Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing

Jie Shen; Ying Zhang; Xihui Chen; Ming-Chang Li

doi:10.1515/geo-2022-0530

Article Open Access

Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing

Jie Shen , Ying Zhang , Xihui Chen and Ming-Chang Li

Published/Copyright: October 3, 2023

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From the journal Open Geosciences Volume 15 Issue 1

Abstract

The economic and ecological implications of infrastructural development in the west coast of Taiwan Strait are critically important for both financial stakeholders and policymakers. This study explores the intricate balance between economic development and ecological sustainability in Yueqing, located on the Western Taiwan Strait. Leveraging remote sensing data for fiscal years 2005, 2010, 2015, and 2020, the analysis reveals stable land use patterns. The data shows that between 2005 and 2020, the predominant land use categories in Yueqing were forestry and agricultural assets, making up 53.30%, 52.81%, 52.85%, and 52.42% of the total study area, respectively. Both asset classes demonstrated an upward trend, contrasting with the diminishing trends observed in grasslands, aquatic zones, and areas allocated for construction. To address the challenges posed by population pressure and limited land resources, the Yueqing government has implemented policies like the “Implementation Measures for Yueqing’s Farmland Protection and Compensation” in 2016 and the “Land Greening Action Plan” in 2020. These policies have effectively managed land transitions and contributed to sustainable development.

Keywords: land use change; economic coordination; geospatial information graph; Yueqing

1 Introduction

Urbanization, as a global socioeconomic phenomenon, is an inevitable outcome of the social and economic development of a country or region to a certain extent. Currently, more than 50% of the global population resides in urban areas, and this proportion is rapidly increasing [1]. While urbanization promotes regional economic growth, it also leads to changes in the natural environment. Since 1978, China has experienced rapid economic development and substantial urban population growth, resulting in significant urbanization expansion. In March 2011, the Chinese government formally approved the “Development Plan for the Economic Zone on the West Coast of the Taiwan Straits.” This plan emphasized the construction of the West Coast Urban Agglomeration as a new economic growth center, marking it as a national strategic development. The plan aimed to achieve coordinated urban–rural development and the formation of a unique spatial system with complementary advantages, reasonable layout, and coordinated development to facilitate harmonious, sustainable, and rapid urbanization in the region. By 2018, the urbanization rate of the West Coast Urban Agglomeration had exceeded the national average, reaching 61.42% [2]. Scholars are interested in understanding the impact of this construction on land use changes as it reflects the degree of coordinated development between ecology and economy in the area.

Land use change involves the organization and arrangement of land use to optimize the structure, layout, and methods within a specific region. It aims to achieve the optimal allocation of land resources in that region. In China, land use planning refers to the comprehensive, strategic, authoritative, long-term, and controllable activity organized by the government at all levels. It involves making overall arrangements and comprehensive deployment of urban and rural land use within a specific regional scope and time period based on the current status and potential of land resources and the demand for various land uses. The planning includes overall land use planning, detailed land use planning, and special land use planning [3].

In recent years, the rapid population and economic growth in China, coupled with land use changes driven by industrialization, urbanization, marketization, and globalization, have become significant subjects of academic research. Numerous scholars have conducted studies on land use changes in China from various dimensions and perspectives, forming a multi-scale research system encompassing national, regional, provincial, urban agglomeration, city, and county levels. For example, Wang et al. [4] conducted a quantitative analysis of land use changes on the Qinghai-Tibet Plateau from 1985 to 2015, identifying the driving factors behind these changes. The results revealed an increase in construction land and a decrease in cultivated land as the main characteristics of land use change, with population and economy identified as the primary driving factors. Feng et al. [5] analyzed the land use change characteristics in the Guangdong–Hong Kong–Macao Greater Bay area from 2010 to 2019 using remote sensing imagery and related data, employing methods such as land use transfer matrix, dynamic change analysis, and land use entropy analysis. Wang et al. [6] analyzed the spatiotemporal changes of land use in the Huang–Huai–Hai River Basin from 1980 to 2010. Their findings provided a basis for ecological environment protection, land use optimization, and farmland construction in the basin. They proposed that human activities were the main drivers of land use changes in the basin, while recent temperature and precipitation increases also played a significant role.

The West-Strait Urban Agglomeration is a vital component of China’s coastal economic belt. It includes cities such as Fuzhou, Xiamen, Quanzhou, Zhangzhou, Longyan, Putian, Sanming, Nanping, and Ningde in the Fujian Province; Wenzhou, Lishui, and Quzhou in the Zhejiang Province; Shangrao, Yingtan, Fuzhou, and Ganzhou in the Jiangxi Province; and Meizhou, Chaozhou, Shantou, and Jieyang in the Guangdong Province. These cities span four provinces and involve various industries, including manufacturing, agriculture, transportation, electronics, petrochemicals, finance, modern services, and tourism. All these sectors rely on land as a foundation for their layout and construction. Given the scarcity and irreversibility of land use, the prudent utilization of land resources becomes imperative. Thus, studying the historical changes in land use within the West-Strait Urban Agglomeration assumes significance in understanding regional and global environmental changes. It also provides theoretical support for ecological environment protection efforts within the agglomeration [7–9]. This study utilized remote sensing imagery from 2005 to 2020, along with RS and GIS, to acquire data on land use and transfer matrices for each period. Taking Yueqing, a county-level city on the west coast of China, as an example, this study analyzed the temporal characteristics and spatial migration patterns of land use types, providing references for land use planning and regional sustainable development.

2 Literature review

Land use change is closely intertwined with factors such as urbanization and ecological projects. The development of cities can influence the transformation of land use types, and in turn, these transformations can impact social and economic development. However, in this process, local governments often undertake ecological projects to foster the harmonization of economy and ecology for the sake of ecological preservation. Land cover changes driven by ecological engineering may lead to shifts in ecological services and functions [10–15]. Failure to consider these changes during development could yield unintended consequences for ecological engineering. Cai et al. [16] observed significant land use changes in the Loess Plateau since the implementation of ecological engineering. Previously, reforestation and afforestation projects in the region primarily focused on transforming sloping farmland, with artificial afforestation being the primary means of vegetation restoration. After 2000, the area of vegetation growth in the Loess Plateau reached 91.9% of the total area, with a substantial increase in forested areas that are now approaching saturation. Hence, regional sustainable development should encompass both ecological conservation and social progress. On the one hand, the response of the ecological environment to socio-economic systems may exhibit a certain degree of lag [17–19]. On the other hand, socio-economic and environmental factors play a crucial role in enhancing the effectiveness of afforestation. As an increasing number of workers engaged in non-agricultural sectors, reducing reliance on food production, the pressure on land as a means of livelihood diminishes, thereby boosting the effectiveness of afforestation efforts [20–22]. Therefore, studying the impact of urban development on land use requires a long-term perspective to observe its effects.

The application of remote sensing data has greatly enriched the theoretical framework in the field of urbanization. However, most studies in this domain tend to focus on exploring the patterns of urban expansion, often selecting plain regions or economically developed areas as research sites, while neglecting cities that have recently entered the accelerated phase of economic growth under the influence of policies. For instance, based on land use data from 2005, 2010, 2015, and 2020 in coastal cities of Jiangsu Province, Hu and Zhang [23] employed ArcGIS 10.7 software and a coupling coordination degree model to analyze the spatiotemporal characteristics of land use evolution in the study area and its equilibrium relationship with economic growth. Similar studies include can be seen in the literature [24–27]. These studies frequently discover that the most significant land use changes in urban areas involve the conversion of arable land to non-agricultural uses. However, the evolution of cities undergoing accelerated development under policy influence can yield more intriguing insights. For instance, Fang et al. [28] investigated the changes in land use and vegetation coverage in Luochuan County of the Loess Plateau using long-term time series NDVI data and Landsat satellite data. The findings indicated that since the implementation of reforestation and afforestation projects, vegetation coverage in Luochuan County increased from 0.6 (in 2000) to 0.9 (in 2020), while the cultivated land area decreased by 481.8 km². Among these changes, 212.8 km² of land with slopes less than 15° were converted into apple orchards, resulting in an increase in vegetation coverage from 0.5 to 0.8. Although the afforestation area on sloping farmland accounted for only 1.5% of the total land area change in Luochuan County, significantly smaller compared to the areas converted to apple orchards (20.0%), the enhanced vegetation coverage in the county primarily resulted from the government’s vigorous promotion of apple tree cultivation. Ma and Zhang [29] analyzed the changes and spatial migration of land use types in the Qinghai Province using interpreted remote sensing images from 1980 to 2020. They employed methods such as the land use dynamic degree index, transfer matrix, and comprehensive index of land use intensity to reveal the extent and spatial shifts of various land use types. Additionally, they examined the relationship between land use/cover types and 23 major socio-economic indicators. The results demonstrated an overall increasing trend in the comprehensive land use dynamic degree, indicating that land use in Qinghai Province has become more active over the study period.

Presently, China’s urbanization process has reached its advanced stages, requiring further supplementation of existing research theories, particularly for cities that have recently entered the accelerated development stage, such as Yueqing. In rapidly developing cities such as Shenzhen, the influence of economic factors often highlights more issues in land expansion, making the patterns of urban expansion more complex. However, there are also studies like Li Rui’s research on Fangzheng County [30]. They selected Landsat TM and OLI remote sensing image data from 2005 to 2020 as the primary data source and utilized GIS and RS technologies to quantitatively extract data on regional land use changes. The findings revealed that despite facing large-scale urban construction, forest land and cultivated land remained the dominant land use types in Fangzheng County. The combined area of these two types accounted for 89.50, 89.94, 89.92, and 87.28% of the total study area in the respective periods, indicating a commendable balance between environmental protection and economic development in the region. Therefore, this study also employed remote sensing images from 2005 to 2020 in Yueqing City, situated in the construction area of the west coast, and utilized Landsat TM and OLI remote sensing image data to derive land use data for each period and land use transition matrices for each stage. The study evaluated the extent of change and the underlying significance of these changes.

3 Methods

3.1 Overview of study area

Yueqing is located on the southeastern coast of Zhejiang Province, China (120°47′–125°15′ east longitude and 27°57′–28°32′ north latitude). Yueqing is adjacent to Yueqing Bay in the east and borders Taizhou City in the north, enjoying a superior geographical location (Figure 1). The terrain belongs to the middle and low mountainous areas in southern Zhejiang and the coastal hills, and the southeastern part is a coastal plain with flat terrain and intersecting rivers, sloping from northwest to southeast. The land area is 1223.3 km², and the sea area is 270 km². Yueqing in Zhejiang Province has a vast sea area in Yueqing Bay, where coastal fisheries and aquaculture are developed, making it a famous national base for mollusk seedlings. At the same time, Yueqing has a developed economy and is one of the earliest and most dynamic regions in China’s market economy development. The level of industrial mechanization is relatively high, and it has the prerequisite for deep integration of primary, secondary, and tertiary industries.

Figure 1

Research area.

3.2 Data collection

This study used four phases of remote sensing images of Yueqing, taken in 2005, 2010, 2015, and 2020. The Landsat 4-5 TM sensor with a resolution of 30 m and Landsat 8 OLI sensor with a resolution of 15 m were selected. The downloaded data were processed using ENVI and ArcGIS software for geometric correction, image registration, mask cutting, and supervised classification [31,32]. The economic data of Yueqing were obtained from the Statistical Yearbook compiled by the local Statistics Bureau, and quantitative analysis was carried out using SPSS software.

3.3 Analysis methods

3.3.1 Analysis of land use change magnitude

The change in the quantity of each land use type is the most significant characteristic of land use change [33,34]. Quantitative analysis of the changes in land use type area during a certain period can reveal the changes in the land use structure and the overall quantity of land use types over time. The calculation formula is as follows:

(1) R b − a = R b − R a ,

where R b − a represents the total magnitude of land use type change during a certain period; and R a and R b represent the initial and final areas of land use change, respectively.

3.3.2 Land use dynamic degree

Land use dynamic degree refers to the degree of change of the same land use type during a certain period, which can reflect the quantity, type, and degree of land use changes during a certain period [35,36]. The calculation formula is as follows:

(2) D = R b − R a R a × 1 T × 100 % ,

where T is the number of years; R a and R b are as defined in equation (1); and D is the dynamic degree of a certain land use type.

The comprehensive land use dynamic degree is a measure of the trend of changes in the area of all land use types during a certain period. The calculation formula is as follows:

(3) LC = ∑ i = 1 n Δ LR i − j 2 ∑ i = 1 n LR i × 1 T × 100 % ,

where Δ LR i − j is the absolute value of the area of land type i converted to land type j during a certain period; LR i is the area of land type i in the initial period; T is as defined in equation (2); n is the number of land use types; and LC is the comprehensive land use dynamic degree.

3.3.3 Land use transfer matrix

The land use transfer matrix is a quantitative description of land use transfer, which can clearly express the spatial change characteristics of land use types [37,38]. The geospatial information mapping method was used to construct the land use transfer spatial pattern of Yueqing. In this study, GIS technology was used to intersect and analyze the land use data of 2005 and 2010, 2010 and 2015, and 2015 and 2020, with the following calculation formula:

(4) N = 10 M a + M b ,

where N is the type code of the newly generated land use transfer unit; and M a and M b are the type code values of the land use before and after the change.

The proportion of land use change can quantify the transfer of land use transfer units and analyze their evolution patterns [39,40], and the calculation formula is as follows:

(5) W = M ij ∑ i = 1 n ∑ j = 1 n M ij × 100 % ,

where W is the change ratio; n is as defined in equation (3); M ij is the area of land use transfer units where land class i is transferred to land class j during a certain period of time.

4 Results

4.1 Spatio-temporal dynamic changes in land use

According to the land use data of Yueqing in 2005, 2010, 2015, and 2020, the land use areas for each period were obtained (Figure 2), as well as the change in the area for each type of land use (Table 1). By analyzing Table 1 and Figure 2, the changes in land use types in Yueqing over 15 years can be summarized: from 2005 to 2020, the main land use types in Yueqing were forests and cultivated land, with forests having the largest area. Within the forests, the areas of forested and shrubland were the main components, accounting for 53.30, 52.81, 52.85, and 52.42%, respectively, during the 4 years. The area of the cultivated land was the second largest, accounting for one-third of the area of forests. The proportions of the cultivated land were 21.64, 21.28, 20.38, and 20.32%, respectively, during the 4 years. In 2005, forests had the largest area among the different land use types, with an area of 741.38 km², followed by cultivated land, construction land, grassland, and water area. In 2010, the water area decreased the most, by 25.95 km², while construction land increased the most, by 13.76 km². The order of proportion was cultivated land, construction land, grassland, and water area. The area of each land use type in 2015 did not differ much from that in 2010. Forest and cultivated land remained the main land use types, with the greatest area conversion being the water area. The area of each land use type changed greatly in 2020, with the cultivated land area showing the largest increase by 18.44 km². The land use structure was cultivated land, construction land, grassland, and water area.

Figure 2

Bar chart of land use type areas from 2005 to 2020.

Table 1

Land use type area change in Yueqing

Land type	2005–2010 (km²)	2010–2015 (km²)	2015–2020 (km²)	2005–2020 (km²)
Arable land	5.0742	12.465	0.9054	18.4446
Forest land	6.7545	−0.5166	5.9841	12.222
Grassland	−1.4679	0.2385	−0.9675	−2.1969
Water area	−0.1638	−25.9452	7.9029	−18.2061
Construction land	−10.197	13.7583	−13.8249	−10.2636

4.2 Analysis of land use change rate

The calculation of land use dynamic degree using formulas (1) and (2) yields the results shown in Table 2.

Table 2

Dynamic degree of land use in Yueqing

Land type Arable land	2005–2010 (km²)		2010–2015 (km²)		2015–2020 (km²)		2005–2020 (km²)
Land type Arable land	U (%)	LC (%)	U (%)	LC (%)	U (%)	LC %	U (%)	LC (%)
Forest land	0.34	0.17	0.88	0.38	0.06	0.21	1.31	0.44
Grassland	0.18		−0.01		0.16		0.34
Water area	−0.66		0.11		−0.43		−0.97
Construction land	−0.08		−7.95		2.75		−6.34
Land type	−0.74		1.05		−1.00		−0.74

From Table 2 and Figure 3, it can be seen that the comprehensive land use dynamic degrees from 2005 to 2010, 2010 to 2015, and 2015 to 2020 were 0.17, 0.38, and 0.21%, respectively. The comprehensive land use dynamic degree was highest from 2010 to 2015, indicating that the overall rate of change in land use in Yueqing increased and then decreased.

Figure 3

Observing the results of the single land use dynamic degree, from 2005 to 2010, arable land showed a rapid growth trend with a growth rate of 0.34%, followed by forest land with a growth rate of 0.18%. Grassland, water area, and construction land showed a decreasing trend, with construction land having the largest decreasing rate of −0.74%, while the changing rates of grassland and water area were −0.66 and −0.08%, respectively. From 2010 to 2015, arable land still had the highest growth rate of 0.88%, followed by grassland, while forest land, water area, and construction land showed a decreasing trend. From 2015 to 2020, as a whole, the land use types showed a slow increasing or decreasing trend. The water area had the highest growth rate of 2.75%, followed by forest land and arable land, while grassland and construction land showed a decreasing trend, with construction land having the most obvious decreasing trend. Overall, from the 15-year land use changes, arable land and forest land showed an increasing trend, while grassland, water area, and construction land showed a decreasing trend.

4.3 Analysis of land use transfer matrix

The land use map model was constructed using formula (3). Based on ArcGIS 10.2 software, the 2005–2010, 2010–2015, and 2015–2020 data were analyzed to obtain the land use transfer maps for the three periods, as shown in Figure 4. Using formulas (4) and (5), the unit rankings and proportional changes of the land use map from 2005 to 2020 were calculated, and the results are shown in Table 3.

Figure 4

Table 3

Ranking of major mapping units for land use change, 2005–2020

Year	Code	Land use transfer type		Number of changed units in the map	Area (in km²)	Change ratio (%)
2005–2010	21	Forest	Cultivated land	5,649	5.0841	0.69
	51	Construction land	Cultivated land	12,113	10.9017	4.09
	12	Cultivated land	Forest	10,560	9.504	3.16
	32	Grass land	Forest	3,173	2.8557	6.63
	52	Construction land	Forest	1,372	1.2348	0.46
	23	Forest	Grass land	1,496	1.3464	0.18
	15	Cultivated land	Construction land	2,255	2.0295	0.67
2010–2015	21	Forest	Cultivated land	3,374	3.0366	0.41
	51	Construction land	Cultivated land	14,044	12.6396	4.57
	12	Cultivated land	Forest	2,515	2.2635	0.76
	15	Cultivated land	Construction land	1,121	1.0089	0.34
	45	Waters	Construction land	4,741	4.2669	10.84
2015–2020	21	Forest	Cultivated land	5,845	5.2605	0.72
	51	Construction land	Cultivated land	4,570	4.113	1.57
	12	Cultivated land	Forest	6,618	5.9562	2.10
	15	Grass land	Forest	1,811	1.6299	3.68
	45	Construction land	Forest	7,041	6.3369	2.41
	23	Forest	Grass land	1,937	1.7433	0.24
	54	Construction land	Waters	7,819	7.0371	2.68
	15	Cultivated land	Construction land	2,650	2.385	0.84

The total land use transfer area from 2005 to 2010 was 32.96 km², and the land use transfer was mainly concentrated between forest land, arable land, and construction land, with the most significant changes occurring in the construction land, which had the largest area of land transfer. The land use transfer structure changed in the period of 2010–2015, with fewer transfers between land use types compared to the previous period. The transfer speed tended to be smooth, and the total land transfer area was 23.22 km² and was mainly concentrated in water areas, arable land, and construction land.

From 2015 to 2020, the type of each map unit changed relatively significantly. The total land use transfer area was 34.46 km², with further expansion of the transformation among the construction land, arable land, forest land, and water areas, and construction land remained the main transfer-out type.

5 Discussion

The observations made in this study provide evidence that ecological and urban development in Yueqing has not exhibited significant conflicts. The changes in land use area from 2005 to 2020 demonstrate a relatively stable pattern. Although the water area has decreased due to lower rainfall, the transformations between farmland, woodland, and construction land have been balanced. The grassland area has remained relatively stable throughout the study period.

Yueqing faces a prominent challenge of balancing its large population with limited land resources, leading to conflicts between people and land. In response, the Yueqing government implemented the “Implementation Measures for Yueqing’s Farmland Protection and Compensation” in 2016. This policy establishes a compensation system to protect farmland and ensure its preservation and productivity, thereby promoting sustainable economic and social development. The woodland area in Yueqing is notably significant, with a gradual increase in forest coverage, particularly in the western mountainous region. However, the forest stock in the Yueqing Bay and coastal plain areas is relatively limited. To address this, the Yueqing government introduced the “Land Greening Action Plan of Adding 10,000 Mu of Forest and Building a Forest City” in 2020, which has significantly contributed to enhancing the forest coverage rate.

To address the contradiction between the supply and demand of construction land and promote the development of new rural areas, China implemented the “Decision on Deepening Reform and Strictly Managing Land Use” in 2004. This decision emphasizes the consolidation of rural construction land and links the reduction of rural construction land to the increase in urban construction land. Considering the transfer of land use in Yueqing from 2005 to 2020, it is evident from Figure 4 and Table 3 that the overall trend was relatively stable, with a notable transfer of land from construction land. These findings suggest that the government’s efforts in economic development have not caused significant damage to the local land ecology. The results of this study indicate that the land use planning and policy measures implemented by the Yueqing government have effectively balanced economic development with environmental protection. The findings demonstrate the compatibility of ecological preservation and urban development, providing valuable insights for sustainable land management and guiding future decision-making processes in Yueqing and similar regions.

6 Conclusion

This study used remote sensing image data from 2005 to 2020 and employed RS and ArcGIS software to obtain land use data and land use transition matrices for different periods. Taking Yueqing as an example, the study analyzed the temporal characteristics of land use types and found that:

The main land use types in Yueqing are forest land and arable land, with forest land being the most prominent, accounting for 53.30, 52.81, 52.85, and 52.42% in 4 years. Over the 15 years, arable land and forest land showed an increasing trend, while grassland, water areas, and construction land showed a decreasing trend.
The total area of land use transition from 2005 to 2010 was 32.96 km², and the transition of land use types was mainly concentrated between forest land, arable land, and construction land, with the largest transition out of construction land. The changes in each land use type from 2010 to 2015 were greatly reduced, with the total transition area mainly concentrated in water areas, arable land, and construction land. The changes in each land use type from 2015 to 2020 were relatively large, with a total transition area of 23.22 km², and construction land still being the main land use type for transition out.

In summary, the relevant land use planning departments on the west coast of the Taiwan Strait should correctly understand the current status of land use planning, combined with the actual situation of land use and economic development, and fully consider the actual development needs of each city, continuously optimize land use planning, and lay a good foundation for the sustainable development of the economy on the west coast of the Taiwan Strait.

The limitations of the study need to be declared. First, the analysis of land use planning and economic development on the west coast of the Taiwan Strait relies heavily on available data sources. The accuracy and reliability of these data sources can have an impact on the validity of the study’s findings. There may be limitations in the quality, resolution, or coverage of the remote sensing data, GIS data, and other sources used for the analysis. This could introduce potential errors or biases in the results and limit the accuracy of the conclusions drawn. Second, the study’s focus on a single city on the west coast of the Taiwan Strait may not capture the full complexity and diversity of land use patterns and economic development in the entire region. Different cities may have unique characteristics, socioeconomic dynamics, and land use challenges that are not fully represented in this analysis. The findings and recommendations derived from this study should be interpreted with caution and may not be directly applicable to other cities or regions on the west coast.

hero0121@skku.edu

Acknowledgments

This research was supported by the PhD studentship of Sungkyunkwan University.

Funding information: This study didn't receive funding support.
Author contributions: Jie Shen contributed to the conceptualization of the study, data collection, analysis, and interpretation, and played a significant role in drafting the manuscript. Ying Zhang and Xihui Chen provided expertise in data processing, contributed to the data analysis and interpretation, and critically reviewed the manuscript. Ming-Chang Li supervised the research project, provided guidance throughout the study, contributed to the conceptualization and design of the research, and played a major role in revising and finalizing the manuscript.
Conflict of interest: Authors state no conflict of interest.
Data availability statement: The data is available upon reasonable request.

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Received: 2023-05-11

Revised: 2023-07-06

Accepted: 2023-07-27

Published Online: 2023-10-03

This work is licensed under the Creative Commons Attribution 4.0 International License.

Articles in the same Issue

https://doi.org/10.1515/geo-2022-0530

Keywords for this article

land use change; economic coordination; geospatial information graph; Yueqing

Creative Commons

BY 4.0