Jinling Zhao

Dongyan Zhang

Juhua Luo

Dacheng Wang

Wenjiang Huang

Identifying Leaf-Scale Wheat Aphids using the Near-Ground Hyperspectral Pushbroom Imaging Spectrometer

Keywords: Hyperspectral remote sensing, leaf-scale, pushbroom imaging spectrometer (PIS), spectral characteristics, wheat aphids

This study is to identify leaf-scale wheat aphids using the nearground hyperspectral Pushbroom Imaging Spectrometer (PIS). Firstly, the spectral characteristics between normal and aphid-infested wheat leaves were compared in spectral reflectance. Concerning the serious aphid damage level, it is obvious that its spectral curve is badly flattened such as green peak (centered around 550 nm), red valley (centered around 680 nm), due to the influence of aphid. Specifically, in the visible spectrum (500-701 nm), the maximum delta (the maximum value minus the minimum value) is 3.3 and it is 7.5 in the nearinfrared spectrum (701-900 nm). Then, the spectral difference and change rate were further analyzed. It seems that both curves show the mirror symmetry and their maximum values are 55.8% and 17.4%, respectively. For the difference curve, the value is negative in the visible spectrum (400-700 nm), which shows that the reflectance of normal wheat leaf is less than that of the serious level. Conversely, it is greater in the near-infrared spectrum (700-900 nm). Finally, based on the high spatial resolution PIS image, ENvironment for Visualizing Images (ENVI-EX) was utilized to extract aphids and the overall accuracy reaches 97%. The result indicates that the PIS is sufficient to identify the wheat aphids and this study can lay a foundation for further applications in precision agriculture using such a hyperspectral imaging system.

Introduction

Wheat (Triticum aestivum L.) is one of the most important cereal crops and it has been used as major consumable commodity by human beings in most areas of the  Author for correspondence: yellowstar0618@163.com. world. However, global climate change has exerted a negative impact on wheat production [START_REF] Ortiz | Climate Change: Can Wheat Beat the Heat?[END_REF]. As a result, the incidence of various kinds of stress is becoming more often, among which diseases and insects have strongly affected the wheat crop. As one of the most important insects in winter wheat, aphids (Rhopalosiphum padi L.) have caused significant losses in its quantity and quality [START_REF] Burd | Biotypic Variation among North American Russian Wheat Aphid (Homoptera: Aphididae) Populations[END_REF]. In addition, insects and diseases in wheat can spread at a fast pace and serious hazards can be induced if they are not timely detected and prevented. Therefore, some effective measures must be taken to reduce production losses and ensure food safety. Nevertheless, on-farm pest management and crop protection strongly depend on diagnosis of disease or insect stresses in fields, while traditional field survey methods are labor-intensive and timeconsuming, and they cannot yield full coverage [START_REF] Lucas | Advances in Plant Disease and Pest Management[END_REF]. Accordingly, it is of great importance to accurately monitor and evaluate the wheat aphids in a noninvasive and nondestructive way. On the other hand, it is also essential for ensuring that pesticide spraying can be limited and more time-and site-specifically [START_REF] Delalieux | Hyperspectral Reflectance and Fluorescence Imaging to Detect Scab Induced Stress in Apple Leaves[END_REF].

With the fast development of advanced sensors and image processing techniques, quantitative remote sensing has facilitated extraordinary advances in monitoring and identifying wheat insects [START_REF] Yang | Differentiating Stress Induced by Greenbugs and Russian Wheat Aphids in Wheat using Remote Sensing[END_REF]. When it is infested, the reflectance spectra are obviously different between healthy and infested plant. To detect such a stress using the remote sensing, it is based on the assumption that insect stress interferes with photosynthesis or physical structure of plant. Furthermore, such destruction affects the absorption of light energy, and thus alters the reflectance spectra at different damage levels [START_REF] Moran | Opportunities and Limitations for Image-Based Remote Sensing in Precision Crop Management[END_REF]. In previous studies, multi-spectral remotely sensed data were usually utilized to identify and monitor the crop stresses in several bands at a large scale in the visible and nearinfrared regions [START_REF] Huang | Identification of Yellow Rust in Wheat using in-situ Spectral Reflectance Measurements and Airborne Hyperspectral Imaging[END_REF][START_REF] Qin | Detection of Rice Sheath Blight for In-Season Disease Management using Multispectral Remote Sensing[END_REF][START_REF] Lan | Development of an Airborne Remote Sensing System for Crop Pest Management: System Integration and Verification[END_REF]. However, the crude spectral resolution of the reflected and emitted energy from the earth is the primary limiting factor in differentiating subtle differences of plant stresses [START_REF] Govender | A Review of Hyperspectral Remote Sensing and its Application in Vegetation and Water Resource Studies[END_REF]. Consequently, hyperspectral remote sensing is currently being investigated by scientists with regard to the detection and identification of plant health/stress status [START_REF] Yang | Assessment of the Severity of Bacterial Leaf Blight in Rice using Canopy Hyperspectral Reflectance[END_REF]. Although airborne and spaceborne hyperspectral sensors can also acquire simultaneously imaging and spectrum in a single scanning process, lack of near-ground prior knowledge of stressed plants has greatly limited such data in the wide applications of precision agriculture. Conversely, near-ground hyperspectral imaging devices can derive the refined spectral characteristics of stressed plants in fields, which can lay the methodological foundation for those hyperspectral data at large spatial scales and further provide the ground truth data for accuracy validation.

By comparing with commonly used hyperspectral spectrometers such as the ASD (Analytical Spectral Devices, Boulder, CO, USA), near-ground hyperspectral imaging device can acquire simultaneously imaging and spectroscopy in a single system, which can be an effective tool in describing the variability between healthy and infected plants. In the last several years, more attention has only been paid to such an imaging device. Several studies have shown that the near-ground hyperspectral imaging systems can detect insect-and disease-infested plants in a non-invasive and non-destructive way [START_REF] Nillson | Remote Sensing and Image Analysis in Plant Pathology[END_REF][START_REF] Mirik | Using Digital Image Analysis and Spectral Reflectance Data to Quantify Damage by Greenbug (Hemitera: Aphididae) in Winter Wheat[END_REF][START_REF] Singh | Identification of Insect-Damaged Wheat Kernels using Short-Wave Near-Infrared Hyperspectral and Digital Colour Imaging[END_REF]. However, corresponding applications and processing techniques have not yet been enough explored in detecting wheat insects or diseases [START_REF] Ye | A Ground-Based Hyperspectral Imaging System for Characterizing Vegetation Spectral Features[END_REF][START_REF] Inoue | An AOTF-Based Hyperspectral Imaging System for Field Use in Ecophysiological and Agricultural Applications[END_REF]. Therefore, based on the Pushbroom Imaging Spectrometer (PIS), the primary objective of this study lies in characterizing the spectral characteristics of leaf-scale wheat aphids and identifying the aphids using the high spatial resolution image.

Materials and Method

Near-Ground Hyperspectral Imaging Device

The hyperspectral imaging device used in our study is the PIS, which was jointly developed by Beijing Research Center for Information Technology in Agriculture and University of Science and Technology of China. This system acquires images by linear array push-broom imaging and Fig. 1 is the demonstration of collecting hyperspectral imaging data. Table 1 shows some key specification parameters of the PIS. It is obvious that PIS is superior to the commonly used ASD field spectrometer in spectral resolution and sampling interval. Additionally, the PIS can also acquire spectro-images with a very high spatial and spectral resolution by which land features of interest can be identified.

Data Acquisition and Preprocessing

Fig. 1. A demonstration of acquiring the spectra combing with image of different aphid damage levels using the PIS.

An experiment was carried out at the experimental farm of Beijing Academy of Agriculture and Forestry Sciences (39.93° N, 116.27° E) on 26 May 2010. At that time, it was just the grain-filling stage of winter wheat, which is the key yield-forming period and is also extremely sensitive to various kinds of biological disasters. In this study, wheat aphids were used as the study object. When aphid-infested symptoms of wheat leaves were evaluated, relative insect damage levels were assessed according to the aphid populations under the help of experienced pathologist. Before using the device to measure wheat leaves, strict laboratory calibration must be firstly performed for ensuring a high radiometric accuracy. When collecting the reflectance spectra, the PIS lens was set at 80 cm over the wheat leaves and halogen lamp irradiation was fixed at a 45° angle. Then, the inverse 2nd leaves were picked up and immediately put onto the scanning platform, and the PIS moved evenly along the track and the image and spectra of wheat leaves on the black cloth were acquired (Fig. 1). Before and after collecting the leaf spectra, white reference panel was used to optimize the instrument. Finally, further processing were required to covert the original DN (Digital Number) values to reflectance by the empirical linear method (Eq. 1). Afterwards, a moving-average smoothing algorithm was utilized to exclude abnormal values and smooth the spectral curves in order to accurately derive the reflectance characteristics between healthy and aphid-infested wheat leaves.

* a DN b   . ( 1
)
Where  is the real reflectance, a and b are the coefficients. When putting the measured spectral value and corresponding DN into Eq. 1, a and b can be obtained by Least-Square Method (LSM), and then  can be obtained.

