Monitoring biomass of water hyacinth by using hyperspectral remote sensing

Jingjing Wang, Ling Sun, Huazhou Liu Institute of Agricultural Economy and Information

Jiangsu Academy of Agricultural Sciences Nanjing, China

Abstract—Establishment biomass monitoring methods of invasive species has an important instructive meaning for control and treatment of invasion, which was also beneficial to national ecological safety and social stability. This study researched the estimation methods of biomass of water hyacinth by hyperspectral remote sensing. The spectral reflectance characteristics were analyzed based on field experiments consisting of different biomass levels. Spectral reflectance and hyperspectral parameters were constructed to be correlated with biomass of water hyacinth and the sensitive parameters were chosen to build biomass estimation models. The result showed that reflectance of near-infrared had better correlation with water hyacinth biomass compared with other wavelength but the correlation is not significant at the 99% confidence level. Red edge parameters including the amplitude of red edge (Dr) and the area of red edge (Dr) and the vegetation indexes including the ration vegetation index (Rnir/ Ro) and normalized difference vegetation index ((Rnir － Ro)/ (Rnir ＋ Ro)) had significant correlation with water hyacinth, especially the latter two parameters which had the correlative coefficient value reaching the 99% confidence level. The estimation models built by sensitive hyperspectral parameters had been verified the precision respectively and the linear regression model based by vegetation index (Rnir/ Ro) got the best accuracy with the RMSE as 1.94 and MRE as 4.26%. The result of this study provide theoretical basis and technology approach for monitoring biomass of water hyacinth by hyperspectral satellite remote sensing.

Keywords-invasive species; water hyacinth; biomass; hyperspectral; reflectance

I. INTRODUCTION Biological invasion is a key threat to global biodiversity

and ecosystem functioning as well as incurring economic costs [1]. Economic sectors adversely impacted by uncontrolled invasion of aquatic plant include water-based navigation, water quality and supply, hydropower, irrigation, fisheries, native species and wildlife [2]. Transportation systems can be impaired by uncontrolled growth of invasive aquatic plant in navigational areas. Water hyacinth is an erect, free-floating, perennial herb and it can tolerate a wide range of habitat condition [3]. Water hyacinth has spread within a hundred years from its home base in Brazil to at least fifty countries around the globe, including China. In China, invasion of water hyacinth is a significant problem for southern area,

predominantly Zhejiang, Fujian, Yunnan and Guangdong. For example, in Fujian, extensive mats of water hyacinth growth in the Jiulong river were responsible for closing the Shuikou hydroelectric station, causing extensive commercial and economic disruption.

Systematic, comprehensive monitoring programs are needed to detect invasions in order to effectively control water hyacinth [4]. Traditional approaches for surveying the biomass of aquatic macrophytes have relied on quadrat and transect-based methods. Such methods are time consuming and often require direct contact with the species which can result in further dispersal [5]. Additionally, aquatic ecosystems are often inaccessible or difficult for field-based monitoring methods.

Remote sensing technology offer potentially valuable tool for monitoring Invasive Alien Species. Aerial photographic techniques have been commonly used for macrophyte mapping [6-8]. Photo-interpretation is largely a subjective process and can be expensive in the long-term [9]. The applied of digital multispectral imagery for mapping and estimating aquatic macrophyte has been most widely studied [10-12]. But the coarse spatial and spectral resolution of multispectral remote sensing imagery present challenges when the infestations are neither continuously widespread, dense, nor monospecfic. Hyperspectral remote sensing technology, characterized by many narrow spectral bands, allows detailed spectra to be acquired for each pixel. Subtle differences in reflection and absorption patterns can be detected resulting in the identification of individual species, higher mapping accuracies, and even the potential for mapping aquatic vegetation that grow at low densities[13-15]. Part of the problem with mapping aquatic plants using digital techniques are that little is known of the detailed high spectral resolution reflectance properties of macrophytes in situ. Study of the reflectance properties of auqtic macrophytes at a high spectral resolution is essential in vegetation remote sensing [16].

In this study, hyperspectral remote sensing technology was used to monitor biomass of water hyacinth. Spectral characteristics of water hyacinth were analyzed based on plot experiment, and spectral response indexes of water hyacinth biomass were and constructed and biomass estimation models were established.

II. METHOD

A. Study area Experimental plots were located in Taihu Lake of Jiangsu

Province, China, where water hyacinth was planted artificially. Field data were obtained from July to October 2010 during different growth stages of water hyacinth. Hyperspectral reflectance of water hyacinth canopy with corresponding biomass field-measurement was measured synchronously.

B. Spectral reflectance measurement Hypecspectral reflectance of water hyacinth was measured

by using a high resolution spectroradiometer (FieldSpec Hand Held, ASD, USA), recording 512 discrete spectral bands over the range 350-1050nm with the spectral resolution 3.5nm. Nadir view measurements for each quadrat were made with the sensor head at 1.0m above the water surface. At each plot a white reference calibration was taken prior to spectral reflectance to normalize all reflectance to a common standard. Five spectroradiometer scans per quadrat were acquired and internally averaged by the system to determine spectral reflectance.

C. Aquatic plant sampling Biomass of water hyacinth was sampled as follows: A 1×1

m square sampling frame was placed on the top of the vegetation. All water hyacinth within the frame was removed. Excess water was removed by shaking the plants. The samples weighed using electronic scale.

D. Hysperspectral parameters selected for biomass estimation Spectra characteristics of vegetation were correlated with

leaf structure, pigment content and nutrition status, which is theoretical basis of biomass estimation based on remote sensing technique [17]. There are many researches showed that the red edge parameters could indicate the vegetation biomass [18]. Some researchers suggested using differential technique to eliminate the background effects [19]. This paper extracted hyperspectral parameters based on vegetation spectral characteristics by using differential technique (as shown in Tab.Ⅰ).

TABLE I. THE DEFINITION OF HYPERSPECTRAL PARAMETERS

Spectral parameters Definition

Rg the maximum reflectance peak in 510~580nm

λg the wavelength of Rg

Ro the minimum reflectance in 640~700nm

λo the wavelength of Ro

Rnir the average reflectance between 760 to 900nm

Db the maximum value of first derivative of

reflectance in 490~530nm λb The wavelength of Db

Dy the maximum value of first derivative of

reflectance in 550~600nm

Spectral parameters Definition

λy The wavelength of Dy

Dr the maximum value of first derivative of

reflectance in 680~750nm λr The wavelength of Dr

SDb the area of of first derivative of reflectance in

490~530nm

SDy the area of first derivative of reflectance in

550~600nm

SDr the area of first derivative of reflectance in

680~750nm Rnir/ Ro ratio vegetation index

(Rnir－Ro)/ (Rnir＋Ro)

normalized difference vegetation index

III. RESULT

A. Charactistics of water hyacinth reflectance The reflectance of water hyacinth is typical vegetation

spectra characteristics (as shown in Fig. 1). The reflectance near 550nm is peak in 400~700 nm and the absorption located at blue and red wavelengths which characteristics can be distinguished from soil, rock and water. The reflectance in 700~800nm is a high reflective plate and also called red edge of vegetation reflective spectra.

B. Statistical analysis of biomass and spectral reflectance To test if the reflectance at a specific wavelength can be

used to estimate biomass, the linear correlative coefficient (R) was calculated as shown in Fig. 2. The reflectance of near infrared wavelength had better correlation with water hyacinth biomass.

Figure 1. Spectral reflectance of water hyacinth canopy

Figure 2. The curve of correlative coefficient between water hyacinth biomass and spectral reflectance

C. Statistical analysis of biomass and hyperspectral parameters The result of correlation analysis between water hyacinth

biomass and hyperspectral location parameters can be found in Tab. Ⅱ that the amplitude of red edge (Dr) had significant correlation with biomass at the 95% confidence level and other location parameters had not the significant correlation with it. The correlation coefficient between biomass and Dr is 0.55, which could not reach the 99% coefficient level.

The result of correlation analysis between water hyacinth biomass and hyperspectral area parameters can be found in Tab. Ⅲ, in which only the area of red edge (SDr) had significant correlation with water hyacinth biomass at the 95% confidence level.

The ratio vegetation index (Rnir/ Ro) and normalized difference vegetation index ((Rnir－Ro)/ (Rnir＋Ro)) constructed by hyperspectral bands had significant correlation with water hyacinth biomass (as shown in Tab. Ⅳ). The correlative value can reach the 99% confidence level.

TABLE II. CORRELATION COEFFICIENTS BETWEEN BIOMASS OF WATER HYACINTH AND HYPERSPECTRAL LOCATION

PARAMETERS

R λg λo λb Db λy Dy λr Dr

Biomass -0.32 -0.22 -0.20 0.43 -0.01 -0.44 -0.25 0.55*

“*”indicates significant at the 95% confidence level and “**” indicates significant at the 99% confidence level

TABLE III. CORRELATION COEFFICIENTS BETWEEN BIOMASS OF WATER HYACINTH AND

HYPERSPECTRAL AREA PARAMETERS

R SDb SDy SDr

Biomass 0.41 0.47 0.58*

“*”indicates significant at the 95% confidence level and “**” indicates significant at the 99% confidence level

TABLE IV. CORRELATION COEFFICIENTS BETWEEN BIOMASS OF WATER HYACINTH AND HYPERSPECTRAL VEGETATION

INDICES

R Rnir/ Ro (Rnir－Ro)/ (Rnir＋Ro) Biomass 0.82** 0.79** “*”indicates significant at the 95% confidence level and “**” indicates significant at the 99%

confidence level (the same below)

D. Estimation models of biomass of water hyacinth The water hyacinth biomass estimated algorithms were

built by forgoing analyses based on least-squares method. The accuracy characteristics determined for the algorithms were root mean square error (RMSE) and mean relative error (MRE)[20]. Sensitive parameters were decided by correlative analyses in advance which included ratio vegetation index (Rnir/ Ro), the normalized difference vegetation index ((Rnir－Ro)/ (Rnir＋Ro)), the amplitude of red edge (Dr) and the area of red edge (SDr), which had significant correlation with water hyacinth biomass at the 95% confidence level.

The four sensitive parameters were used to build water hyacinth biomass estimation algorithms separately and comparisons of the accuracy were shown in Tab.Ⅴ. Algorithms built by the two vegetation indexes (Rnir/ Ro) and ((Rnir － Ro)/ (Rnir ＋ Ro)) had better accuracy. The linear regression algorithm built by (Rnir/ Ro) had best accuracy with the MRE less than 5%.

IV. CONCLUSION In this study, hyperspectral remote sensing data were used

to estimate water hyacinth biomass. Spectral reflectance, hyperspectral location parameters, hyperspectral area parameters and vegetation indexes were constructed based on the spectra characteristics of water hyacinth to correlate with the biomass of water hyacinth to find out the sensitive hyperspectral parameters of biomass of water hyacinth.

The near-infrared reflectance had better correlation with water hyacinth biomass but the correlation coefficient can not reach the 99% confidence level. The red edge parameters Dr and SDr had been proved sensitive with water hyacinth biomass, which was consist with some study result in other vegetation. The vegetation indexes constructed by hyperspectral bands shown most significant correlation with water hyacinth biomass and the correlative coefficient could reach the 99% confidence level. The biomass estimated algorithms accuracy test result showed that regression models built by vegetation indexes (Rnir/ Ro) and ((Rnir － Ro)/ (Rnir ＋ Ro)) got good estimation precision with the MRE less than 10%. The linear regression estimation model built by (Rnir/ Ro) could have the best accuracy with the MRE 4.26%.

This study employed field hyperspectral reflectance to research spectra characteristics of water hyacinth and build water hyacinth biomass estimated algorithms. Additionally, further research is being made using hyperspectral remote image data to get more scale application.

TABLE V. CORRELATION COEFFICIENTS BETWEEN BIOMASS OF WATER HYACINTH AND HYPERSPECTRAL VEGETATION

INDICES

Parameters Regression Equation RMSE MRE(%) Rnir/ Ro y = 0.8595×x - 1.8017 1.94 4.26

(Rnir－Ro)/ (Rnir＋Ro)

y = 227.92×x - 190.64 2.07 5.30

Dr y = 673.57×x + 8.9564 2.81 12.93 SDr y = 17.816×x + 8.5505 2.77 12.22

In the regression equation y represents the biomass of water hyacinth and x represents sensitive hyperspectral parameter respectively

ACKNOWLEDGMENT This research was supported by the National Science and

Technology Ministry through National Key Technology Research Program (Project No. 2009BAC63B02). The ASD to enable spectral measurements to be made was kindly lent by Dr Ying Zhang, Nanjing Normal University of China. We are grateful to Institute of Agricultural Resources and Environment of Jiangsu Academy of Agricultural Sciences for assistance in laboratory analysis.

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