2009 Urban Remote Sensing Joint Event

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The Quantitative Estimation of Periurban Vegetation

Ecology Using Hyperspectral Remote Sensing

LU Xia

School of Geodesy & Geomatics Engineering

Huaihai Institute of Technology

Lianyungang, Jiangsu province,China

E-mail: luxia1210@163.com

HU Zhenqi The Institute of Land Reclamation and Ecological Restoration;

China University of Mining &Technology(Beijing);

Beijing,China;

E-mail:huzq@cumtb.edu.cn

GUO Shuyan School of Geodesy & Geomatics Engineering

Huaihai Institute of Technology

Lianyungang, Jiangsu province,China

E-mail: shuyanguo1@126.com

Abstract—The environment of the mountainous area is one of

important parts in urban ecological and economic system. It has

an impact on he stabilization of urban ecological environment.

Mentougou District in Beijing, a mountainous area, had

abundant mineral resources originally. Many years’ exploitation

activities made the mineral resources exhausted. It is an

inevitable objective law that each mining site has to face. Of

course, we know that it brought enormous benefits for the local

government and the mine group, while it also had caused

tremendous environmental damage. In order to realize the peri-

urban ecological barrier of Beijing, the damaged ecosystem must

be restored and the rapid succession of the architecture of plant

community should be finished successfully. The paper took the

Wangpingcun coal mine as a case study, the estimation models of

the biophysical and biochemical parameters of vegetation were

established by hyperspectral remote sensing technology. The

fresh weight estimation model of vegetation was built based on

the vegetation index of R752/R548 by thrice function method.

The estimation model of chlorophyll concentration (SPAD) was

established through linear four-point interpolation technique.

The total nitrogen content estimation model was created based

on derivative spectrum among the absorption bands ranging

from 350 nm to 2500 nm through the multivariate regression

modelling. Based on the construction of above models, the spatial

distribution maps of biophysical and biochemical parameters of

vegetation were extracted by the parameter mapping and

regularities of distribution were revealed. The research results

showed that the precisions of above estimation models were very

high. The multiple determinant coefficients (R2) of fresh weight

and SPAD and the total nitrogen content estimation models of

vegetation were 0.883, 0.814 and 0.795 respectively. The spatial

distribution maps indicated that the chlorophyll concentration

and fresh weight of vegetation around the coal wastes pile were

the minimum. The more the SPAD and fresh weight became, the

farther the distance away from the coal wastes pile became. The

regularity of distribution of the total nitrogen content was

completely opposite to the fresh weight and SPAD. It is

concluded that estimating biophysical and biochemical

parameters of vegetation using hyperspectral remote sensing is

completely possible. It has theoretical significance for restoring

service function of ecosystem and transforming regional

industrial structure.

I. INTRODUCTION

Hyperspectral remote sensing technology was created in eight decade of last century. It has been a hot of international remote sensing technology since the beginning of nine decade of last century [1]. Hyperspectral remote sensing is a technology by using many narrow electromagnetic wave bands ranging over ultraviolet, visible light, near infrared, middle infrared and thermal infrared band to get many narrow and continuous spectra. Therefore, spectra dimension is added based on traditional two dimensions and a unique three dimensions remote sensing is formed. Because of such characteristics, many experts applied hyperspectral remote sensing technology successfully to geology, oceanography, atmosphere, environmental remote sensing, and agriculture. Agricultural application mainly included vegetation information identification and biophysical and biochemical parameters inversion of vegetation. Of course, a small amount of researches such as disease and insect damage, heavy metal pollution, and microelement of vegetation were studied in recent years. TONG Qingxi[2] used theoretical model between LAI and hyperspectral remote sensing image to extract LAI. GONG Peng[3] applied hyperspectral remote sensing data to analyze classification of conifer trees. TANG Wanlong[4] analyzed the best estimation model of nitrogen and phosphor content of winter wheat based on spectral properties. NIU Zheng[5]used field measurement spectra data to study biochemical components of leaf. Curran[6]applied standard first derivative, normalized absorption depth, and normalized absorption area to estimate biochemical components of leaf. Although hyperspectral remote sensing has so many applications, application in exhausted coal mining sites is considerably little. ZHANG Shaojie and CAO Daiyong[7]simply introduced the flow of environmental monitor applying hyperspectral remote sensing technique. CHI Guangyu[8]used the abnormity of reflectance spectra of vegetation to indirectly monitor soil pollution, water pollution, and air pollution in Dexing mining area. K.L.Smith, M.D.Steven, and J.J.Colls[9] applied the ratio of derivative of reflectance spectrum at 725nm to that at 702nm to analyze