3

Results and Discussion

Analysis of Spectral Characteristics of Leaf-Scale Wheat Aphids

On the high-resolution PIS image (Fig. 2(a)), two pixels including a healthy leaf point and an aphid-covered leaf point were selected. Fig. 2(b) is the comparative result of their spectral reflectance curves in the 500-900 nm spectral range. It is obvious that the spectral reflectance values are markedly different between normal and aphid leaves. Furthermore, between 500 nm and 701 nm, the reflectance of aphid leaf is larger than that of normal leaf, while it is smaller in the 701-900 nm spectral regions. In the visible spectrum (500-701 nm), the maximum delta (the maximum value minus the minimum value) is 3.3 and it is 7.5 in the near-infrared spectrum (701-900 nm).

Concerning the locations of the minimum value, they are very similar between normal and aphid pixels, which are 664 nm and 661 nm, respectively. However, they are very different for the locations of maximum values which are 785 nm and 852 nm, respectively.

Comparing the spectral curves between normal and aphid-infested leaves, it can be found that both of them show the typical spectral characteristics of green vegetation: green peak centered around 550 nm, red valley centered 680 nm and high nearinfrared reflectance centered around 780 nm, but they are different at specific spectral values for different damage levels. Furthermore, comparing the reflectance between the visible and near-infrared spectra, the spectral differences are more obvious in the near-infrared spectrum. Nevertheless, the spectral curve of aphid-affected leaf is badly flattened especially for the characteristic bands such as green peak, red valley owning to the influence of aphid.

Fig. 2. The comparative of spectral curves (b) between normal and aphid-infested leaves on the PIS image (a).

Additionally, the analysis of spectral difference and change rate were also performed between normal and seriously aphid-affected leaves (Fig. 3). Due to the constraint of spectral range, the PIS can only obtain the spectral characteristics in the visible and partial near-infrared wavelength. In the 400-900 nm spectral regions, it can be found that they are very different in the spectral difference and change rate curves. It seems that they show the mirror symmetry and the maximum change rate and difference are 55.8% and 17.4%, respectively. As can be seen in the difference curve, the value is negative in the visible spectrum (400-700 nm), which shows that the reflectance of the normal wheat leaf is less than that of the serious level. Conversely, it is greater in the near-infrared spectrum. This phenomenon shows that the reflectance is determined by different factors in the visible and near-infrared spectral ranges. For the change rate curve, it fluctuates more severely in the visible spectrum than in the near-infrared spectrum, which shows that aphids exerted more evident impact in the visible spectrum.

Difference=f Normal -f Serious ; Chang rate=Difference/f Normal ; f is the spectral reflectance. The phenomenon for the change trend of spectral curves is that aphids cover the top of wheat leaf and they destroy the cell structure and change the chlorophyll content. In the visible spectral range, the reflectance of wheat leaf is mainly determined by various pigments, among which, chlorophyll is a leading one. When aphids break the wheat leaf, chlorophyll content will accordingly decrease which result in an increase of reflectance owning to the decreasing ability of chlorophyll absorption. Additionally, the secretions of aphids adhere to the leaf top can absorb some dust and other materials and increase the reflectance to a certain degree. Conversely, the reflectance depends on the cell structure in the near-infrared spectrum. Aphids destroy the cell structure of wheat leaf, so the reflectance of aphid-infected leaf decreases compared with the normal leaf.

Identifying Aphids using the PIS Image

Based on the high spatial resolution PIS image, aphids were identified. Due to the only leaf-scale analysis in this study, it is easy to separate aphids from leaf background. Here, the ENVI-EX (ENvironment for Visualizing Images, Research Systems, Inc.) feature extraction module was used owning to the obvious aphid texture and single background. As shown in Fig. 4(a), there are five necessary steps to complete the object-oriented classification including choosing the scale parameter, merging the object primitives, refining the objects using a threshold value for just one band of the image and it is an optional step, extraction of the attributes, and objectoriented classification based on rules or examples. As can be seen on the aphid identified image (Fig. 4(b)), it is obvious that most aphids were extracted from normal wheat leaves. To evaluate the classification accuracy, 30 aphid pixels were randomly selected in the ENVI environment, and then they were found on the classification map using the link displays tool o ENVI. The comparative result showed that 29 points were correctly identified and the overall accuracy reached 97%.

Conclusion

In our study, near-ground hyperspectral imaging data (PIS) were used to identify leafscale wheat aphids. By analyzing the spectral characteristics between normal and aphid-affected wheat leaf and classifying aphids using the PIS image, it has been proved to be an effective tool for identifying aphids using such a hyperspectral imaging system. According to the above analysis, we drew several conclusions: (1) the PIS can combine imaging with spectrum in a single system compared with commonly used non-imaging ASD spectrometer; (2) the PIS can collect the pixel-topixel spectrum, so it can obtain the pure spectral information on the basis of identification of targets of interest; (3) when the PIS is used in the canopy-scale wheat aphids, it will be more accurate to identify the targets of interest by using simultaneously spectral and spatial information of the PIS; (4) owning to very high spatial resolution of the PIS image, it will be very suitable for identifying the targets of interest using the object-oriented classification method.

Fig. 3 .

 3 Fig. 3. Analysis of spectral difference and change rate between healthy and seriously aphidaffected wheat leaves.

Fig. 4 .

 4 Fig. 4. The technical flowchart for identifying leaf-scale aphids using the ENVI-EX objectoriented classification module (a) and (b) is the high-resolution image acquired by the PIS spectrometer and the identification result.

Table 1 .

 1 Some key specification parameters of the PIS imaging system.

	Sensor parameters	PIS
	Spectrum range	400~1000 nm
	Spectral resolution	2 nm
	Sampling interval	0.7 nm
	FOV	16°
	Spatial resolution	5-10 mm
	Pixel dimension	7.4 μm×7.4 μm
	Image resolution	1400 (Spatial dimension) × 1024 (Spectral dimension)

Acknowledgements

The project was supported by China Postdoctoral Science Foundation funded project (20110490317), National Natural Science Foundation of China ( 41071276), Program of Ministry of Agriculture (200903010), Special Funds for Major State Basic Research Project (2007CB714406), and Postdoctoral Science Foundation of Beijing Academy of Agriculture and Forestry Sciences (2011).


Jian Wang
email: wangjian@caas.net.cn

Research on Application of Web Log Analysis Method in Agriculture Website Improvement

Keywords: Web log analysis, information behavior, agricultural users, agricultural website

With the advance of agricultural modernization, agriculture website was increasingly becoming a major tool for farmers getting information about life and production.

How to make the analysis of the needs of farmers effectively to help them to find the information from the ocean of information and resources of the Internet they were interested in had become an urgent and important issue. In this paper, we used the website of Agridata as an example and focused on the solution for the problem. A way was proposed for analyzing and mining the web log of Agridata, which integrated statistical analysis and cluster analysis. By this method, could information behaviors of users be grasped when browsing the website. It was important significance in improving the structure and content of agriculture website, which could provide better services for farmers and improve the level of modernization of agricultural production.

Introduction

As the emphasis on the rural information continues to deepen in our country, the development environment for rural information was constantly optimized and farmers' awareness of information technology was growing. More and more famers knew the technology for getting information of production and living from Internet, which could fundamentally change the situation of information isolated from the outside world to remote villages. Therefore, agriculture web site provided a great convenience to farmers in eliminating poverty, agricultural sales, employment, medical treatment and education. Thus, agriculture web site played a positive role for improving living conditions in rural areas and safeguarding national unity and stability. However, during the process of building agriculture web site, as the special nature of agriculture, farmers' behavior of access information had some special needs. How to grasp the information needs of agricultural users was important for improving agriculture web site. This was an urgent problem.

Web log was a kind of file which record some information of user access to web pages which included records of Browse, search, information downloads, and other message of site visits. We could obtain varies information about visits and utilization of website resource and properties of users by calculating and analyzing web server log. This information could help us optimize the information resources and improve quality and efficiency of web services. At present, many commercial Web sites had analyzed user behavior through the log analysis techniques to improve web services and this area had become a research focus. For example, Zhang xuehong(2005) discussed the actual value of user behaviors analysis, home page designing and customer service with the method of log analysis and introduced one process of homepage log analysis. Fengchunhui(2010) studied the web log applied in distance education using data mining technology and obtained a good result. Furthermore, Xuping (2010) and Wengchangping(2010) also studied web log playing the role of mining users information behaviors to improve website with their background.

However, the study background of user information behavior analysis based on web log which had referenced from existing literatures were for academic professionals such as library users and distance education students. This method was very little used for analyzing the information behaviors of web users in rural areas. To resolve this problem, in this passage, the Web access log of the agricultural scientific data center was used as an object and we discussed the method for studying the information behavior of users of agricultural methods with statistical analysis and cluster analysis. Then we conducted experiments with the Web access log of the national agricultural scientific data center and analyzed the pattern of information behavior of its user. With these results, several advices on this website of the agricultural scientific data center were made for providing a better service to agricultural users. The web log of Agridata was some log files about website visits existing in the web server which included IP address of website visitors, URL of explored pages, date and time of visits, path of exploring, etc. Usually, as different setting of web server, the format of log files had three types: National Center for Supercomputing Applications Combined (NCSA), Microsoft IIS Format and W3C Extended Log File Format. The log file format of Agridata was W3C Extended Log File Format.

Materials and methods

web log of the agricultural scientific data center collection

The format and meaning of the fields in the log file was shown in table 1.

Web log data processing

Web log file gotten from website sever directly contained very rich information.

Only a part of this information was needed to analyze information behavior of website users. How to extract this useful information from the log file correctly and completely was the key for analysis of user information behavior. This process was called web log data processing.

