Next Article in Journal
Analyzing Spatio-Temporal Characteristics of Cultivated Land Fragmentation and Their Influencing Factors in a Rapidly Developing Region: A Case Study in Guangdong Province, China
Previous Article in Journal
Exploring the Role of Transit Ridership as a Proxy for Regional Centrality in Moderating the Relationship between the 3Ds and Street-Level Pedestrian Volume: Evidence from Seoul, Korea
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Land
Volume 11
Issue 10
10.3390/land11101751
land-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Examining the Spatial Variations of Land Use Change and Its Impact Factors in a Coastal Area in Vietnam

by
Yutian Liang
1,2,3,*,
Jiaqi Zeng
1 and
Shangqian Li
1
1
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, China
2
China Regional Coordinated Development and Rural Construction Institute, Sun Yat-sen University, Guangzhou 510275, China
3
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
*
Author to whom correspondence should be addressed.
Land 2022, 11(10), 1751; https://doi.org/10.3390/land11101751
Submission received: 13 September 2022 / Revised: 1 October 2022 / Accepted: 2 October 2022 / Published: 9 October 2022
Browse Figures
Review Reports Versions Notes

Abstract

:
Controlling land use change in coastal areas is one of the world’s sustainable development goals and a great challenge. Existing research includes in-depth studies of land use change in relatively developed regions, but research on economically less developed but fast-growing regions is lacking. Since the reform and opening up in Vietnam, the influences of globalization have prompted the economy of the coastal area to develop rapidly, making it one of the less developed but rapidly developing regions where human activities and global changes vigorously interact. Therefore, taking the coastal area of Vietnam as the study area, we used the land use change index and random forest model to analyze the spatial variations of land use change and its impact factors. The research shows that: (1) land use shows a trend of continuous and rapid increase in construction land, with the proportion of construction land increasing from 2.72% in 2000 to 4.40% in 2020. However, natural landscapes, such as forests and grasslands, are decreasing. (2) Land use also shows obvious spatial variation characteristics, which are mainly manifested in the differences in change rate, development intensity, and distribution characteristics. Among them, the region with the largest rate of change was the Central Coastal Area. The region with the highest development intensity is the Mekong River Delta. (3) The main factors affecting land use change are foreign direct investment (FDI), the industrialization index, and population. Based on that, we analyzed the mechanism influencing the above factors from the perspectives of urbanization and population growth, and industrialization and park construction, as well as globalization and FDI, which can explain well the relationship between the impact factors and the spatial variation. This study can provide a valuable decision-making reference for formulating reasonable regional land development policies and is a good example of land use research for other rapidly developing areas.

