Lake, it was found that the emergent plants, water body and cultivated land were the main land use types. During 1974 and 2007, the water body and emergent plants areas were decreased respectively by 28.7% and 20.8%, while the cultivated land areas were increased by 69.7%. And the heterogeneity and landscape fragmentation degree of different landscape types decreased constantly, but the latter wasn't to the significant difference level.

(2) During 1988 and 2008, the main pollutants (NH 4 + -N, COD Mn , BOD 5 , TP, TN) concentrations changed greatly with obvious spatial-temporal characteristics by analyzing the water quality data, and the pollutants concentrations showed increasing tendency. The pollution of North Zone was most severe in the whole lake, and the pollution was more serious in summer of normal and dry years, while the pollution was serious at the beginning of summer flood in wet years, and then became less.

(3) The local climate changed obviously in Baiyangdian Lake with Mann-Kendall test, which the precipitation decreased, while the air temperature and evaporation increased. The warming and drying climate tendency intensified water eco-environmental degradation, and the precipitation was the main impact factor on the changes water eco-environment in lake.

(4) Sharp increasing of human activity directly caused ecological/environmental hydrology regime in upstream watershed of Baiyangdian Lake changed obviously, which made it changed from a natural controlled lake-wetland to a totally artificial regulated lake.

of organo chlorine pesticides in the Baiyangdian Lake freshwater food web of North China. Arch Environ Contam Toxicol, 58(3):700-710 (2009) 18. Zhang M Y. Study on the change of eco-environment in Baiyangdian watershed based on RS, GIS and landscape pattern analysis. Nanjing: Nanjing Normal University: pp1-58 (2004)

Fig. 2

 2 Fig.2 Different Kinds of Monitoring Points The pollutants in Baiyangdian Lake are from many sources, widely distributed, and constituted complexly. The main pollution indicators of water quality in Baiyangdian Lake are NH 4 + -N, COD Mn (Chemical Oxygen Demand, Permanganate Index), BOD 5 (Biochemical Oxygen Demand after 5 days ), TP (Total Phosphorus) and TN (Total Nitrogen). According to water sources and water flow characteristics in Baiyangdian Lake, it can be divided into three pollution zones, which is south

( 4 ) 9 )( 5 )

 495 Landscape evenness index Shannon evenness index (SHEI): It reflects non-uniformity degree of each patch area distribution in landscape. It is represented by the ratio of diversity index and the maximum value. The formula is: SHEI=SHDI/lnN (Dynamic change index of landscape pattern Landscape type dynamic change index is the changing degree of a certain landscape type area per unit time. It can quantitatively characterize the changing speed of landscapes, which has a positive effect on comparing region differences of landscape type changing and predicting the changing tendency of landscape type in the future. The calculating formula is:

Fig. 3 Fig. 4

 34 Fig.3 Annual Average Water Level in Baiyangdian Lake between 1950 and 2008

Fig. 5

 5 showed the dynamic changes of NH 4 + -N, COD Mn , BOD 5 , TP, TN in south pollution zone, north pollution zone, and mixed pollution zone from 1988 to 2008. From it, we can find that North pollution zone was polluted most seriously in the three pollution zones, where the concentrations of NH 4 + -N and COD Mn in 1994 and 1999-2008 were worse than water of Grade V. The concentrations of them were the highest in 2004, where the concentration of NH 4 + -N was 16 times the water quality standard of Grade V (2mg/L), and the concentration of COD Mn was 2.2

Fig. 5

 5 Fig.5 Annual Pollutants Concentrations Dynamics

Fig. 8

 8 Fig.7 Seasonal and Years Evaporations of Baiyangdian

Fig. 10 Ten 4 . 1 . 4 1 )Fig. 12 Ten﹡﹡

 10414112 Fig.9 Seasonal and Years Average Temperatures in Baiyangdian Lake

3.217 billion m 3 ,

 3 and agricultural water was 2.696 billion m 3 , which accounted for 83.8% of the total water used. The construction of controlled reservoir in upstream watershed also greatly promotes the development of irrigation and drainage engineering. So far, 35 irrigation districts above 667 hectares have been constructed, among which there are 4 large irrigation districts (Shahe, Tanghe, Yishui, Fanglaizhuo) above 2001 hectares and 31 medium irrigation districts (above 667 hectares and smaller than 2001 hectares). The whole effective irrigation area of cultivated land in watershed is 78706

Fig. 13 The

 13 Fig.13 The Dynamic Changes of the Concentrations of River Pollutants in Baiyangdian

2 Monitoring on Landscape Pattern of Water Ecosystem with Remote Sensing and Its Analysis Methods 2.2.1 Data Sources of Remote Sensing Images and Its Disposal Methods The

  pollution zone that undertakes water from south branch water system (mainly including Zhulong River, Xiaoyi River, and Tang River), north pollution zone that undertakes water from north branch water system (such as Fu River, Cao River, Baigouyin River, Bao River, Ping River, and so on), and mixed

	zone of the two pollution zone. The observation data (NH 4 + -N, COD Mn , BOD 5 , TP) of long time
	observation spots in the three zones from 1988 to 2008 were collected, including 1988 (restoration of
	water after the first drying up), 1991, 1994, 1996, 1999, and 2002-2008, the observation frequency was
	different from once per month to once per season; and each month's observation material of TN from
	2002 to 2008. And the water quality monitoring data of the estuaries of Cao River and Fu River were
	collected, which was the most serious polluted location in North branch water system of Baiyangdian
	Lake watershed. The locations of water quality monitoring spots were shown in Fig.2.
	2.Landsat MSS/TM
	remote sensing images of studied region were chosen as the data source, which was Sep. 3, 1974, Aug.
	28, 1987 (Dried-up Year), Jul. 13, 1996 (Wet Year), and Aug. 24, 2007. The software ENVI 4.5 was
	used to modify atmosphere of the images, and the map of roads in Baiyangdian Lake basin with a scale
	of 1:100, 000 were used to carry on Geometric Calibration and Registration. Actuality Map of Land
	Use in 1983, Administrative Map, and DEM images were used to help interpret Baiyangdian Lake
	wetland landscape pattern, as well as to enhance the accuracy of interpreting land type. Referring to

