Journal of Global Change Data & Discovery2019.3(3):252-258

[PDF] [DATASET]

Citation:Li, J. L., Tian, P., Shao, S. Y., et al..East China Sea Coastline Dataset (1990-2015) .Journal of Global Change Data & Discovery2019.3(3):252-258 .DOI: 10.3974/geodp.2019.03.05 .

DOI: 10

East China Sea Coastline Dataset (1990-2015)

Li, J. L.1,2*Tian, P.1,2Shao, S. Y. 1,2Zhao, M. Q.1,2

1. Donghai Institute, Ningbo University, Ningbo 315211, China??

2. Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo 315211, China

Abstract:Continental coastline in the East China Sea is between 23°37′N-31°46′N and between 117°11′E-122°08′E. Starting from Yangtze Estuary Qidongzui and extending to Port of Tielu where Fujian and Guangdong borders, the continental coastline spans 8 latitudes from south to north and stretches over Shanghai, Zhejiang and Fujian. The position of the continental coastline was determined and corrected based on Landsat TM/OLI images (special resolution: 30 m) following image interpretation and waveband edge detection, and then spatial and temporal distribution data of the continental coastline in the East China Sea during 1990-2015 (5-year intervals) were obtained. By reference to national basic coastline functional planning types and according to natural state and artificial use mode of the continental coastline, the coastline was divided into two major types: natural coastline and artificial coastline, where the former was subdivided into bedrock coastline, gravel coastline, sludge coastline and estuary coastline, and the latter was subdivided into aquaculture coastline, port coastline, constructed coastline and protective coastline. Through related models, transitional intensity and fractal dimension data of the coastline were calculated and jointly constituted a spatial and temporal change dataset of the continental coastline and its types with 5-year intervals in the East China Sea (1990-2015). Statistical results of the data indicated that total length of the continental coastline in the East China Sea was 4,720.74 km (2015). From 1990 to 2015, the proportion occupied by the length of natural coastline in the total coastline length declined from 68.06% to 46.12% and that occupied by artificial coastline increased from 31.94% to 53.88%. The contents of this dataset included the following data in the East China Sea during 1990-2015: (1) spatial distribution data of the continental coastline and its types with 5-year intervals (.shp); (2) Structured data of continental coastline types (.xlsx); (3) fractal dimensions of coastlines in different regions (.xlsx); (4) coastline change intensity (.xlsx). Saved mainly in .shp and .xlsx formats, this dataset consisted of 43 data files with data size of 3.68 MB (compressed into 1 file with data size of 1.70 MB).

Keywords:coastline; coastline fractal dimension; coastline use intensity; the East China Sea

1 Introduction

The East China Sea is an important constituent part of China’s sea area, and its coastal zone is even a zone where human activities are the most active and intensive with the evolution of its coastline resources obviously influenced by human activities[1]. Understanding spatial distribution, spatial structure, transitional intensity and complexity of the coastline in the East China Sea can improve regional coastline resource protection, reasonable utilization of coastal landscapes and effective governance and planning of regional eco-environment. Remote sensing image data of the East China Sea in 6 periods during 1990-2015 were collected in this study. According to continental coastline type features in the East China Sea and national basic coastline functional zoning types, the coastline classification system and corresponding interpretation signs of the East China Sea were established, followed by man-machine interactive interpretation of remote sensing images in the study area via ArcGIS and ENVI platforms and extraction of continental coastline position and type information needed in this study. Based on coastline data in the East China Sea, models related to transitional intensity and fractal dimension of the coastline were introduced to calculate spatial and temporal distribution and fractal dimensions of the continental coastline types in the East China Sea as well as coastline transitional intensity dataset.

2 Metadata of Dataset

Name, author, geographic area, data year, time resolution, special resolution, dataset composition, data publishing and shared service platform and data sharing policy of East China Sea coastline changes dataset in five-year increments (1990-2015) [2] are shown in Table 1.