1. Introduction

Globalization, as an important trend, has introduced extraordinary challenges and possibilities under the sustainable development goals for 2030 (SDG 2030). It is an important issue of global concern and has a profound influence on land use [1,2,3]. Coastal areas, as regions with concentrated influence from various human activities, provide a promising focal point for studying land use and land cover change (LUCC) in the context of globalization [4]. Coastal areas are characterised by way of high-level socioeconomic development and excessive population density. At present, about 11% of the world’s population lives in coastal areas inside 10 km of the coast. Of these, 65% live in coastal cities [5,6]. A giant population concentration leads to an abnormal scale and intensity of human development on coastal land [7]. Therefore, in contrast with other ecosystems, coastal areas are more prone to urbanization, globalization, and industrialization [8,9]. Due to the elements influencing their interaction, land use models in coastal areas are continuously changing with human activities and socioeconomic development [4,10]. In coastal areas with alternative forms of land use, such as built-up areas and urban development, land development frequently proceeds under a high-efficiency utilization mode, such as port-facing industries, development of industrial parks, and exclusive monetary areas [7,11,12,13]. Currently, the dynamic monitoring of land use change in coastal areas has become a necessary aspect of research on the impact of human activities on world change [14]. Research on land use change in coastal areas has explored the dynamic mechanism of land use change via the analysis and empirical comparison of cases in different regions [15,16,17,18]. Research has typically been targeted on hot spots and typical areas. Coastal land use change has been studied from distinctive perspectives. Most research has focused on the pattern of spatial variation in coastal land use change and the forces impacting these changes.
Existing research on land use change in coastal areas falls into two categories: (1) Spatial differentiation patterns of land use change in coastal areas. Studies in some areas have proven that natural landscapes, such as forests and grasslands, are increasing. The eastern coastal region of the United States is a typical example. There, gradual increases in recent years have resulted in forest coverage higher than 50% [19]. In addition, in Ban Don Bay, Thailand, the aerial extent of forest/mangrove land cover is also increasing, whilst areas of agriculture and wasteland are reducing [20]. However, research in other areas has proven that the areas of forest and grassland are decreasing, whilst construction land and farmland are increasing. In hot spots, such as the coastal vicinity of China, especially in the coastal area of northern Jiangsu [21], the coastal areas of Zhejiang Province [22], and the Bohai coastal area [23], the enlargement of construction land is especially pronounced. Other regions, such as the northwest coast of Mexico [24], the coastal areas of Turkey [25], and the northwest coastal area of Egypt [26], exhibit that there has been a significant expansion in construction land and farmland but a pronounced decrease in natural landscape areas. Studies in Southeast Asia also exhibit that natural landscapes, such as mangroves, are being destroyed and degraded by human settlements, agriculture, and coastal industrial park development [27,28]. (2) Studies on the factors impacting land use change in coastal areas. Research suggests that urbanization, industrialization, financial growth, and other human activities are the most important factors impacting land use change. In Costa Rica, human activities have a pronounced impact on coastal areas [29]. In the coastal area of Guangdong Province [30] and the Beijing-Tianjin-Hebei region [31], urbanization and industrialization have been the most important factors that led to amazing changes in the landscape pattern alongside the coastal areas. High population density, fast economic growth, and urbanization are regarded as the primary factors leading to the conversion of agricultural land in China [32]. In the Cochin coastal area of India, economic growth and industrialization have caused a large expansion of construction land [33]. Moreover, industrial park development, especially in areas surrounding Jakarta, was once the predominant factor inflicting extensive land use change in Indonesia [34]. Urbanization also influenced LUCC in India [35]. In summary, the existing research indicates that there are two different development tendencies of land use change: the growth of natural landscapes and the growth of construction land. Concurrently, in terms of influence factors, though the impact of urbanization, industrialization, and economic growth on land use change has been confirmed, the coastal resource use pattern and related land use change vary between and within nations. These changing patterns also depend on prominent factors such as natural background, population density, and urban development [36]. Specific to a certain region, the factors impacting land use change require further study.
Recently, scholars worldwide have designed many tools to give an explanation for land use change. These have included principal component and correlation analysis models, a couple of linear regression models, a global-scale dynamic land use allocation model, nonlinear models such as canonical correspondence analysis, cellular automata models, and system dynamics models [37,38,39,40]. These models have simulated the plausible causes of LUCC to a constrained degree. However, the linear regression model can only replicate the alternate in total land use in the region, and it is challenging to disclose the relationship between spatial heterogeneity and driving force. The cellular automata mannequin can simulate biophysical problems in LUCC; however, it is now not appropriate for the simulation of human decisions. The system dynamics model can fully reflect on the driving factors in the LUCC process; however, it lacks the potential to deal with spatial factors [41]. By adopting Earth Observation Data, the entropy that can be calculated from remote sensing data can be used as a proxy data predictor for the intensity of landscape fragmentation [42]. Some scholars have also studied the dynamics and spatial determinants of land change by integrating Landsat imagery and spatiotemporal economic data [43].
Canonical responsiveness analysis primarily based on the nonlinear speculation can intuitively learn about the socioeconomic driving mechanisms of land use change and divulge the interplay between land use change and socioeconomic development [44,45]. However, it can’t evaluate the relative importance of every impact factor. The random forest model is a data-mining approach based on random sampling and random feature selection. Its benefits are: (1) it can avoid overfitting, with moderate complexity and excessive accuracy; (2) an internally generated out-of-bag accuracy can be obtained to estimate the model performance; and (3) it can evaluate the significance of variables, which performs a key role in exploring the mechanism of construction land growth [46]. The random forest model is a natural nonlinear modeling approach that is appropriate for examining complicated datasets with many unknown features, such as the evolution and expansion of construction land. The random forest model has been extensively used to simulate land use change and city expansion [47,48]. The simulation accuracy is also high, which suggests that it is superb in fitting complicated problems and measuring the importance of factors. Therefore, we used the random forest model to determine the significance of every driving factor in the expansion of construction land.
In the context of economic globalization, with the continuous integration of developing areas into the global system, the pace of improvement in exceptionally poorly developed areas exceeds that of some more established regions. Dramatic and speedy economic improvement will have more and more profound impact on land-use change [1,2,49]. When examining the factors influencing land-use change, quite a few questions must be examined. For example, what is the vogue of evolving land use changes in a fast-growing region? What are the evolutionary characteristics of construction and ecological lands? What spatial variation characteristics will land use change present in specific regions? What are the main factors that cause this differentiation? Exploring the above questions can assist the creation of a valuable decision-making reference for formulating reasonable regional land development policies and represent a good example of land use research in other less-developed areas.
Vietnam is a typical developing but fast-growing region. In 2019, the per capita GDP was once only USD 2715, ranking 132 in the world. Since 2008, Vietnam’s GDP growth rate has remained above 5% and it reached 7.08% in 2018, making it the fastest-growing country in Southeast Asia. In addition, it additionally presents three important development characteristics: fast urbanization, continuous industrialization, and in-depth economic reforms (“opening up”). First, since 1990, the city population of Vietnam has increased by almost 21.56 million, with the percentage growing from 20.3% to 36.6%. Second, since 2000, Vietnam has transformed about 64,000 hectares of natural land to industrial land, accounting for 68% of the total natural land area converted. Third, since 1988, Vietnam’s foreign direct investment (FDI) has experienced a rapid growth trend in both the number of projects and the amount invested. As the frontier of Vietnam’s reform and opening up and representing an important engine to drive Vietnam’s economic development, the coastal area of Vietnam is much more developed than inland areas [5,6]. It is the area where human activity is the most intense [8,9]. This was a key element in our choice of the coastal zone of Vietnam as the focal point for this study.
The objectives of this study were: (1) to quantify and characterize the pattern of spatial variation in land use from 2000 to 2020 and reveal the trends and characteristics of the spatial variation of land-use change, (2) to identify the major drivers of land-use change, and (3) to determine the relationship between these drivers and the spatial variations.