  1 Water Ecosystem Landscape Pattern Evaluating Indexes of Baiyangdian Lake in 1974-2007

					Patch level				Landscape level
					PA/hm 2 (PR/%)				PN	PD	FRAC_MN SHDI SHEI
	Items	Emergent	Water	Cultivated	Forest/grass	Urban	Unused	/ind	/ind/hm 2
		plants	area		land		land		land	land
	1974	12928.2 (35.4)	11355.4 (31.1)	8999.6 (24.7)	1643.2 (4.5)		582.3 (1.6)	998.8 (2.7)	20109 0.55	1.10	1.37 0.77
	1987	15923.4 (43.6)	2662.7 (7.3)	13531.8 (37.1)	1527.8 (4.2)		820.4 (2.3)	2041.4 (5.6)	23560 0.65	1.08	1.29 0.72
	1996	14228.6 (39.0)	11462.8 (31.4)	8406.9 (23.0)	1005.1 (2.8)		1257.7 (3.5)	146.2 (0.4)	19894 0.54	1.09	1.31 0.73
	2007	10243.8 (28.1)	8091.4 (22.2)	15272.5 (41.8)	1283.2 (3.5)		1490.1 (4.1)	126.5 (0.4)	19788 0.54	1.08	1.32 0.74
	Tab.2 Transfer Probability Matrix of Water Ecosystem Landscape Pattern in Baiyangdian Lake (1974-2007) (%)
	Landscape	Dynamic	Emergent	Water	Cultivated	Forest/grass	Urban	Unused
		types	dgree	plants		area	land	land	land	land
				K
	Emergent plants	-1.60		50.4	27.29	4.75	6.55	2.61	9.9
	Water area		-2.21	13.35	51.36	3.49	7.14	5.69	4.42
	Cultivated land		5.36	31.07	17.94	81.16	69.18	12.74	76.21
	Forest/grass land	-1.69		2.23		1.47	5.46	8.84	9.16	6.22
	Urban land	11.99		2.66		1.57	4.89	7.87	69.44	2.93
	Unused land		-6.72		0.28		0.37	0.25	0.42	0.37	0.32

4 Analysis on Driving Mechanism of Water Eco-environment Evolution 4.1 Climatic Changes in Baiyangdian Lake 4.1.1 Precipitation Precipitation

  , as one of the main ways to income water in Baiyangdian Lake, had a vital effect on the evolution of water eco-environment. Average annual precipitation was 514.1mm in lake, and Fig.6showed the results of precipitation in lake from 1974 to 2008. From it, we can find that the annual precipitation in Baiyangdian Lake changed greatly and the largest precipitation from 1955 to 2008 were 909mm (in 1996), 3.6 times as much as the smallest annual precipitation

	P/mm	1000
	Precipitation	700 800 900
		600
		500
		400
		200 300			R 2 = 0.0018 y = -0.9137x + 2345.5		y = 19.453x -38154 R 2 = 0.3365		R 2 = 0.5639 y = 21.399x -42424
		100
													Date
		0
		1955	1960	1965	1970	1975	1980	1985	1990	1995	2000	2005	2010
			Fig.6 Annual Precipitations of Baiyangdian Lake
	4.1.2 Evaporation Fig.

252mm (in 1997). The precipitation showed a slow decreasing tendency before 1980s, while, the precipitation decreased rapidly after 1980s. It was present the phenomenon of obvious continuous wet and dry year. For instance, it was dry year occurred from 1983 to 1987 for five years, which directly caused Baiyangdian Lake dried up with a duration of 1650 days.

1 Eco-hydrological Process in Upstream Watershed Changed Greatly, Directly Causing the Runoff into Lake Decreased Sharply Watersheds

  4 Relativity between Water Level and Meteorological Factors are the source of lakes, and lakes are the converging of watersheds. Watersheds and lakes form a dynamic system which shows mutual feedback

	Pearson correlation	Precipitation	Air temperature	Evaporation	Solar radiation
	Water level	0.483 ﹡﹡	-0.326	-0.314	-0.008
	Annotation:	﹡﹡ Correlation is significant at the 0.01 level(2-tailed).
	4.2	Human Activity
	4.2.

  5 Situation of Different Reservoirs Supplying Water to Baiyangdian Lake

	1997	11	Angezhuang reservoir	79.97	51.98		26	Yellow river	200.00	100.00
	1998	12	Angezhuang reservoir	33.06	21.50	2008	27	Yellow river	312.00	157.60
	1999	13	Angezhuang reservoir	27.43	17.80	2009	28	Angezhuang reservoir	70.00	20.00
	2000	14 15	Angezhuang reservoir Wangkuai reservoir	31.11 75.00	15.00 40.60	----Total --	----	--2351.56	--1090.36
	Year Times	Water supplement source	Released water (million m³ )	Water flowing into Baiyangdian (million m³ )	Year Times	Water supplement source	Released water (million m³ )	Water flowing into Baiyangdiar (million m³ )
		1 2	Angezhuang reservoir Xidayang reservoir	25.07 11.23	9.79 4.51	2001	16 17	Angezhuang reservoir Wangkuai reservoir	50.87 100.79	21.64 45.13
	1983	3	Xidayang reservoi	51.75	15.10		18	Xidayang reservoi	50.15	35.01
		4	Xidayang reservoi	26.49	7.38	2002	19	Xidayang reservoi	38.73	19.74
		5	Angezhuang reservoir	19.10	5.77		20	Wangkuai reservoir	59.14	31.04
	1984	6 7	Wangkuai reservoir Xidayang reservoi	44.75 31.16	14.31 12.19	2003 2004	21 22	Wangkuai reservoir Yuecheng reservoir	234.97 417.00	116.34 159.00
		8	Wangkuai reservoir	45.14	27.09	2005	23	Angezhuang reservoir	83.50	42.51
	1992	9 10	Xidayang reservoi Angezhuang reservoir	30.10 34.13	16.21 18.80	2006	24 25	Angezhuang reservoir Wangkuai reservoir	32.00 114.60	8.28 48.44

Acknowledgements

.We are grateful for financial support by the Program for New Century Excellent Talents in Unversity (NETC-10-0708), National Basic Research Program of PR China (973 Project, 2009CB421100), the National Natural Science Foundation for Innovation Team of China (No. 40701189) and the Science Fund of China Postdoctor (No.20080430072).


Hao Yang

Dongyan Zhang

Wenjiang Huang

Zhongling Gao

Xiaodong Yang

Cunjun Li

Jihua Wang

Application and Evaluation of Wavelet-based Denoising Method in Hyperspectral Imagery Data

Keywords: imaging hyper-spectrometer, noise, filtering, wavelet analysis, quantitative evaluation

The imaging hyper-spectrometer is highly susceptible to the presence of noise and its noise removal is regularly necessary before any derivative analysis. A wavelet-based(WT) method is developed to remove noise of hyperspectral imagery data, and commonly used denoising methods such as Savitzky-Golay method(SG), moving average method(MA), and median filter method(MF) are compared with it. Smoothing index(SI) and comprehensive evaluation indicator(η) are designed to evaluate the performance of the four denoising methods quantitatively. The study is based on hyperspectral data of wheat leaves, collected by Pushbroom Imaging Spectrometer (PIS) and ASD Fieldspec-FR2500 (ASD) in the key growth periods. According to SI andη, the denoising performance of the four methods shows that WT>SG=MA>MF and WT>MA>MF>SG, respectively. The comparison results reveal that WT works much better than the others with the SI value 0.28 andηvalue 5.74E-05. So the wavelet-based method proposed in this paper is an optimal choice to filter the noise, in terms of balancing the contradiction between the smoothing and feature reservation ability.