Table 1 Metadata summary of East China Sea coastline changes dataset in five-year increments (1990-2015)

Items

Description

Dataset full name

East China Sea coastline changes dataset in five-year increments (1990-2015)

Dataset short name

Coastline_ESC_1990-2015

Author information

Jialin Li X-4440-2019, Donghai Institute, Ningbo University, Ningbo, China; Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo, China, nbnj2001 @163.com

Peng Tian X-4435-2019, Donghai Institute, Ningbo University, Ningbo, China; Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo, China, tppyang @163.com

Shuyao Shao X-4674-2019, Donghai Institute, Ningbo University, Ningbo, China; Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo, China, vickyssy @163.com

Mengqi Zhao X-4671-2019, Donghai Institute, Ningbo University, Ningbo, China; Department of Geography & Spatial Information Techniques, Ningbo University, Ningbo, China,1318561­2309@163.com

Geographical area

The East China Sea

Data age

1990??1995??2000??2005??2010??2015

Temporal resolution

5years Spatial resolution 30 m

Data format

.shp??.xlxs Data size 1.70 MB??After compression??

Dataset

Temporal and spatial distribution of the continental coastline of the East China Sea: structural changes in the continental coastline of the East China Sea, changes in coastline fractal dimension, and coastline intensity

(To be continued on the next page)

(Continued)

Items

Description

Foundations

NSFC-Zhejiang Joint Fund for the Integration of Industrialization (U1609203); National Social Science Fund (16ZDA050)

Data Publisher

Global Change Research Data Publishing & Repository http://www.geodoi.ac.cn

Address

No. A11, Datun Road, Chaoyang District, Beijing (100101), Institute of Geographic Sciences and Natural Resources Research, CAS

Data sharing policy

Data of Global Change Research Data Publishing & Repository (in both Chinese and English), entity data (in both Chinese and English) and theses published through Journal of Global Change Data & Discovery. Their sharing policies are as follows: (1) Data are open to the whole society for free in the most convenient way through the internet and are available for free browsing and downloading by users; (2) Using the “data”, the final user should label data sources in the reference or at proper position according to the quoted format; (3) Value-added service users or users transmitting and spreading (e.g. via computer server) the “data” in any form need to sign a written agreement with editorial department of Journal of Global Change Data & Discovery to gain approval; (4) the author who extracts partial records in the “data” to create new data should abide by the 10% quotation principle, namely the data records extracted from the dataset shall be less than 10% of total records in the new dataset, and meanwhile, it’s necessary to label data sources for the extracted data records[3].

Communication and Searchable System

DOI, DCI, CSCD, WDS/ISC, GEOSS, China GEOSS

3 Data Research and Development Method

According to coastline type features in the East China Sea and natural state and artificial use mode of the coastline types and by reference to national basic coastline functional planning types, coastlines in 6 periods during 1990-2015 were divided into natural coastline and artificial coastline, both of which were then subdivided into several second-level types (Table 2). From international angle, average high-water line is generally used to indicate coastlines, so according to research needs and actual situation of the study area and based on an analysis of

different reflective spectral features of surface features nearby the coastline, single-band (the 5th waveband) edge detection of the processed remote sensing images in different periods was carried out so that there was an obvious sea-land boundary line. Man-machine interactive interpretation was implemented on this basis, and the position of the coastline and its types were corrected by reference to average high-water line method[4].

3.1 Algorithm Principle

Coastline transitional intensity, which is the percentage of annual change of coastline length in the area, can objectively describe spatial and temporal change features of coastline length in the study area[5], and the specific calculation formula is as below:

(1)

Where LCIij is coastline transitional intensity from year i to year j in the area, %; Li and Lj are coastline lengths in year i and year j, respectively (km). Positive and negative LCIij values represent coastline shortening and increasing, and absolute LCIij value can express coastline transitional intensity.

The change of coastline fractal dimension can reflect the change of coastline curvature and complexity, both of which will increase with coastline fractal dimension[6]. Fractal dimensions of the continental coastline in the East China Sea in different periods were calculated based on the grid method via Matlab in this study. By reference to the existing research results[7], ArcGIS was firstly utilized to generate square grids which could cover the overall coastline in the East China Sea and calculate the needed grid number N(ε), which will vary from length ε of square grids, and according to the fractal theory:

(2)

Where A is a constant; D is coastline fractal dimension which ranges from 1 to 2.

