Xin Xu

Hua Yu

Zheng

Hao Zhang

Lei Xi

The Soil Nutrient Spatial Interpolation Algorithm Based on KNN and IDW

Keywords: IDW, KNN, KD-Tree, Soil Nutrient, Spatial Interpolation

For breaking the limitation of the GIS platform and realizing the soil nutrients spatial interpolation algorithm for any points in the monitoring area to transplant to the mobile platforms, this paper established the spatial index of the soil nutrient sampling points utilizing the K-D Tree as the space splitting algorithm of the soil nutrient sampling points. On this basis, the K nearest neighbor search of the soil nutrient sampling points was also implemented employing KNN algorithm. Finally, the soil nutrient spatial interpolation was realized based on KNN and IDW algorithm. Meanwhile, the accuracy of the algorithm and the influence to the different soil nutrient elements affected by the K value in KNN algorithm were also verified. The results show that the soil nutrient spatial interpolation algorithm was viable to predict the element contents of soil nutrient during the running time was less than 3s. To reach the best accuracy, the values of the proximal point K for predicting the PH, organic matter, rapid available phosphorus and rapid available potassium should be set as 85, 15, the largest sample space and 65 respectively. The optimal average absolute error of the pH, organic matter, rapid available phosphorus and rapid available potassium was 0.0405, 0.3870, 0.0015 respectively.

Introduction

Due to the problems of the obtaining the sampling points data of the soil testing and formulated fertilization such as difficult sampling [START_REF] Jing-San | The Existing Problems and Countermeasures of Unit Soil Samples Collecting in Soil Testing and Formulated Fertilization[END_REF] , heavy workload [START_REF] Yue | The Existing Problems and Countermeasures in Soil Testing and Formulated Fertilization Project[END_REF] , high cost [START_REF] Donghai | The Existing Problems and Countermeasures in Soil Testing and Formulated Fertilization[END_REF] and so on, the data points during the fertilizing decision stage in the process of testing soil for formulated fertilization was not enough to achieve the complete coverage for the field parcel in each region. Therefore, the sampling point soil nutrient data of the currently unknown spatial points were obtained on the basis of interpolation operation of the existing sampling point data frequently in real application. The utilizing of spatial interpolation technology was commonly used method. In the method, the statistical methods were applied to the nutrient data of some smaller density soil sample points to conduct interpolation operation to the data from the points that were not sampled to form the more dense point data distribution or the area data of different areal unit. The soil nutrient values of the points which were not sampled were predicted scientifically. Then, the precise fertilization could be implemented to the whole area.

Following the rapid development of the mobile intelligent terminal and the distributed computing technology, the computing and data processing of more and more applications was transferred to client-side 错误!未找到引用源。 . Due to the frequent disconnection [START_REF] Yisheng | Processing model of mobile data in disconnecting computing environments[END_REF] and low reliability [START_REF] Tao | The Development and Application of GIS under Mobile Computing Environment[END_REF] of mobile GIS, most of space interpolation for mobile side had to rely on the service side in the using. It made the usage scenarios of the mobile-oriented space interpolation limited. Thus, making full advantage of modern computer technology could provide more configurable, extensible and customizable distributed applications and made traditional soil testing and formulated fertilization recommendation system running in embedded devices such as high performance and low cost mobile intelligent terminal possible 错误!未找到引用源 。7,8]. However, most of current soil testing and formulated fertilization system 错误!未找到引用源。9错误!未找到引用 源。 and soil nutrient spatial interpolation technology 错误!未找到引用源。2-错误!未找到引用源。 were based on the GIS platform and hard to be transplanted to mobile platform. Hence, to realize the soil nutrient inquiry to any land parcel and the fertilization information recommendation by the users through intelligent mobile terminals and the personalized recommendation of the soil testing and fertilizer recommendation technology by fully integrating mobile technology and soil testing formula technology, the researching of a type of soil nutrient spatial interpolation technology which was divorced from the limitation of the GIS platform was significant for the application and popularization of soil testing formula fertilization technology.

2

Material and Methods

Source of Material

The data in this paper came from the 238 soil nutrient sampling points in 10 thousand mu precision agriculture model production field of Changge city, Henan provice in 2014. The sampling point data was consist of sampling number, numbering of land parcel, soil texture, preceding crop, PH values, organic matter, rapidly available phosphorus, rapidly available potassium, latitude and longitude information.

Research Method

Inverse Distance Weighted (IDW) Interpolation

At present, there were many kinds of methods for the soil nutrient interpolation. In which methods, the Inverse Distance Weighted interpolation (IDW) and Kriging interpolation 错误!未找到引用源。15错误!未找到引用源。 were considered to be the most widely used two common interpolation methods. The Kriging interpolation was often influenced by many factors in actual application 错误!未找到引用源。18错误!未找到引用源。 . Considering the space complexity of Kriging interpolation algorithm, the Inverse Distance Weighted interpolation was adopted in this study to design the interpolation algorithm for the soil nutrient data.

The inverse distance weighted interpolation was based on the theory of "like similarity principle". Through the calculating of the inverse distance weighted average of the discrete points of nearby area, the value in the cell was also calculated. According to the principle that the point with closer distance had the greater weighting values, the value of the estimate point was fitted employing the linear weighting of some neighboring points around.

This algorithm was a simple and effective data interpolation method with relatively fast computing speed. However, it also had the obvious flaws. For example, the method only considered the space distance between the estimation points and the interpolation points. Moreover, the weight calculation used in the algorithm lacked the exact physical basis 错误!未找到引用源 。20] .

K-Nearest Neighbor (KNN) Algorithm and Spatial Index of Soil Nutrient Sampling Points Based on K-D Tree

The soil nutrient content of the sampling point was not only affected by the change of the sample point distance, but also was affected by many other factors such as soil texture variation. Therefore, theK-Nearest Neighbor algorithm was also introduced in the process of the adjacent points selecting and searching during the process of the interpolation to the soil nutrient using inverse distance weighted interpolation to improve the IDW algorithm. K-Nearest Neighbor (KNN) sorting algorithm was proposed by Cover and Hart in 1967 错误!未找到引用源。 . It was a simple algorithm based on analogy and had become one of the most mature machine learning algorithms in theory at present. The idea of the algorithm was: if most of the k most similar (i.e., the adjacent) samples of a sample in the characteristic space belonged to a category, then the sample also belonged to this category. If the query point and the positive integer K were given, the k closest data to the query point were also found from the data set.

The realization of the KNN algorithm was based on the establishment of the spatial index for data. The commonly used algorithms for establishment of a spatial index in GIS interpolation algorithm has K-D tree, K-D-B tree, BSP tree, R tree series, quadtree, grid and many other spatial index [START_REF] Zebao | Research of Spatial Indexes Optimization and Implementation[END_REF] . K-D (K dimension search binary tree) was a main memory data structure to generalize binary tree to multidimensional data and a type of binary tree in K dimension space. It was also a type of dynamic index structure for the segmentation of data space and suited for the space point target indexing [START_REF] Chaode | The Review of Spatial Indexes in GIS[END_REF] . Therefore, this paper constructed the spatial index algorithm of the soil nutrient utilizing the K-D tree.

Workflow Design

The idea of the soil nutrient spatial interpolation algorithm based on KNN and IDW proposed in this paper was: Constructing the KD-Tree firstly to establish the index space of the soil nutrient data sampling points. After that, the sampling point data set was further screened. The specific method was: using the given soil nutrient training data set, the tree structure data index was constructed adopting the space partitioning tree KD tree to conduct the hierarchical division for the search space of soil sampling point. After that, the fast matching was conducted to the new input sampling point data to find the nearest neighboring K sampling points of the sampling point in the training data set. The K sampling point data would be used as the spatial interpolation data. The algorithm process was shown in Figure 1. (2) Conducting the data pre-processing to the sampling point data and removing critical value such as the outliers and the value beyond administrative boundary and so on to reduce the measuring error of the interpolation results;

(3) Constructing the KD tree and establishing the spatial index of the sampling point data;

(4) Conducting the space partition to the soil nutrient sampling point data using the KD tree;

(5) Conducting the screening to current data set utilizing the K-Nearest Neighbor algorithm to seek the nearest neighbor sampling point data of the current interpolation sampling point data to form the preparative interpolation data set; (6) Traversing the current sample point data set, judging the data whether was accordant with the soil texture of the current sampling point. If the data was inconsonant with the soil texture of the current sampling point, the data would be discarded. (7) Selecting the final interpolation data set based on the step 6; (8) Judging whether the number of the data in final data set "cout" was less than 2, if "cout" was less than 2, this time interpolation was fail and returned the "false" information. Otherwise, the process continued.

(9) Looping through the data set and calculating the weighted average of the current unknown sample points with the current data points. (10) Calculating and outputting the interpolation results based on inverse distance weighted interpolation.