Introduction

Hyperspectral imaging system is characterized by integrating both spectroscopy and imaging techniques in one system to provide detailed information of target which otherwise cannot be achieved with either conventional imaging or spectroscopy alone [START_REF] Wang | Basis and Application of Quantitative Remote Sensing in Agriculture[M[END_REF] . The simultaneous spatial and spectral information provided by this system along with its non-destructive and chemical-free nature nominated this technology to be a deliberated tool for monitoring crop diseases [START_REF] Singh | Detection of insect-damaged wheat kernels using near-infrared hyperspectral Imaging[END_REF] , vegetation nutrition [START_REF] Nguyen Do Trong | Prediction of optimal cooking time for boiled potatoes by hyperspectral imaging[END_REF] , food safety [START_REF] Liu | Categorization of pork quality using Gabor filter-based hyperspectral imaging technology[END_REF] etc.

But the obtained data is highly susceptible to the presence of noise. The sensors because of their narrow bandwidth are not able to capture a lot of energy and cause self-generated noise inside the sensor. Variations and atmospheric absorption of sun illumination also reduce the amount of recorded spectral signal [START_REF] Rozni | Trends and Issues in Noise Reduction for Hyperspectral Vegetation Reflectance Spectra[END_REF] . Therefore, noise removal is regularly necessary before any derivative investigation.

Many scholars have developed different methods for how to reduce noise of spectral signal [START_REF] Ying | A New De-Noising Technique for Spectra Based on Mexican Hat Wavelet[END_REF][START_REF] Zhou Dan | Wavelet Analysis and Its Application in Denoising the Spectrum of Hyperspectral Image[END_REF][START_REF] Hu | Noise reduction of hyperspectral data using singular spectral analysis[END_REF][START_REF] Jin | A General Framework for High-Dimensional Data Reduction Using Unsupervised Bayesian Model[END_REF] and 2-dimension image [START_REF] Lei | Hyperspectral Imagery Denoising Method Based on Wavelets[END_REF][START_REF] Chen | Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis[END_REF][START_REF] Gómez-Chova | Correction of systematic spatial noise in push-broom hyperspectral sensors: application to CHRIS/PROBA images[END_REF][START_REF] Quang | Image smoothing of multispectral imagery based on the HNN and geo-statistics[END_REF] . At the beginning, noise reduction applied spatial average method directly, such as moving-average or median filter method based on multi-points. But it was found that in the shortwave infrared spectrum, the mean and median filter cannot remove noise satisfactorily and a slight jaggedness remains [START_REF] Schmidt | Smoothing vegetation spectra with wavelets[END_REF] . With the development of fast Fourier transform and discrete Fourier transform, noise reduction method in frequency domain [START_REF] Atkinson | Wavelet-based hyperspectral image estimation[END_REF] become acceptable. Moreover, a number of methods for hyperspectral data dimensionality reduction have been developed, such as principal component analysis (PCA) [START_REF] Chen | Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis[END_REF][START_REF] Jolliffe | Principal component analysis[END_REF][START_REF] Weiwei | A noise removal method for hyperspectral data based on Contourlet transformation and PCA analysis[END_REF] , linear discriminate analysis (LDA) [START_REF] Fukunaga | Introduction to statistical pattern recognition[END_REF] etc. Especially in *Corresponding author. E-Mail:wangjh@nercita.org.cn recent years, booming wavelet technology aims to provide a new way of thinking, there are many wavelet-based and improved filter has been widely applied [START_REF] Zhou Dan | Wavelet Analysis and Its Application in Denoising the Spectrum of Hyperspectral Image[END_REF][START_REF] Lei | Hyperspectral Imagery Denoising Method Based on Wavelets[END_REF][START_REF] Othman | Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage[END_REF][START_REF] Mingxiang | Quantitative Evaluation of Soil Hyperspectra Denoising with Different Filters[END_REF] . However, these denoising methods aforementioned were designed mainly for field spectrometer(ASD, e.g.), or spspaceborne and airborne hyperspectral sensors(CHRIS/PROBA, OMIS, e.g.) where noise was caused by atmosphere and water vapour etc. Rarely research about denoising of imaging hyper-spectmeter data was reported. Recently, a new field-based Pushbroom Imaging Spectrometer(PIS) is developed independently by own. So more original data can be acquired, nevertheless coupled with more noise signal, which due to physical instruments, sun's illumination, field environment condition and others. An ideal method which is simple and effective to reduce noise of the collected data is an actual need. Furthermore, as to the evaluation of denoising effect, most research is based on visual judgment or simple signal noise ratio(SNR), which always can not satisfy requirements for their complex nature. This paper attempts to find an optimal denoising method aiming at homemade Imaging Spectrometer (PIS). The rest of this paper is structured as follows. Section 2 describes the hyperspectral imagery data collection and introduces four denoising methods along with the quantitative evaluation. Section 3 presents the denoising result and evaluating result. Conclusions are drawn in Section 4.

Materials and Methods

Hyperspectral data collection

The study site located in the National Experimental Station for Precision Agriculture, the Changping district of Beijing, China (40°11′N, 116°27′E). The cultivar of winter wheat, Jing 411 was chosen as research material and sown in October, 2009. When the winter wheat grown at jointing stage, 30 leaves samples were picked and corresponding hyperspectra was collected by PIS (Pushbroom Imaging Spectrometer) and ASD (FieldSpec®3portable spectrometer) simultaneously. The detailed process was as follows: Put leaves flatly on the black cloth and corresponding spectrum was measured with 120cm distance from the lens of PIS, the experimental time was from 10:30am to 15:30pm in a sunny and windless day.

PIS was developed by National Engineering Research Center for Information Technology in Agriculture and University of Science and Technology of China. Before the instrument was applied, the laboratory calibration had been carried out to determine location of the wavelengths, spectral response function, radiation accuracy and spatial displacement etc. The specific technical parameters of PIS and ASD were presented in Table 1.

Data Pre-processing

Hyperspectral imagery data can be viewed as a 3-dimensions imagery sequence, which is constituted by 2-dimensions image and 1-dimension spectrum. The 2-dimensions image describes target's spatial information feature while 1-dimension spectrum information reveals spectral profile feature of every pixel in image. For every pixel in image acquired by PIS, a sequence of digital number(DN) was obtained with spectrum interval of about 0.7nm.