 

Table 2 Continental coastline classification system of the East China

Coastline type

Extraction explanation

Natural coastline

Bedrock coastline

Bedrock coastal zone is generally of large slope. Sea-land boundary line which can be obviously identified in the remote sensing image is determined as coastline.

Gravel coastline

Located on the sandy coast, it is generally flat and straight, white and bright stripe is presented on the standard false color combinational image with clear and uniform texture.

Sludge coastline

Located on mud-flat coast with irregular shape, where saline-alkali tolerant plants are red or dark red after binding of standard false color wavebands.

Estuary coastline

Boundary line of estuary and ocean, it is deep blue on the image, and both estuary coastlines can be well distinguished on the remote sensing image.

Artificial coastline

Aquaculture coastline

Artificially constructed dam used for aquaculture, it presents banding-like white color in the image, in it are aquaculture ponds with regular shape, internal dykes are also white with rough texture, and position of aquaculture coastline is determined at the external boundary of causeways of aquaculture ponds.

Port coastline

Formed by dock basin and shipping port with obvious bright white strips, so coastline is determined as the line connecting it with the land.

Constructed coastline

Coastline formed by buildings in urban and rural residential areas and industrial buildings, presenting irregular bright white color in the remote sensing image, it is usually encircled by artificial causeway, it has obvious boundary line with seawater, and outer edge of the dam is defined as the coastline position.

Protective coastline

Other shore protection works (non-aquaculture areas) separating land and sea, they are bright white and banding-shaped surface features in the remote sensing data, most of them are constructed using concrete, and outside them are generally dark-colored sludge beach, so the outer edge of the seawall is defined as the coastline position.

 

After logarithms are simultaneously taken from both sides of equation (2), linear fitting is performed to obtain:

(3)

In accordance with stipulations formulated by State Bureau of Quality Technical Supervision, in the digitalization process of a topographic map with basic scale, resolution is generally 0.3-0.5 mm map unit. By reference to the conversion formula and measuring scale of the East China Sea map, this value is converted into on-site distance which can serve as grid length ε[6] measuring coastline length, and the conversion formula is:

(4)

Where Q is scale denominator. Values with grid side lengths of 1,000 m and 2, 500 m and without corresponding common measuring scale are added to Table 3 so that intervals of grid side length are uniform, and then grid side length sequence of coastline fractal dimensions in the East China Sea is constructed.

3.2Technical Route

As shown in Figure 1, Landsat remote sensing image data in the East China Sea during 1990-2015 were collected, and the original remote sensing images were preprocessed, including geometric correction, waveband synthesis, image mosaicking and clipping of the study area. After then, a coastline classification system of the study area and interpretation signs of different coastlines were established so as to extract spatial distribution of continental coastlines in the East China Sea; on this basis, coastline fractal analysis and grid method were utilized to calculate fractal dimensions of coastlines in the East China Sea, and then complexity of coastline face profile was quantitatively expressed; coastline transitional intensity model was introduced to evaluate basic development and utilization features of continental coastlines in the East China Sea.

 

Table 3 Grid side length sequence

Grid length ε (m)

Corresponding scale denominator Q

Grid length ε (m)

Corresponding scale denominator Q

600

2,000,000

1,800

6,000,000

900

3,000,000

2,500

/

1,000

/

3,000

10,000,000

1,100

3,500,000

4,500

15,000,000

1,200

4,000,000

6,000

20,000,000

1,500

5,000,000

7,500

25,000,000

Figure 1 Data development technology roadmap

4 Data Results and Verification

4.1 Dataset Composition

This data mainly included a vector file (.shp format) of spatial distribution map of continental coastlines in the East China Sea and a form file (.xlsx) of structural change, transitional intensity and fractal dimension calculated using related models, where vector data included spatial distribution maps of continental coastlines in the East China Sea in 1990, 1995, 2000, 2005, 2010 and 2015, and spatial distribution maps in 6 periods were exported out of ArcGIS10.5; .xlsx file mainly presented results calculated using vector data, and the corresponding constitution diagram was made in Excel. The dataset size was 1.70 MB after compression.

