Neural Network Retrieval Model of Chlorophyll-a Concentration in Bohai Bay

Studies in Surveying and Mapping Science (SSMS) Volume 3, 2015

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Neural Network Retrieval Model of Chlorophyll-a Concentration in Bohai Bay Huilin Ye*1, Jingqin Mu2, Guoqing Yao3 College of Information Engineering, China University of Geosciences(Beijing),Beijing,China;

1,3

College of Computer Science, Tangshan Normal University, Tangshan Hebei,China

2

hlye@cugb.edu.cn; 2mujingqin@163.com; 3gqyao@cugb.edu.cn

*1

Abstract Based on in-situ measurements of chlorophyll-a concentration and ETM images of Bohai bay, a neural network retrieval model is built up in this paper. According to the spectral characteristics of Bohai bay coastal water, analysis is conducted to decide the correlation between the reflectance of the first 4 bands of ETM and chlorophyll-a concentration. The BP neural network model is constructed, consisting of 4 input nodes (the reflectance of the first 4 bands of ETM), 8 hidden nodes and 1 output node, by comparing the performance of the network with different numbers of hidden nodes. The result indicates that the BP neural network model has a high accuracy with R2 achieved 0.956 and the BP neural network model provides a reliable basis and method for monitoring chlorophyll-a concentration in water by remote sensing technology quickly and accurately.

Keywords Bohai Bay; Chlorophyll-a Concentration; BP Neural Network Model

Introduction Chlorophyll-a concentration in water is intimately linked with the amounts and types of algae, so chlorophyll-a concentration is a composite indicator to reflect the phytoplankton biomass in water, and also is a vital parameter to evaluate the quality of sea water, organic pollution levels and fishery resources. Detecting, analyzing and simulating chlorophyll-a concentration and its space-time distribution characteristics accurately have important research significances in grasping the nearshore marine fishery resources, monitoring the red tides and evaluating eutrophication condition. Traditional determination method of chlorophyll-a, mainly depending on underway sampling by survey vessels and measuring in the lab, is costly and time-consuming. And sampling point by point dispersively cannot meet the demands of measuring continuously and synchronously for a long range and a long time. Also it is difficult to characterize the distribution of water quality indicators in space and time change. Using remote sensing to estimate the chlorophyll-a concentration in sea can make up for the defects of common methods.

Since the 1970s, kinds of analysis methods based on remote sensing data have been proposed by scholars at home and abroad. Chen, She, Lv, Li, Li and Wu have respectively built regression models for retrieving chlorophyll-a concentration using remote sensing images and in-situ measurements while Zhan, Wu, Zhao, Wang, Shen and He have established retrieval models of chlorophyll-a concentration in lakes, reservoirs or even seas by neural network technology. According to the theoretical foundation and the amount of measured data, the methods of retrieving water quality parameters by remote sensing data mainly fall into 3 classes: statistical methods, semi-empirical and semi-analytical methods and theoretical methods.

Study Area The Bohai sea is the largest semi-enclosed inland sea in China, located in 37°7´-41°0´ north latitude, 117°35´-121°10´ east longitude, with a total area of 77000 km2 and the average depth of 18m. It is mainly composed of Liaodong bay, Bohai bay and Laizhou bay. Bohai bay is a shallow sea basin surrounded by lands, mostly silt and soft clay. The seabed terrain inclines towards the center of the bay. Plenty of suspended sand is injected while Luanhe river, Haihe river, the Yellow River and Xiaoqinghe river feed into the bay. The sediment mostly is silt and silty sand silt.

Due to the increasing economic activities in the Bohai Rim in recent years, the water ecosystem of the Bohai sea has been seriously affected by accepting a large number of terrigenous pollutant every year. Because of its semi31

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Studies in Surveying and Mapping Science (SSMS) Volume 3, 2015

enclosed geographical environment, the seawater exchange capacity is poor and the self-purification ability is low.

The environmental problem of the Bohai sea is increasingly prominent while the nearshore water is seriously eutrophication and red tides occur frequently.

Research Method Chlorophyll-a concentration measured data from Bohai bay coastal water have been obtained from October 2007 to August 2010 in the study, ranging from 0.00097 to 18.5µg/L. ETM images with little cloud cover are selected based on the sampling time. 62 samples are acquired, and images acquisition time and sampling time are shown in Table 1. All the samples are randomly divided into two groups: 46 samples for model training and 16 samples for model testing.

TABLE 1 TABLE OF DATA SAMPLING

Image Acquisition Time

Sampling Time

Number of Samples

October 20th 2007

October 9th 2007

2

October 6th 2008

October 1st 2008

5

April 16th 2009 May 2nd 2009 May 18th 2009

May 1st 2009

6 6 6

September 23th 2009 October 9th 2009

October 1st 2009

7 8

April 3rd 2010 June 6th 2010

May 12th 2010

5 2

July 24th 2010 September 10th 2010

August 15th 2010

8 7

Pretreatment of ETM Images The reflectance of water is far below most other features. In addition to the strong reflectance in blue and green light, the absorption in other optical wavelengths is obvious, especially in the near-infrared. But the spectral reflectance curve changes when the water contains other substances. The reflectance rises obviously in the nearinfrared when the water contains chlorophyll-a. The higher chlorophyll-a concentration is, the more significantly the reflectance in the near-infrared rises. Simultaneously, reflectance decreases in blue light and increases in green light. Therefore, the first 4 bands of ETM are used in this study for establishing a retrieval model.

ETM L1T image products after system radiation correction and geometry correction by ground control points are used in this study. Due to the influences of the sensor and atmosphere, spectral features of the same target in multitemporal images have extensive differences, so that the extraction of image information may be affected. So the atmospheric correction must be done. First, the images are calibrated by radiometric calibration tool of ENVI 5.1 and header files of ETM, so the voltage or digital quantitative values (DN) recorded by the sensor can be converted into the absolute radiation brightness values (emissivity). Then the atmospheric correction for ETM images is done

by ENVI 5.1 FLAASH atmospheric correction module. FLAASH atmospheric correction is the most useful atmospheric correction tool currently, which can restore high-fidelity spectrum information of surface features and obtain the real physical model parameters more accurately such as surface temperature, reflectivity and emissivity.

Selection of Pixel Arrays In order to weaken the impact of random noise and reduce the geometric error, the reflectance of the sample points could be replaced by the average reflectance of their adjacent pixel arrays from the images. In this study, the reflectance of the sample points and the average reflectance of their adjacent 3×3、5×5、7×7、9×9、11×11 pixel arrays are extracted. And the average reflectance of all sample points is calculated which is shown in Figure 1. As shown in the figure, the average reflectance is relatively stable when the 5×5 pixel array is taken and it does not change significantly as the pixels increases. So the reflectance of the sample points should be replaced by the average reflectance of their adjacent 5×5 pixel arrays.

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FIG. 1 AVERAGE REFLECTANCE OF PIXEL ARRAYS OF ALL SAMPLE POINTS

Establishment of BP Neural Network Model Because of the complexity of the optical properties of Bohai bay water, general methods cannot retrieve the chorophyll-a concentration well. So a neural network model is built.

Artificial Neural Network is a theoretically mathematic model which can imitate the activities of human brains and is a large-scale nonlinear adaptive system which has a strong capability of self-learning, self-organization, parallel processing of information and nonlinear fault tolerance. It is proved in theory that the network with the deviation, at least one “S� hidden layer and a linear output layer can approach any rational function. In recent years, artificial neural network has been used in retrieval problems by ocean color remote sensing, particularly in the field of applications of eutrophication prediction. Back-Propagation Neural Network is a multilayer feedforward network based on error back-propagation algorithm and the network as a whole has the function of an approximate function by means of its self-learning function for determining the coupling weights between neurons so it can apply to the modeling research of nonlinear system. BP neural network, as one of the most commonly used artificial neural network model, has been applied to the prediction research of algae growth, water quality condition et al.

In this study, a BP neural network model that is composed of an input layer, a hidden layer and an output layer is used to retrieve chlorophyll-a concentration of Bohai bay with the reflectance of the first 4 bands of ETM images (after the FLAASH atmospheric correction) as the input nodes, the measured values of chlorophyll-a concentration as the output node, the hyperbolic tangent function as the activation function and the Levenberg-Marquardt algorithm as the training method. At present, the determination of the number of hidden layer nodes cannot refer to a clear reference standard and it is generally chosen by experience. But the determination of the number of nodes affects the performance of the network directly. Too many nodes may weaken the generalization ability of the network while the precision of the network may be poor with too few nodes. After comparing the results (as shown in Table 2), the number of hidden layer nodes is 8 when the network performs best (R2=0.956,RMSE=0.672).

Therefore, the BP neural network model is established consisting of 4 input nodes, 8 hidden nodes and 1 output node (the structure of the network as shown in Figure 2). Figure 3 demonstrates the scatter plot of chlorophyll-a measurements and outputs of BP neural network model. And the measurements of chlorophyll-a concentration and outputs of BP neural network model of the testing group are listed in Table 3.

TABLE 2 R2 AND RMSE OF DIFFERENT NUMBERS OF HIDDEN NODES

Number of Hidden Nodes

1

2

3

4

5

6

7

8

9

10

11

12

R2

0.886

0.899

0.943

0.948

0.952

0.944

0.945

0.956

0.945

0.946

0.945

0.945

RMSE

1.078

0.993

0.746

0.709

0.683

0.741

0.738

0.672

0.754

0.735

0.752

0.734

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Input Layer

Hidden Layer

Output Layer

Band1 reflectance

Band2 reflectance

……

Band3 reflectance

Chlorophyll-a concentration

Band4 reflectance

8 nodes FIG. 2 STRUCTURE DRAWING OF BP NEURAL NETWORK MODEL

FIG. 3 SCATTER PLOT OF CHLOROPHYLL-A MEASUREMENTS AND OUTPUTS OF BP NEURAL NETWORK MODEL TABLE 3 CHLOROPHYLL-A MEASUREMENTS AND OUTPUTS OF BP NEURAL NETWORK MODEL OF TESTING GROUP

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Measured Value(µg/L)

Output of Model(µg/L)

Relative Error(%)

0.001

0.003

203.15%

0.690

1.022

48.08%

0.750

0.487

35.13%

0.780

1.966

152.05%

0.790

0.180

77.26%

0.980

4.708

380.43%

1.270

1.036

18.42%

1.870

3.476

85.91%

11.800

4.946

58.08%

18.500

5.589

69.79%

1.120

1.034

7.72%

1.210

1.987

64.24%

1.300

2.178

67.57%

1.380

4.676

238.85%

1.510

0.160

89.39%

1.780

1.079

39.37%

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Conclusions The Bohai bay water is intensely affected by human activities and has a complex water composition so that a single model cannot retrieve its chlorophyll-a concentration well. Artificial neural network model has a better effect on inversion due to its strong capability of self-learning, self-organization, parallel processing of information and nonlinear fault tolerance. Therefore, neural network model is of great importance in developing analysis methods based on remote sensing and provides a reliable method for monitoring chlorophyll-a concentration in water rapidly and accurately using remote sensing technology. So it has a great development potential and a wide application prospect.

However, the inversion accuracy of the neural network model in this paper is mainly restricted by the following aspects: (1) Marine ecosystem itself is very complex, and has great uncertainties and chaos. Simulation of its development and changes accurately is very difficult, even impossible.

(2) The inversion accuracy is insufficient because the measured data have random errors and strictly synchronous ETM images are not obtainable.

(3) FLAASH atmospheric correction method for calibration of ETM images depends on the atmospheric parameters and the precision of instrument calibration. Inevitably there are still some errors in calibration results that may affect the inversion accuracy.

(4) There are no uniform standards for the structure design of BP neural network (such as the number of hidden layer nodes) and the selection of network parameters (such as learning rate) at present. So it is affected by human factors (like experience). In order to improve the generalization ability of the network, it is necessary to design the training set rationally that a lot of tests must be done to obtain the satisfactory structure and parameters, and defects of the network model cannot be ignored.

ACKNOWLEDGMENT

The study was funded by Tangshan Basic Instruction Project In Industrial Application (No.12140204A-4).The Landsat-7 images were obtained from USGS website.

REFERENCES

[1]

Yanzhe Zhang, Xiaoshen Zheng, Bo Zhang.“Study On Retrieval Of Chlorophyll Concentration By Remote Sensing In Bohai Sea”. Journal Of Tianjing University Of Science & Technology, 2010,25(1):51-53.

[2]

Xiaozhou Shu, Qiu Yin, Dingbo Kuang.“Relationship Between Algal Chlorophyll Concentration And Spectral Reflectance Of Inland Water”.Journal Of Remote Sensing, 2000,4(1):41-45.

[3]

Li Qian, Wenling Liu, Wei Li, Xiaoshen Zheng.“Distribution Characteristics Of Surface Chlorophyll-A Concentrations In the Bohai Sea”. Journal Of Salt And Chemical Industry, 2010,39(5):20-24.

[4]

Anxin Mei, Wanglu Peng, Qiming Qin, Huiping Liu.“An Introduction To Remote Sensing”.Beijing: Higher Education Press,2001.

[5]

Chuqun Chen, Ping Shi, Qingwen Mao.“Study On Modeling Chlorophyll Concentration Of Surface Coastal Water Using TM Data”.Remote Sensing Of Environment China, 1996,03:168-176.

[6]

Fengning She, Qiming Cai, Yuwei Chen, Xuwen Li.“Quantitative Analysis On Chlorophyll-a Concentration In Taihu Lake Using Thematic Mapper Data”. Journal Of Lake Sciences, 1996,03:201-207.

[7]

Heng Lv, Nan Jiang, Liancong Luo.“Quantitative Retrieval Of Chlorophyll-a By Remote Sensing In Taihu Lake Based On TM Data”.Scientia Geographica Sinica, 2006,26(4):472-476.

[8]

Jilong Li, Manyi Jin.“Applications Of Landsat TM Data For Mapping Of Red Tide Distribution In Bohai Bay”.XXI ISPRS Congress 2008,2009:405-408.

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[9]

Studies in Surveying and Mapping Science (SSMS) Volume 3, 2015

Miao Li, Shuying Zang, Changshan Wu.“Inversion Of Chlorophyll-a Concentration Based On TM Remote Sensing Image In Keqin Lake”. Journal Of Agro-environment Science,2012,12:2473-2479.

[10] Mingquan Wu, Song Han, Yongqing Zhao, Zheng Niu.“Quantitative Estimation Of Chlorophyll-a And Total Suspended Matter Concentration With Landsat TM”.Remote Sensing Information, 2012,27(4):91-95.

[11] Haigang Zhang, Ping Shi, Chuqun Chen.“Retrieve Water Chlorophyll Concentration Using Neural Network“.Chinese Science Bulletin, 2000,45(17):1879-1884.

[12] Jinghong Wu, Xiuhuan Yang, Baoying Tang, Jinrong Li, Zhanxia Zhang.“The Research Of The Relation Between Red Tide And Environmental Factors In Aotou, Daya Bay”.Acta Scientiarum Naturalium Universitatis Sunyatseni,2001,40(3):37-40.

[13] Dongzhi Zhao, Yuan Qu, Fengshou Zhang, Ling Zhao, Pifu Cong.”A Neural Network Model For Estimating Sea Surface Chlorophyll From Thematic Mapper Imagery”.Marine Environmental Science,2001(1):16-21.

[14] Jianping Wang, Shengtong Cheng, Haifeng Jia, Zhishi Wang, Yuhua Deng. “An Artificial Neural Network Model For Lake Color Inversion Using TM Imagery”. Environmental Science,2003,02:73-76.

[15] Chunyan Sheng, Chuqun Chen, Haigang Zhan.”Inverse Of Chlorophyll Concentration In Zhujiang River Estuary Using Artificial Neural Network”. Journal Of Tropical Oceanography,2005(6):38-43.

[16] Enye He, Hai Li, Xiangxiang Ren, Yi Cai, Jing Yang.”The Application Of BP Neural Network In The Prediction Of Chlorophyll-a Concentration In The Bohai Gulf”.Marine Forecasts, 2008,25(2):1-10.

[17] Xiru Xu.”Physical Of Remote Sensing”.Beijing: Peking University Press,2005.02.

[18] Xiaoshen Zheng, Peigen Lin.”Study On Algorithm Of Chlorophyll Concentration Retrieval In Bohai Bay Based On TM Data”. Journal Of Tianjing University Of Science & Technology, 2010,25(6):51-53.

[19] Zongli Jiang.”Introduction To Artificial Neural Networks”.Beijing: Higher Education Press, 2001,8:10-13.

[20] Zhongqiang Yi. “Review Of Research On Eutrophication Prediction In Lakes And Reservoirs With Intelligent Algorithm“.Water Resources And Power, 2010,28(8):33-36,26.

[21] Shuang Cong. “Neural Network Theory And Applications With MATLAB Toolboxes”.Hefei: Press Of University Of Science And Technology Of China,1998.

[22] Xianquan Xiang, Jianhua Tao.”Prediction Of Chl-a In Bohai Bay By Genetic Neural Network And Fuzzy Recognition”.

Marine Environmental Science, 2011,30(2):239-242,278.

Huilin Ye was born in Zhoushan Zhejiang province, China, in 1989, studying for a M.S. in Cartography and Geographic Information Engineering in China University Of Geosciences Beijing(CUGB), China and received the B.S. in GIS from CUGB in 2012. Her research direction is remote sensing information processing and application.

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