Algorithm Implementation

Based on the above algorithm design, the process of the algorithm implementation consisted of the following three steps: 1. KD tree Constructing, 2. K neighboring searching, 3. the inverse distance weighted interpolation calculation.

KD tree Constructing

The KD tree data structure was established firstly in the algorithm. After that, the KD tree was constructed based on KD tree data structure. The steps of the algorithm were as follows:

(1) If the sampling point data sets "Dataset" was empty, the empty KD tree was returned. Otherwise, the node generation program would be executed.

(2) Determine the maximum variance dimension the "Split" domain. The maximum variances in each space dimension of all the sampling point data which prepared for interpolation were counted. The maximal dimension in their variance was elected as the space division dimension. The quantitative value was used as the value of the "Split" domain.

(3) The root Node "Node" was confirmed. The data of the selected space partition dimension was sorted. The sampling point data located in the middle was used as the data of the "Node" node.

(4) The left subspace "leftNodeData" and the right subspace "rightNodeData" of the "Node" were determined. The data of the sampling points whose space dimension was less than the "Node" node was used as the data of the left subspace of the "Node" node. The data of the sampling points whose space dimension was larger than the "Node" node was used as the data of the right subspace of the "Node" node.

(5) The above process was recursive to conduct the space division partition for the "leftNodeData" and the "rightNodeData" until the subspace had not data.

K-Nearest Neighbor Searching

The KD tree nearest neighbor search query algorithm was adopted in this study to construct the KD tree "Kd". Adopting the target point "target" as the input, employing the obtained K nearest neighboring "nearest" sets of the "target" as the output, the steps of the K search algorithm establishment were as follows:

(1) The leaf nodes containing the target point "target" were found from the KD tree. From the root node, the KD tree was recursively searched downward. If the coordinates of the current dimension of the target point "target" was less than the coordinate of the split points, the target point "target" was moved to the left child node space. Otherwise, it was moved to the right child node.

(2) The leaf node searched in step 1 was used as the current nearest point "currnearest".

(3) The other possible nearest points were backtracked recursively. If there was a node closer the target point "target" than the current nearest point "currnearest", the value of the current "currnearest" was updated.

(4) When the backtracking backed to the root node, the search ended. The last nearest point "currnearest" was the nearest neighboring point of the "target".

(5) The current nearest neighboring point was deposited in the "nearest" set and removed from the tree of Kd.

(6) The above process was repeated until the number of set elements in the "nearest" was equal to K. Stopped the search and returned back to the "nearest" set.

Inverse Distance Weighted Interpolation

Based on the KD tree generating algorithm, the spatial index tree of the current sample sampling points was constructed. On the basis of this, the K nearest-neighbor sampling points were searched out combining k-nearest neighbor algorithm to form the nearest-neighbor set "nearest". Employing the nearest-neighbor set "nearest" and the target point "target" as the input, the sampling points data of the current sampling points was conducted the spatial interpolation processing adopting the inverse distance weighted interpolation. The algorithm steps were as follows:

(1) Calculating the distance between the target point "target" and the sampling points in the nearest-neighbor set "nearest". Traversal the "nearest" set and calculating the distance from the "target" to each nearest-neighbor sampling point.

(2) Calculating the weight of each point. The weight of each sampling point in the nearest-neighbor set "nearest" was calculated to be used as the weight during the interpolation.

(3) Calculating the results of the weighted interpolation. Traversal the "nearest" neighboring set. The results was worked out based on the weight of each point and returned.

Adopting the data from 238 soil nutrient sampling points in 10 thousand mu precision agriculture model production field of Changge city, Henan provice in 2014 as the data source, the ARM CPU architecture as mobile hardware environment(1.6 GHz, 4 core), the above soil nutrient interpolation calculation method was realized based on the Android 4.4 mobile platform. As shown in figure 2, the pH, organic material, rapid available phosphorus and rapid available potassium in the sample data were conducted the spatial interpolation processing and effect analysis during the algorithm implementation efficiencywas less than 3s.

Fig. 2. Algorithm running results

The Figure 2 showed the results page after interpolation to the 10 nearest-neighbor sampling point data. By the program, the nutrient content of the current point was predicted through the interpolation to the pH, organic matter, rapid available phosphorus and rapid available potassium of the current point and contrasted with the actual value.

The effect analysis of algorithm adopting the cross validation method: a small number of samples of the sampling data of nutrients were screened and reserved to be not involved in the interpolation. These reserved samples were conducted comparative evaluation with the interpolation results. That meant that the model established by the most of the sample was used to conduct the small sample prediction. Meanwhile, the error of prediction was also obtained. The mean absolute error, average relative error and root-mean-square error was used as the standard of measuring the interpolation precision 错误!未找到引用源。错误!未找到引用源。 .

In this experiment, the randomly selected 50 sample points from the total samples were used as the data waiting for interpolation and need to be predicted. The rest of the sample points were used as the interpolation data. After many times of experiment, the influence of the different number of the nearest-neighbor to the result data of the sampling point was tested under the same soil texture. The interpolation results analysis were shown in the tables below: Because the algorithms was designed based on mobile computing, it could not only guarantee the accuracy but also provide high efficiency. Through many times of experiment contrasting, when the execution efficiency was less than 3s, the variation situation of the soil nutrients interpolation error in the algorithm was examined in 100 neighboring set. The comparing results as follows:

(1) The prediction error of Ph decreased gradually with the increase of the neighbor point firstly. The prediction error reach minimum when the number of the neighbor point increased to 85. The mean absolute error, average relative error and root-mean-square error was 0.0405, 0.064052 and 0.4360 respectively. Then, the error increased gradually again.

(2) The prediction error of organic material decreased gradually with the increase of the neighbor point firstly. The prediction error reach minimum when the number of the neighbor point increased to 15. The mean absolute error, average relative error and root-mean-square error was 0.3870, 0.1417 and 2.4147 respectively. Then, the error increased gradually again.

(3) The prediction error of rapid available phosphorus decreased gradually with the increase of the neighbor point.

(4) The prediction error of rapid available potassium decreased gradually with the increase of the neighbor point. The prediction error reach minimum when the number of the neighbor point increased to 65. The mean absolute error, average relative error and root-mean-square error was 0.0015, 0.1885 and 23.9818 respectively. Then, the error increased gradually again.

7 . 9 .Fig. 1 .

 791 Fig. 1. The process design of algorithm (1) Reading the current sampling point data set and ready for operation;(2) Conducting the data pre-processing to the sampling point data and removing critical value such as the outliers and the value beyond administrative boundary and so on to reduce the measuring error of the interpolation results;(3) Constructing the KD tree and establishing the spatial index of the sampling point data;(4) Conducting the space partition to the soil nutrient sampling point data using the KD tree;(5) Conducting the screening to current data set utilizing the K-Nearest Neighbor algorithm to seek the nearest neighbor sampling point data of the current interpolation sampling point data to form the preparative interpolation data set;(6) Traversing the current sample point data set, judging the data whether was accordant with the soil texture of the current sampling point. If the data was inconsonant with the soil texture of the current sampling point, the data would be discarded.(7) Selecting the final interpolation data set based on the step 6; (8) Judging whether the number of the data in final data set "cout" was less than 2, if "cout" was less than 2, this time interpolation was fail and returned the "false" information. Otherwise, the process continued.(9) Looping through the data set and calculating the weighted average of the current unknown sample points with the current data points.(10) Calculating and outputting the interpolation results based on inverse distance weighted interpolation.

Table 1 .

 1 Results Statistic of pH Values

	Nu mber of adjacen t points	Ma ximum value	Absolute error Mini mum value	Mean value	Maximum value	Relative error Minimum value	Mean value	Root-mean-square error
	5	1.2	-1.75	0.0365	0.203725	0.004342	0.068 8	0.468 1
	10	1.17	-1.72	0.0405	0.200233	0.004342	0.066 89	0.456 9
	15	1.15	-1.7	0.0390	0.197905	0.004342	0.065 512	0.448 4
	20	1.14	-1.7	0.0405	0.197905	0.004292	0.065 741	0.447 8
	25	1.13	-1.7	0.0395	0.197905	0.002894	0.065 31	0.444 8
	30	1.12	-1.7	0.0390	0.197905	0.002894	0.065 089	0.443 1
	35	1.12	-1.7	0.0390	0.197905	0.002894	0.065 234	0.442 7
	40	1.12	-1.7	0.0385	0.197905	0.002894	0.065 159	0.442 2
	45	1.12	-1.7	0.0380	0.197905	0.002894	0.064 95	0.441 5
	50	1.11	-1.69	0.0395	0.19674	0.002861	0.064 609	0.438 8
	55	1.11	-1.69	0.0385	0.19674	0.002861	0.064 465	0.438 3
	60	1.11	-1.69	0.0390	0.19674	0.002861	0.064 537	0.438 6
	65	1.11	-1.69	0.0385	0.19674	0.002861	0.064 317	0.438 0
	70	1.11	-1.69	0.0385	0.19674	0.002861	0.064 317	0.438 0
	75	1.11	-1.68	0.0400	0.195576	0.002861	0.064 119	0.436 6
	80	1.1	-1.68	0.0405	0.195576	0.002861	0.064 052	0.436 2
	85	1.1	-1.68	0.0405	0.195576	0.002861	0.064 052	0.436 0
	90	1.1	-1.68	0.0425	0.195576	0.002861	0.064 205	0.436 3
	95	1.1	-1.68	0.0435	0.195576	0.001431	0.064 063	0.436 4
	100	1.1	-1.68	0.0440	0.195576	0.001431	0.063 994	0.436 5

Table 2 .