For the purpose of follow-up quantitive research, DN value of wheat leaves must be transformed to relative reflectance which involved with instrument's calibration. In calibration, linear empirical method was adopted.

DN = aρ+b

(1)

In Eq.( 1), the coefficient a and b was determined by experiment data and least square method. And then the reflectanceρwas calculated.

ASD data is also be transformed to reflectance through standard reference white panel.

Denoising Methods

In this paper, aiming to the quantitative analysis of crop physiological and biochemical parameters, a wavelet transform method was explored to remove noise of hyperspectral imagery data, and common denoising methods such as Savitzky-Golay method(SG), moving average method(MA) and median filter method(MF) were also chosen and compared with it. Some indicators were designed to evaluate abilities of noise removal.

Wavelet Transform

Denoising techniques based on the wavelet transform, also known as wavelet shrinkage has been proposed to eliminate noise in various types of signals, by applying the transform and zeroing the coefficients below a certain threshold value, on the basis that noise coefficients would have lower amplitude than the coefficients corresponding to the feature being studied. Additionally, the coefficients remaining after the threshold operation may be reduced by the threshold value as well; this is known as soft threshold. It has been shown by Donoho and Johnstone (1994) that by using the appropriate threshold value, soft threshold results in the optimum denoising with the least distortion in the signal.

In this paper, threshold-based wavelet denoising algorithm includes mainly three steps.

1) The discrete wavelet transform and selection of wavelet filter and number of decomposition layers:

( ) ( ) ( )

Y d Y f Y z  (3)
In Eq.(3), Y(d)、 Y(f)、 Y(z) is high-frequency wavelet transform coefficient of noisy data, truth data and noise data.

2) Choose a threshold for every level high-frequency coefficient and wavelet transform coefficient ˆ()

Yf

was estimated by the threshold. Soft threshold and hard threshold can be used such as Eq.( 4) and Eq.( 5). T is threshold in them.

For soft threshold:

sgn( ( ))( ( ) ) ( ) ˆ() 0 ( ) Y d Y d T Y d T Yf Y d T       (4) For hard threshold: ( ) ( ) ˆ() 0 ( ) Y d Y d T Yf Y d T       (5) 3) Wavelet reconstruction ˆ()
Yf to get estimation of ˆ() ft:

1 ˆ( ) ( ) f t Y f    (6)
In Eq.( 6),,-1is the operator of wavelet inverse transformatin.

The wavelet transform filter method lies in the choice of the threshold scheme, commonly used scheme is Rigrsure, Sqtwolog, Heursure and Minimaxi. In them, Rigrsure selects an adaptive threshold based on Stein unbiased likelihood principle, Sqtwolog adopts fixed threshold 2 log( ( )) length x  , Heursure is a mixture of the first two, Minimaxi selects the mean square extreme error under non-ideal circumstance as the threshold.

The denoising effect is influenced by threshold adjustment ways. If the adjustments, there are two ways: one is that threshold is adjusted according to the first level noise(global threshold), while the other one is according to each level noise(hierarchy threshold).

Savitzky-Golay filter, Moving average, Median filter

The method of Savitzky-Golay filtering for hyperspectral analysis is discussed in detail by King et al. (1999). Savitzky-Golay filtering has its roots in least squares polynomial smoothing which is to find a smoothed value for each point in the spectrum of a subset of data within a window. The window contains the point to be smoothed in the center position of the window as well as several of its neighbors to either side in the spectrum. All the data within the window are used to perform the least squares fit, but only the central point is smoothed for each window position. The other points are smoothed by moving the window across the spectrum point by point, performing a least squares approximation to the windowed data at each location.

Moving average takes the mean spectral value of all points within a specified window as the new value of the middle point of the window(Tsai and Philpot, 1998). The method is solely based on linear calculations and has one key parameter, the filter size. The median filter is similar to moving average but it is median value not average value.

Evaluating Methods

Smoothing methods may cause some loss of information or change the original spectral so they will produce incorrect results in subsequent analyses such as red edge discrimination. Thus, smoothing techniques besides being powerful in the smoothing ability, should be used in the way that preserves the absorption and local spectral features like minima, maxima and inflection points. Fine filtering method evaluation criteria should be that the denoised spectral curve remains curve characteristics (curve shape and location of characteristics), and is as smooth as possible.

A smoothing index(SI) was designed to measure the smoothness of denoised spectral curve. In Eq. (7), n is number of wavebands,  ,  is the denoised spectrum and original spectrum respectively; the SI is smaller, it means the denoised spectrum is smoother.

Smoothing index:

1 1 1 1 ˆ| | = | | n i i i n i i i SI             (7)
Signal-to-Noise Ratio(SNR), Mean Square Error(MSE), Spectral Angle(SA), were also be used to evaluate smoothing effect. As the denoising effect is proportional to SNR while MSE is inversely proportional to SA, we designed a comprehensive evaluation indicator to ensue the consistency of the aforementioned three indicators. They are defined as follows:

Mean Square Error:

1 2 1 () n ii i MSE n      (8)
Signal-to-Noise Ratio:

n i i n i i i SNR         (9)
Spectral Angle:

1 2 2 1 1 ârccos n i i n n i i i i SA          (10)
Comprehensive evaluation indicator:

MSE SA = SNR   (11)
In above equations,  ,  is the denoised spectrum and original spectrum, n is number of wavebands respectively.

Results and analysis

The result of applying wavelet transform denoising together with other methods was demonstrated in Figure 1. We chosen an area in the middle of leave and got its DNs average in the area in every band, so an average spectrum was acquired. In fact, this process meant the correlation between pixels in spatial domain is utilized. The average spectra were showed in blue curves in Figure 1. Red curves displayed the results after noise removal. All algorithm was realized in MATLAB platform. In wavelet transform filter, sqtwolog, soft threshold, hierarchy threshold scheme were selected, the number of decomposition layers was set to 5, wavelet radix chosen "symlets20". For SG filter, the number of envelope iterations was 2 and window size was 5. And in MA and MF filter, the filter step was 5.

For four methods, the effects of noise removal can be seen in the Fig. 1. The obvious noises were removed and the whole shapes of spectral curves were preserved. When compared to the other, WT was smoother than others, especially in wavelength greater than 480nm. Contrasting the curves in 750-950nm, some small disturbances remained in Fig. 1(b,c,d) while Fig. 1(a) was smooth.