2009 Urban Remote Sensing Joint Event

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plants, which were located in oil polluted soil. GAN Fuping[10] applied the maximum absorption depth near 685nm to identify vegetation pollution. LU Xia[11]applied red edge position to analyze heavy metal stress of vegetation in mining sites. In order to farther analyze hyperspectral remote sensing application to biophysical and biochemical parameters of vegetation in mining sites, the author used Hyperion hyperspectral remote sensing data, field spectra measurement of vegetation, SPAD of vegetation, biomass of vegetation, and total nitrogen content to establish estimation models of biophysical and biochemical parameters of vegetation by multiple stepwise linear regression method and curve simulation method. Subsequently, we attained spatial distribution maps of these parameters of vegetation by parameter mapping and discovered spatial distribution regularities of distribution. To sum up, this research will help to restore ecological service function and realize ordered succession of vegetational type.

II. STUDY SITE AND ECOLOGICAL DAMAGE ANALYSIS

A. Study Site

The study was conducted in Wangping town and Beiling agency. There is Wangpingcun coal mining site, located in Wangping town. It is one of the state-owned coal mines controlled by Beijing Mining Bureau. The length from east to west is 5.4 km and its width from north to south is 2.5 km. The coal deposits belong to the C-P periods of the Paleozoic Era. The coal type is anthracite.

B. Ecological Damage Analysis

Exploitation of coal resources will inevitably cause ecological damage. From the point of view of an ecosystem, ecological damage can be classified as landscape damage, environmental damage and biological damage.

Damage to the landscape in our study site mainly includes piles of coal waste, abandoned construction sites and houses, mining subsidence and cracking. The piles of coal waste occupy an area of almost 215,000m2. A large area of exhausted drifts under the surface is caused by coal mining activities and resulted in surface subsidence (cracks in the surface) leading to large areas of abandoned houses (17,013 m2) in Lvjiapo village. In our study, eleven surface subsidence sites were investigated with an area of 2149.095 m2. Cracks in the study area are classified into three types: ground fissures, building cracks and mountain cracks. Fourteen soil and waste samples were collected within the area of a circle, with its center at the Jidou pile of coal waste and a radius of 1500 m. The change in the amount of heavy metals of each sample as a function of the distance between the sample collection position and the coal pile is discussed, which indicate that Cu and Cr pollution in the study area is not a problem, but Cd pollution is serious. Comparing the nutrients in each sample with the grading standards of soil nutrient content, we found that the amounts of organic matter and total nitrogen in all soil samples are deficient, while the total nitrogen in the coal waste samples was relatively higher than the standard value.

Rapidly available phosphorus and potassium, as well as the alkali-hydrolyzable nitrogen in all samples were abundant and did not contain a risk of potential pollution. Biological damage included that vegetational cover and the number and types of wild species decreased greatly. In addition, the rate of incidence of disease in the population of the coal mining site rose gently.

It was concluded that Lvjiapo village, Nanjian village, Dongwangping village, and Xiwangping village was the most serious damage area. Vegetation samples and spectra collection and chemical tests mainly focused on the area. Comparing vegetation ecology of serious damage area with other area by hyperspectral remote sensing technology was the method taken to analyze periurban vegetation ecology in our paper.

III. DATA ACQUISITION AND IMAGE

PREPROCESSING

A. Acquisition and Preprocessing of Hyperion

Cloud-free imaging spectrometer data were acquired on June 15, 2006 with Hyperion instrumentation. The Hyperion level 1B data were provided as calibrated radiance in 220 contiguous channels from 400-2500nm with a spectral resolution of 10 nm and a spatial resolution of 30 m. The level 1B Hyperion radiance data were first corrected for “streaking” or variation in balance among vertical columns in the track direction of the image data, a product of the “pushbroom” design of the sensor, most evident in the shortwave infrared channels. Image processing includes the choice of useful bands, the restoration of bad lines, the removal of vertical bars, apparent surface reflectance rebuilding, image resizing and geometric correction. Among these pretreatments, radiance data were transformed to apparent surface reflectance by using the FLAASH atmospheric correction program. It was theoretical basis of quantitative estimation of biophysical and biochemical parameters of vegetation.

B. Field Measurement and Collection of Vegetation Samples

and Chemical Tests

Field spectrometer data and vegetation samples in 24

homogeneous plots (30 m 30 m) were collected when the

Earth Observing One satellite passed over the study site in the Wangpingcun coal mining area. Canopy-based spectra were obtained from a portable ASD FieldSpec-FRTM spectro-radiometer. Each vegetation sample consisted of a composite of leaves collected from several heights in the canopy. A semi-micro kjeldahl approach, with an auto analyzer determination method, was used to extract nitrogen content.