 2 Results Statistic of Organic Material

	Nu		Absolute error			Relative error		Root-
	mber							mean-
	of	Maxi	Mini	Mean	Maximu	Minimu	Mean	square
	adjacen	mum	mum	value	m value	m value	value	error

Table 3

 3 Results Statistic of Rapidly Available Phosphorus

	Nu		Absolute error			Relative error
	mber							Root-
	of adjace nt	Ma ximum value	Minim um value	Mean value	Maximu m value	Minimu m value	Mean value	mean-square error
	points
	5	8.16	-18.28	-1.7905	1.1209	0.0019	0.3219	6.9795
	10	8.15	-17.75	-1.7320	1.1195	0.0102	0.3178	6.6646

Table 4

 4 Results Statistic of Rapidly Available Potassium

	Nu		Absolute error			Relative error
	mber of adjace nt	Ma ximum value	Minim um value	Mean value	Maximu m value	Minimu m value	Mean value	Root-mean-square error
	points
	5	57.7 1	-47.5	0.0200	0.4809	0.0110	0.196 4	24.2538
	10	57.1 6	-48.44	-0.4010	0.5057	0.0180	0.195 8	24.3808
	15	56.7 4	-49.41	-0.2720	0.5072	0.0124	0.194 2	24.3740
	20	56.5 2	-50.03	-0.3100	0.4966	0.0090	0.192 3	24.2862
	25	56.2 5	-50.09	-0.2755	0.4954	0.0084	0.190 8	24.1878
	30	55.9 5	-50.21	-0.2020	0.4999	0.0037	0.189 9	24.1507
	35	55.7 3	-50.29	-0.0700	0.5020	0.0015	0.189 7	24.1325
	40	55.5 7	-50.35	-0.0160	0.5028	0.0012	0.189 3	24.0965
	45	55.4 1	-50.49	-0.0160	0.5035	0.0011	0.188 7	24.0536
	50	55.2 8	-50.56	0.0645	0.5024	0.0023	0.189 1	24.0341

Acknowledgment

Funds for this research was provided by the Henan Province Scientific and Technological Achievements Transformation Plan(132201110025), the National 12th Five-year Science and Technology Support Plan (2014BAD10B06), the Colleges and Universities in Henan Province Key Scientific Research Project (15A520019).

Conclusions

The K-D Tree was used as the space division algorithm for the soil nutrient sampling point in this study. The spatial index of the soil nutrient sampling point was also established. On this basis, the K-nearest neighbor search of the soil nutrient sampling point was realized using KNN algorithm. Furthermore, combined with KNN and IDW algorithm, the soil nutrients spatial interpolation algorithm was implemented and breaked away from the limitation of GIS platform.

(1) The experiment showed that the soil nutrient spatial interpolation algorithm based on KNN and IDW was effective and feasible for predicting the content values of the Ph, organic matter, rapid available phosphorus and rapidly available potassium in soil.

(2) This paper tested the influence to different soil nutrient elements affected by the K value in KNN algorithm. The result showed that the neighboring points for the prediction of Ph, organic matter, rapid available phosphorus and rapid available potassium of respectively take 85, 15, maximal sample space, 65 could achieve the best accuracy. Of which, the optimal average absolute errors of Ph, organic matter, rapidly available potassium were 0.0405, 0.3870, and 0.0015 respectively.

(3) Through the analysis of the error size, it was found that the interpolation precision to pH and organic matter of the algorithm is higher than the interpolation precision of the rapid available phosphorus and rapid available potassium. The error of the rapid available phosphorus and rapid available potassium was larger. This was largely relevant with their large spatial variability [START_REF] Ke | Spatital Variants and Sampling Strategies of Soil Properties for Precision Agriculture[END_REF] .

Following the rapid development of the mobile intelligent terminal and the distributed computing technology, more and more applications transferred the computing and the data processing to thin client. The soil nutrient spatial interpolation algorithm based on KNN and IDW enabled the traditional soil testing and formulated fertilization recommended system to run on embedded devices such as high performance and low cost mobile intelligent terminal. The advantages of the mobile technology and the soil testing formula technology could thus fully integrate to achieve the positioning and query to the soil nutrient and recommended fertilization information of any plot for the users through the intelligent mobile platform in the fields. It had an important significance for the accurate utilizing of the soil testing and formulated fertilization technology and the resolving of the problem of "the last kilometer" for the scientific fertilization information promotion.


Xin Zhang
email: czhangx@nercita.org.cn

Wengang Zheng
email: ezhengwg@nercita.org.cn

Lu Yu

Wenli Wang

A Review on Leaf Temperature Sensor: Measurement Methods and Application

Keywords: leaf temperature, sensor, infrared temperature measurement, infrared thermal imaging, leaf temperature model

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

Introduction

Plant leaf temperature is the surface temperature of leaf where is exposed in the atmosphere. The leaf temperature is influenced by the physiological structure of plant itself and the meteorological factors such as solar radiation, air temperature and wind. Leaf temperature varies at different times. In the plants and crops planting, it is more and more seriously to request people to accurately measure plant leaf temperature, so we can understand the relationship between the internal energy, physiological status and environmental factors. Studying the changes of leaf temperature is important for the research on field evapotranspiration, irrigation, variety breeding and yield forecasting.

In 1875, the German E.Askenasy explained the concept of leaf temperature, but because of the backward measure technology, the development of leaf temperature application was slow. To measure leaf temperature, thermal resistance could be used [START_REF] Hackl | A comparison of plant temperature as measured by thermal imaging and infrared thermometry[END_REF] . With the development of the thermocouple technology, the leaf temperature was measured by thermocouple. The leaf temperature was measured with thermocouple to study the winter transpiration of greenhouse cucumber in South China [START_REF] Weihong | Measurement and simulation of cucumber canopy transpiration in a subtropical modern greenhouse under winter [J][END_REF] . However, due to directly contacting the leaf, the heat conduction is easy to occur, which makes the big error of measurement. It consumes time and the range of temperature is limited [START_REF] Gaoming | Plant temperature and measurement[END_REF] . With the rapid development of infrared technology, scientists began to measure the plant leaf temperature with the infrared radiation thermometer, which realized the non-contacting measurement of leaf temperature. T. KALYAR et al [START_REF] Kalyar | Utilization of leaf temperature for the selection of leaf gas-exchange traits to induce heat resistance in sunflower (Helianthus annuus L.) [J][END_REF] measured the leaf temperature and used the characteristics of the leaf gas exchange to induce the heat resistance of sunflower. With the infrared technology becoming mature, the thermal infrared imager was produced and applied in the agricultural gradually. The sorghum leaf temperature was measured by infrared imaging instrument to study the water status of the plants under drought stress [START_REF] Yitao | Relationship between leaf temperature and water status in sorghum under drought stress [J][END_REF] . The development of leaf temperature measurement technology laid a solid foundation for people to understand the physiological index of plants and the growth of crops.

Starting from the research on leaf temperature measurement, the experiment status and achievements of leaf temperature sensors' application was described and the significance of leaf temperature measurement was showed by the paper. It is summarized that there are several common measurement methods including thermal resistance measurement, thermocouple measurement, infrared temperature measurement, infrared thermal imaging measurement and leaf temperature model. The characteristics and application of five temperature measurement methods were introduced and the problems and development direction of the leaf temperature measurement were pointed out, which provided a reference for the further research on the leaf temperature sensors.

Temperature measurement theory and sensing requirement

Leaf temperature is the surface temperature of the plant leaf, which affects the photosynthesis and transpiration of plants, and it is used to express the temperature of plant and analyze the physiological activities of plants. The degree of plants' drought or water can be indicated and the growth rate and output of the crops can be affected by leaf temperature. There are a lot of methods for leaf temperature measurement such as thermal resistance temperature measurement, thermocouple temperature measurement, infrared temperature measurement, infrared thermal imaging temperature measurement.

In addition, the temperature of plant leaves can be obtained by the leaf temperature model. According to different conditions and measurement requirements, the suitable measurement method can be chosen.

In the practical application, there are many specific requirements for the leaf temperature sensor. Firstly, the leaf temperature varies from time to time, and the temperature range should be satisfied with the leaf temperature sensor. Secondly, the stability should be guaranteed when the leaf temperature sensor measures in the harsh environment, such as humidity, high temperature and other environment. In addition, to obtain the accurate temperature data in the experiment, the influence of external factors on the leaf temperature sensor should be eliminated as large as possible, which can ensure the measurement accuracy.

The status of the leaf temperature sensor in application experiment

The leaf temperature is measured by the leaf temperature sensor. At present, the leaf temperature sensor is widely used in the experiment of plants especially crops. The leaf temperature not only affects the physiological and energy changes of the plants, which reflects the health status of plants, but also reflects the water status of crops, which can guide crop irrigation and drought resistant genotype selection. In addition, the output of crops has a close relationship with the leaf temperature. Here will introduce the experiment status of the specific application of leaf temperature sensor in these aspects.

Application experiment of the leaf temperature sensor in the physiological health of plants

Leaf temperature can reflect the physiological activity of plants, and the growth of plants can be monitored by measuring the leaf temperature. Fig. 1 shows the leaf temperature is measured by IRT-P5 temperature sensor produced by apogee [START_REF] Omer | Using Leaf Temperature as a Nondestructive Procedure to Detect Root Rot Stress in Geranium[END_REF] , mounted at the top of plant leaves 4 inches, measuring angle is 65 degrees, to measure the temperatures of geranium infected with the pathogen and control group.

We could see that the leaf temperature of geranium infected with the pathogen was significantly higher than the temperature of control group. Then it concluded that leaf temperature could predict whether geranium Infective pathogen. In order to study the relationship between temperature and physiological index, the German TFI-50 infrared thermometer was used to measure plant leaf temperature in calm sunny weather [START_REF] Xiaotong | Leaf Temperature and Physiological Traits of Panicle under Different Soil Water Potential[END_REF] . It showed that the higher the stress was, the higher the leaf temperature became, the higher the MDA content was, the lower the content of chlorophyll became, and the physiological characters of the leaves could be judged in a certain degree. The infrared thermography was used to detect physiological parameters of plants in non-contacting and non-destructive manner [START_REF] Xu | Early detection of plant disease using infrared thermal imaging[END_REF] .

Winterhalter et al [START_REF] Loï C Winterhalter | High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage[J][END_REF] used infrared thermometer (Germany, KT15D) to measure plant canopy temperature, which was used to study the high throughput detection and measurement of canopy water quality. Different temperatures were detected by infrared thermal imaging technology between infected tobacco mosaic virus TMV-U1 and uninfected leaves. The results showed that before the visual symptoms appeared the leaf temperature was 0.5-1.3℃ lower than the healthy leaf. Under controlled conditions, the difference of the leaf temperature can be used to distinguish the diseased leaves and healthy leaves. To study the changes of the biomass of three different winter rape infected with A.brassicae and A.brassicicola, the leaf temperature was measured with infrared imaging technology [10] . The results showed that the temperature of diseased leaves was 3-5℃ higher than that of the healthy leaves, in the large area, it could also find the obvious changes of lesion area temperature.

Although the research process did not find that the two pathogenic bacteria had any different influence on winter rape, it was found that the thermal imaging technology had great potential in the application of the plant epidemic disease, resistance breeding and crop protection.

Application experiment of the leaf temperature sensor in the irrigation guidance and drought resistant genotype screening

The leaf temperature can reflect the water status of crops and guide the crop irrigation and drought resistant genotype selection. The leaf temperature was measured from the beet leaf 1cm with the infrared thermometer (LaboratoriesC-1600 Linear) [START_REF] Shaw | Responses of sugar beet (Beta vulgaris L.) to drought and nutrient deficiency stress [J][END_REF] . In order to promote the leaf transpiration, tried to select the new leaves when measured, and the time was in the morning 9: 00-10: 00. Study found that under drought conditions the beet leaf temperature difference is not significant between genotype and put forward that using leaf temperature difference between the condition of normal irrigation control and drought stress could better distinguish. A high resolution thermal imager was used to get maize leaves infrared image under conditions of water stress and normal irrigation and found that the leaf temperature obviously changed in two different treatments [START_REF] Liu | Maize leaf temperature responses to drought: Thermal imaging and quantitative trait loci (QTL) mapping [J][END_REF] . The study showed that the temperature of maize seedling could significantly reflect the drought resistance of maize under drought stress and the leaf temperature difference could be regarded as an index of drought tolerance screening of maize at seedling stage. Fig. 2 shows the thermal infrared imager of corn canopy temperature is measured and recorded to study the ability of different maize varieties to adapt to drought [START_REF] Atherton | Phenotyping maize for adaptation to drought[END_REF] .

Fig. 2. Canopy temperature measured by infrared thermal imager

Using the portable infrared thermometer (the SK-8700) to measure corn leaf temperature [START_REF] Yunpeng | Diurnal variation in leaf-air temperature difference and the hybrid difference in maize under different drought stress[END_REF] , the visual angle is 45 degrees and the distance from the leaf is10cm. It studied the daily difference of corn leaf-air temperature and the relationship between the temperature and the environment factors in detail.

The experiment showed that corn leaf-air temperature difference are relatively stable in full water supplied conditions, basically maintained at the range of -4.0-1.0℃.The leaf-air temperature difference showed an increasing trend under drought stress and the difference was 5℃ between severe stress and adequate water supply. The leaf-air temperature difference can not only well reflect the moisture content of the corn plant but also be used as an important indicator of the drought state of corn (as shown in Fig. 3).

Application experiment of the leaf temperature sensor in output forecasting

The leaf temperature is also important in the output forecasting. Han et al [START_REF] Han | The relation between leaf temperature and water condition in boot stage on rice [J][END_REF] using the BAU-I infrared thermometer to measure leaf temperature, from the side away from the rice leaf 1cm determined, and studied the relationship between leaf temperature and water conditions at the booting stage and yield through the experiment. A hand-held infrared thermometer (domestic M312216) was used to observe leaf temperature and study the relationship between leaf temperature of early rice and late rice and production [START_REF] Ziming | The study on double-season rice leaves temperature change law and its relationship with yield[END_REF] . From the table 1, the rice varieties and the cultivation measure are reasonably selected, leaf temperature of late growth period is reduced and leaf-air temperature difference are increased, rice yields will be raised. In addition, at the same growth conditions of planting density, irrigation and fertilization, genotypic differences among varieties led to the leaf-air temperature difference. Therefore, leaf-air temperature difference can be the selection index of high yield breeding for rice in different growth stages. There were many researchers who used infrared thermometer to measure crop canopy temperature, and to study the relationship between canopy temperature and canopy-air temperature difference and crop yield [START_REF] Shan | Genetic differences in canopy temperature of different early-rice varieties and its relationship with yield [J][END_REF][START_REF] Xuemin | Relationships between yield characteristics and canopy temperature of peanut [J][END_REF] . Note: * and * * respectively, expressed at 0.05 and 0.01 levels of significant difference.

Since modern times, scientists have studied the method of measuring plant temperatures and until now it has developed into a very mature technology. The methods include contacting measurement of temperature, non-contacting measurement of temperature and leaf temperature model. This paper mainly introduces thermal resistance temperature measurement, thermocouple temperature measurement, infrared temperature measurement, infrared thermal imaging measurement and leaf temperature model. Each method has its own characteristics, the table 2 shows the technical parameters, advantages and disadvantages and the specific application of these methods in detail. The thermal resistance is a temperature sensor which is made of known resistance varying with temperature, such as platinum resistance temperature measurement. Thermal resistance temperature measurement often uses constant current source of three wires to drive the resistance sensor in order to obtain the voltage of thermal resistance. After voltage amplification, A/D conversion and SCM processing, the temperature of the object will be obtained. The thermal resistance is the method of contact temperature measurement, which has a lot of advantage including high accuracy, simple operation and low cost. At present, the research on using thermal resistance to measure leaf temperature is not too much. The changes of carbon dioxide concentration in winter wheat canopy were studied using TL series glass thermal resistor temperature measurement [START_REF] Baohua | Studies on the changing patterns of CO 2 concentration in winter wheat canopy [J][END_REF] . The LT-1M leaf temperature sensor [START_REF]Sensors and systems for monitoring growing plants[END_REF] (as showed in Fig. 4) has a subminiature touch probe that measures leaf temperature. The lightweight stainless steel wire clip holds high precision glass packaging thermistor, and the probe is very small and specially designed, which almost makes no effect on the natural temperature of the plant leaves. LT-1M leaf temperature sensor has high measurement precision, installed simply and user can customize the length of waterproof cable, which can avoid the effect of humidity environment when measuring the leaf temperature. It can also be used in plant physiological and ecological monitoring system and photosynthesis measuring instrument. Thermocouple is also a method of contact temperature measurement which can be used to directly measure the crops leaf temperature. It is easily carried and operated, and its price is cheap. At present, the CB-0231 thermocouple temperature measurement instrument produced by CID eco scientific instrument co., ltd is often applied in plant leaf temperature measurement. CB-0231 [START_REF]CB-0231 thermocouple thermometer[END_REF] is a precision temperature measurement instrument which is first applied in physiological ecology and teaching in our country. According to the temperature difference and electromotive force principle, the thermocouple probe has the advantages of small volume, small heat capacity and fast response to measure the continuous trace changing temperature of the organisms. It is not only widely used in plant physiological ecology freezing injury, fruit storage and refrigeration but also used to measure the surface temperature. The CB-0231 thermocouple thermometer was used to measure the leaf temperature to study the effects of soil water stress on quercus variables leaf temperature [START_REF] Xiaoming | Influence of soil water stress on Quercus variabilis leaf temperature and characteristics of chlorophyll fluorescence[END_REF] .

4.2

Non-contacting measurement of leaf temperature

Infrared temperature measurement

All objects above zero degree are constantly emitting infrared radiation energy to the surrounding. It is the basis of the infrared radiation temperature measurement to obtain the object surface temperature accurately by measuring its infrared energy [START_REF] Zifei | Infrared thermometers outline[END_REF] . According to the infrared radiation of the object, infrared thermometer relies on its internal optical system to gather the infrared radiation energy on the infrared sensor and converts into electrical signal. After the amplification circuit, the compensation circuit and the linear processing, the detected object temperature is displayed in the terminal. The infrared temperature measurement system includes optical system, infrared sensor, signal amplifier and signal processing and display output. The core is the infrared sensor which converts the incident radiation into a measurable electrical signal [START_REF] Jiefu | Principle of infrared thermometer and problem of application[END_REF] .

The first infrared thermometer appeared in 1931 and its strong advantage attracted the world's attention. As early as in the 1960s he used infrared thermometer to measure the plant temperature [START_REF] Tanner | Plant temperature[END_REF] .

Later researchers and ecological scholars constantly tried to measured plant leaf temperature by infrared thermometer and analyzed the physiological status of plant, and studied the growth of plant crops. Infrared temperature measurement is non-contacted and different from the traditional temperature measurement. It has advantages of quick response, high accuracy and reliability, wide range and it is not easily damaged [START_REF] Xia | Design of infrared thermometer based on single-chip microcomputer[END_REF] . In recent years, with the rapid and smooth development of China's infrared products market, a few foreign companies have increased the market share quickly and owned considerable advantages. The infrared temperature measurement instrument has broad market prospect and economic benefit so that its application will be more and more extensive. The MI series infrared radiometer [START_REF]Infrared Radiometer Meters MI-200 Series[END_REF] (as showed in Fig. 5) produced by the Apogee company has been widely used in plant leaf temperature measurement. Infrared radiometer receives object infrared radiant energy of probe field of view and converts it into electrical signal so that the object surface temperature will be measured of. It can quickly measure the application environment and its response time is only about 0.6 seconds. The recording of the germanium probe is equipped with a radiation shield, which can effectively reduce the error of the measurement data caused by abrupt changes of environment temperature. Because of high accuracy, high sensitivity and a variety of field of view, infrared radiometer is very suitable for the measurement of plant leaf temperature. All objects whose nature temperature is higher than the absolute zero are emitting radiated energy in the form of electromagnetic wave, including 0.7-1um of infrared light wave. The infrared temperature has a high temperature effect, which is the basis of infrared thermal imaging. Infrared thermal imaging is the technology that receives infrared radiation by infrared detectors and changes to video of thermal image by signal processing system. It transforms the thermal distribution of the object into visual images and displays the gray or pseudo color on the monitor, and then gets the temperature distribution field information [START_REF] Li Yunhong | Research on temperature measurement technology and application of infrared thermal imaging[END_REF] .

Since early 1980, far infrared imaging technology has firstly applied to agriculture and environmental detection. The real-time observation of plant leaves is possible with the features of multi-function, high accuracy and resolution. The application of far infrared imaging technology in plant research has caused a boom [START_REF] Kitaya | The effect of gravity on surface temperatures of plant leaves [J][END_REF][START_REF] Mustilli | S Arabidopsis OST1 protein kinase mediates the regulation of stomatal aperture by abscisic acid and acts upstream of reactive oxygen species production[END_REF][START_REF] Merlot | Use of infrared thermal imaging to isolate Arabidopsis mutants defective in stomatal regulation [J][END_REF] . The results of the study were remarkable and the object of the study was also widened. The infrared thermal image (as showed in Fig. 6) is recorded with the FLIR A310 infrared camera (as showed in Fig. 7) which is produced by FLIR Systems [32] , with a spectral infrared range of wavelength λ from 7.5 to 13 um, a temperature range of -20 to +120℃ and an accuracy of ±2%. The FLIR A300 infrared camera comes with the 18mm standard lens providing a 25°×19° field of view and 320×240 resolution. With its composite video output, the camera is a good choice for measuring leaf temperature. The FLIR A310 owns its built-in analysis, alarm functionality and independent communication technology. It has multi-camera utility software including the FLIR IP config and FLIR IR monitor.

Leaf temperature model

The leaf temperature model is a simulation model in the greenhouse, which regards the environment conditions (air temperature, air humidity and other parameters) as the driving variable. This paper mainly introduces the model between environment temperature and leaf temperature. The model takes leaf temperature as the dependent variable and environment temperature as the independent variable, building their fitted equation. The model method has good forecast effect and can guide actual production. In foreign countries, it was feasible to control the greenhouse environment according to the leaf temperature and establish the mechanism model of the leaf temperature [START_REF] Rohmy | Environmental control in greenhouse based on phytomonitoring-leaf temperature as a factor controlling greenhouse environments[END_REF] .Yao et al [START_REF] Yao Zhenkun | A simulation model of the relationship between tomato leaf temperature and ambient factors in solar greenhouse[END_REF] established the leaf temperature simulation model based on the mechanism model of the blade energy balance.

Using the common cucumber as the experimental material in the simple sunlight greenhouse, the model of plant leaf temperature was established [START_REF] Li | Solar greenhouse in northern crop leaf temperature and physiological adaptability mechanism model[END_REF] . The study showed the change regulation of the environment temperature to leaf temperature in a day and analyzed the physiological adaptive mechanism of the leaf temperature. From the Fig. 8, the environment temperature and leaf temperature are fitted and the equation is: y=1.188a-7.6662. The 'y' was the leaf temperature and the 'a' was environment temperature, coefficient of determination was R2=0.9128. The equation was significantly associated with 0.01 levels and verified good correlation between leaf temperature and environment temperature. There are many researches on the application of leaf temperature. Although the technology of leaf temperature measurement is developing and many advanced products are produced, there are some problems in the application of leaf temperature and measurement methods that are mainly reflected in the following aspects:

(1) The leading wire of thermal resistance sensor and the connecting wire of the resistance have a great influence on the measurement results. The leading wire of thermal resistance is often in the measured temperature environment which fluctuates seriously and varies with temperature. It is so difficult to estimate and correct that causes larger measurement error [START_REF] Ting | A new method of lead compensation for three-wire resistance temperature detectors[END_REF] . Because the platinum is expensive metals, it is generally used for high precision industrial measurement and rarely applied in plant temperature measurement.

(2) Because the thermocouple is belong to contacting measurement [START_REF] Xueli | Analysis and correction method of thermocouple temperature measurement error[END_REF] , it is necessary to absorb heat from the leaves and bring heat conduction, which will influence on the surface environment of leaves.

Because of the disadvantages of hard workload and longtime response, the thermocouple temperature measurement is not suitable for large area measurement. In addition, contacting with the leaves easily leads to the change of the leaf environment and affects the growth of the crops and the measurement precision. Many experts and scholars measures leaf temperature by thermocouple measurements, but it is difficult for thermocouple to overcome the limitations of radiation and heat conduction. So thermocouple temperature measurement is not too widely used in plant leaf measurement .

(3) Infrared thermometer is affected by the leaf emission rate which is a very complex parameter. So it is difficult to obtain the real temperature. The result needs to be corrected for material emissivity and temperature measurement data is processed difficultly. In addition, the infrared thermometer is greatly influenced by the environment, such as dust, steam, carbon dioxide, etc [START_REF] Jilin | Radiation temperature measurement and calibration technology[END_REF] .

(4) There are some problems in the infrared thermal imaging technology [START_REF] Enliang | Application and development of infrared thermal imaging technology in intelligent monitoring[END_REF] . Firstly, the infrared imaging camera depends on the temperature difference to form images, but leaf-air temperature difference is generally small, so the contrast of infrared thermal image is low and the ability of resolving details is poor. Secondly, the infrared imaging camera could not realize the target through transparent barrier, such as the window glass because it can't make the infrared imaging camera detect temperature difference. Thirdly, high cost and price is the biggest factor that limits the thermal imaging camera products widely used.

Development direction

The research on the leaf temperature in our country started relatively late and it has not reached to the world leader level in the application and measurement of leaf temperature, so there is a lot of room for developing. Firstly, research on the application of leaf temperature is still in the primary stage and using leaf temperature to study evapotranspiration of crop, irrigation, variety breeding and yield forecast should be further improved. The application of leaf temperature should be developed toward the direction of precision agriculture. Secondly, when measuring the crop leaf temperature we should study the effects on temperature at different moments and different parts of leaves and the degree of young and old leaves may make leaf temperature different. It is the key for application of leaf temperature to measure temperature accurately. We should pay attention to the development of leaf temperature measuring instrument and the key technology and improving the accuracy of measurement, which is the guarantee for the healthy and rapid development of our country agriculture.

Conclusion

Now more and more experts and scholars pay more attention to the study of crop leaf temperature, and leaf temperature has become one of the most important factors in the process of crop growth. Plant leaf temperature is one of the most important parameters of plant affecting its healthy growth. It is significant for the study on crop evapotranspiration, irrigation, variety breeding and yield forecast. In order to measure leaf temperature, thermal resistance measurement, thermocouple measurement, infrared temperature measurement, infrared thermal imaging measurement and the leaf temperature model can be used. Each method has its own characteristics in measuring the leaf temperature.

Obviously, infrared temperature measurement has more advantages in leaf temperature measurement.

This non-contact type of measurement has advantages of wide range and fast response and it is suitable for large area measurement. However, the factors that affect the accuracy of infrared temperature measurement, such as emissivity, distance, environment temperature and atmospheric absorption must be noticed. The accuracy of infrared sensors and the key technology must be improved. Reducing the cost of infrared instrument production and the use of infrared temperature measurement will be more common.

Fig. 1 .

 1 Fig. 1. Geranium plants being measured at the measurement station by Infrared radiometer

Fig. 3 .

 3 Fig. 3. Characteristic of diurnal variation of leaf-air temperature difference for maize hybrids at the stage of 13th leaf expanded under different drought stress

Fig. 4 .

 4 Fig. 4. The LT-1M leaf temperature sensor

Fig. 5 .

 5 Fig. 5. Apogee MI series infrared radiometer

Fig. 6 .Fig. 7 .

 67 Fig. 6. The infrared thermal image of leaf

Fig. 8 .

 8 Fig. 8. The fitting of leaf temperature and environment temperature

Table 1 .

 1 The correlation between leaf temperature and yield of 14:00 during rice growth period

		no-super rice		super rice		Correlation coefficient
	growth period						r
		JinYou463 JinYou402 JinYou458 GanXin203 LuLiangYou996
	leaf stage	2.2	1.8	2.1	1.7	2.3	0.5748
	differentiation	2.5	2.4	2.8	2.6	2.6	0.6734*
	heading stage	2.8	2.6	2.9	2.7	2.9	0.8329**
	after heading	2.4	2.3	2.6	3.0	2.7	0.5727
	plot output /(t/hm 2 )	6.71	6.22	7.45	7.12	8.11

Table 2 .

 2 Technology parameters, advantages and disadvantages of four methods of temperature measurement

	method	product	range	precision	advantage	disadvantage
	thermal resistance	LT-1M leaf			High precision Simple
		temperature sensor	0-50 ℃	0.15 ℃

Acknowledgement

The research was supported by the National High Technology Research and Development Program("863"Program) of China (2013AA103005).


Dong Liang
email: dliang@ahu.edu.cn

Ling Teng

Linsheng Huang
email: clinsheng0808@163.com

Xinhua Xie
email: dxiexinhuavip@163.com

Yan Zuo

Jingling Zhao

Dynamic Analysis of Urban Landscape Patterns of Vegetation Coverage Based on Multitemporal Landsat Dataset

Keywords: landscape pattern, vegetation coverage, normalized difference vegetation index (NDVI), Landsat, remote sensing

Dynamic monitoring of vegetation coverage changes, especially on a relatively large temporal scale, have important practical significance in urban planning and environmental protection. The objective of this study is to dynamically investigate the urban landscape patterns of vegetation coverage based on remote sensing techniques. Multi-temporal Landsat images of 1990, 2000 and 2013 were firstly used to produce three vegetation coverage maps of Hefei City, Anhui Province, China with five grades using the NDVI (Normalized Difference Vegetation Index) dimidiate pixel model. Subsequently, a total of eight landscape pattern indictors in FRAGSTATS 4.2 were selected to analyze the dynamic characteristics of area, quantity and density for the study area with different vegetation coverage grades. The results showed that 1) the dominant vegetation coverage of 1990, 2000 and 2013 were the high vegetation coverage, the moderate vegetation coverage and the moderate-to-high vegetation coverage, respectively. The acreage of non-vegetation coverage increased by 1.89%, while the high vegetation coverage decreased by 10.48% from 1990 to 2013; 2) the quantity and density of patches decreased by 33.42% and 33.41% during 1990-2013. Shannon's diversity index and Shannon's evenness index increased from 0.92 in 1990 to 0.97 in 2000, and then declined to 0.96 in 2013; and 3) the contagion index had an upward trend and conversely the aggregation index showed no significant changes, but both of them were close to 1 during 1990-2013. In comparison with natural influences, the primary driving forces causing the changes were ascribed to human factors including the rapid population growth and fast-growing urban areas.

Introduction

Vegetation is a general term that describes the phytocoenosium covering the land, which not only provides large amount of material resources (e.g., vegetation products and vegetation by-product), but also helps in maintaining soil and water, conserving water, fixing sand, purifying air, regulating climate and so on. Furthermore, it plays an important role in maintaining the ecological balance and promoting regional sustainable development [START_REF] Bengochea-Morancho | A hedonic valuation of urban green areas[END_REF][START_REF] Tang | Assessing spatiotemporal variations of greenness in the Baltimore-Washington corridor area [J][END_REF][START_REF] Gong | Ecological environment effect analysis of Wetland change in Beijing region using GIS and RS [C]// Urban Remote Sensing Event[END_REF]. With the continuous improvement of socio-economics, the built-up areas gradually expanded, and spatial regions are becoming more concentrated due to human activities, especially in urban areas. Consequently, quantitative monitoring of the spatio-temporal changes of urban vegetation cover has attracted more attention in urban planning and environmental protection [START_REF] Dale | The relationship between land-use change and climate change [J][END_REF][START_REF] Kalnay | Impact of urbanization and land-use change on climate[END_REF]. In traditional vegetation monitoring, statistical methods with lower updating frequency are usually used, but they are rather time-consuming, laborious, and costly, especially failing to monitor the changes of vegetation coverage on a large scale. Conversely, remote sensing has facilitated extraordinary advances in analyzing urban vegetation coverage changes by its real-time and wide coverage features, which has become a significant measurement for monitoring the urban ecological environment.

In recent years, different ecological problems have appeared in urban regions, and some landscape ecology approaches to combining remote sensing have been applied to explore the urban landscape pattern and its change process [START_REF] Tang | Spatial analysis and temporal simulation of landscape patterns in two petroleum-oriented cities [J][END_REF][START_REF] Griffith | A Landscape Ecology Approach to Assessing Development Impacts in the Tropics: A Geothermal Energy Example in Hawaii [J][END_REF][START_REF] Yin | Multi-scale analysis of landscape pattern of Hunan Province [C]// Earth Observation and Remote Sensing Applications (EORSA)[END_REF][START_REF] Peng | A study in stability of the regional land-use landscape pattern[END_REF]. Tang et al. linked spatial pattern and biophysical parameters of urban vegetation by multi-temporal Landsat imagery [START_REF] Tang | Linking Spatial Pattern and Biophysical Parameters of Urban Vegetation by Multitemporal Landsat Imagery[END_REF]. Liu et al. monitored vegetation cover changes in Hefei based on normalized difference vegetation index (NDVI) dimidiate pixel model [START_REF] Liu | Monitoring vegetation-cover changes based on NDVI dimidiate pixel model[END_REF]. Deng et al. used MODIS-NDVI to monitor the vegetation coverage of Shangri La in Northwest Yunnan [START_REF] Gong | Dynamic monitoring of vegetation coverage in Shangri-La county of northwest Yunnan based on MODIS-NDVI[END_REF]. It can be found that in the previous studies, multi-temporal Landsat imagery including TM and ETM + were primarily used on a regional scale, while MODIS-NDVI products were mainly used on a national or continental scale. In addition, the methods used in the studies were primarily concentered on how to calculate the vegetation coverage precisely for monitoring the spatio-temporal vegetation changes. Conversely, the urban landscape patterns of vegetation coverage have not fully been investigated and explored. Specifically, two primary limitations can be found in the aforementioned studies. On the one hand, most of the studies are based on the changes in vegetation quantity and spatial distribution derived from remote sensing imagery. On the other hand, most remote sensing data are focused on Landsat TM/ETM + (before 2010) and didn't integrate the new data of Landsat8 launched in 2013.

In this study, Hefei, the capital city of Anhui Province, China were taken as the study area and three Landsat imageries (Landsat 5 TM images of 1990 and 2000 and Landsat 8 OLI (Operational Land Imager) image of 2013) were firstly used to calculate and classify the urban vegetation coverage grades based on the NDVI dimidiate pixel model., Subsequently, the landscape patterns of vegetation coverage were dynamically analyzed in FRAGSTATS 4.2 software. Finally, the primary driving forces were also investigated.

Data and Methodology

Study Area

Hefei, the capital city of Anhui Province, China is located between 30°56' N-32°32' N and 116°40' E-117°58' E, with an area of more than 11408.48 km 2 . It has a subtropical monsoon climate, with an annual average temperature of 15.7 °C, an annual average precipitation of 1000 mm and an annual average sunshine of 2100 hours. As of 2014, the city has a total resident population of 7.696 million. The general trend of topography is high in the north, southeast and southwest, and low in central and southern parts.

Data Source and Pre-Processing

Landsat (name indicating Land+ Satellite) imagery is available since 1972 from seven satellites in the Landsat series, especially the newly launched Landsat8 in 2013. The time-series remotely sensed imagery with various spectral and spatial resolutions in Landsat series provides good data sources for dynamic analysis of urban vegetation coverage. In this study, multi-temporal Landsat satellite images were acquired and used to dynamically investigate the landscape pattern changes of urban vegetation coverage. Specifically, the cloud-free Landsat5 TM data (path 121/row 38) of 1990 and 2000 and Landsat 8 OLI image of 2013 were acquired (Table 1 and Figure 1), where the data of 2000 and 2013 was derived from the United States Geological Survey (USGS), and the data of 1990 was derived from the Global Land Cover Facility (GLCF). In addition, the vector data of Hefei was also used to mask the three Landsat images. The Landsat 5 TM in 1990 derived from the GLCF is the Geocover data set which is a collection of high resolution satellite imagery provided in a standardized, orthorectified format. . While the Landsat 5 TM in 2000 and Landsat 8 OLI in 2013 derived from the USGS are primary products and they were processed by referring to the data of 1990. The quadratic polynomial geometric correction model was used to geometrically correct the images of 2000 and 2013 within 0.5 pixels. Radiometric calibration and atmospheric correction were also performed. Finally, the vector file of Hefei was utilized to mask the three images.

Method

As shown in Figure 2, two primary steps would be required. Firstly, the NDVI images of three pre-processed images were respectively calculated according to the Eq. 1.

Then, the appropriate values of NDVI veg and NDVI soil for three periods were selected from corresponding Landsat images, and the vegetation coverage were calculated in accordance with the Eq. 2. Finally, different landscape indices were calculated in FRAGSTATS 4.2 software for further analyzing the vegetation landscape patterns in the study area. In comparison with other VIs, NDVI (Eq.1) has been widely used in the monitoring of vegetation biomass by remote sensing techonology. It is not only related to the plant distribution density, but also reflects the plant growth and the spatial distribution density of plant growth factors [START_REF] Wang | Diagnosis of Vegetation Recovery in Mountainous Regions After the Wenchuan Earthquake[END_REF][START_REF] Wang | Vegetation temperature condition index and its application for drought monitoring[END_REF].

NIR red NIR red NDVI      (1)
Where, NIR  and red  represent the reflectance of the near infrared (NIR) band and the red band. For the Landsat 5 TM image, NIR corresponds to the Band 4 and red corresponds to the Band 3. For the Landsat 8 OLI image, they correspond to the Band 5 and the Band 4, respectively.

Calculation of Vegetation Coverage

Vegetation coverage refers to the ratio of the vegetation canopy area in vertical projection and the total soil area, namely planting soil ratio [START_REF] Adams | A Light Interception Method for Measuring Row Crop Ground Cover[END_REF]. In our study, and the NDVI dimidiate pixel model was selected to estimate the vegetation coverage (Eq.2), which is a simple and practical remote sensing estimation model by assuming that the earth's surface corresponding to the pixel is composited by the vegetation cover regions and non-vegetation regions [START_REF] Leprieur | Evaluation of the performance of various vegetation indices to retrieve vegetation cover from AVHRR data[END_REF][START_REF] Zribi | Derivation Of Wild Vegetation Cover Density In Semi-Arid Regions: Ers2/Sar Evaluation [J][END_REF]. Similarly, the observed spectral information by remote sensing sensor is also composited by the linear weighted synthesis of the two components, and the weight of each factor is the proportion of the pixel area [START_REF] Wang | Vegetation temperature condition index and its application for drought monitoring[END_REF].

soil c veg soil NDVI NDVI F NDVI NDVI    (2)
Where, F c represents the vegetation coverage, while NDVI veg and NDVI soil were the NDVI values of pure vegetation and pure soil, respectively.

Selection and Calculation of Landscape Pattern Indices

Landscape indices are usually used in landscape ecology quantitative research methods, which can describe the landscape patterns and corresponding changes, and establish a link between the landscape pattern and changing process [START_REF] O'neill | Indices of landscape pattern[END_REF]. In this study, the landscape indices were calculated in FRAGSTATS 4.2 software which can provide more than 50 landscape indices at three levels (patches, landscape types and landscape). A total of eight classes can be divided according to the measurement aspects of landscape pattern, which are Area/Density/Edge Index, Shape Index, Core Area Index, Independent/Near Index, Contrast Index, Contagion/Discrete Degree Index, Connectivity Index and Diversity Index. Specifically, two aspects of landscape patterns were mainly carried out. One is the amount distribution of patches of different vegetation coverage grades to describe the structure and changes, which includes Number of Patch (NP), Patches Area (AREA), Aggregation Index (AI), Percentage of Landscape (PLAND), Shannon's Evenness Index (SHEI), Shannon's Diversity Index (SHDI), etc. The other is the spatial form and distribution characteristics of patches for describing spatial pattern and changes, which are measured by Contagion Index (CONTAG), Fractal Dimension Index (FRAC), Patch Cohesion Index (COHESION), etc. [START_REF] Zhen | Landscape spatial analysis technology and application (First Edition)[END_REF]. In our study, PLAND, NP, PD, AREA-MN, COHESION, CONTAG, SHDI and SHEI were selected by referring to [START_REF] Jones | Monitoring Environmental Quality at the Landscape Scale[END_REF] to describe the vegetation spatial pattern changes (Table 2).

3

Results and Discussion

Classification Maps of Vegetation Coverage

Three vegetation coverage maps were produced according to the Eq. 2 (Fig. 3).

Five grades were categorized: Grade 1: non-vegetation coverage ( 0 20 ) . It showed that the spatial distribution of vegetation coverage in Hefei showed an obvious characteristics, The peripheral vegetation coverage was higher and the central city was lower in 1990, 2000 and 2013. In 1990, the vegetation coverage of the study area was good, the high vegetation coverage was mainly in the south, southwest and northeast of, and the area of non-vegetation coverage was mainly concentrated in the central city. From 1990 to 2013, the vegetation coverage of the study area had been reduced obviously. The high vegetation coverage in southwest and northeast had been degraded into low and moderate-to-high vegetation coverage and the area of nonvegetation coverage in the central city showed an obvious trend of expanding. The regions with obvious decreasing vegetation coverage can be ascribed to active regional economic developments [START_REF] Chen | Study of information mechanism Remote Sensing (First Edition)[END_REF]. On the other hand, the changed vegetation coverage regions can also reflect the development paths for the study area. In general, the urban sprawl has been transferred from central urban area to the periphery. To describe the landscape heterogeneity and fragmentation, the number of patches was used. As shown in Table 3, the distribution of the number of patches for different vegetation coverage grades could be also obtained (Figure 4b). It showed that the total number of patches in the study area reduced by 278555 during 1990-2013. Specifically, the number of patches of Grade 1 increased from 74867 to 74908 during 1990-2000, and then decreased to 47698 in 2013. From 1990 to 2013,that of Grade 2 and Grade 5 decreased by 42237 and 8497, respectively. That of Grade 3 and Grade 4 decreased from 259384 to 170252 and from 215976 to 183300 respectively during 1990-2000, and then decreased by 12546 and 66298. It was obvious that the number of patch of Grade 3 changed greatly during 1990-2013 (Figure 4).

The increase of Shannon's Diversity Index indicated that various types of patches in the landscape showed the equalized distribution. Larger evenness index showed that the landscape patches in area had more uniform distribution. The results showed that SHDI and SHEI were relatively high in the study area. The Shannon's Diversity Index increased from 1.4785 to 1.5526 and the Shannon's Evenness Index increased from 0.9186 to 0.9647 during 1990-2013. LAND 100.00 100.00 100.00 833488 739925 554933 1.4785 1.5612 1.5526 0.9186 0.9700 0.9647

The Changes in Landscape Pattern

Patch Density Index, Mean Match Area Index, Cohesion Index and Contagion index were selected to analyze the landscape pattern changes in space of different vegetation coverage. As shown in Table 4, patch density distribution of different vegetation coverage of three years could be obtained (Figure 5 a). From 1990 to 2013, the total patch density of landscape in the study area reduced from 72.93% to 48.56%. For urban areas, patch density decreased at different levels during 1990-2013. For Grade 1, patch density remained unchanged from 1990 to 2000, however it decreased from 6.55% to 4.17% during 1990-2013. For Grade 2 and Grade 5, the patch density increased firstly and then decreased, which showed that patches of two grades were getting less fragmented in the entire period. Similarly, Grade 3 and Grade 4 declined respectively during 1990-2000 and 2000-2013. The largest patch density zones were Grade 3, Grade 2 and Grade 3, respectively. The results could be explained that the lands of the three grades had become more fragmented. Conversely, the smallest patch density zones of three years were the Grade 1, which showed there were lower degree of fragmentation degree in these areas of Grade 1.

Concerning on the AREAR-MN (Table 3), comparison of the number of patches for different vegetation coverage grades could also be obtained (Figure 5 b). There was an increase for all the grades except the Grade 5e. For Grade 1, the mean patch area increased significantly by 1.52 hm 2 , which indicated that the vegetation coverage was fragmentated in the study area during 1990-2013.

From 1990 to 2013, Contagion Index tended to increase and there was no significant changes for Patch Cohesion Index, but both of them were close to 1, which showed that the landscape overall trend gathered, and good natural connectivity. Grade 1, Grade 4 and Grade 5 in the three periods were close to 1, which indicated that the aggregation degree was higher. However, the Patch Cohesion Index of Grade 2 and Grade 3 were relatively lower, which indicated that those regions of three grades had lower degree of aggregation, but they increased for s in 2013.

Analysis of Driving Forces

Driving forces analysis is a way of understanding and accounting for vegetation coverage changes potentially caused by different factors such as regional climate change and human factors [START_REF] Zhou | Research on spatial pattern of humaninduced vegetation degradation and restoration:a case study of Shaanxi Province [J][END_REF]. Environmental factors, such as slope, aspect, elevation, temperature, precipitation, etc., usually determine the vegetation growth and agricultural development in the fragile ecosystem [START_REF] Wang | Land-Cover Changes And Its Impacts On Ecological Variables In The Headwaters Area Of The Yangtze River, China [J][END_REF]. Conversely, Hefei city is the capital city and the influences of human factors paly a highly significant role in changing the vegetation coverage in comparison with regional climate change. Fastgrowing urban areas have changed the natural vegetation coverage, and demographic and economic development are the primary driving factors for the growing metropolitan cities [START_REF] George | China's land resources and land-use change: insights from the 1996 land survey[END_REF]. In this study, three driving forces were investigated including the rapid population growth, the fast-growing urban the higher urbanization level.

Economic Development

The urban sprawl for a fast-growing city is substantially driven by the urgent requirements of social and economic development. Consequently, the natural vegetation coverage is changed to adapt to the human activities. Hefei has experienced a remarkable period of rapid economic growth spanning in more than 20 years from 1978 to 2010 (Figure 6). In 1990, the local gross domestic product (GDP) was just 58.19 (100 million) Yuan, but it had increased to 324.73 (100 million) of 2000 and 4672.91 (100 million) of 2013. Similarly, the GDP per capita also showed a rapid growth trend. It was 458, 7481 and 61555 Yuan in 1990, 2000, 2013, respectively. In more than 20 years, the GDP in Hefei had increased by 7,930% and the GDP per capita have greatly increased by 13340%. It was inevitable that the natural vegetation coverage had been extremely change along with the improvement in economic development. Specifically, the total land for residential areas and mining was just 66,477.64 (10,000) in 1990, but it reached 78,036.95 (10,000) in 2000 and 364040 (10,000) in 2013, respectively. Similarly, the traffic land area was just 13,593.15 (10,000) in 1990, but it reached 48,710 (10,000) in 2013. At the same time, arable land areas have kept a decreasing trend in Hefei (Figure 7 b). The total arable land areas was 507,868.42 (10,000), and it decreased to 494,464.80 (10,000) in 2000 and 336,025 (10,000) in 2013. In more than 20 years, the arable land increased by 33.84%.

Conclusions

The landscape pattern of vegetation coverage can be dynamically explored based on multi-temporal remote sensing imagery. In general, the high vegetation coverage was significantly reduced, and the moderate-to-high degree had little changes, while low vegetation coverage and moderate vegetation coverage was significantly increased. Specifically, the high vegetation coverage in Hefei had decreased markedly from 1990 to 2000, but the situation had become better from 2000 to 2013. Furthermore, the degree of landscape fragmentation had decreased from 1990 to 2013, while landscape diversity index and evenness index were higher, which showed that the distribution of each patch type was balanced and distributed relatively evenly in area. To find out the driving forces causing the changes of vegetation coverage, three types of driving factors were primarily discussed, including the economic development, growth of population and construction areas, and institutional policies. Multi-temporal remote sensing images are useful to monitor ecological changes of regional vegetation. Additionally, it is extremely necessary to find out the driving forces causing the changes of vegetation coverage.

Fig. 1 .

 1 The false-color composite images of 1990, 2000 and 2013 of Hefei

Fig. 2 .

 2 Fig. 2. Flow chart of calculating vegetation coverage and performing landscape pattern analysis

  patch types as well as its maximum in the landscape * denotes that i represents the patch type; j represents the amount of patches; m represents the total number of all the patch types; n represents the number of a certain patch type; P i represented the circumference of the ith patch type.

Fig. 3 . 3 . 2

 332 Fig. 3. Three spatial distribution maps of five-grade vegetation coverage

Fig. 4 .

 4 Comparison of PLAND and NP for five vegetation coverage grades among three periods

Fig. 5 .

 5 Comparison of PD and AREA-MN of for different coverage grades

Fig. 6 .

 6 Fig. 6. Economic development in Hefei during 1990-2013

7 .

 7 Area changes of housing construction, traffic land and arable land Fig.Changes of population and artificially changed areas in Hefei during 1990-2013

Table 1 .

 1 Multi-temporal Landsat images used in the study

	Year	Path/Row	Acquisition Time	Sensor	Resolution (m)
	1990	121/38	1990.9.19	Landsat 5 TM	30
	2000	121/38	2000.9.25	Landsat 5 TM	30
	2013	121/38	2013.9.20	Landsat 8 OLI	30

Table 2 . Landscape pattern indices and their ecological significance *

 2

	Index and formula	Level	Ecological significance
			Reflecting the abundance
	PLAND p 	Landscape	ratio of a certain type of
	i
		class	patches in the landscape.

Table 3 . Spatial landscape pattern properties of vegetation coverage Type

 3

		Percentage of landscape (%)	Patches number (a)	Shannon's Diversity Index	Shannon's Diversity Index
			PLAND			NP			SHDI			SHEI
		1990	2000	2013	1990	2000	2013	1990	2000	2013	1990	2000	2013
	1	12.35	12.28	14.25	74867	74908	47698
	2	8.25	13.90	10.89	189087 198783 146850
	3	15.54	27.61	21.18	259384 170252 157706	1.4785 1.5612 1.5526 0.9186 0.9700 0.9647
	4	28.36	25.75	28.67	215976 183300 117002
	5	35.50	20.46	25.02	94174	112682 85677

Table 4 . Spatial landscape pattern properties of vegetation coverage type

 4

			Patch density (%)		Mean patch area(hm 2 )		Patch Cohesion (%)	Contagion Index (%)
			(PD)			(AREA-MN)			(COHESION)			(CONTAG)
		1990	2000	2013	1990	2000	2013	1990	2000	2013	1990	2000	2013
	1	6.55	6.55	4.17	1.89	1.87	3.41	98.90	98.89	99.52
	2	16.55	17.39	12.85	0.50	0.80	0.85	76.73	98.83	88.67
	3	22.70	14.90	13.80	0.68	1.85	1.54	88.09	99.59	98.02	26.43	3.26	26.45
	4	18.90	16.04	10.24	1.50	1.61	2.80	98.96	98.65	99.24
	5	8.24	9.86	7.50	4.31	2.08	3.34	98.85	99.30	99.41
	LAND	72.93	64.74	48.56	1.37	1.54	2.06	98.29	99.29	98.97	26.43	23.26	26.45

Acknowledgment

The project was supported by Anhui Provincial Natural Science Foundation (No. 1408085QF126), the Open Research Fund of Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences (No. 2014LDE012), and the Leadership Introduction Project of Academy and Technology of Anhui University (No. 10117700024).