As usual, there were four steps in web log data processing: data cleaning, user identification, session identification and access fragment identification. The flow chart of web log data processing was shown as figure 1.

Data cleaning

The purpose of data cleaning was deleting the information unrelated to analysis the user information behaviors from web log files, combining similar records, summarizing and dealing with records of error accessing. As usual, HTML files in web log were associated with the user session. On the other hand, during the process of visiting page, images, video and audio files in the page were also downloaded and visit recorders of these files were written in the log. These Fig1. The flow chart of web log data processing deleted with the method of URL suffix filter. But it was worth noting that the method of URL suffix filter was used with understanding website content to avoid the loss of some important user sessions. For example, for a page which main content was image, we should not simply delete records of images access from log file when using the method of URL suffix filter for data cleaning. Meanwhile, we should define a set of rules to retain this information as a basis for analysis. On the other hand, the operating records of network administrators, web spider and web crawler could be filter out during the process of data cleaning.

User identification

The purpose of user identification was analyzing information of users. As the local cache, proxy servers and firewalls were used in web, such as different users using the same proxy servers, the computer with the same IP address at the same time having different systems and browsers, etc. it was difficult for identifying users with IP address. Usually, during the process of research, we could set some rules to simplify the classification process: when the same IP address used different systems and browsers at the same time, users with the IP address could be considered as different users; when the same IP address had visited a group of pages which were not topological link, users with the IP address could be considered as different users and so on. On the other hand, as people in research institutions were relatively concentrated geographically while farmers were more scattered, we could set a rule to infer these two kinds of people: the grope with high concentration IP address were researchers, otherwise the group with low concentration IP address were farmers.

2.2.3Session identification

A session was a valid user to access the service. Details on session identification were processes of identifying users' requests for consecutive pages to get users' message of information behaviors and interesting objection. The purpose of session identification was creating page clustering for each user's information behaviors, which could provide "material" for analysis information behaviors of user. Usually, for a log file of long series access time, the same user may repeatedly visit the same page. Thus, we should set a rule: when time intervals of request for any two adjacent pages were over a set threshold, we could regard this process as restarting a session.

Access fragment identification

The nature of access fragment identification was supplementing the above process of session identification. It could mean that this process determined whether any important request in file log could be identified. In this process, the method we applied was similar to the way of user identification which set a rule: when pages between current request and last request were not hypertext links, this page the user current request was called from the local cache. In this case, we should check the reference log to determine the source of the current user request. When several pages in visit history contained links to current requested page, we used the page that visit time was closest to the current one as a current source. If some web log was missing, we could add the missing page to the user's session file by the method of analysis of site topology.

Log data mining and user information behavior analysis

After data of web log processing, a large number of redundant information was filtered out and the useful information could be mined to get the information behavior of network users. Usually, there were two steps in this process: user behavior information mining and user information behavior analysis.

User behavior information mining

User information behavior is a concept of diversity, on one hand, only when information was useful to users, had the information existence value; on the other hand, users could not do anything without information. It was meaning that information and users relied on each other. Thus, we can determine that during the process of users' information behaviors, users could absorb certain information and create information related to it. Information was given new life during the process.

In general, the user's information behavior was determined by a variety of characteristics of the user, such as the user's knowledge structure, behavior, etc. In this paper, we focused on the need for Improve the Agridata website efficiency with the method of analysis agricultural network user's information behavior. So characteristics we chosen from the web log data mining in this study were as follows: click-through of a page, users' traffic, the view number of pages, traveling time of users, access duration, network traffic, the visit number of a single page, etc.

User information behavior analysis

According to the characteristics of users' information behavior, we could analyze the information behavior of a user to grasp the user's psychology, which was a great significant for improving services effect of website. As usual, this analysis included two methods: statistical analysis and classification clustering.

(1) Statistical analysis This was a most commonly method for analyzing user access patterns, which studied how to measure, observe, express and summary the number characteristics of objection. In this passage, we could compute parameters such as mean, maximum, minimum, etc, with a variety of information of users who visited Agridata website to estimate the behavior information pattern of users. To some characteristics that were not easy to see from data, we used graphics-assisted method for analysis, such as Scatter plots, histograms, line charts, box diagrams and so on, to show trend of changes intuitively. In the actual analysis process of this passage, we computed the distribution of site visits and page views changed with the length of visit. These results of statistical analysis could help to determine rationality of Agridata topology map and convenience of users' access to resources, which could useful for improving structure of the website.

(2)Cluster analysis This was an important method for data partitioning or packet processing. Currently, the cluster analysis algorithm could be divided into the method, the level of methods, density-based methods, grid-based methods and model-based methods and so on. As there were large amounts of data resources in Agridata and the purpose of users was not clear (the meaning of users could not be accurately expressed with mathematical expressions) when they searched data in Agridata, in this passage, we applied fuzzy clustering analysis method for analyzing users' information behaviors. This method combined with fuzzy set theory and the traditional clustering method. The principle of this approach was as follows: first, the given objection was divided into a number of equivalence classes with the way of fuzzy equivalence relation, which was meaning that establishing fuzzy similar matrix to express similarity between each sample. Then, we clustered with these equivalence classes directly. During the process of specific implementations, we clustered pages of the same database users visited and regarded these pages as a whole for analysis. To these pages, we could compute some parameters such as click-through, access duration and navigation path, for analysis and displaying the users' information behaviors totally. Furthermore, this method for analysis could one-sidedness caused by reference to a single characteristic value Based on the above analysis, in the cluster analysis, we let } ,...., , { future direction of development and related factors of agriculture users' information behaviors. In this passage, we analyzed the chosen log files with WebLog Expert, a common log analysis tools. Some results gotten from the experiment were shown in After clustered this feature, we computed the interest value for the database we The overall interest value of these seven databases could be gotten from the mean of all users' interest values for these seven databases, which were shown in table 3. From analysis results above, we could infer information behavior characteristics of users who visited the website of Agridata.

(1) From data of table 3, we could infer that the interest value of images was the highest, which were 28% higher than the interest value for text database and 56% higher than the interest value for data database. It fully confirmed that acceptance of users for image was higher than that of text and data for data. After further studying data in table 3 and results of the statistical analysis, we could find that IP addresses of users who access image databases were many and scattered, the one for text databases was second and IP addresses for data databases were the most concentrated. It could mean that agricultural workers and farmers preferred image data while numerical data were often used by agricultural researchers.

(2) From results of the statistical analysis, we could determine that the access time period of users was very scattered, which could mean that users could visit the website at any time of a day. In general, in the day time (about 8:00 to 18:00), click-through of the website was maximum, around 74% of all click-through and IP addresses of visited were more concentrated. So we could infer that agricultural researchers in institutions were more willing to use Agridata than that of farmers.

(3) From results of statistical analysis for main page and search pages residence time of users, we determined that IP addresses of users whose pages residence time was shorter had displayed more concentrated while longer pages residence time of users whose IP addresses were more scattered. Thus, we could infer that farmers were much weaker for adapting to main page and search pages than agricultural researchers did.

From results of the analysis in this passage, the website of Agridata should be improved in three aspects. Firstly, we should strengthen publicity efforts to farmers for Agridata; secondly, the main page and search pages of Agridata should be simplified to make them easier to use; finally, the number of image databases should be increased to meet the need for farmers. If more farmers visited the website of Agridata for life and production, we could sure that Agridata should provide better services for farmers and promote agricultural production and research.

Conclusions

In this passage, the Web access log was used as an object and we discussed the method for studying the information behavior of users of agricultural methods with statistical analysis and cluster analysis. Then we conducted experiments with the Web access log of the national agricultural scientific data center and analyzed the pattern of information behavior of its user. With these results, several advices on this website were made for providing a better service to agricultural users and promoting agricultural production and research.

  sever included name of computer, name of website, IP address and port number

  information were useless for analysis the user information behaviors and should be

Figure 2 .

 2 figure 3.

Table 2 .

 2 Details 7 databases which users visited from the website in log files should be extracted and clustered based on databases for the analysis. To simplify computing, we chose page residence time of users as a characteristic for analyzing.

Figure3.F

  Figure3. Results from analysis with WebLog Expert

Table 1 .

 1 The format and meaning of the fields

	D	I	Index	Log File Format W3C Extended	Meaning of fields
		1	Host IP	c-ip	IP address or domain name of the client
					computer which send HTTP request to sever.
		2	User name	cs-username	user ID and host of users who send
				cs-host	HTTP request to sever
		3	Date and	Date、Time	Date and time of HTTP request to sever
			time		send by user
		4	HTTP	cs-method、	HTTP request to sever send by user
			Request	cs-uri-stem
				cs-uri-query

Table 3 .