IV. QUANTITATIVE ESTIMATION MODELS OF

VEGETATION ECOLOGY

A. Estimation Model of Vegetation Fresh Weight

Biomass, an important indicator of ecological system health, was estimated by hyperspectral remote sensing to

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provide important parameters for quantitative measurement of ecological assets.

1) Selection of pixel spectra of vegetation

From above introduction, we can know that the size of each plot is the same as the spatial resolution of Hyperion image. Therefore, we located GPS of each vegetation sample on the Hyperion image. In theory, we should obtain twenty-eight sample points on the image. However, because of close distance between spectra test location and GPS error, only fourteen GPS points of samples were identified by visualization on Hyperion image. Vegetation type was mainly brambles.

2) Choice of Spectral Character Variables and Correlative Analysis

About twenty variables were chosen in this paper, among which reflectance spectra, first derivative of reflectance spectra, logarithm of reflectance spectra based on multivariate, while vegetation indices such as RVI, NDVI, RDVI, R750/R550, R700/R550, and TVI based on mono-variant. Making correlation analysis of above variables and fresh weight of vegetation samples, we discovered a significant correlation between fresh weight and vegetation index R752/R548. The correlation coefficient was up to 0.883. Corresponding Hyperion bands were band 750nm and band 550nm.

3) Estimation Model of Fresh Weight of Vegetation Samples

Scatter plot between vegetation index R752/R548 and fresh weight was performed. Curve simulation based on sample points were carried out. They were showed in Fig.1. We knew that thrice function estimated highly fresh weight of vegetation samples. The estimation model of fresh weight and R2 were respectively:

Y= 0918X3 3.679X2 26.530 (1)

R2 = 0.883 (2)

Figure 1. Scatter plot of vegetation index R752/R548 and fresh weight

B. Estimation Model of SPAD of Vegetation Samples

SPAD is a benign indicator of nutrition stress and photosynthesis ability of vegetation during the procession of whole growth. Many experts had paid special attention to SPAD and chlorophyll content researches by hyperspectral remote sensing technology. In addition, research methods of SPAD and chlorophyll content have been developed quickly, one of which is analysis technology based on spectral position analysis. However, which kind of spectral position to study SPAD depends on field spectra measurement. In this paper, we took red edge position (REP) as an indicator, which was extracted by field spectra of twenty-eight vegetation samples through four-point interpolation method. Then, estimation model of SPAD was constructed by exponential simulation method. The estimation model of SPAD and R2 were respectively:

Y=1.798E-27e0.091X (3)

R2 = 0.814 (4)

Scatter plot of SPAD was showed in Fig.2. In the graph,

REP of each vegetation sample was independent variable and SPAD of each vegetation sample was dependent variable. We also knew that correlation coefficient between red edge position and SPAD was very high. It was concluded that estimating SPAD by red edge position (REP) through exponential function was feasible.

C. Estimation Model of Total Nitrogen Content

1) Selection of Spectral Character Variable

Nitrogen element is not only one of important indicators for evaluating photosynthesis rate and total nutrition status, but also one of important reference indicators for evaluating the growth status of forests. We at first chose character absorption bands corresponding to forty-two biochemical components ranging from 350nm to 2400nm and absorption bands domain established by Zhi Huang and Brian J. Turner. Then, we took the first derivative of these absorption bands as spectral character variables.

2) Correlation Analysis between Spectra Character Variables and Total Nitrogen Content

We performed the correlation analysis between first derivative spectra and total nitrogen content by using SPSS software. Correlation analysis results were listed in table I. It was indicated that the highest correlation coefficient was up to 0.671.

3) Estimation Model of Total Nitrogen Content

According to above correlation analysis results, we applied multivariate stepwise linear regression method to establish estimation model of total nitrogen content. Therefore, serious problems such as low correlativity of independent variables and ordering of correlation coefficients must be considered when using the method. Nitrogen content model by stepwise linear regression method and R2 were respectively:

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Figure 2. Scatter plot of REP and SPAD

Y=2.07 439.397D 91 D 2306

D 678 (5)

R2 = 0.795 (6)

In the model, independent variable was first derivative of

band 917nm, 2306nm, and 678nm. Multiple determinant coefficient was 0.795.

