Urban Green Vegetation Stress Conditions Diagnosis Based on Hyperspectral Database-A Case Study of

Xuzhou

Qian Xiaojin, Shen Qiu, Liang Liang*, Zhang Lianpeng, Wang Lijuan

School of Geodesy and Geomatics Jiangsu Normal University

Xuzhou, China liangliang198119@gmail.com

Wang Shuzhan School of Chemistry and Chemical Engineering

Jiangsu Normal University Xuzhou, China

Abstract—In order to provide scientific support for the management of the urban green vegetation, taking Xuzhou city as an example, this paper proposed a method to diagnose stress conditions of vegetation rapidly by using a green vegetation spectral database. Under laboratory conditions, 303 samples of leaf reflectivity spectra taken from 25 kinds of green vegetation in urban areas were acquired by the means of AvaSpec-2048x14-USB2 spectrometer integrating sphere reflectivity measurement. When collecting spectral data, the visual observation and other traditional detection methods were used to diagnose the stress conditions of each vegetation sample, which could be divided into four levels including normal, mild stress, moderate stress and severe stress. After these procedures, a spectral database which can diagnose stress conditions of green vegetation in Xuzhou city rapidly was established on the platform of the software Environment for Visualizing Images (ENVI). To verify the ability of the database to diagnose stress conditions of green vegetation, 113 unknown test samples were introduced into the database. Firstly, spectral data were preprocessed by the methods of smoothing in order to eliminate the influence of background information. Secondly, on the basis of analyzing spectral feature of green vegetation in different stress conditions, the methods of spectral matching analysis in database, including Spectral Feature Fitting, Spectral Angle Mapper and Comprehensive Matching were used for matching analysis to diagnose the stress levels of 113 unknown vegetation samples. And then the matching accuracy which based on the traditional detection methods was evaluated. The results showed that the feature band which is capable of diagnosing the stress conditions of green vegetation mainly focused on the areas of green peak, red-absorption band and red edge in the reflection spectra curves. The matching accuracy of Spectral Feature Fitting, Spectral Angle Mapper and Comprehensive Matching reached 78.5%, 75.6% and 83.4%, respectively. The result indicates that it is feasible to diagnose the stress conditions of green vegetation using the method of spectral matching, and this method is expected to be a supplementary and alternative of traditional detection methods.

Keywords — green vegetation; hyperspectra; diagnosis; stress condition

I. INTRODUCTION Urban greening construction plays an irreplaceable role in

protection of urban ecological environment and maintenance of human living environment. So it is particularly important to take some effective greening construction measures to promote urban harmony and sustainable development. However, many cities usually pay little attention to green vegetation management, which wastes plenty of money and manpower. Therefore, monitoring and maintaining green vegetation is of great significance in mastering their health dynamic to maintain greening achievements. Traditional detection methods of green vegetation growth are time-consuming, costly and do great damage to green vegetation. In addition, they can only get the point-source information, which is difficult to be operated on the macroscopic scale. As a result, they bring great difficulties to the green vegetation detection, severely affecting its comprehensiveness, timeliness and objectivity [1]. Compared with traditional ground survey, hyperspectral remote sensing technology has more advantages in speed and resource consumption. Furthermore, it can improve the data accuracy and management ability of green health in order to timely supply urban planning and decision-making departments with accurate health data of green space [2].

As a main achievement of remote sensing technology in the 1980s, hyperspectral remote sensing technology has powerful function of data acquisition on the macroscopic scale, low consumption, high speed and noninvasive advantages in biological information acquisition [3], which can make up for the disadvantages of the methods above. This paper took Xuzhou city, Jiangsu province as an example, focusing on urban green vegetation, and put forward a method of establishing green vegetation spectral database for rapid diagnosis of green vegetation stress conditions. In addition, based on the analysis about the characteristics of green vegetation spectra under different stress conditions, this method adopted spectral matching technology to match unknown green vegetation samples in order to determine their levels of stress. This process was completed in ENVI.

* corresponding author Sponsored by the National Innovation and Entrepreneurship Training

Program for Undergraduate (No.201310320030 and No. 201310320048), the Natural Science Foundation of Jiangsu, China (No. BK2012145 and No. BK20140237) and the Natural Science Research Project for Universities of Jiangsu Province, China (No.12KJB420001).

II. MATERIALS AND METHODES

A. General Situation of Experimental Areas Xuzhou city is located in east longitude 116°22'-118°40',

north latitude 33°43'-34°58', and is about 210 kilometers long from east to west and 140 kilometers wide from north to south. It occupies an area of 11258 square kilometers, 11% of the whole areas of Jiangsu province. Xuzhou city locates in the warm temperate semi-humid monsoon climate area, and its four seasons are also distinct--cool in summer and warm in winter. This paper took Xuzhou city as an example, and mainly collected green vegetation leaves in Tongshan district for the spectra acquisition and the construction of spectral database. The geographical position of experimental areas is presented in Fig. 1.

B. Experimental Method 1) Leaf collection

Leaf collection was conducted under the climate conditions of clear weather and appropriate temperature. In the process of acquisition, leaves were sealed into labeled bags (with leaf information) in order to keep the leaves fresh and prevent moisture loss. After that, they were taken back to the laboratory in the treated preservation box. Finally, there were 25 tree species being collected, whose data were 303 pieces of spectra.

2) Leaf stress level evaluation Atmospheric pollution stress, water stress, temperature

stress and plant diseases and insect pests can pose a threat to green vegetation. In this paper, green vegetation were divided into 4 levels: normal, mild stress, moderate stress and severe stress by observing the bare parts of green vegetation leaves, stems, tree trunks, branches and roots [2]. The symptoms of the green vegetation under different stress conditions are presented in Table 1.

3) Spectra acquisition In this paper, spectra were collected by the means of

AvaSpec-2048x14-USB2 spectrometer integrating sphere reflectivity measurement. During the experiments, sample test was conducted in the dark room and made halogen tungsten lamp the only light source in order to reduce the light impurity. What is more, the head of optical fiber probe kept the vertical with samples, and the average value after repeated measurement was taken as the final value [4]. The middle veins avoided being exposed when leaves were lighted on.

Fig. 1. The geographical position of experimental areas in Xuzhou city.

TABLE I. URBAN GREEN VEGETATION STRESS SYMPTOMS AND CLASSIFICATION STANDARDS

The degree of stress

Level code Symptoms

Normal 1 Leaf color is pure; veins are clear and

complete; leaf tip and leaf margin are hale and smooth, without defects.

Mild stress 2

The color of the leaves has a slight change; the veins are still clear and complete; leaves have defects or stripes; leaves are slightly dry or pathological or wither.

Moderate stress 3 Leaf sides present injury and necrosis,

leaves are also seriously dry.

Severe stress 4 The color of the leaves changes a lot, and massive defects or stripes appear.

C. Data Processing and Spectral Database Construction Spectral database is the set of all kinds of surface feature

spectra reflectivity data, which are measured by spectrometer in certain condition. It plays an important role in the accurate interpretation of remote sensing image information, the rapid matching of unknown surface features and improving the level of classification recognition. This provides the basis for people to understand, diagnose and match surface features [5]. In this paper, urban green vegetation went through spectra reflectivity measurement. After the smoothing of the measured spectra curves, the user-defined spectral database was established on the platform of ENVI, whose file format is ASCII. The green vegetation spectral database involved in much common green vegetation, such as magnolia, camphor, privet, coral tree and so on.

III. RESULTS AND ANALYSIS

A. Spectra Characteristic Analysis of the Leaves Under Different Stress Conditions In accordance with the method described in the section

“Leaf collection”, training samples were selected according to the field survey between April and June in 2014. Field survey areas included street green space, residential green space, etc. in Tongshan district. There were 303 experimental data being collected, and 190 random samples of them were used to be the training set, which included 123 normal spectra, 27 mild stress spectra, 28 moderate stress spectra and 12 severe stress spectra .Then the rest were the prediction set, which consisted of 75 normal spectra, 14 mild stress spectra, 14 moderate stress spectra and 10 severe stress spectra. Taking magnolia as an instance, the analysis of green vegetation characteristic under different stress conditions are shown in Fig. 2.

Green vegetation spectra are mainly caused by leaf chlorophyll, other biological, chemical composition and the light absorption of the cellular structure [6]. Spectra curves of normal magnolia have typical green vegetation spectra characteristics: because the absorption of chlorophyll in blue and red band is strong, and in green band is relatively weak, normal magnolia leaf spectra reflectivity in the green band is relatively high, forming the obvious green peak [7]. Moreover,

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Fig. 2. Magnolia spectral curves under different stress conditions

strong reflection of the cell walls of mesophyll cells causes normal magnolia leaf spectrum curve after the 730 nanometers to form obvious near-infrared high steps and a sharp red edge between the valley of red (formed by the 680 nanometers red-absorption band) and near-infrared high steps. In terms of theory, green vegetation weakens its ability to absorb red light because their chlorophyll decreases, lutein increases and cellular structure is destroyed, which results in the phenomenon that green peak is low, red edge moves in the direction of blue band (blue shift) and red edge’s slope drops. The results showed in Fig. 1 are consistent with this theory: compared with the normal magnolia, the green peak of stressed magnolia leaf is low, red-absorption weakens and red edge slope also tends to decline. The stronger the stress level is, the more obvious these phenomena are. Even green peak of severe stress magnolia leaf gradually disappears and its red edge is not obvious any more.

B. Spectral Matching Analysis Spectral matching technology based on spectral database

mainly uses reference spectra to diagnose unknown spectra and distinguish the characteristics of surface features. According to the similarities of reference spectra and unknown spectra [8], spectral database can diagnose the species and stress conditions of unknown green vegetation. Besides, it adopted Spectral Feature Fitting, Spectral Angle Mapper and the method respectively setting the weight of the above two matching methods and Binary Encoding to 0.4, 0.3, 0.3 (referred to as Comprehensive Matching) to match and analyze green vegetation spectra under different stress conditions. The results are shown in Fig. 3.

For green vegetation under the same stress condition, three matching methods all can match up well. The matching accuracy of Spectral Feature Fitting, Spectral Angle Mapper and Comprehensive Matching reaches 78.7%, 75.2% and 84.8%, respectively. Moreover, the matching accuracy of normal, mild stress, moderate stress and severe stress reaches 90.0%, 76.9%, 72.2% and 79.2%, respectively. It can be found that normal leaves and severe stress leaves are easily matched by the way of spectral matching, but mild stress and moderate stress leaves are not easy to be matched. This is because mild stress spectra curves are consistent with normal spectra curves,

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Fig. 3. Matching accuracy of green vegetation under different stress conditions in training set

and it is difficult to accurately distinguish between them. Meanwhile, moderate stress between mild stress and severe stress is difficult to be accurately assessed with the naked eye. In general, Comprehensive Matching has the best effect, and normal and severe stress leaves are easily matched.

To further verify its adaptability to unknown samples, spectral database were used to forecast the prediction set. The matching methods above were adopted to accomplish matching and analysis of green vegetation spectra under different stress conditions. The matching results of green vegetation are shown in Fig. 4.

In the prediction set, the matching accuracy of Spectral Feature Fitting, Spectral Angle Mapper and Comprehensive Matching reaches 78.2%, 75.9% and 82.0%, respectively. Although the Spectra Angle Mapper and Comprehensive Matching’s matching effects on normal spectra are the same, but the matching accuracy of Comprehensive Matching to different stress conditions is obviously higher than Spectral Feature Fitting and Spectral Angle Mapper. Also, the matching accuracy of normal, mild stress, moderate stress and severe stress reaches 86.2%, 73.8%, 71.4% and 83.3%, respectively. It can be seen that matching accuracy of normal and severe stress spectra is higher, and mild and moderate stress spectra are not easy to be matched. The results are consistent with those in the training set and show that spectral database has good adaptability to unknown samples.

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Fig. 4. Matching accuracy of green vegetation under different stress conditions in prediction set

IV. DICUSSION AND CONCLUSION It is feasible to collect 303 leaf spectra by the means of

AvaSpec-2048x14-USB2 spectrometer integrating sphere reflectivity measurement and build green vegetation spectral database for rapid diagnosis of their stress conditions. Furthermore, the matching accuracy of Spectral Feature Fitting, Spectral Angle Mapper and Comprehensive Matching reaches 78.5%, 75.6% and 83.4%, respectively. It provides a quick and convenient method for monitoring and managing urban green vegetation. At the same time, according to the comparative analysis of different spectral matching methods, it can be found that Comprehensive Matching works best. In addition, a conclusion can be reached that the feature bands to diagnose the stress conditions of green vegetation mainly focus on the spectra reflectivity curves of green peak, red-absorption band and red edge.

However, there are some limitations of this paper. Firstly, as for spectra pretreatment, it only smoothes spectra curves and does not adopt other pretreatment methods to comparative analysis for a optimal result. Secondly, due to the limitation of time and conditions, the data samples are not large enough, causing that matching results may have certain limitations. Apart from that, parts of the spectra measurement results can not fully reflect the characteristics of green vegetation spectra, which results from instrument such as narrow spectral range. As a result, it remains to be further complemented and promoted in the future.

ACKNOWLEDGMENT The authors thank to Hou Fei, Mo Yunhua , Xia Meijuan,

Xie Minghang, from School of Geodesy and Geomatics, Jiangsu Normal University, China for their assistance in collecting the spectra data.

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