Evaluating methods in section 2.4 were applied to further assess the effect of denoising quantitatively. The result was showed in Table 2. In Table 2, two critical indicators should be concerned: SI andη. SI represents smoothness, the less means the smoother. WT had the minimum value of 0.28, MF had the maximum of 0.36. The denoising effect was WT>SG=MA>MF.η measured denoising ability from another view. Similarly, WT had the minimum value of 5.74E-05, it meant WT had the best performance. The effect was WT>MA>MF>SG. As a result, both of evaluating indicators showed WT had the best denoising performance.

Above result was based on region of pixels. For single pixel, the result of WT filter was showed in Fig. 2. The red curve represented denoising spectrum. It can be found WT was still very efficient though much noise existed in this case. So the WT filter was stable in denoisng, especially in >480nm wavelength range. When the selected region from PIS image was same to the view field of ASD, the same target was measured. Owing to filter algorithm was built-in ASD, the obtained ASD spectrum had little noise and can be viewed as reference spectrum. Denoised PIS spectrum by WT and the corresponding ASD spectrum was showed in Fig. 3. As shown, the former approach to the later. The trend was very consistent though absolute reflectance differences. The absolute reflectance difference can be explained by the two instrument sensitivity itself, illumination condition, calibration accuracy, and time etc.

Discussion and conclusions

This study presented an approach to removing noise of hyperspectral imagery data which were collected by PIS. Aiming to the quantitative inversion of crop physiological and biochemical parameters, the wavelet-based method was developed, and commonly used denoising methods such as Savitzky-Golay method(SG), moving average method(MA), and median filter method(MF) were compared with it. And a smoothness index and a comprehensive evaluation indicator were designed to appraise denoising effects. The denoised effects of four methods were assessed by them. For index SI, the denoising effect was WT>SG=MA>MF; forη, the result was WT>MA>MF>SG. So WT had the best noise removal performance. The denosing result by WT was also compared with ASD data, the effect was close to each other. As a result, WT filter was a very efficient means to removing noise for hyperspectral imagery data.

Though WT method showed powerful denoised ability, there was space for further research. Firstly, this paper focused mainly on spectrum dimension, and put little consideration on image dimension noise reduction. Secondly, for evaluation of denoised effect, the characteristic preservation ability can be evaluated through detailed key feature such as location of small peak and valley which depends on specific application. Denoising data was applied to biochemical parameters inversion, and then best denoising method was evaluated. In addition, the factors, such as illumination, temperature, wind, and human are all affect the experimental results, so how to ensure optimal spectrum information that reflects truth need further to study.

Figure 1 .

 1 Figure 1. The results of four denoising methods

Figure 2 .

 2 Figure 2. Denoising result of single pixel by WT Figure 3. Denoising Spectra of ASD and PIS by WT

Table 1 .

 1 Specific technical parameters of PIS and ASD

	Parameters	PIS	ASD
	Spectral Range	400-1000nm 350-2500nm
	Spectrum Resolution	To 2nm	3nm@700nm; 10nm@1400nm&2100nm
	Spectrum Interval	0.7nm	1.4nm,350-1000nm; 2nm, 1000-2500nm
	Spatial Resolution	To 5mm	/
	Field of View	16°	25°

Table 2 .

 2 The statistics of evaluation results for four methods

	Indicator	SI	SNR	MSE	SA	η
	WT	0.28	36.56	0.14	0.015	5.74E-05
	SG	0.35	35.12	0.20	0.018	1.03E-04
	MA	0.35	36.15	0.16	0.016	7.08E-05
	MF	0.36	35.65	0.18	0.016	8.08E-05

Acknowledgements

This work was subsidized by the Beijing Natural Science Foundation (Project Number: 4092016), China Special Funds for Major State Basic Research (Project Number: 2007CB714406, 2011CB311806), and China Postdoctoral Science Foundation funded project (Project Number: 20110490317). The authors are grateful to Mr. Weiguo Li, and Mrs. Hong Chang for data collection.


Xiaohe Gu

Yuchun Pan

Xin He

Jihua Wang

Estimation of maize planting area through the fusion of multi-source images

Keywords: maize, planting area, MODIS, TM, remote sensing, wavelet transform, minimum distance

The limitations on spatial resolution and on the availability and measurement accuracy of remote sensing images are the primary problems in the estimation of the large-scale planting area for maize. The integration of mid-and low-resolution images is the one of primary methods used for the estimation of large-scale crop planting areas using remote sensing. The use of a single-temporal thematic mapper (TM) image results in a low accuracy of maize recognition, so a mid-scale time-series normalized difference vegetation index (NDVI) dataset, which was derived from the fusion of the moderate-resolution imaging spectroradiometer (MODIS) and TM images based on the wavelet transform, was established. The planting area was estimated using the minimum distance model and the accuracy was evaluated using in-situ samples. The results show that the estimation of the maize-sown area based on the time-series NDVI information of the integrated images reached high levels of gross and position accuracy (89% and 90%), indicating that this method could fully utilize the time-series information from the MODIS images and the spatial resolution of a TM image. The use of the difference in phenophases among fall crops enables the effective classification of the spatial distribution of these crops.

Introduction

Maize is one of the primary crops in China, the planting area and yield of which comprise one-fifth of all crops [START_REF] Yuzhu | A preliminary study on the method for corn yield estimation by remote sensing in north China[J][END_REF] . Accurate information on the planting area and distribution of maize are important for effective yield estimation, agricultural management, and national food security [START_REF] Shuisen | Review of research advances in remote sensing monitoring of grain crop area[END_REF] . The spatial characteristics of maize distribution in China include complex planting structures and fragmented planting parcels. The limitations on spatial resolution and on the availability and measurement of the accuracy of remote sensing images are the primary problems in the estimation of the large-scale planting area for maize [START_REF] Gu Xiaohe | Consistency study between MODIS and TM on winter wheat plant area monitoring[J][END_REF] .