V. SPATIAL DISTRIBUTION INFORMATION EXTRACTION OF BIOCHEMICAL AND BIOPHYSICAL

PARAMETERS

According to above models of biophysical and biochemical parameters of vegetation in exhausted mining site, we applied each model to Hyperion image by parameter mapping method and obtained spatial distribution map (showed in Fig.3, Fig.4, and Fig.5) of fresh weight, SPAD, and total nitrogen content respectively. Fig.3 denoted spatial distribution map of fresh weight of vegetation. Fig.4 denoted spatial map of SPAD of vegetation. Fig.5 denoted spatial distribution map of total nitrogen content of vegetation. Here, in three maps, white area represented non-vegetation cover area, which was obtained by mask technique, other colors represented different amount of fresh weight, SPAD, and nitrogen content of vegetation.

After analyzing the spatial distribution characteristics, it is concluded that fresh weight and SPAD of vegetation around coal waste piles in total study site are lower than that away from coal waste piles. That is to say, spatial distribution of fresh weight and SPAD of vegetation in study site is in direct ratio to the distance away from coal waste piles. However, spatial regularities of distribution of nitrogen content is contrary to fresh weight and SPAD. The minimum of nitrogen content was 1.029% (of dry matter) and the maximum was 3.582% (of dry matter). We primarily analyze the probable reason. It is caused by the migration and precipitation of nitrogen element in coal wastes. Large amount of nitrogen element are gradually migrated to ambient soil and deposited

Figure 3. The spatial distribution map of fresh weight

gradually and absorbed by vegetation under the influence of wind and rain. Although we should do more research work in the future, in order to completely understand the actual reason, research result has special reference to vegetation restoration in study site. In addition, we can use nitrogen element in coal waste piles to improve soil fertility only to achieve soil betterment and better usage of coal wates piles. Once soil fertility is enhanced, vegetation and corps in the soil will grow healthily.

TABLE I . CORRELATION ANALYSIS RESULTS

Correlation analysis

Band (nm) correlation coefficient r

431 0.584

505 -0.597

508 -0.578

616 0.526

678 -0.619

912 0.549

917 -0.671

1057 0.530

1522 -0.564

1736 0.530

2019 -0.602

2020 0.583

2270 0.519

2303 0.536

2306 0.657

2338 0.519

2424 -0.573

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VI. CONCLUSIONS

From above analysis, it is concluded that:

1) Vegetation index R752/R548 can be used to estimate model of fresh weight and R2 is 0.883.

2) SPAD estimation model precision constructed by four-point interpolation method is very high and R2 was 0.814.

3) Total nitrogen content estimation model precision constructed by taking first derivative spectra as variables is very high and R2 was 0.795.

4) SPAD and fresh weight of vegetation is inversely proportional to the distance away from coal waste piles, while total nitrogen content of vegetation is contrary.

Mentougou district is located in the west of Beijing. Coal resources are exhausted and ecological environment has been damaged greatly. However, ecological status of Mentougou district has important impact on ecological environment of Beijing. Therefore, vegetation ecology research in this paper by hyperspectral remote sensing technology is needed in order to improve environmental protection and the sustainable development of Beijing.

In addition, if we have more capital, we will spend much more time on the following research.

Relative research results showed that there were heavy metal pollution in the soil. The heavy metal pollution identification of vegetation in study site should be studied in order to understand growth status of vegetation and discuss the relation between heavy metal content in soil and that in vegetation.

ACKNOWLEDGMENT

The paper mainly comes from my doctoral dissertation. Actually, the paper is one part of my dissertation. While Hyperion data order and field spectra measurements and chemical test of vegetation biochemical parameters and soil samples were provided by my superviser Pro. Hu. Here I want to greatly thank Pro. HU Zhenqi to provide considerable helps and conducts for my dissertation. I thank KANG Jingtao, LI Haixia, XU Xianlei, and CHEN Tao to help to do field spectra of vegetation in Wangpingcun coal mining sites. I thank ZHANG Hongguang, who comes from Beijing Academy of Geology of Nuclear Industry, to help us to measure reflectance spectra of vegetation, soil and coal wastes. In addition, I want to thank LIU Weijie to help to write some programs to extract REP (red edge position) in order to analyze the inner relationship between SPAD of vegetation samples, which were collected in coal mining sites and REP. Finally, I want to thank teacher YAN, who come from China University of Agriculture, to rent SPAD-502 instrument to measure SPAD value of vegetation samples, which can replace chlorophyll concentration.

Figure 4. The spatial distribution map of SPAD

Figure 5. The spatial distribution map of nitrogen content

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