 3 the overall interest value of databases

	number	Name of database	the overall interest value
	1	agricultural insect in china database	0.51
	2	natural enemies spider database	0.49
	3	Parameter of organic fertilizer database	0.29
	4	Fertilizer parameters database	0.35
	5	quality standard of rice database	0.48
	6	quality standard of tea database	0.35
	7	quality standard of green food database	0.33

1 n

In the experiment of this passage, we chose the website of Agridata as the representative of agricultural sites and analyzed information behaviors of its users.As thinking about the life and production situation of agriculture network users, in this passage, we chose web log files in July 2010 as the objection of experiments.Such sample we chosen could not only reflect needs of agriculture users during the busy farming season, but also avoid deviations of information behaviors in the general sense caused by various holidays.In this passage, we used the analysis method combining with statistical analysis and cluster analysis. The flow chart of analysis was shown in figure2.During the process of analysis, after processing 40873 records in the log file chosen as the objection of the experiment, we computed some parameters such as time distribution of access site, access duration, the amount of download data and the concentration of IP address, etc, to provide the basis for analysis information behaviors of agriculture users. These parameters also helped us to determine the

Acknowledgements: Funding for this research was provided by "basic scientific research special fund of nonprofit research institutions at the central level" (2011j-1-06)

Where n j ,...., 2 , 1  , ij  represented the interest value of a user i U to a set of pages W for the characteristic j X


Hanming He

Jianmeng Feng

Spatial Pattern of Plant Specimen and Its Implications in Conservation Biology in Hengduan Mountains of Southwest China

Keywords: :Altitudinal difference against area, Hengduan mountains, Population density, Spatial pattern, Specimen density, Species density, Vascular plants

In the past, the spatial patterns of specimen have not been paid enough attention. In this study, we probed the spatial pattern of plant specimen and its implications in conservation biology in Hengduan Mountains, one of the hotspots of global biodiversity, based on the dataset extracted from Chinese Virtual Herbarium. The results showed that there were big differences of specimen density among the units of the studied area. High specimen density was mainly found in the boundary regions of Northwest Yunnan, Southwest Sichuan and Southeast Tibet, the central part and the southern part of the studied area. The interest of the collectors or botanists was mainly focused on the units with low population density (low disturbance of human activity) and high complexity of topography. With the increase of specimen density, an increasing trend of species density was observed. Most of the units studied were not paid enough attention in the history of specimen collection. If we collected specimen at higher density in blank or marginalized area, we may have much more chances to find more taxonomies. The spatial pattern of specimen density may shape our understanding of the spatial pattern of specie diversity. We should not only inherit specimen from our predecessors, but also, we should probe into specimen repositories to understand biodiversity status and its spatial pattern.

Introduction

Taxonomic records and their conservation are the basis of biodiversity research [START_REF] Bickel | What museum collections can reveal about species accumulation, richness, and rarity: an example from the Diptera[END_REF][START_REF] Ponder | Evaluation of museum collection data for use in biodiversity assessment[END_REF]Rudolf & Torsren, 2004). With the accumulation of specimen in the long history of specimen collection, its implications in conservation biology are being paid much more attention in these days [START_REF] Ponder | Evaluation of museum collection data for use in biodiversity assessment[END_REF]). The inquiry into large specimen discussions can help us understand the characteristics and the spatial patterns of biodiversity in local or global scale [START_REF] Chew | Teasing apart the effects of taphonomic and sampling bias on species diversity estimates using gis[END_REF][START_REF] Greenstreet | Assessing the sampling effort required to estimate alpha species diversity in the groundfish assemblages of the North Sea[END_REF]Rudolf & Torsren, 2004). Also, it lends us a hand to detect the hotspots, in which lots of energy and time have been inputted in the past. Moreover, the probe into huge specimen discussions can help us to find or predict the blank or marginalized area neglected in the history of specimen collection more or less, and in which we may find more Taxonomies un-discovered in the past [START_REF] Radim | Is Sampling subjectivity a distorting factor in surveys for vegetation diversity?[END_REF]Rudolf & Torsren, 2004). Therefore, the investigation on the spatial pattern of specimen and its implication in conservation biology can not be under-estimated.

Myers et al. (2000) published their research on biodiversity hotspots at global scale in Nature

Magazine. The conclusions disclosed that Hengduan Mountains in Southwest China held high richness of biodiversity, and was one of 25 biodiversity hotspots in the world. Therefore, the study on the biodiversity in this region has drawn extensive and intensive attention [START_REF] Sun | Evolution of Arctic-Tertiary Xora in Himalayan-Hengduan Mountains[END_REF]. So, there was a long history of plant specimen collection, and a large amount of plant specimen from the studied area were accumulated [START_REF] Wu | The Hengduan Mountains flora and her significance[END_REF], which made the inquiry into the spatial pattern of plant specimen and its implications feasible. However, until now, no relevant study was reported.

Supported by China National Science & Technology Infrastructure Center (SCNSTIC), Chinese

Virtual Herbarium (CVH, www.cvh.org.cn) is aimed to provide essential and comprehensive information of plant specimen in China (Institute of Botany, Chinese Academy of Sciences, 2010). The herbarium covers most of plant specimen resources in China, with more than 2.5 million plant specimen digitalized and updated online. Until now, the herbarium is believed as the most complete database of plant specimen in China, serving us with taxonomic and distribution information of plant specimen across China with high reliability. So, our aim is to explore the spatial patterns of plant specimen and its implication in conservation biology in Hengduan Mountains, based on the dataset extracted from CVH. We hope this study can help us understand and protect plant diversity in the studied area. It must be noted that in this paper, we only focused the vascular plants in Hengduan Mountains.

Results

Frequency distribution of specimen density and the discreteness analysis

There were big differences of specimen density among 87 units studied (Fig. 1). The lowest density was zero, while the highest density was 3.4 specimens per km2. There were 46 units (accounting for 52.9% of all units) whose specimen density was lower than 0.1 specimen per km2. Among 87 units studied, there were 78 units whose density was lower than 1 specimen per km2. It implied that most of the units displayed low specimen density, while high density was only observed in a small fraction of units studied. The discreteness analysis showed that the discreteness coefficient was 1.7, markedly higher than 1, which meant that the distribution of specimen density among 87 units studied was extremely uneven.

Spatial pattern of specimen density and its interpretation

High specimen density was observed in the boundary regions of the Northwest Yunnan, Southwest Sichuan and Southeast Tibet, and we also found the high density in the southern units and the central units of Hengduan Mountains (Fig. 2). Except these units, low specimen density or blank units were detected in most of the units studied. Pearson's correlation indicated that with the increase of population density, there was a slight decreasing trend of specimen density among 85 units studied, except irregular data points of two units (Fig. 3). There was strong correlation between specimen density and altitudinal difference against area (topographical complexity), and with the increase of the altitudinal difference against area, an increasing trend of specimen density can be predicted (Fig. 4). Fig. 4. Correlation between Altitudinal difference against area and specimen density

Correlation between specimen density and species density

We observed that there was strong correlation between specimen density and species density, and with the increase of specimen density, an increasing trend of species density can be perceived (Fig. 5). It implied that in the units with lower specimen density, lower species density can be probably predicted, and vice versa. Fig. 5. Correlation between species density and specimen density

The implications in conservation biology

The discreteness analysis indicated that the extreme unevenness of specimen density can be observed among the units studied, and the spatial pattern of specimen density also showed that the higher density was mainly found in the boundary regions of the three provinces, the central units and the south units of the studied area, which implied that these units were hotspots in the history of specimen collection and biodiversity research. However, in most of the units studied, the density was very low, and even some of them were blank area. So, we can infer that most of the units studied were paid little attention, or were overlooked in the history of specimen collection, more or less. The Pearson's correlation indicated that with the increase of altitudinal difference against area, an increasing trend of specimen density can be observed. Also, the results showed that there was slight negative correlation between population density and specimen density. It may imply that the interest of the collectors or botanists was mainly focused on the units with lower population density (low human disturbance) and higher complexity of topography, probably because raw or undisturbed vegetation and high richness of plant diversity in units with complex topography attracted their attention.

Previous study showed that if we conducted intensive specimen collection in the blank or marginalized regions, there were much more chances to find more taxonomies un-discovered in the regions in the past, compared with the attempt in biodiversity hotspots (Rudolf & Torsren, 2004). In this paper, both discreteness analysis and the spatial pattern indicated that lots of blank or marginalized units were overlooked in the history of specimen collection. Therefore, we tentatively deduced that if

we collected specimen at higher density in these blank or marginalized area, we may discover more species or taxonomies, which may enrich and refine our understanding of biodiversity in Hengduan Mountains and its spatial pattern.

In the results, we observed that there was strong correlation between species density and specimen density. It implied that higher intensity of specimen collection may enrich our findings in plant taxonomy in the studied area. It also indicated that specimen density would possibly affect our understanding of biodiversity in the studied area. If the intensity of specimen collection was low or not enough, we may underestimate the biodiversity in the studied area. However, if we conducted specimen collection at higher or enough intensity, we may find more species or taxonomies, and the results may more accurately reflect the biodiversity status in the studied area. Recently, Van Liefferinge (2010) observed that the investigation at higher sampling density may more reasonably reflect the biodiversity of fish in small brooks in Belgium. The studies in fungus also obtained similar conclusion [START_REF] Whitcomb | Assessing diversity of arbuscular mycorrhizal fungi in a local community: role of sampling effort and spatial heterogeneity[END_REF]. Moreover, in the field of genetics diversity research, the studies testified that sampling density is an important factor affecting our understanding of genetics diversity [START_REF] Alberton | Sampling effects on the assessment of genetic diversity of rhizobia associated with soybean and common bean[END_REF][START_REF] Miyamoto | What sampling is needed for reliable estimations of genetic diversity in Fraxinus excelsior L[END_REF][START_REF] Muirhead | Identifying the source of species invasions: sampling intensity vs. J. genetic diversity[END_REF]). Other relevant studies also supported our conclusions [START_REF] Chew | Teasing apart the effects of taphonomic and sampling bias on species diversity estimates using gis[END_REF][START_REF] Greenstreet | Assessing the sampling effort required to estimate alpha species diversity in the groundfish assemblages of the North Sea[END_REF]. Therefore, the implications of the intensity of specimen collection can not be under-estimated

The strong correlation between specimen density and species diversity also hinted that the spatial pattern of specimen density may shape our understanding of the spatial pattern of species richness.

