Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data

doi:10.4236/jcc.2014.22002

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Journal of Computer and Communications, 2014, 2, 8-13

Published Online January 2014 (http://www.scirp.org/journal/jcc)

http://dx.doi.org/10.4236/jcc.2014.22002

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Monitoring the Sewage Draining in Shenzhen Reservoirs

Using Hyperspectral Data

Yin Li1, Wei Pan1, Xiaomao Yang2, Qinglin Tian1

1CNNC Beijing Research Institute of Uranium Geology (BRIUG), Beijing, China; 2College of Environmental Sciences and Engi-

neering, Peking University, Beijing, China.

Email: [email protected]

Received September 2013

ABSTRACT

Freshwater resources are regarded as the foundation of urban development and assure the sustainable prosper-

ity of the city. The contaminations of fresh water in reservoirs can threaten safety of people directly and force

the Industrial processes to be suspended. Therefore, developing a method to detect the potential locations where

contaminated water drains off into the reservoirs efficiently and precisely is a challenging task but in urgent

need. In this research, we used the air-borne sensor Hymap to get the hyperspectral data of Shenzhen. Finally we

find a way to invert five water quality parameters (Suspended Solid (SS), Total Nitrogen (TN), Total Phosphorus

(TP), Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD5)) from the Hymap image and we

distinguish the clear water and polluted water on the image successfully.

KEYWORDS

Hyperspectral; Water; Hymap; Shenzhen Reservoirs

1. Introduction

Shenzhen is a modern international metropolis with de-

veloped industrial and densely population. Freshwater

resources are regarded as the foundation of Urban de-

velopment and assure the sustainable prosperity of the

city. The contaminations of fresh water in reservoirs can

threaten safety of people directly and force the Industrial

processes to be suspended. There are 171 reservoirs and

396 pound s in Shenzhen, 17 of the reservoirs are middle

size. Total capacity is 611 million cubic meters, each

year to provide 350 million cubic meters of drinking wa-

ter. Although the enormous amounts of water in the re-

servoirs, the per capita is less than 200 cubic meters,

about 1/12 of the national rate. It’s an urgent task to pre-

vent contamination of existing freshwater resources.

There are so many reservoirs in Shenzhen that it’s too

difficult to monitor the sewage draining in Shenzhen

reservoirs by manual effort. The reservoirs in Shenzhen

are surrounded by a large number of farms and industrial

parks; a way to monitor the sewage draining quickly and

effectively is in urgent need [1-3].

Monitoring water quality by remote sensing has grad-

ually developed from qualitative to quantitative and the

number of parameters is increasing. We can invert Chlo-

rophyll a, suspended solids, yellow substance, transpa-

rency, turbidity, water temperature, etc., and the retrieval

accuracy continues to improve [4].

2. Data and Process

2.1. Data

We get 34 water samples for this paper, 14 of them are

clear water and the other 20 of them are contaminated.

The locations of the samples are widespread in Shenzhen

and typically in the inlet, centre or outlet of the reservoirs.

Collecting time is October 31 to November 7, a period of

eight days.

Recode the locations of the 34 water samples by GPS

(the detail is shown in Figure 1), take note of water

transparency, water depth, surface temperature and other

related data. We had performed a chemical analysis on

those water samples (Including SS, TP, TN, BOD5 and

COD etc.). See Table 1 below for details.

We used ASD Fieldspce (R) Pro to measure the spec-

trums of the sample when we collected the samples. ASD

Fieldspce (R) Pro can measure continually the spectrums

from 350 nm to 2500 nm, the spectral resolution is 1 nm

and the number of output bands is 2501. The process

measurement was in clear, calm days. We take ten spec-

trums for each sample, then take the average as the orig-

Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data

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Figure 1. The locations of the 34 samples.

Table 1. Detail of the Biochemical parameters of samples.

Biochemical

parameters Clear water Polluted water

mean max min me a n ma x min

CODcr

Cr6+

Cyanide

BOD5

24.01

_ _ _

17.64

1.88

5.47

5.59

0.17

43.3

_ _ _

12.1

18.4

10.7

0.73

11.6

_ _ _

0.25

1.67

2.01

0.025

188.71

0.022

0.52

0.27

0.47

0.052

108.85

23.27

29.93

41.09

3.38

419

0.051

3.09

0.78

3.95

0.211

767

50.2

90.2

6.1

71.5

0.007

0.061

0.025

0.08

0.005

10.6

16.7

0.539

inal reflective spectrum of the sample. The data got by

ASD is synchronous Hymap data, which has 128 bands,

continue from 400 nm to 2500 nm. The spectral resolu-

tion of Hymap is 15 ~ 18 nm.

2.2. Processing of Data

Due to the spectrometer response differently in each

band, there is some noise in the original reflective spec-

trums. The original reflective spectrums were smoothed

by Mean Filter and the spectrum in 350 nm to 1300 nm

has been selected to be regarded the spectrum of the

samples. The details of the spectrum are show in the

Figure 2 below.

2.3. Spectrum Analysis

The most important influencing factors on inland water

spectrum are chlorophyll a and suspended solids, other

parameters are difficult to find independent spectral cha-

racteristics, we need extra information of the relationship

between different elements and invert them by remote

sensing indirectly.

Derivative spectrum can be mathematically calculated

from reflective spectrum. It shows the positions of min-

imum and the maximum bending point in reflection

spectrums. Research shows that derivative spectrum is

very sensitive to signal to noise ratio of the spectrum, but

it’s less sensitive to signal to noise ratio in lower differ-

ence, and it widely used in practical application. The re-

sult of correlation analysis is showed in the figures (Fig-

ures 3 and 4) below .

The figures show that the all six parameters (SS, TN,

AN, BOD5, TP, COD) have low correlations with all the

original reflective spectrum bands, but the some of them

have a high correlation with the derivative spectrum

bands. The derivative spectrum has a strong ability to

invert water parameters, but the derivative spectrum must

be calculated from high spectral resolution spectrum data.

To monitor the sewage draining quickly and efficiently,

we need to use the airborne or satellitic remote sensing,

but the spectrums get from aircrafts can’t have a high

spectral resolution like ASD, so the idea that getting in-

formation from derivative spectrum doesn’t work.

The figure also shows five parameters (SS, TN, BOD5,

TP, COD) have some consistent correlation with the de-

rivative spectrum bands, In band 150, band 200 and some

other bands, the correlation is significantly higher than

others. It indicates the five parameters have comprehen-

sive representations in some range of the spectrum. In

Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data

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Figure 2. Spectrum of the samples.

Figure 3. Correlations with original reflective spectrum bands.

this study, we choose six obvious reflective (or absorp-

tive) peaks. The detail is shown below in Table 2.

Count all the area of the six peaks in the 34 original

reflective spectrums, choose ten spectrums randomly,

build simultaneous equation with the peaks area and the

all six parameters, then use the model to approach the

parameters of the 34 samples. The result is given in the

Figure 5 below.

The figures show that the approach works well except

AN. This way can be used in invert the parameters of the

Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data

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Figure 4. Correlations with original reflective spectrum bands.

Figure 5. Result of approach the parameters. a. Sample of a Table footnote.

Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data

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water. Analyze the relationship between with the area of

the six peaks, the result indicates the peak 2 (from 684

nm to 706 nm) and the peak 4 (from 783 nm to 834 nm)

can be used to distinguish the polluted water and the

clear water. As the Figure 6 show s that the clear water

and polluted water are located separately, almost in two

lines. It means that we can use an index calculated from

the airborne or satellitic data.

2.4. Image Analysis

Use the GPS information of the 34 samples to find the

corresponding Hymap data. Because of the lower spec-

tral resolution and the influence of the atmosphere, the

spectrums of the samples are different with the data get

by ASD at some wavelength. The peak from 757 nm to

767 nm and the peak from 988 nm to 1155 nm do show

in the spectrums get from the Hymap image, but the peak

2 and peak 4 are still obvious in the Hymap spectrums.

Using the band 25, band 26, band 27, band 18, band 19

and band 20 to calculate the area of peak 2 and peak 4

and using the ratio of area of peak 2 to area of peak 4 as

the index to distinguish the clear water and polluted wa-

ter. The results are shown below. The red points indicate

the locations of polluted water.

As the Figure 7 sh ow s, we can identify the inlet of the

polluted water quickly and efficiently and it is also useful

in detecting the small inlet which is too small to find by

eyes.

3. Conclusion

Because of the complex aquatic environment of Shenz-

hen, SS, TN, AN, BOD5, TP and COD are not composed

of certain particles, they don’t have any certain spectral

characteristics. For all six parameters have low correla-

tions with all the original reflective spectrum bands, we

can’t use any single bands or ratio of two bands to invert

those parameters quantitatively. Derivative spectrum is

sensitive to the changes in the proportion of SS, TN, AN,

BOD5, TP and COD, some bands have high correlations

with the five parameters, it can work well with data that

have high signal to noise ratio and high spectral resolu-

tion, but we need to further study how to use this tech-

nique on Hymap image. The area of the six peaks can be

used for inverting the proportion of SS, TN, AN, BOD 5,

TP and COD accurately; it can work effectively in mon-

itoring the sewage draining in Shenzhen reservoirs; the

physical significance of this technique needs further

study. The ratio of area of peak 2 to area of peak 4 is

effective in distinguishing the polluted water and clear

Figure 6. The relationship between peak 2 and peak 4. a. Clear water are shown by blue points and polluted water are shown

by red points.

Monitoring the Sewage Draining in Shenzhen Reservoirs Using Hyperspectral Data

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(a)

(b)

(c)

(d)

(e)

Figure 7. The result of image analysis.

Table 2. Detail of the peaks.

Reflective

peaks Centre

point (nm) Begin

point (nm) End

point (nm)

561 510 617

706 684 730

760 757 767

807 783 834

1068 988 1155

675 648 700

water, it can be used on Hymap image successfully, and

it’s a quick and effective way to monitor the sewage

draining in Shenzhen reservoirs.

REFERENCES

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[2] S. J. Li and X. J. Wang, “The Spectral Features Analysis

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Water Quality Parameters,” Geography and Territorial

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ing Hyperspectral Remote Sensing Data in Taihu Lake

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