4.2 Data Results

Data were interpreted based on remote sensing images of the coastlines in 6 periods during 1990-2015, and coastal type distributions in the East China Sea in different periods were obtained (Figure 2). Meanwhile, length percentages of different coastline types in different periods were calculated within the scope of coastal zone in the East China Sea, and an area graph was drawn (Figure 3) to analyze coastline type structure. On the whole, up to 2015, the length proportion of natural coastline in total coastline length in the East China Sea continued to drop from 68.06% in 1990 to 46.12% by nearly 14.18%, while the proportion of artificial coastline already reached 53.88%.

Figure 2 Temporal and spatial distribution of the continental coastline from 1990 to 2015 in the
East China Sea

Figure 3 Structural changes in the continental coastline of the East China Sea

Coastline transitional intensities (Figure 4) in the East China Sea in 6 periods were calculated according to equation (1). During the 25 years, overall transitional intensity of coastlines in the East China Sea was -0.38%. On the whole, transitional intensities of the coastlines in different phases presented fluctuating trends, where transitional intensity during 2005-2010 reached the maximum, being -0.96%, coastline was lengthened transiently during 1995-2000 with transitional intensity of 0.14%, so 1995-2000 was a phase with the slowest coastline change.

The continental coastline is long in the East China Sea with significant spatial differences in coastline forms. Therefore, coastal fractal dimensions (Figure 5) in the selected city and provinces were calculated to reflect coastline curvature and complexity changes in each region. The results showed that average fractal dimension of coastlines in the East China Sea presented a declining trend in a fluctuating way with the form developing towards flat, straight and regular direction. Fractal dimension of continental coastline in Fujian Province was higher than average overall value in the East China Sea, while the situation was the contrary for Shanghai City and Zhejiang Province.

Figure 4 The intensity of coastline changes in various provinces and cities in the East China Sea
from 1990 to 2015

Figure 5 Spatio-temporal variation of fractal dimension of shoreline in each region

Conclusion

As an important sea area in the middle of China, the East China Sea is of enormous marine economic development potential, so it’s more necessary to understand spatial and temporal distribution and transition of the coastal zone. The established continental coastline dataset in the East China Sea will be of critical significance for studying continental coastline change in the East China Sea, understanding coastline development and use intensity, analyzing regional landscape layout change and protecting regional eco-environment, and it is especially an indispensable basic geographic database[8,9].

References

[1] Zhang, J. Y., Su, F. Z., Zuo, X. L., et al. Research on the spatial differentiation of coastal land development surrounding South China Sea [J]. Acta Geographica Sinica, 2015, 70(2): 319-332.

[2] Li, J. L., Tian, P., Shao, S. Y., et al. East China Sea coastline changes dataset in five-year increments (1990-2015) [DB/OL]. Global Change Research Data Publishing & Repository, 2019. DOI: 10.3974/geodb.2019.04.14.V1.

[3] GCdataPR Editorial Office. GCdataPR data sharing policy [OL]. DOI: 10.3974/dp.policy.2014.05 (Updated 2017).

[4] Gao, Z. Q., Liu, X. Y., Ning, J. C., et al. Analysis on changes in coastline and reclamation area and its causes based on 30-year satellite data in China [J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(12): 140-147.

[5] Shi, Y. Y., Lv, X., Huang, X. J., et al. Arable land use transitions and its response of ecosystem services value change in Jiangsu coastal areas [J]. Journal of Natural Resources, 2017, 32(6): 961-976.

[6] Hou, X. Y., Wu, T., Hou, W., et al. Characteristics of coastline changes in mainland China since the early 1940s [J]. Scientia Sinica (Terrae), 2016, 46(8): 1065-1075.

[7] Ma, J. H., Liu, D. X., Chen, Y. Q., et al. Random prefractal dimension and length uncertainty of the continental coastline of China [J]. Geographical Research, 2015, 34(2): 319-327.

[8] Li, J. L., Ye, M. Y., Pu, R. L,et al. Spatiotemporal change patterns of coastlines in Zhejiang province, China, over the last twenty-five years [J]. Sustainability, 2018, 10(2): 477.

[9] Liu, C., Shi, R. X., Zhang, Y. H., et al. Global multiple scale shorelines dataset based on Google Earth images (2015) [DB/OL]. Global Change Research Data Publishing & Repository, 2019. DOI: 10.3974 /geodb.2019.04.13.V1.

Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum