2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE.

Automated detection of coastline using Landsat TM based on water index and

edge detection methods

Zhang Xu-kai, Zhang xia, Lan Qiong-qiong Muhammad Hasan Ali Baig

Institute of remote sensing application Chinese Academy of Sciences

Beijing, China zhangxk@irsa.ac.cn

Zhang Xu-kai, Lan Qiong-qiong Muhammad Hasan Ali Baig

Graduate University of Chinese Academy of Sciences Beijing, China

Abstract—This paper presents a comprehensive and automated approach to detect coastline accurately and efficiently, which integrates MNDWI (Modified Normalized Difference Water Index), Otsu algorithm and canny edge detection. Landsat TM data was employed in Qinhuangdao coastal zone as a case study. First, by analyzing the ground truth data, the coasts in Qinhuangdao were classified into four classes : sandy coast, bedrock coast, artificial coast and muddy coast, and then detection result of coastline was achieved on the basis of these types. Finally, the coastlines detected in September 2006 and September 2011, were compared together. Some changes caused by anthropogenic activities were monitored. The overall accuracy of coastline detection, verified by using GPS data was 92%.

Keywords: remote sensing; automated detection; coastline; MNDWI; Canny edge detection

I. INTRODUCTION As coastline is the boundary between land and ocean, its

geographic location, orientation, length and such properties are fundamental for navigation, coastal resource management, coast erosion monitoring, and environmental protection. Remote sensing is becoming a highly effective and efficient coastline detection technique, due to its advantages of high timeliness, large scale and low cost [1]. A variety of remote sensing images and methods have been used to detect coastlines. In principle, the accuracy of the coastline detection depends on the spatial resolution of the remote sensing images [2]; however, due to the cost of high-resolution images, medium-resolution images such as Landsat TM have been widely used to detect coastline, excepting the demand for high mapping scale. In view of coastline detection using Landsat TM, Frazier et al. used simple density slicing of TM band5 to detect coastline when sea water was clear [3]; Manavalan et al. used TM band4 to extract water body such as river and lake successfully [4]; Kevin et al. suggested that TM band7 was suitable for the monitoring of the change of coastline [5].

Automated coastline detection from remote sensing images belongs to the detection problem in the field of computer vision and image processing, in which edge detection and image segmentation are two conventional approaches to the boundary detection [6]. Lee and Jurkevich proposed a coastline detection technique on the basis of an edge detection algorithm, and suggested that a comprehensive procedure was required for automated detection of coastline [7]. In short, this paper proposes a comprehensive and automated approach to detect coastline using Landsat TM data.

II. METHODOLOGY

A. Data and Preprocessing The study site is Qinhuangdao coastal zone in China, with

the latitude ranging from 39°38′N to 40°2′N and longitude 119°12′E to 119°51′E. Two Landsat TM images, acquired on September 16, 2006 and September 6, 2011 respectively, were collected with a spatial resolution of 30 m. Only band2 and band5 for each image were used in this study. Image preprocessing was conducted on these two TM images, including radiometric correction and precise geometric correction and registration between the two-date images. The error of geometric correction and registration is less than 0.5 pixels. Besides, according to ground truth data of coastal zone in Qinhuangdao, the coasts were classified into four classes: sandy coast, bedrock coast, artificial coast and muddy coast. Moreover, in September 2011, a field experiment was accomplished to measure coastline’s location using GPS.

B. Calculation of MNDWI Xu proposed MNDWI (Modified Normalized Difference

Water Index) to extract water body more effectively and accurately than NDWI (Normalized Difference Water Index). It was proved that MNDWI could remove shadow noise from water information without using sophisticated procedures [8]. When using Landsat TM data in this study, MNDWI was

2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE

Figure 1. TM image (Left) and MNDWI of TM image (Right) acquired in September 2006

calculated by the following equation:

2 5 2 5( ) / ( )band band band bandMNDWI TM TM TM TM= − + (1)

As shown in figure 1, sea water was enhanced efficiently after calculating MNDWI and it was prepared for the next step to detect coastline.

C. Coastline Detection by Canny Integrating Otsu After the calculation of MNDWI, in the key step of this

method, detection of coastline was achieved automatically on the basis of the coastal type mentioned above. For the sake of clarity, a flow chart is given in figure 2 to outline the approach of coastline detection used in this paper.

Figure 2. Flow chart of the approach of coastline detection

As shown in figure 2, considering the different character of each coastal type, different approach was applicated to detect the coastlines. To remove the disturbance of pixels whose brightness is between sand and non-sand, coastlines of sandy coast were detected by integrating median filter, Otsu method and canny edge detection. Because wetland in muddy coast consists of great amount of water, making muddy coast similar with sea in MNDWI image, coastlines of muddy coast were detected by integrating gray stretching with canny edge detection. Due to obvious character, coastlines of bedrock coast and artificial coast were only detected by integrating image sharpening, Otsu method and canny edge detection.

