Water Body Extraction for the Landsat TM Imagery of Hulun Lake

Chunzhe Zhao

1,2

, Xueying Li

, Rong Xu

1,2,*

and Jiang Xiong

Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher

Education, Chongqing Three Gorges University, Wanzhou, Chongqing, China

Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology, Chongqing Three

Gorges University, Wanzhou, Chongqing, China

School of Computer Science and Engineering, Chongqing Three Gorges University, Wanzhou, Chongqing, China

School of Mathematics and Statistics, Chongqing Three Gorges University, Wanzhou, Chongqing, China

Keywords: Hulun Lake Wate, Reextraction, Ostu Algorithm, Cyclic Thresholding.

Abstract:

Based on the Landsat TM imagery, several common lake water extraction methods are compared via

extracting the Hulun Lake water body. The thresholds in these methods are determined by the Otsu and the

Iteration method. It is found that the water area in the image can be extracted using these methods, and the

application effects decrease in the order of NDWI, MNDWI, MIR, normal method of spectral relationship.

The thresholds determined by the Otsu and the Iteration method are almost equal. The thresholds from

Iteration method are more accurate, which means that these two algorithms are feasible in the identification

of the lake water body in this region.

Corresponding author.

1 INTRODUCTION

Since the 1970s, remote sensing technology has

been widely used in the extraction of water body

information due to its advantages of large

monitoring area, short imaging period, and

abundant information volume (Cao

et al., 2022).

Especially, the water body extraction is essential in

water resources survey, flood analysis, and

environmental monitoring. The accuracy of water

body extraction affects the quality of follow-up

surveys and assessments. Therefore, it is an

attractive topic to extract water bodies from remote

sensing images accurately and quickly (Dong et

al.,2022; Li et al.,2022).

Considering water accounts for 74% of the

Earth's surface, water condition differs under the

different geomorphological and hydrological

characteristics. Hence, several methods are

proposed for extracting water bodies (Anusha et

al.,2022; Ma et al.,2007; MaFeeters,1996;

McCormack et al.,2022; Lu et al.,2011; Soman and

Indu,2022). Zhang, Minghua combine the improved

spectral relationship method with the threshold

method to construct a multi-conditional spectral

relationship model, and used to extract information

on the water in the polar high mountains and

achieved good results (Zhang,2008). The decision

tree is employed in the automatic extraction of the

water body (Du et al., 2001; Li and Wang, 2007).

Hu, Zhengguang et al. proposes the algorithm based

on the AVHRR data combined with the

double-boundary extraction and the decomposing of

the mixed pixels (Hu et al.,2007). The high

accuracy and feasibility of the algorithm is verified

in monitoring the lake area changes in northeast of

China and Inner Mongolia. Xu, Hanqiu and Cao,

Ronglong optimize and improve NDWI separately

(Xu, 2006; Cao et al., 2008). Both of them increase

the accuracy of water extraction. Although the

information extraction methods of water body

mainly include single-band threshold method,

exponential model method, normal method of

spectral relationship, image classification method,

and so on, the index method of water body and the

normal method of spectral relationship are widely

used for water body extraction since their high

precision.

The extraction information of lake water body is

the basis for the dynamic monitoring of lakes.

354

Zhao, C., Li, X., Xu, R. and Xiong, J.

Water Body Extraction for the Landsat TM Imagery of Hulun Lake.

DOI: 10.5220/0011927800003612

In Proceedings of the 3rd International Symposium on Automation, Information and Computing (ISAIC 2022), pages 354-359

ISBN: 978-989-758-622-4; ISSN: 2975-9463

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

However, due to the complex interaction process

among lake water bodies, surrounding land objects,

solar radiation, and the atmosphere, the presence of

mixed pixels has caused by the multiple

interference of mountain shadows and other noises,

which makes the extraction difficulty and

uncertainty of lake water body increased (Xu et

al.,2021). Previous studies have shown that most

methods of water extraction have certain regional

limitations, such as: the method applied to a certain

lake may not be suitable for another lake, and the

size of the threshold in the water extraction model

has different effects on the extraction results (Sarp

and Ozcelik,2017). The thresholds in the existing

studies are mostly determined based on experience

through repeated human-computer interaction tests,

and are rarely determined using a threshold

algorithm for image segmentation. The effects of

different threshold algorithms on water extraction

results are also rarely analysed.