Recently, most of biodiversity research in the studied area focused on interpreting species richness patterns with climatic and topographical factors (Fu et ). But, our results implied that the spatial pattern of specimen density may be a strong potential factor, and may shape or even distort our understanding of spatial patterns of biodiversity.

Considering the unevenness of the spatial pattern of specimen density and lots of blank or marginalized units, we tentatively assumed that we had not fully understood the spatial pattern of plant species diversity in Hengduan Mountains. Rodrigo & Michael (2007) investigated the fern diversity in Bolivia, and found that sampling density strongly shaped their understanding of spatial patterns of fern diversity, which in some extents supported our conclusions.

The results showed that with the increasing of specimen density, the increasing trend of species density can be evidently observed. It may imply that the overall intensity of specimen collection was not enough. We should increase our intensity of specimen collection to enrich our understanding of plant diversity in the studied area. And it may also imply that the plant specimen in our dataset only represent a small fraction of plant species in the studied area. In 2004, Rudolf & Torsren explored the species diversity of Euscelidia, and revealed that the specimen in museums only represented 50% of species in reality (Rudolf & Torsren, 2004), which in some extents supported our results.

The specimen was important and fundamental resources in conservation biology, and we should input much energy and time on the collection, arrangement of specimen and the construction of specimen repository. However, in the past, the specimen's implications in conservation biology have not been paid enough attention in the studied area. In this paper, we suggested that we should not only inherit specimen from our predecessors. But also, we should probe into the specimen discussions to understand, assess the biodiversity status and its spatial pattern, and evaluate its hotspots and marginalized area. Furthermore, compared with other methods, the research based on specimen repository is one of the most efficient methods at the lowest costs (Rudolf & Torsren, 2004), therefore, we should fully exploit the resources of specimen repository [START_REF] Ponder | Evaluation of museum collection data for use in biodiversity assessment[END_REF]Rudolf & Torsren, 2004).

Conclusions

In this study, we probed the spatial pattern of plant specimen and its implications in conservation biology in Hengduan Mountain. The results showed that there were big differences of specimen density among the units of the studied area. High specimen density was mainly found in the boundary regions of Northwest Yunnan, Southwest Sichuan and Southeast Tibet, the central part and the southern part of the studied area. The interest of the collectors or botanists was mainly focused on the units with low population density (low disturbance of human activity) and high complexity of topography. With the increase of specimen density, an increasing trend of species density was observed. Most of the units studied were not paid enough attention in the history of specimen collection.

Fig. 1 .

 1 Fig.1. Frequency distribution of specimen density in the units of Hengduan Mountains

Fig. 2 .

 2 Fig.2. Spatial patterns of specimen density of vascular plants in Hengduan Mountains

  al. 2007; Sherman et al. 2008; Yao et al. 2010;

	Zhang et al. 2009

Data collection and methodsAll information of plant specimen in the studied area, including taxonomic and distribution information, was extracted from Chinese Virtual Herbarium. Then, we built a database of plant specimen of Hengduan Mountains, with more than 120,000 specimens and the information of plant specimen in each unit (county). Species richness in each unit was estimated from the specimen information. The topographical information and population density were from literatures[START_REF] Zhao | Album of Yunnan[END_REF]. To exclude the effects of area on the spatial pattern of specimen, we adopted specimen density to represent the amount of plant specimen in each unit. In the similar way, we adopted species density to stand for species richness (Logarithm transformation of area was conducted). In this paper, we adopted altitudinal difference against area (logarithm transformed) to represent topographical complexity, and the altitudinal difference was the difference between the highest altitude and lowest altitude in each unit studied.Based on the dataset, dispersion coefficient was estimated to probe the differences of specimen density among 87 units studied. With the help of ArcGIS Desktop 8.3, we examined the spatial pattern of specimen density. To explain the pattern, Pearson's correlation between specimen density and population density, altitudinal difference against area was conducted. To check our hypothesis that the spatial pattern of specimen may shape our understanding of the patterns of species diversity, we conducted Pearson's correlation between specimen density and species density. Above analysis and drawings were completed with the help of ArcGIS Desktop 8.3、MS Excel and SPSS.

2 Foundation: This study was supported by Scientific Foundation of Yunnan (2010CD077)


Haihua Wang
email: wanghaihua@cau.edu.cn

Minzan Li

Yane Zhang

Determination of Corn Nutrient Status under N&K Stressed Condition Using Hyperspectral Analysis

Keywords: Hyperspectral Reflectance, Corn, Nutrient, MLR, PLS

Variable fertilization for crops, like corn, depends on monitoring nutrition condition. Thus, hyperspectral reflectance was used to predict chlorophyll and total nitrogen content under different N,K treatments during corn growth. The area of the experimental field was divided into 3 strips with different nitrogen treatment (N1:0 kg/ha-low, N2:314 kg/ha-normal, and N3:653 kg/ha-high). In each strip, there were 3 repetitions of 3 potassium treatments( K1:0 kg/ha-low, K2:214 kg/ha-normal, and K3:500 kg/ha-high). The results show that growth stages happened in advance under low nitrogen treatment and reflectance intensity, total N, and chlorophyll content are all largest under N2K1 in shooting stage. GNDVI(R2=0.88, RMSE=0.08) performs well for chlorophyll prediction under N3K2. In addition, MLR has the potential of determination of chlorophyll(R2=0.94, RMSE=0.02) and total N(R2=0.97, RMSE=0.09) in trumpet stage, as well as PLSR for chlorophyll(R2=0.99, SEC=0.01;SEP=0.09) and total N (R2=0.96, SEC=0.11;SEP=0.47) in trumpet stage.

Introduction

The corn (Zea mays) is cultivated large scale as significant food and economic plant. More inorganic fertilizers are undertaken to gain higher yield in conventional way, however it has been proved that excessive nitrogen(N) fertilizer will reduce the yield and harm environment. So, it is important to give growers a reasonable advice based on growth nutrition condition. Since chlorophyll content is related to response to nutrition condition, the traditional fertilization is executed based on observing leaf color. But, in fact, this method is not reliable because growth is under complex conditions. Compared to chemical analysis, optical method is nondestructive and fast. Near infrared spectral technology has been used widely in past decades and proved the potential of prediction of growth condition [START_REF] Norris | Design and development of a new moisture meter[END_REF][START_REF] Gausman | Optical Parameters of Leaves of 30 Plant Species[END_REF][START_REF] Walburg | Effect of Nitrogen Nutrition on Growth, Yield and Reflectance Characteristics of Corn Canopies[END_REF][START_REF] Shibayama | A Spectroradiometer for Field Use: V I. Radiometric Estimation for Chlorophyll Index of Rice Canopy[END_REF][START_REF] Yan | Near-Infrared Spectrum Analysis and Application[END_REF][START_REF] Li | Spectral Analysis Technology and Application[END_REF][START_REF] Wang | Remote Sensing Quantitative Theory and Application in Agriculture[END_REF]. Tang shows that the contents of chlorophyll and carotenoid are corresponded to spectra [START_REF] Tang | Study on hyper Spectral characteristics of corn leaves and their correlation to chlorophyll and carotenoid[END_REF]. Cheng reports LAI and Chlorophyll can be predicted by the first derivative spectra of corn canopies [START_REF] Cheng | Physiological response and spectral characteristics of summer corn under nutrient stress condition[END_REF]. Different treatment of nitrogen(N) and potassium(K) can induce growth nutrition change. This paper described the changes of total nitrogen and chlorophyll content, tested by chemical method, in corn leaves under different match of N and K, then studied how to use hyperspectral analysis to predict total N and chlorophyll content. Wang reported that potassium(K) could affect the spectra of corn leaf [START_REF] Wang | Nutrients Change and Spectral Response of Spring Corn Leaf for Varying Amounts of Potassium Fertilization[END_REF]. Walburg considered that ratio of NIR to red was more advantage than red in N detection [START_REF] Walburg | Effect of Nitrogen Nutrition on Growth, Yield and Reflectance Characteristics of Corn Canopies[END_REF]. Cheng found some sensitive wavelength regions in different growth stages under different nutrient treatments (N,P, and K) [START_REF] Cheng | Physiological response and spectral characteristics of summer corn under nutrient stress condition[END_REF] .

Materials and methods

Experiment design

Nongda 86 cultivar was selected as experimental corn and was sowed on April 25, 2009 at Shangzhuang Experimental Station of China Agricultural University, Beijing. Fertility of soil was detected before fertilization control. The area of the experimental field was about 12 ha and was divided into 3 strips with different nitrogen treatment (N1:0 kg/ha-low, N2:314 kg/ha-normal, and N3:653 kg/hahigh). In each strip, there were 3 repetitions of 3 potassium treatments( K1:0 kg/ha-low, K2:214 kg/ha-normal, and K3:500 kg/ha-high). So the total number of samples was 54. In addition, water and other fertilizers were treated under normal condition。

Spectra

Spectra was obtained under no wind condition between 10:00 and 13:00 and the dates were related to corn growth stages(Table 1). Trumpet and anthesis-silking stage were illustrated in Figure 1a and 1b respectively. FieldSpec spectrometer (ASD Co., USA) was used to obtain reflectance spectra which covered from 325 to1075nm with 1nm resolution. Spectra were collected in the middle part of the fourth(up to down) corn leaves. Three repetitions for each leaf were averaged for data processing. The standard white board was used as reference for each 3 leaves by equation ( 1) : R = (R S -Dark) / (R W -Dark) , [START_REF] Norris | Design and development of a new moisture meter[END_REF] where R S is reflectance of corn leaf; R W is reflectance of the standard white board; and Dark means that no light enters the spectrometer. Considered that nitrogen may shift in filling stage, data was sampled from ear leaves.

Determination of chlorophyll and total nitrogen content

The same leaves were packaged with plastic bags for determination of chlorophyll and total nitrogen content after spectra collection. Chemical experiment and SP-2102 UV spectrophotometer (Cany Precision Instruments Co., Ltd, Shanghai China) were used together for chlorophyll content. After veins in leaf were removed, leaf was cut into small fragments. Then 0.4g fragments and 25ml solution with 1 for 95% ethanol and 2 for 99% acetone were mixed in a test tube. 24 hours later, the chlorophyll-containing solution was put into the spectrophotometer and A645, A652 and A663 readings were recorded for calculate the chlorophyll content by equation ( 2):

C T = 20.29A645+8.05A663 or C T = 20.29A652 , (2)
where C T is a response value to total chlorophyll content(mg﹒L _1 )。 The equation (3):

Chl = C T •V ×10 -3 / W , (3)
was used calculate chlorophyll content Chl(mg﹒g -1 ), where V is volume of extract liquor,25ml;W is fresh weight of leaf,0.4g。 To total nitrogen detection, rest fragments were dried in oven and grinded into powder. 0.4g powder and 6.2g CuSO 4 • 5H 2 O were mixed, then reacted with 60ml concentrated sulfuric acid in a tube. The tube was heat at 500℃ for 2 hours, then was cooled. Kjeldahl (FOSS NIRSystems, Inc., US) was used to determinate total nitrogen content(mg. g -1 ) of the leaf in solution.

PLSR analysis and multiple linear regression

Log (1/R) was often used as absorbance for spectral preprocessing. First and second derivative were also taken into eliminate the baseline drift error. Compared the derivative spectra of high and low total N content, we selected a set of G, R, NIR wavelengths. Then single-band spectra and some VIs(Vegetable Indexes), like red edge slope, NDVI, GNDIV, DVI, RVI and SVI, were used to predict chlorophyll and total nitrogen content. MLR(Multi Linear Regression) and PLSR (PLS Regression) were also introduced to determinate chlorophyll and total nitrogen content in this paper, which were all multivariate data processing methods and fit to hyper spectra. PLS (Partial Least Squares) was based on MLR and PCA (Principle Component Analysis), and PCA had the potential for dimension reduction under considerable variables. So, PLSR was more powerful to calibrate spectrum data than MLR. However, Over-fitting can happen to validation sometimes. In this paper, MATLAB (Math Works Inc., Natick, MA, USA) was used for data processing tool。 To low nitrogen treatment, the chlorophyll content reach highest in early shooting stage(June, 11) , and the total content decreased at shooting stage(June 23). The same growth stages happen in advance for low nitrogen treatment. Under low nitrogen treatment, the high N content in filling stage also proved that grain is difficult to get more nitrogen nutrient. In addition, Figure 2 and 3 indicated that the total N content under high N treatment deceased later than chlorophyll content, and later than normal N treatment. Different NK treatments were studied in-depth except low nitrogen treatment, which showed growth ahead and poor performance of nitrogen absorption in filling stage based on the above analysis. Figure 4 and5 show that total N and chlorophyll content of leaves under N2K1 in the shooting stage are highest. Followed by N3K3 and other normal N, the lowest under N3K2 and N3K1. It can be concluded that normal nitrogen treatment is fit to low potassium and high nitrogen needs high potassium. Total N (mg.g-1) The correlation coefficient between total N content and chlorophyll content was given in Table 2, excluding filling stage where they are significant different. Three treatments, where the correlation was higher than 0.8, are N1K3(R2 = 0.83; RMSE = 0.15), N2K1(R2 = 0.81; RMSE = 0.12), and N2K3(R2 = 0.81; RMSE = 0.07). Under low N,K treatment, although growth stage was earlier, the correlation of chlorophyll and total N was higher. In contrast, high nitrogen treatments had low correlation, which indicated that excess nitrogen would affect transform nitrogen to chlorophyll.

Result and Discussion

Chlorophyll and total nitrogen content

Leaf spectrum

Typical spectra of corn leaves (Anthesis-silking stage; The sample S5, total N = 4.1234%; S26, total N = 2.0707%) are illustrated in Figure . 8. Wavelength ranges below 405 and above 978 were cut with high noise. In the visible region, most of the light was absorbed and there was a small peak in 553nm related to green band. The spectra curve jump to strong reflectance in near-infrared region through red edge (685~750nm), which can be related to the internal organizational structure of corn leaf. Reflectance .

N1K2 N1K1 N2K3 N2K2 N2K1 N3K3 N3K2 N3K1 2 .3 .4 .5 .6 .7 .8 N1K3
Fig. 10. Reflectance in each stage at 818nm

Wavelength selection and VIs regression

Figure 11 shows Second derivative spectra of log(1/R) of the same corn leaves shown in Figure 8. 553,680,760 and 818nm were selected as green, red, red edge, and nearinfrared wavelengths. Single-band spectra and some VIs, like red edge slope, NDVI, GNDIV, DVI, RVI and SVI, were used to predict chlorophyll and total nitrogen content. The best performance was the correlation between GNDVI(R 2 = 0.88, RMSE = 0.08) and chlorophyll content under N3K2, followed by G (R 2 = 0.79, RMSE = 0.13) under N2K2 and NDVI (R 2 = 0.78, RMSE = 0.11) under N3K2. To total N, all top 3 performances happened in anthesis-silking stage(Stage 6), including G (R 2 = 0.88, RMSE = 0.12), R (R 2 = 0.83, RMSE = 0.14) and GDNVI (R 2 = 0.81, RMSE = 0.15) respectively.

PLSR for total N and chlorophyll content

Some efficient results is shown in Table 3 by PLSR for total N and chlorophyll content in different growth stage. Better performances for Chlorophyll content were shooting stage (R 2 = 0.95, SEC = 0.03; SEP = 0.12) and trumpet stage (R 2 = 0.99, SEC = 0.01; SEP = 0.09) respectively. Better results for total N content were filling stage (R 2 = 1.00, SEC = 0.02; SEP = 0.58) and trumpet stage (R 2 = 0.96, SEC = 0.11; SEP = 0.47) respectively. Compare the four results, prediction for total N content is not perfect with high SEP in validation. In addition, The PLSR results for total N and chlorophyll content under different N,K treatment are good for calibration, but overfitting for validation.

In general, total N and chlorophyll content can be predicted in trumpet and shooting stage using reflectance spectra for assessment of corn nutrition condition.

MLR analysis for total N and chlorophyll content

The intensity of log(1/R) at R, G, NIR, and the slope of red edge were selected to implement MLR to predict total N and chlorophyll content. The result is described in Table 4. The best correlation (R 2) for chlorophyll content prediction was 0.94 (RMSE = 0.02), and for total N content was 0.97 (RMSE = 0.09) in trumpet stage(June 30).

Conclusion

Chlorophyll and total N content increased early, then decreased until filling stage when reproductive growth started. The correlation between Chlorophyll and total N content was analyzed because growth stages happened in advance under low nitrogen treatment. Chlorophyll and total N content under the treatments of normal nitrogen and low potassium performed best in shooting stage, which means high nitrogen is not necessary.

Reflectance under N2K1 and N3K3, similar to total N content and chlorophyll content, were more strong than N3K2, N2K3,N1K2 in shooting stage at both 553 and 818nm wavelength. GNDVI(R 2 = 0.88, RMSE = 0.08) was better than other VIs for prediction of chlorophyll content under N3K2, as well as G (R 2 = 0.88, RMSE = 0.12) was better for total N content prediction in anthesis-silking stage. In trumpet stage, the correlation of Multiple Linear Regression(553, 680, 760 and 818) for prediction of Chlorophyll and total N content are 0.94(RMSE = 0.02) and 0.97 (RMSE = 0.09) respectively, and PLSR also had good performance for total N content (R 2 = 0.96, SEC = 0.11; SEP = 0.47) and chlorophyll content (R 2 = 0.95, SEC = 0.03; SEP = 0.12). In a word, total N and chlorophyll content can be predicted in trumpet and shooting stage or some N,K treatments using reflectance spectra for assessment of corn nutrition condition.

Fig. 1 .

 1 Images of (a) Trumpet stage and (b)Anthesis-silking stage

Figure 2

 2 Figure 2 and 3 show chlorophyll and total N content under different N treatments in different growth stages respectively. Chlorophyll content increased early, then decreased. Total N content had the same change as Chlorophyll, but increased again in filling stage(July 15) for the reason that corn steps into reproductive growth.To low nitrogen treatment, the chlorophyll content reach highest in early shooting stage(June, 11) , and the total content decreased at shooting stage(June 23). The same growth stages happen in advance for low nitrogen treatment. Under low nitrogen treatment, the high N content in filling stage also proved that grain is difficult to get more nitrogen nutrient. In addition, Figure2and 3 indicated that the total N content under high N treatment deceased later than chlorophyll content, and later than normal N treatment.

Fig. 2 . 5 *Fig. 3 .

 253 Fig. 2. Chlorophyll content in each stage under different nitrogen treatments

Fig. 4 .Fig. 5 . 3 . 2

 4532 Fig. 4. Total N content in each stage under different N,K treatments

Fig. 7 .

 7 Fig. 7. Correlation between total N and chlorophyll at Trumpet stage (June 30)

Fig. 8 .Figure 9

 89 Fig. 8. Reflectance spectra of two corn leaves with high and low total N content Figure 9 and 10 show spectral reflectance at 553 and 818nm under different N, K treatments in different stages respectively. Overall, changes of reflectance intensity are like wave, and there are two peaks in the shooting stage(Stage 4) and anthesissilking(Stage 6) stage. The reflectance intensity at 553nm is lowest in trumpet stage(Stage 5). N2K1, N3K3 were more strong and N3K2, N2K3,N1K2 were lower in shooting stage at both wavelength. It was similar to the result of leaf total N content and chlorophyll content. Compared to other N,K treatments, the intensity of reflectance under N2K1, N3K2 change obviously in trumpet and anthesis-silking stage(Stage 6). High nitrogen treatments increased gradually, and low nitrogen treatment declined in general.

Fig. 11 .

 11 Fig. 11. Second derivative spectra of log(1/R) of the same corn leaves shown in Figure 8

Table 1 .

 1 Growth stages of corn(2009)

	Stage No.	1	2	3	4	5	6	7
	Name	Seedling		Shooting		Trumpet Anthesis-silking Filling
	Date	5-27	6-02 6-11 6-23	6-30	7-07	7-15

Table 2 .

 2 Correlation between total N and Chlorophyll under different treatments

	N,K treatment	R2	RMSE
	N1K3	0.83	0.15
	N1K2	0.78	0.19
	N1K1	0.31	0.32
	N2K3	0.81	0.07
	N2K2	0.67	0.19
	N2K1	0.81	0.12
	N3K3	0.24	0.18
	N3K2	0.06	0.23
	N3K1	0.22	0.29

Table 3 .

 3 PLSR for total N and chlorophyll content in different growth stage(5 factors)

		Chlorophyll content		Total N content
	Stage	Calibration	Validation	Stage	Calibration	Validation
		R2	SEC R2	SEP		R2	SEC R2	SEP
	Shooting 0.95 0.03 0.71 0.12 Trumpet 0.96 0.11 0.72 0.47
	Trumpet 0.99 0.01 0.70 0.09	Filling	1.00 0.02 0.53 0.58

Table 4 .

 4 MLR for N and chlorophyll content in different growth stage

	Date	R 2 for Chlorophyll	SEC	R 2 for total N	SEC
	5-27	0.72	0.07	0.26	0.13
	6-02	0.54	0.02	0.66	0.04
	6-11	0.79	0.05	0.89	0.02
	6-23	0.75	0.05	0.78	0.13
	6-30	0.94	0.02	0.97	0.09
	7-07	0.51	0.14	0.92	0.10
	7-15	0.48	0.23	0.60	0.21
	Table 5 shows that the best correlation (R 2) for chlorophyll content prediction is 0.96

* Corresponding author, Email: gpac@cau.edu.cn

Acknowledgments.

This study was supported by Chinese National Programs for High Technology Research and Development Research Fund (2011AA100703) and NSFC Programs of China (U0931001).


Guobing Pan

Jianping Pan

Research in crop land suitability analysis based on GIS

Keywords: crop land, Suitable assessment, The geographic information system

This paper based on GIS land suitable assessment principle and method, use supermap to analysis suitability of crop land of liao city. The suitability of comprehensive analysis was conducted in Study area for three crops .First, choosing representative and having the same impact the suitability of natural factors and social economic factors of the three kinds of crops; Then, interpolation and empowerment of overlap and analysis in supermap, and the appropriate level of land were classified according to land on crops. Thus can adjust measures to local conditions to crops layout and farming system reform and adjustment, the biggest production potential play land. Experimental results show that in the complex crop suitability assessment process, GIS is an effective, possiblel means.

Introduction

The land suitable assessment is to evaluate the suitability for a certain purpose land suitable degree of process. Through the comprehensive analysis of natural factors and the social economic factors which have influence on the land, divide the land into some level according to the suitability under the specified way, in order to show that the appropriateness of appropriate degree for a variety of use .At present, the wide application of GIS in land suitable assessment, make land suitable assessment more flexible, science. A single use land suitable assessment has been much attention, but evaluation basically all around economic crop, lack major crops on land suitability analysis research [1~3] .

Liao city located in the western of shandong province, north latitude 35 ° 47 '-37 °, 03 ° east longitude'115°16′-116°30′, the total area is 8590 square kilometers ,the city is in the Yellow River alluvial plain, flat and tilt from southwest to northeast, average rail slope is about 1/7500, altitude is 27.5 -49.0 meters,belong to temperate and monsoonal climate area, has the remarkable season change and monsoon climate characteristics, belong to semi-arid continental climate. Large land degrees is for 62.8 -64.8, dry degree is in 1.7 -1.9. Overall, agricultural climate resource is more abundant and suitable for growing DuoZhong crops. The climate resources is also rich and from the Yellow River water for irrigation, the climate for DuoZhong crops has better adaptability, therefore, the planting industry has a long history, become the main crops north China cotton, wheat, corn on the region [2~5] . Due to the different influence on the land in liaocity of each factor differences, And with the quickening of the process of urbanization, unreasonable planting and reclamation, so as to make the land resources will not be used reasonably, efficiently. This paper discuss how to apply GIS t0 crop land the suitability analysis in liaocity.

research methods

The research area (HuangFanOu)of this paper is located in north China plain, the main crops are wheat, corn and cotton. The region in the terrain, sunshine and rainfall, some natural factors change caused by small scale, the requirements of the conditions of land crop of great similarity, that is, in this regions have three crops in the suitability of basic level is consistent. Based on this law, this paper this can be three crops suitability degree analysis. First, choose is representative of the three kinds of crops, to have the same impact the suitability of natural factors and social economic factors; Then, in supermap interpolation, and empowerment of overlap and analysis, and according to land on crops, the appropriate level of land were classified. Thus can adjust measures to reform and adjustment for local conditions to crops layout and farming system, play the biggest production potential of land and create conditions for the rational utilization of land include making land planning.

evaluation factors selected

Land suitability level of each assessment unit is a comprehensive effect of Comprehensive role, reasonable choice of evaluation factors is the premise to ensure the suitability of the land quality, therefore, in selecting a factor should be followed in several principles

Leading-the main factor restricting the use of land. Difference-choosing the factors which have obvious difference and can appear near the value in the study area.

Independence-each factors can't appear causality. The possibility-factors must have the corresponding information. According to the above principles, this paper selects 5 major factor as a land of liao city suitable assessment factors, namely: soil types (the class), soil texture, buried deep underground water level, soil fertility and soil pollution, among them soil fertility was expressed through the organic matter, total nitrogen, alkali solution nitrogen, rapidly-available phosphorus and potassium, soil pollution was expressed through the cadmium, mercury, lead, arsenic, chromium and copper six kinds of heavy metal. Through these five factors, we will get soil type distribution, soil texture map, buried deep underground water level distribution, soil fertility distribution, soil pollution distribution, then, this five distribution empowerment overlap add to get comprehensive evaluation distribution , and then get comprehensive evaluation distribution based on the land suitability of crops degree.

The fund of structure engineering of chongqing jiaotong university(CQSLBF-Y011-4) The fund of experiment teaching and reform of chongqing jiaotong university(SY1201003) 4 The experiment 4.1 data preparation 1、 First, find a paper map, the map evenly distributed sampling points, then conduct field sampling according to the sample point distribution and the needed factor data.

2、Through the scanner, scanning liao city land map and forming grid data, using this grid data as a screen for reproduction, working in a vectorial. After scanning ,the grid data was stored for JPG format. Then use PhotoShop to pretreatment grid data ,improve the contrast of raster images and other quality, in order to improve the quality and efficiency of the vector .

3 、 Map in digital and attribute editor in supermap, getting sample point distribution (figure 1).

Figure 1 sample point distribution 4.2 suitability analysis process 1、soil type analysis

(1) First, score different soil types (table 1), then build " Soil types _v" column in the sample point data attributes list, the value is listed in table 1 for the "score". Set up the analysis environment. (2) Choosing inverse distance square weights interpolation method to do interpolation analysis of soil types, making special charts to get soil type distribution (figure 2).

Figure 2 soil type distribution

2、the soil texture analysis As for soil types analysis, first of all expert scoring (such as table 2), and then build " Soil types _v" column in the sample point data attributes list, the value is listed in table 2 for the "score". Finally interpolation soil texture map obtained (figure 3). 4). Therefore, we combined with the local cropping system and the requirements of the fertility of the soil, the choose organic matter, total nitrogen, alkali solution nitrogen, phosphorus and phosphorus rapidly-available potassium as soil fertility evaluation index, and obtains the single soil fertility index level (table 4). (2) the index weight determination The index weights mean every soil fertility factor refer to comprehensive fertility soil of contribution. It is the key issue of comprehensive evaluation of fertility how to determine the weight of single fertility index. This paper represents the weight of each index by the analysis of correlation coefficient between each index. Calculation method is: first it calculates the correlation coefficient of single fertility index with SPSS software (table 5), and then it calculates average value( r )of the correlation coefficients of a fertility index and other fertility index. And it uses the average of all fertility index correlation coefficient average absolute value and the sum total

Table 3 Groundwater depth allocation

(  r ) ratio(  r r
) as the single fertility index in soil fertility of the comprehensive contribution value or weight (table 6) (3) Soil fertility changes evaluation According to the results of the above and the application of the soil quality index and method, it quantitative reveals different areas of comprehensive fertility changes computation formula is:

   ) I W ( i i

IFI

Among them, for soil fertility index IFI comprehensive, Wi said each single evaluation indexes weights, Ii for each single evaluation index level, so, the size of the values IFI said the comprehensive soil fertility levels. And according to the change of the value range, IFI will soil fertility is divided into four levels (table 7), and then based on the above analysis and calculation get soil fertility grading figure (figure 5)

5、 soil pollution analysis

At present heavy metal pollution of the cultivated land area accounts for about 1/5 of the total area of arable land in China, every year because of pollution land it cuts food about 10 million Ton , and another 12 million Ton food pollutants exceed standard, both of them the direct economic loss are more than 20 billion RMB. The soil pollution are provided with latent, hysteresis, cumulative, not reversible and difficult management. through the analysis of the data acquisition, data and expert consultation, we think it's scientific and reasonable that the soil pollution as important factor. We use six heavy metals such as mercury, cadmium, lead, arsenic, chromium and copper to show pollution of soil condition in this analysis. According to each of the mean and the maximum metal content and the maximum value of elements, it calculates the heavy metal pollution index (nemero index), formula is:

2 L C L C P 2 i i 2 i i g 平均 最大 ) ( ) (  
Ci is the measured elements of i; Li is the highest tolerances elements of i. (the secondary standard of GB 15618-1995); Ci /Li is elements pollution value of i; (Ci /Li )最大 is the largest maximum of Ci/Li;(Ci /Li )平均 is the sum of the average of Ci/Li . According to above analysis calculate comprehensive index, with comprehensive pollution index method pollution level division standard, it divides heavy metal composite pollution values of the division standard grades of the study area in the study area and the heavy metal pollution values (table 8). According to above analysis results, first it constructs " Pg " column in attributes list, second enters each Pg value, then interpolation analysis and heavy classification, last it gains the soil pollution grading graph (figure 6).

table 8 the comprehensive pollution index of soil and classification

Comprehensive

6、 land comprehensive evaluation analysis

Thought the above 5 results in this paper, the analytical process gives a certain weight stack according to the local conditions to grow crops on soil types factor, the soil texture factor, the underground water level buried depth factor, soil fertility factor and the soil pollution factor. In the paper it uses three scale two steps analytic hierarchy processes to identify weight

The basic principle: the first step is to compare between the two factors to a comparison matrix by three scale; The second step is to use comparison matrix and mathematical formula to obtain structure matrix; The third step is to use consistency check with the matrix, use the important factor of the maximum eigenvalue to represent the corresponding level. The specific procedure is as follows:

1、It compares between the two factors for quantitative with the next type, then composes comparative matrix by numerical value (table 9). Kij is the comparison quantitative value of factor "i" and factor "j", also it's the relevant elements of the matrix. If i = j, K11 ,K22 ,----,K66 ,it means the factor compare themselves. Diagonal of comparative matrix the number is 1.If the comparison matrix appear 0, 1, 2 in the triangle then other value is 2, 1 and 0 in the corresponding position under triangle in table 9 ij 0 i factor is less important than j factor K 1 i factor is as important as j factor 2 i factor is more important than j factor

      ( ) ( ) ( )
             ) K 1](K ) K - )/(K B - (1 * ) K - 1/[(K K K 1 K K / 1 B * K K R j i min max m j i j i min max m j i ij ) ( ) ( ) ( ) (

7、conclusion

Based on GIS land suitability assessment principle and method, it used supermap to analyzing suitably crops of Liao city land, than we get the following:

1、With united analyzing respectively of rivers and soil type distribution map, groundwater depth buried depth distribution, it can be found soil salinity is most serious, groundwater depth is the biggest in GuanXian. But five big rivers through Liao city are not through GuanXian (WeiHe and Maxia river are all in the edge) and some of the main canal perennial dry, therefore, in the region we should increase the water resources and avoid the soil salinity become heavier. Also we should adjust the agricultural structure and change the agricultural development model.

2、Liao city agricultural land quality gradually reduce from the east to the west, therefore, it should pay attention to adjusting agricultural layout. In the east we must give full play to the rich soil water resources and fertile soil advantages to improve crop quality and output. In the west land we can adjust measures to local conditions reform land conditions to develop forestry, orchards and other production.

3、The soil heavy metal pollution reach light pollution levels in some areas, the crops has caused the damage in a certain degree in the region. Therefore, some polluting factories should be banned that prevent soil pollution become worse.

It is concluded that the present situation and the existence problems in Liao city crop land suitability assessment process of agricultural land. It shows that GIS is an effective and operational technology in complex evaluation process to the crops suitability.

Figure 4

 4 Figure 4 buried deep underground water level classification figure

Figure 5

 5 Figure 5 soil fertility grading figure

  Figure 6 soil pollution grading figure

Figure 7

 7 Figure 7 land comprehensive evaluation distribution4 conclusion

Table 1 soil type allocation

 1

	Soil types	score
	Sand soil meadow	1
	Meadows solonchak	1

Table 2 score soil texture

 2

	Soil texture	score
	Soil mass sand	1
	Clay soil mass	1
	Stick loam accounting	2
	Sandy loam stick	2
	Sticky loam	2
	Sandy loam	3

loam Figure 3 soil texture map 3、groundwater depth analysis According to the underground water level in the region, score suitability of major crops (

table 3 )

 3 , then interpolation analysis in the supermap and get in groundwater depth distribution, according to table 3 classify the buried depth of underground water level distribution, get buried depth underground water level classification figure (figure

Table 4 soil fertility index level

 4

	Organic matter	Total	Alkali solution	Rapidly-	Rapidly-	level
	(g/kg)	nitrogen	nitrogen	available	available
		(g/kg)	(ppm)	phosphorus	potassium
				(ppm)	(ppm)
	0.329 -0.6	0.018 -0.05	20.65 -30	Null	6.45 -30	6
	0.6 -1	0.05 -0.075	30 -60	3.8 -5	30 -50	5
	1 -2	0.075 -0.1	60 -90	5 -10	50 -100	4
	2 -2.1072	0.1 -0.1372	90 -120	10 -20	100 -150	3
	Null	Null	120 -123	20 -40	150 -200	2
	Null	Null	Null	40 -79	200 -331	1

Table 5 correlation coefficient

 5

		Organic	Total	Alkali	Rapidly-	Rapidly-
		matter	nitrogen	solution	available	available
				nitrogen	phosphorus	potassium
	Organic matter 1
	Total nitrogen	0.959	1
	Alkali solution	0.035	0.076	1
	nitrogen
	Rapidly-	-0.0	-0.38	0.563	1
	available
	phosphorus
	Rapidly-	0.036	0.92	0.222	0.137	1
	available
	potassium

Table 6 each fertility index correlation coefficient and weight

 6

	Fertility index system	Correlationcoefficient average	Weight coefficient
	Organic matter	0.238	0.192
	Total nitrogen	0.394	0.316
	Alkali solution nitrogen	0.224	0.180
	Rapidly-available phosphorus	0.06	0.048
	Rapidly-available potassium	0.329	0.264

table 10 structure judgment matrix

  Rij is the table structure in the judgment matrix element, Ki, Kj respectively represent each table corresponding factor accumulate value (  Through the above analysis, the soil type distribution, soil texture map, groundwater depth grading figure, soil pollution grading figure and soil fertility hierarchical graph empowerment overlap add (Table 11), we can get the land comprehensive evaluation distribution (FIG 7)

	table 11 each factor weight
	factor	K1	K2	K3	K4	K5
	weights	0.1232	0.2597	0.0582	0.0296	0.5293
					5
	 1 Kmax 、Kmin, means the sum of the most important factor and the least i 5 Ki ),Kmax 、 Kmin are the maximum and the minimum of   1 i important factor related coefficient, then in the paper Bm=9+1=10; So we can get tectonic judgment matrix (table 10). Then it uses matlab software to solve the maximum eigenvalue λmax =5.2855, and(5.2855-5)/(5-1)=0.071375<0.1, it can pass the consistency check, so the maximum eigenvalue vector matrix:W=[0.2034,0.4286,0.0961,0.04886,0.8737] Then, it gets the weight value factor by unification (table 11). factor R1 R2 R3 R4 R5 Soil types R1 1.0000 0.3077 3.2500 5.5000 0.1818 Soil texture R2 3.2500 1.0000 5.5000 7.7500 0.3077 Buried deep underground water level R3 0.3077 0.1818 1.0000 3.2500 0.1290 Soil pollution R4 0.1818 0.1290 0.3077 1.0000 0.1000 Ki ; Bm generally instead by soil fertility R5 5.5000 3.2500 7.7500 10.0000 1.0000