Canny edge detection integrating Otsu method used in this paper was proposed by Li et al [9~11]. Canny edge detection gets two threshold values, namely, threshold1 and threshold2, whose relationship is usually threshold1=0.5* threshold2. Threshold1 is the key of controlling effect of edge detecting, while increasing the value of threshold2, most of the noise will be wiped off. However, some useful edge information was lost. To detect edge automatically, threshold2 was calculated by Otsu method [10], and threshold1 was calculated as threshold1=0.5* threshold2.

In summary, the Otsu method could calculate the high threshold which was significant to the Canny edge detection. In addition, this threshold could be used in the Canny edge detection to detect object’s edge such as coastlines in MNDWI images. The Otsu method can solve the threshold’s setting in Canny edge detection and improve the effect of edge-distilling of Canny edge detection.

III. RESULTS AND DISCUSSION

A. Results of Coastline Detection The result of coastline detection by this comprehensive

and automated method was output into GIS software, overlapped with TM images and shown in figure 3. The coastline detected by our method overlaps TM image well.

2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE

Figure 3. Coastline detected and TM image in 2011overlapped

Besides, Coastlines detected from TM images of 2006 and 2011 were overlapped to analyze the changes between 2006 and 2011.

Figure 4. Coastlines detected in 2006 and 2011 overlapped: black line is of 2006 and red one is of 2011.

As shown in figure 4, two obvious changes of coastlines in 2006 and 2011 are detected: muddy coastline in wetland of Geziwo in Beidaihe district and artificial coastline of shipyard and dock in Shanhaiguan district. That two obvious changes were amplified in figure 5 and figure 6.

Figure 5. Coastlines of muddy coast in 2006 and 2011 overlapped: black line is of 2006 and red one is of 2011.

Figure 6. Coastlines of artificial coast in 2006 and 2011 overlapped: black line is of 2006 and red one is of 2011.

As shown in figure 5, coastline in wetland of Geziwo in Beidaihe district moved in the direction of land between 2006 and 2011. Wetland is defined as water area by MNDWI because of its high water content; therefore, the change of muddy coastline shows that the area of wetland is increasing when water area is increasing. After investigation, the government of Qinhuangdao spent much effort in improving the environment of wetland, making the area of wetland increasing. As shown in figure 6, coastline of shipyard and dock in Shanhaiguan district moved in the direction of sea between 2006 and 2011. It's known that the shipyard and dock of Shanhaiguan district were expanded during 2006 and 2011, because the shape of coastline was regular.

B. Results of Accuracy verification Generally speaking, coastlines detected from remote

sensing images are affected by tidal variations, especially the sandy coast and muddy coast with gentle slope. Taking into account the maximum water level increase and coastal slopes, Guariglia states that the coastline, including sandy coast with gentle slope, detected from TM images whose resolution is 30 m, isn't interfered by tidal factor [12]. The maximum water level increase was obtained from Qinhuangdao tidal station and coastal slopes were calculated using two-date tidal height and the distance between coastlines straightly detected from TM

2012 Second International Workshop on Earth Observation and Remote Sensing Applications

978-1-4673-1946-1/12/$31.00 ©2012 IEEE

images [1]. After the calculation, on September 16, 2006 and September 6, 2011, the maximum shift in coastline position was about 12.5m, less than 30m. Therefore, tidal effects were not considered when coastlines were detected by our method. After detecting coastlines from TM images, coastlines of September 2011 were output into GIS software and the accuracy was verified using GPS data collected in September 2011.

TABLE I. RESULT OF ACCURACY VERIFICATION

In the pixel Out of pixel

Sandy coast 25 0

Artificial coast 23 2

Muddy coast 19 6

Bedrock coast 25 0

25 GPS points of each coastal type were used to verify the accuracy, moreover, it was successful to detect coastline only when GPS point was in the pixel detected by our method. As shown in table 1, the overall accuracy of detecting coastlines is 92%, only 2 point of artificial coast and 6 points of muddy coast are not successful to detect. The most possible reason is that it's hard to determine GPS points of muddy coast because of its obsolete sign of coastlines.

IV. CONCLUSIONS The coastlines detected could overlap TM images well,

without considering tidal influence because of coarse resolution of TM. Besides, the overall accuracy verified with GPS data is 92%, as a result of that most points located by GPS and coastlines overlap well. Moreover, the changes of coastlines between 2006 and 2011 are detected. From figure5, coastlines extended towards the land during that period, however, with an opposite result from figure6. After investigation, it’s obvious that the changes of coastline are affected by anthropogenic activities and the results are consistent with the fact.

In this study, MNDWI, Otsu method and canny edge detection were integrated to detect the coastline automatically. According to the results of coastline detection, it proves that coastline can be detected automatically and effectively by our method. In addition, a better result of coastline detection can be achieved, by classifying the coasts and taking corresponding measures. Finally, the changes of coastlines between 2006 and

2011 were detected, mainly caused by anthropogenic activities during that period. However, automated classification should be considered in future study to improve the detection result.

ACKNOWLEDGMENT This study was supported by the Special Public Welfare

Project of the Ministry of Land and Resources of the People’s Republic of China (Grant No. 201011019-07).

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