In this study, Landsat TM is used as the data

source, and several common water extraction

methods are used to extract the water information of

the Hulun Lake. This paper compares and analyses

the impact of different threshold algorithms on

water extraction results, and supports improving the

automation of water body information recognition.

2 RESEARCH AREA AND ITS

DATA

Hulun Lake (48°30′-49°20′N and 116°58′-117°48′E)

lies in the southern suburb of Manzhouli City,

Hulunbeier League. Its location is between

Xinbarhuzuoqi and Xinbaerhuyouqi, and the lake

area belongs to the temperate semi-arid climate

zone. The source of the lake water mainly depends

on the surface runoff and the atmospheric

precipitation. The rivers are flowing into Hulun

Lake are mostly seasonal rivers. The main rivers

into the lake are the Krulun River in the southwest

and the Wuerxun River in the southeast.

The experimental data has taken from the

Landsat TM image of September 7, 2010, with a

landscape number of 125-26. The product is

Level1T, it has been subjected to system radiation

correction, ground control point geometry

correction, and DEM terrain correction, including 7

bands of data. Data pre-processing is mainly image

cropping..

Figure 1: Landsat TM image.

3 LAKE WATER INFORMATION

EXTRACTION METHOD

To extract the water body of the Hulun Lake,

several common water extraction methods are

employed, such like single-band threshold method,

normal method of spectral relationship and

exponential model method. The threshold required

in the above method has determined by a threshold

algorithm.

3.1 Water Extraction Method

Based on the single-band threshold method, the

water body has a low reflectance in the near

infrared and short wave infrared bands, and it has

easily distinguished from other features. For this

feature, a threshold K can be determined in the TM5

band, the water body is smaller than the threshold,

and other features are larger than the threshold.The

extraction model of water body is as follows.

TM5<K . (1)

With the normal method of spectral relationship,

Zhou, Chenghu et al. analyse the spectral

characteristics of ETM+ images. It is found that

water has the spectral relationship between

TM2+TM3 and TM4+TM5. This feature can be

Water Body Extraction for the Landsat TM Imagery of Hulun Lake

355

used to extract water information (Zhou et al.,

1999). The extraction model of water body is.

TM2+TM3 TM4+TM5 >0（

）-

（

）

(2)

Besides, there are many index models for water

extraction, such as the Normalized Difference

Water Index (NDWI), the Modified Normalized

Difference Water Index (MNDWI), and

Normalized Difference Vegetation Index (NDVI).

In this study, the formula of NDWI and MNDWI

are expressed by:

NDWI=(TM2-TM4)/(TM2+TM4), （3）

MNDWI=(TM2-TM5)/(TM2+TM5). （4）

Based on the contrast between the green and

near-infrared bands, NDWI can suppress

vegetation information to the maximum extent to

achieve the purpose of highlighting water

information (

McFeeters,1996). MNDWI is an

improvement to MDWI that better suppresses

building information and improves the accuracy

of water extraction. The MNDWI and NDWI

values of the water body are higher than other

ground objects, so the water body information

can have extracted by setting the corresponding

thresholds for the two indexes. The water

extraction models are

DWI>M , (5)

MNDWI>N , (6)

where M and N are threshold values.

3.2 Threshold Algorithm

In order to compare the applicability of different

threshold algorithms, the Ostu method and the

Iteration method are employed.

The Ostu method is based on the separability

of the target and background categories in the

image. The basic principle is to divide the grey

histogram of the image into two parts with the

optimal threshold, such that the inter-class

variance between the two parts achieves the

maximum value. That is the maximized

separation. The Ostu algorithm is expressed as

follows.

a. Read the grayscale image.

b. Let k=0, and t(0) be the smallest grayscale value.

c. Divide the image into Class C

and C

based on t(k).

d. Calculate the possibility, the mean value and the

variance of Class C

and C

e. Calculate the inter-class variance between of Class

and C

f. Let t(k+1)=t(k)+0.005, k=k+1.

g. Repeat Step c-f until t(k) exceeds the largest

grayscale value.

h. Find the largest inter-class variance, and the

corresponding t(k) is the threshold.

The Iteration algorithm is a method of selecting an

appropriate threshold in the image segmentation

process, also known as the cyclic threshold

method. It is based on the approximation of the

idea of threshold iteration, using the program to

calculate the appropriate segmentation threshold

automatically. The guideline of the Iteration

algorithm is as follows.

a. Read the grayscale image, then find the smallest

grayscale value t

min

and the largest grayscale value

max

b. Let k=0 and t(0)=(t

min

max

)/2.

c. Divide the image into Class C

and C

based on t(k).

d. Let t

be the mean value Class C

, and t

be the

mean value Class C

e. Let t(k+1)=(t

)/2 and k=k+1.

g. Repeat Step c-e until |t(k)-t(k-1)|<0.005.

h. The last t(k) is the threshold.

According to the former guidelines, the Ostu

method and the Iteration method are implemented.

For the Ostu method, the threshold initial value t(0)

takes the minimum gray value T

min

of the image and

increments by 0.005 steps each time until t(k) is

greater than or equal to the maximum gray value

max

of the image; A t(k) divides the pixels on the

image into two categories. After calculating the

variance between the classes in turn, the t (k) when

the variance between the classes is the largest has

taken as the optimal threshold. In the Iteration

method, we first find out the minimum grey value

min

and the maximum grey value T

max

in the image.

We can set the average value to T(0), divide the

pixels into two categories according to T(0), and

calculate the two average grey values of the class

pixel, and then the average of the current two

average grey values is used as the next threshold,

and the process is repeated until T(k+1)-T(k)<0.005

ends. T(k+1) at this time is the optimal threshold.

The normal method of spectral relationship

(formula (2)) does not need to set a threshold value,

and the difference image formed by

(TM2+TM3)-(TM4+TM5) has binarized to obtain a

water body extraction result. For the single-band

threshold method, two thresholds of the TM5 band

have obtained by the Ostu method and the Iteration

method, so that there are extraction results of water

body. The exponential model method needs to obtain

NDWI and MNDWI images based on TM images,

and then apply the Ostu method and Iteration

method to calculate the threshold values, and obtain

four extraction results of water body.

ISAIC 2022 - International Symposium on Automation, Information and Computing

356

4 WATER EXTRACTION

RESULTS AND ACCURACY

ANALYSIS

On the TM composite pseudo-colour image, the

water body is blue-black and the vegetation is

bright red or light red. The Visual Image

Interpretation has used to delineate the Hulun

Lake boundary as a reference data for testing the

water extraction results of each method. In order

to scientifically and objectively evaluate the

experimental results of various methods for

extracting water body information, this paper

qualitatively and quantitatively evaluates the

water body extraction results from two aspects:

visual effect and area precision.

4.1 Water Extraction Results

Since the thresholds obtained by the Ostu method

and the Iteration method are relatively close, this

paper only evaluates the visual effects of the water

extraction results obtained by the former.