Previous studies show that the integration of mid-and low-resolution images is the primary methods used for investigating large-scale crop planting areas [START_REF] Van Niel | A simple method to improve field-level rice identification: toward operational monitoring with satellite remote sensing[J][END_REF][START_REF] Xiao | Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images[END_REF][START_REF] Gao | Land-use induced changes in topsoil organic carbon stock of paddy fields using MODIS and TM/ETM analysis: A case study of Wujiang County, China[J][END_REF][START_REF] Wu Mingquan | Mapping paddy fields in large areas, based on time series multi-sensors data[END_REF] . A mid-resolution image has a high level of accuracy in crop recognition, but lacks availability and coverage [START_REF] Jinshui | HJ-1 remotely sensed data and sampling method for wheat area estimation[END_REF][START_REF] Li | Remote sensing measurement of corn planting area based on field-data[END_REF] . On the other hand, a low-resolution image enables the acquisition of long time-series information that match crop growth [START_REF] Xiaohuan | Extraction of multi-crop planting areas from MODIS data[END_REF][START_REF] Wenbo | Remote sensing monitoring of winter wheat areas using MODIS data[END_REF] . Thus, utilizing mid-and low-resolution images to conduct crop recognition through the integration of the multi-source data is necessary. Currently available studies on crop planting area estimation primarily focus on winter wheat [START_REF] Meichen | Monitoring planting area and growth situation of irrigation-land and dry-land winter wheat based on TM and MODIS data[END_REF][START_REF] Gai Yongqin | Agricultural drought monitoring based on TM and MODIS remote sensing data-a case study of HebeiProvince[J][END_REF] and rarely emphasize maize. A number of crops are synchronized with maize, so this crop is of great significance for research on the estimation of planting areas through the integration of mid-and low-resolution images.

The Yuanyang County in Henan province was selected as the study area. The data used in the current paper included multi-temporal moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) images and a single-temporal thematic mapper (TM) NDVI image. A mid-scale time-series NDVI dataset, which was derived from the fusion of the MODIS and TM images based on wavelet transform, was established because of the low accuracy of maize recognition when using only a single-temporal TM image. The planting area was then estimated using the minimum distance model and the accuracy was evaluated using in-situ samples.

Study area and data preprocess 2.1 Study Area

The Yuanyang County in Henan province, a typical agricultural region where the planting structures are stable and the fall crops include maize and paddy, was chosen as the study domain. The phenological differences of the crops are often used to select the temporal remote sensing data for crop recognition. Based on the crop phenology in Yuanyang County, growing time is generally from mid-June to mid-October. The synchronized fall crops include paddy, peanut, and soybean, which tend to obstruct maize recognition.

Data Preprocess

The remote sensing data used in the current paper included MODIS and TM images covering the entirety of Yuanyang County. The Earth Observing System/MODIS data were composed of the NDVI images with a maximum synthesis of 16 d downloaded from the NASA website. The MODIS images were captured from May 24 to October 30, 2008, with a spatial resolution of 250 m. On the other hand, the TM image was captured on August 25, 2008, with a spatial resolution of 30 m. The data preprocess included geometric correction, atmospheric calibration, and projection transformation. The polynomial model of bilinear interpolation, which could ensure an error of less than 0.5 pixels, was used for the geometric correction. The 6S model was used for the atmospheric calibration. The projection type used for all spatial data used the Universal Transverse Mercatol system, whereas the geographic coordinate system was WGS 84. Farmland parcel data, which were digitized from SPOT5 fusion images, provided the basic unit for the in-situ sample location and accuracy evaluation. The in-situ data included 49 sample parcels from August 2008, the attribute of which includes the parcel and planting area percentages for maize. The in-situ samples were primarily used to evaluate the accuracy of the method.

Method and Technical Process

The technical processes for the estimation of the maize planting area using integrated MODIS and TM images including wavelet transform fusion, reconstruction of time-series NDVI, classification by minimum distance model, and accuracy evaluation, are in this section.

Fusion Model of Wavelet Transform

According to the theory and algorithm of wavelet transform, an image could be divided into one approximate component and three detail components. The approximate and detail components represent low-and high-frequency information, respectively. Using the inverse wavelet transform, high frequency information derived from the high-resolution image and low-frequency information derived from the multi-spectrum image could be transformed into the fusion image.

The algorithm of the wavelet transform model derived from the orthogonal basis of compact supported wavelets can be shown as follows:

j 1 r c j 1 j 1 r c j 2 j 1 r c j 3 j 1 r c j C H H C D H G C D G H C D G G C                  (1)
where J is the decomposing order of the wavelet transform, C is the matrix of the original image, G is the high-pass filter, H is the low-pass filter, C j is the low-frequency component, and D j represents the three high-frequency components.

The key parameters of the wavelet transform fusion model include the wavelet basis, decomposition level, and fusion rule. The wavelet fusion is unable to provide a uniform theory and method for multi-source sensors because of the various remote sensor types and different spatial resolutions. Thus, performing numerous experiments was necessary to analyze the optimal wavelet basis, decomposition level, and fusion rule of the wavelet fusion between the MODIS and TM images.

After several experiments, the fusion of the wavelet transform between the MODIS and TM data was conducted using the BIOR6.8 wavelet basis and three decomposition layers and employing the fusion rule of high-low frequency conversion. Figures 2 and3 show the final fusion image.

Reconstruction and Analysis of the Time-series NDVI

The NDVI is the optimal indicator for the features, growth condition, and coverage of vegetation. In the current study, the standard growth curves of the primary fall crops were obtained using the time-series fusion image from the NDVI with a resolution of 30 m. First, using the located in-situ samples, pure parcel samples from the NDVI time-series fusion images, including maize, paddy, peanut, and soybean, were selected. As shown in Figure 4, the reflectance of the red band decreased when the coverage and leaf area index of the four crops increased, whereas the reflectance of the near infrared band increased along with the phenological changes. Thus, each of the four crops had peaks in the NDVI time-series curves.

The peak of maize in the NDVI curve appeared between the end of July and the first week of August, which corresponded to the grain-filling stage. The peak resulted from the end of the nutrition growth and the beginning of the complete reproductive growth of maize.

The features of the NDVI curves of the four crops were different to a certain extent. At the early-growth stages, the NDVI value of maize was difficult to distinguish because it was similar to that of paddy and soybean. However, at the late-growth stages, the NDVI values of maize and peanut rapidly decreased and showed significant differences with the NDVI values of paddy and soybean.

Minimum Distance Classifier

The selection of an effective distance measurement is important for the algorithm of the minimum distance classifier. The universal distance parameters include the Euclidean distance (ED) and the Mahalanobis distance, among others. In the current study, the typical ED was selected to determine the pixel classification using the minimum distance model. The function of Euclidean distance is as follows:

2 E r t d (NDVI NDVI )   (2)

Accuracy Evaluation

The in-situ sample parcels were assumed to have the real values, and the accuracy of the estimation of the maize planting area was evaluated based on the fusion of the MODIS and TM images with the indexes of position accuracy and gross accuracy.

The contributions of the different parcels varied because of the area differences of each farmland parcel. Taking the parcel area as weight, the function of the position accuracy is as follows:

  n i io i i1 i n i i1 1 p p * A K *100% A        (3)
where P i is the estimated percentage of maize in one parcel, P io is the maize percentage of the in-situ sample parcel, A i is the area of a certain parcel and n is the number of in-situ samples.

The evaluation of the gross accuracy was performed using the planting area account of all in-situ sample parcels as the true value. The function of the gross accuracy is as follows:

n i i o i1 o p * A A K (1 ) *100% A       (4)
where P i is the estimated percentage of maize in one parcel, A i is the area of a certain parcel, n is the number of in-situ samples of maize and A o is the account of maize planting area of the in-situ sample parcels.

Results

The time-series curves of the four crops show that the NDVI features of several crops were similar to that of maize. At the seedling stage, the NDVI values of maize, paddy, and soybean were near that of one another, indicating that the growth conditions of the three crops were similar. At the jointing stage, the NDVI value of maize was close to that of the other crops, which were hardly distinguished. At the maturity stage, the NDVI value of maize was close to that of peanut when they were divided. However, the NDVI value of peanut was different with that of maize at the seedling stage, so the spatial distribution could be extracted using the stratified classification method.

Considering the differences in terms of phenomena and growing process among the studies crops, the spatial distribution of the NDVI values of peanut and maize at the seedling stage were extracted first. Then, the total spatial distributions of maize and peanut were distinguished based on their NDVI characteristics at the maturity stage. Finally, the joint of both spatial distributions was considered as that of maize, through which the account of the maize planting area in Yuanyang County could be estimated.

The maize planting area was also derived using a supervised classification of the in-situ samples. Considering the in-situ sample parcels as the true value, the accuracy of the supervised classification and time-series analyses were evaluated. The position and gross accuracies of maize derived using supervised classification were 62.89% and 49.01%, respectively. The estimated accuracies were derived from an improved time-series analysis from which the position and gross accuracies were 78.76% and 88.57%, respectively.

Conclusion and Discussion

The fusion of MODIS and TM images based on wavelet transform enabled the plotting of the standard growth curves of the primary fall crops with a 30 m resolution, and the maize planting area was estimated using a minimum distance classifier. The results show that the estimation of the maize-sown area based on the time-series NDIV information of the fusion images could yield high values of gross and position accuracy (89% and 90%), indicating that this method could fully utilize the time-series information from the MODIS images and the spatial resolution of a TM image.

The present study requires further improvement. First, studying the estimation method for the maize planting area using daily time-series images is necessary because the MODIS NDVI data are synthesized from the maximum synthesis value of 16 d and because the phenomena observed from the fall crops are similar. Second, although the current study improved the spatial resolution of time-series data from 250 to 30 m, the spatial character of maize distribution, including the complex planting structure and fragmented planting parcels, result in mixed pixels. Therefore, studying soft classification or mixed pixel decomposition is necessary to reduce the effect of mixed pixels.

Fig. 1 .

 1 Fig. 1. MODIS and TM images covering Yuanyang County in 2008

Fig. 2 .Fig. 3 .

 23 Fig. 2. Fusion image from the MODIS and TM images based on the wavelet transform

Fig . 4 .

 4 Fig .4. NDVI time-series curves of the fall crops

Table 1 .

 1 Analysis of the accuracy of the maize planting area derived from the supervised classification and

	time-series analysis methods.
	method	position accuracy gross accuracy
	supervised classification	62.89%	49.01%
	time-series analysis	78.76%	88.57%

Foundation: The National Natural Science Funds (No. 41001199), Beijing Excellent Talent Program (No. PYZZ090416001998) and Beijing Scientific New Star Program (No. 2010B024) First author biography: Xiaohe Gu (1979-),


Sijia Tao

Jingming Li

Junhui Li

Jianbo Tang

Jinrui Mi

Longlian Zhao

Discriminant Analysis of Red Wines from Different Aging Ways by Information Fusion of NIR and MIR Spectra

Keywords: Information fusion, NIR, MIR, Wine, Aging way

Two different levels' information fusion of near infrared (NIR) spectra and mid infrared (MIR) spectra coming from red wines aged in different ways has been investigated. A total of 96 red wines, including 44 wines aged in oak barrel, 26 wines aged in stainless steel tank with oak chips and 26 wines aged in stainless steel tank, were analyzed. The NIR transmission spectra and MIR attenuated total reflectance (ATR) spectra of samples were collected. For data level fusion, the NIR and MIR spectra were max-min normalized respectively and then merged. For feature level fusion, five PCs and db3 coefficients of 3-layer wavelet decomposition were calculated. Discriminant models of the three different aging wines were established using DPLS and Fisher method before and after fusion. All the fusion methods mentioned above have achieved better results on classification than individual spectroscopy. These results suggest that the spectral fusion of NIR and MIR is a promising technology for discriminating different aging wines.

Introduction

The red wine is welcomed by customers at home and abroad for its rich nutritional value. The wine aging process is the wine's mature process. It is a necessary procedure in the production of the aged wine. Different aging ways had great effects on the content of polysaccharides and wine chromaticity. So that it can affect the quality of the wine.

The research from home and abroad using spectroscopy studying wine are mainly focused on two aspects. On one hand, the application of mid infrared spectroscopy alone is widely used for classifying the varieties [START_REF] Roussel | Fusion of aroma, FT-IR and UV sensor data based on the Bayesian inference. Application to the discrimination of white grape varieties[END_REF] [START_REF] Picque | Discrimination of red wines according to their geographical origin and vintage year by the use of mid-infrared spectroscopy[END_REF] , vintages [START_REF] Edelmann | Rapid method for the discrimination of red wine cultivars based on mid-infrared spectroscopy of phenolic wine extracts[END_REF] and geographical region of red wine. On the other hand, near infrared spectroscopy is applied for quantitative analysis which is becoming a new rapid analysis technology that it is primarily applied for contents of samples as well as wine [1][4] . Although a large number of studies have been published, there are very few systematic investigations exploring classification of different aging ways of red wines to our knowledge.

Information fusion is the merging of information from disparate sources, thus we can obtain more accurate and reliable description than single source achieved. The information of MIR comes from the frequency of the atomic vibration between the molecules while the information of NIR comes from the conjunction of vibration frequency and times frequency. The MIR has a lower limit of detection of 10 -5 ~10 -6 compared with NIR of 10 -3 ~10 -4 . Based on the different characteristics of NIR and MIR, the information fusion of near infrared spectra and mid infrared spectra is supposed to get better classification results.

Here, two different levels' information fusion, data level fusion and feature level fusion, are studied, and the applicability of spectra fusion technique to discriminate different aging wines is investigated. The aim of this study is to realize the rapid and reliable discrimination of wines aged in three different ways.

Experimental

Wine Samples

A total of 96 red wines were analyzed in this study, including 44 wines aged in oak barrel, 26 wines aged in stainless steel tank with oak chips and 26 wines aged in stainless steel tank. The wine samples were from two kinds of grape varieties (Cabernet Sauvignon and Merlot) ranged from 2003 to 2007 vintages. The red wine samples were collected from wineries in Changli and Huailai of Hebei, China.

Instruments and procedures

Spectra collection

NIR transmission spectra: Wine samples were scanned in transmission mode using FT-NIR spectrometer of MPA (Bruker Optics), equipped with a tungsten lamp and a cooled PbS detector. Samples were scanned in a rectangular cuvette with a 1 mm path length. The spectra were measured by summing 32 scans acquired over the wavelength range 12500~3900cm -1 at a resolution of 4 cm -1 . The spectrum of each sample was obtained in triplicate and the mean of the three measurements was used for classification. NIR spectra are shown in Fig. 1.

Fig. 1 Near infrared spectra of red wine samples

MIR ATR spectra:

The MIR spectra of 96 wine samples were obtained from 550 to 4000 cm -1 on TENSOR 27 (Bruker Optics) FT-MIR instrument with an ATR attachment. The experiment used air as reference in room temperature. For both background and samples, the spectra were measured by summing 32 scans acquired at a resolution of 4 cm -1 . MIR spectra of the wine samples are shown in Fig. 2.

Chemometric techniques for classification analysis

The spectra before and after fusion were used to develop discrimination models for different wine aging ways using two pattern recognition methods, discriminant partial least squares (DPLS) and Fisher discriminant analysis (FDA). DPLS is an application of Partial Least Squares (PLS) [5] for discrimination analysis. Different from PLS, the output variable of DPLS was expressed as a binary variable, while the PLS was concentration value. Generally a critical threshold was pre-set, by comparing the predictive value with the threshold, the samples' categories were determined. FDA has been widely used in the domain of pattern recognition. The Fisher discriminant criterion was to find a linear projection that the classes of the samples are separated with maximal ratio of between-class and within-class variability [6] .

Results and discussion

Data level fusion

For data level fusion, the NIR and MIR spectra are preprocessed (min-max normalized) respectively. These two sources of information are merged then.

To develop the discriminant models, 96 wine samples were divided into two sets randomly, 2/3 training set and 1/3 for validation sets. Both sets were independent; the training set was used to build discriminant models and the validation set was only used for testing the models. Then DPLS and FDA modeling methods were used to develop classification models of different aging wines before and after fusion respectively. To test the stability of the models, wine samples were divided into training and validation set for 10 times randomly.

Feature level fusion

Two kinds of feature extraction methods, PCA and wavelet transform, were used to achieve the goal of feature level fusion.

Principal components analysis (PCA)

PCA [START_REF] Vandeginsten | Handbook of Chemometrics and Quali-metrics: Part B[END_REF] was a widely used feature extraction method for reduction the number of variables showing co-linearity. In this study, five PCs were extracted from NIR spectra and MIR spectra respectively, then these PCs were merged as the input of the chemomtric methods in order to get the fused classification discrimination through modeling at the feature level. FDA method was applied to build the classification models of three different aging wines using the NIR spectra, MIR spectra and the merged spectra. Table 3 shows the classification result of the 96 red wine samples. As it can be seen from table 3, the recognition accuracy of training set and validation set was 95.95% and 98.67 respectively. When compared with the results of NIR and MIR spectra, it can be easily found the accuracies were improved after spectra information fusion

Wavelet transform

Wavelet transform [START_REF] Mallat | Singularity Detection and Processing with Wavelet[END_REF] is now being adopted for a vast number of applications. Wavelets are mathematical functions that cut up data into different frequency components, and then study each component with a resolution matched to its scale. These coefficients can then be compressed more easily because the information is statistically concentrated in just a few coefficients.

The db3 wavelet was chose for 3-layer wavelet decomposition. The approximate coefficients of the 3rd layer were used to information fusion. Then DPLS and FDA modeling methods were used to develop classification models of different aging wines before and after fusion respectively.

we can obtain 99.52% as the recognition accuracy of training set and 95.96% as the recognition accuracy of validation set by taking an average of ten times as a result when use the DPLS model. While for the FDA method, the recognition accuracy of training set is 97.61% and the recognition accuracy of validation set is 98.65%. It can be found the accuracy was improved after information fusion.

Conclusions

In this paper, two different levels' information fusion of NIR and MIR spectra of three different aging wines were studied. For classifying three different aged wines, discriminant models are developed to analysis fused spectra with FDA and DPLS methods. The recognition accuracies were all increased to a certain extent after spectra fusion. For data level fusion, the recognition accuracies of validation set were improved to 96.59% and 97.98% by DPLS and FDA respectively. For feature level fusion, the recognition accuracies of validation set were improved to 98.67% using DPLS when PCA was used while the recognition accuracies of validation set were improved to 95.96% and 98.65% respectively by DPLS and FDA when we used wavelet transform. All the results above showed that information fusion of NIR and MIR spectra combined with the pattern recognition methods (DPLS and FDA) had the capability to extract feature of red wines and play a good role in discriminating wines from different aging systems.

However, some work still can be done for better results: some other pattern recognition methods could be tried, and the level of information fusion could be extended to the decision level [START_REF] Windeatt | Binary labeling and decision-level fusion[END_REF] . It should be surely useful to extend the study in this direction.

Fig. 2

 2 Fig. 2 Mid infrared spectra of red wine samples

  Table 1 and table 2 show the mean recognition accuracy of cross-validation (training set) and prediction. From table 1 and table 2 we can see, both recognition accuracy of training set and recognition accuracy of validation set are improved. For DPLS model, the recognition accuracy of training set was improved to 99.06% while the recognition accuracy of validation set was advanced to 96.59%. For FDA model, the recognition accuracy of raining set and validation set were 98.01% and 97.98%, respectively.

	Table 1. Classification results for 3 different aged wine samples by DPLS
		Recognition accuracy(%)	Recognition accuracy(%)
		of training set	of validation set
	NIR	90.60	86.02
	MIR	97.81	96.25
	After fusion	99.06	96.59
	Table 2. Classification results for 3 different aged wine samples by FDA
		Recognition accuracy(%)	Recognition accuracy(%)
		of training set	of validation set
	NIR	93.68	93.66
	MIR	96.00	97.34
	After fusion	98.01	97.98

Table 3.

 3. Classification results for 3 different aged wine samples by FDA

		Recognition accuracy(%)	Recognition accuracy (%)
		of training set	of validation set
	NIR	90.63	87.50
	MIR	94.38	91.87
	After fusion	95.95	98.67

Corresponding author. Tel.:010-62737414, E-mail address: taosijia530@163.com Acknowledgements: This study was supported by National Science Foundation of China (Project No: 31101289) and Chinese Universities Scientific Fund (Project No:

2009JS104).