Figure 2 shows the results of water extraction by

each method, and Figure 3 is a partial enlarged view

of Figure 2, which is showing the results of water

extraction on the south bank of the lake. Referring to

the TM composite pseudo-colour image, the

shoreline of the east, west, and north shores of Hulun

Lake extracted by the single-band threshold method

is basically the same as the Visual Image

Interpretation, but some of the shoals on the south

bank are misunderstood, and there is a large

misplacement phenomenon. The extraction result of

this method is less than ideal, which has related to

the threshold setting on the one hand and the

principle of the method on the other hand. The

results of the inter-spectral relationship extraction

method are more accurate than the single-band

threshold method, and there are fewer shoals, which

have mentioned mistakenly. The results of NDWI

and MNDWI extraction are relatively close. The

latter have more shoals, but the rivers in the south

eastern shore have extracted well.

In general, the former methods have achieved

good results in the obvious east, west and north

shores of the water and land boundaries, but the

extraction effect on the south bank (Figure 3) is poor,

mainly due to the wrong shoal and some high water

content. The vegetation area, which also reflects the

lower threshold (high) determined by the threshold

algorithm.

Figure 2: Water extraction results of each method.

Figure 3: Water body extraction results on the south bank

of the lake.

4.2 Accuracy Evaluation

The accuracy of the lake surface area obtained by

each method has calculated based on the visually

interpreted lake area (Table 1). Among them, the

area of the three-band threshold method, NDWI and

MNDWI is the area when the threshold has

determined by the Ostu method. It can be seen that

the accuracy of the area obtained by several methods

is high, and the order of the area accuracy from high

to low is NDWI, MNDWI, normal method of

Water Body Extraction for the Landsat TM Imagery of Hulun Lake

357

spectral relationship, single-band threshold method.

This has consistent with the results of the visual

evaluation. At the same time, it has found that the

NDWI and MNDWI index models can achieve

better results, when the threshold has determined by

the automatic thresholding algorithm.

Table 2 shows the thresholds calculated by the

Ostu method and Iteration method. It can be seen

that for the single-band threshold method, the results

obtained by the two threshold algorithms are almost

identical; for the two index models NDWI and

MNDWI, the difference between the two threshold

algorithms is smaller, and the threshold obtained by

the Iteration method is more accurate. However, it

should have noted that some above extraction

methods of water body have mistakenly mentioned a

part of the non-water body, and it can be seen that

the threshold obtained by the automatic thresholding

algorithm is low (high).

Table 1: Comparison of water extraction results of each

method.

Method

Area

（km

）

Reference

area（km

）

Area

accuracy

Single band

threshold

1800.95 1772.15 98.37

Inter-spectral

relationshi

1781.13

1772.15

99.49

NDWI 1775.12

1772.15

99.83

MNDWI 1779.23

1772.15

99.60

Table 2: Threshold calculation results.

Method

Maximum interclass

variance method

Iteration

method

Single band

threshold

57 56

NDWI 0.020 0.025

MNDWI 0.080 0.086

5 CONCLUSIONS

For the Hulun Lake, the NDWI and MNDWI

extraction results are better, if the threshold value of

water body extraction is determined by the automatic

thresholding algorithm. The area of the lake has

interpreted visually, and its area accuracy is above

99%. The single-band threshold method is relatively

poor. The reason is related to the threshold algorithm.

On the other hand, the reason is that it has a great

relationship with the principle of the method; the

results have extracted by the normal method of

spectral relationship, which are between the

single-band method and the exponential model

method, and the accuracy is high.

In this paper, the lake area extracted by various

methods has evaluated quantitatively with the area as

a reference index, and the positioning accuracy of

each extraction result will have evaluated in the

future. In addition, the threshold value obtained by

the automatic thresholding algorithm in the study

area is different from the artificially extraction

threshold set of water body in other areas. The

reason for further study has needed, and the result

may help to improve the accuracy of the threshold

algorithm.

ACKNOWLEDGEMENTS

This research is supported by the Scientific and

Technological Research Program of Chongqing

Municipal Education Commission (Grant No.

KJQN202001233), the Natural Science Foundation

of Chongqing, China (Grant No.

cstc2018jcyjAX0725), the Key Laboratory of

Chongqing Municipal Institutions of Higher

Education (Grant No. [2017]3), and the Program of

Chongqing Development and Reform Commission

(Grant No. 2017[1007]).

REFERENCES

Anusha, B.N., Babu, K. R., Kumar, B. P., Kumar, P. R.,

and Rajasekhar, M.(2022). Geospatial approaches for

monitoring and mapping of water resources in

semi-arid regions of Southern India, Environmental

challenges, 8:100569.

Cao, H., Han, L., and Li, L. (2022). Changes in extent of

open-surface water bodies in china's yellow river basin

(2000-2020) using google earth engine cloud platform.

Anthropocene, 39: 100346.

Cao, R. L., Li, C. J., Liu, L. Y., Wang, J. H., and Yan, G. J.

(2008). Extracting miyun reservoir’s water area and

monitoring its change based on a revised normalized

different water index. Science of surveying and

mapping, 33(2):158-160.

Dong, Y. T., Fan, L. B., Zhao, J., Huang, S. S., Geiß, C.,

Wang, L. Z., and Taubenbock, H. (2022). Mapping of

small water bodies with integrated spatial information

for time series images of optical remote sensing.

Journal of hydrology, 614(Part B):128580.

Du, J. K., Huang, Y. S., Feng, X. Z., and Wang, Z. L.

(2001). Study on water bodies extraction and

classification from spot image. Journal of remote

ISAIC 2022 - International Symposium on Automation, Information and Computing

358

sensing, 5(3):214-219.

Hu, Z. G., Wang, Y. T., Chi, T. H., Liu, S. H., and Bi, J. T.

(2007). Monitoring lake areas based on mixed pixel

decomposition combined with double-edge extraction.

Remote sensing information, (3):34-38.

Li, K., and Wang, Y. Y. (2007). Study on the modified

automatic water bodies extraction model from tm

image. Journal of water resources & water engineering,

18(6):20-22.

Li, Y. S., Dang, B., Zhang, Y. J., and Du, Z. H. (2022).

Water body classification from high-resolution optical

remote sensing imagery: Achievements and

perspectives. ISPRS journal of photogrammetry and

remote sensing, 187:306-327.

Lu, S. L., Wu, B. F., Yan, N. N., and Wang, H.

(2011).Water body mapping method with HJ-1A/B

satellite imagery. International journal of applied earth

observation and geoinformation, 13:428-434.

Ma, M., Wang, X., Veroustraete, F., and Dong, L. (2007).

Change in area of ebinur lake during the 1998-2005

period. International journal of remote sensing, 28:(24),

5523 -5533.

McCormack, T., Campany, J., and Naughton, O. (2022). A

methodology for mapping annual flood extent using

multi-temporal Sentinel-1 imagery, Remote Sensing of

Environment, 282:113273.

McFeeters, S. K. (1996). The use of the normalized

difference water index in the delineation of open water

features. International journal of remote sensing, 1996,

17:1425-1432.

Sarp, G., and Ozcelik, M. (2017). Water body extraction

and change detection using time series: A case study of

Lake Burdur. Turkey, Journal of Taibah University for

science, 11(3):381-391.

Soman, M. K., and Indu, J. (2022). Sentinel-1 based Inland

water dynamics mapping system. Environmental

modelling & software, 149:105305.

Xu, H. Q. (2006). Modification of normalized difference

water index to enhance open water feature in remotely

sensed imagery. International journal of remote

sensing, 27(14):3025-3033.

Xu, Y. Y., Lin, J., Zhao, J. W., and Zhu, X.Y. (2021). New

method improves extraction accuracy of lake water

bodies in Central Asia. Journal of hydrology, 603 (Part

D): 127180.

Zhang, M. H. (2008). Extracting water-body information

with improved model of spectral relationship in a

higher mountain area. Geography and geo-information

science, 24(2): 14-16.

Zhou, C. H., Luo, J. C., Yang, X. M., Yang, C. J., and Liu,

Q. S. (1999). Understanding and analysis of remote

sensing imagery. Beijing: Science Press.

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