2. Materials and Methods

2.1. Study Area

Vietnam is in the eastern portion of the Indo-China Peninsula, with geographical coordinates between 8°10′ N, 102°09′ E and 23°24′ N, 109°30′ E. The study area is the coastal province of Vietnam. Using the Vietnam Statistical Yearbook and considering the economic development and geographical location, we divided the study area into three regions (northern, central, and southern) and three subregions: the Red River Delta, Central Coastal Area, and Mekong River Delta (Figure 1). The Red River Delta is in northern Vietnam, near the capital city of Hanoi, with a high level of economic development. Located in southern Vietnam and driven by the proximity of Ho Chi Minh City, the economic development of the Mekong River Delta is relatively high. The Central Coastal Area is near the coastal zone, spanning a wide latitude, and its economic development level is relatively average, with Da Nang as the main city. In 2018, the population of Vietnam was 95.5404 million, mostly distributed in the Red River Delta, Mekong Delta, and southeast coastal areas.

2.2. Data

Global land use data from 2000/2010/2020 are from the GlobeLand30 2020 database. GlobeLand30 was developed using the POK method and covers ten land categories. GlobeLand30, a 30-meter resolution global land cover data product, was developed by the National Geomatics Center of China.1 The images for land cover classification are chiefly 30-meter multispectral images, including TM5 ETM+, and OLI multispectral images of Landsat (USA) and HJ-1 (China Environment and Disaster Reduction Satellite). Eight land cover types were selected for analysis in this study (Table 1). The elevation, slope, and population data were obtained from World Pop,2 with a resolution of 100 m. Line features such as roads, waterways, railways, coastlines, and rivers were obtained from natural earth3 and Open Street Map.4 Point features such as city centers, townships, and industrial parks were extracted from Open Development Vietnam.5 The economic and social statistical data used in this study mainly came from the Vietnam Statistical Yearbook.

2.3. Methods

2.3.1. Land Use Change Analysis Method

Comprehensive Index of Land Use Intensity

The calculation of the comprehensive index of land use intensity is commonly used to express the land use intensity [50]. The comprehensive index of land use intensity is normally used to specify the land use intensity, which can be described as:
L a = 100 ∗ ∑ i = 1 n ( A i   ∗   C i )
where La is the land use intensity comprehensive index in a certain region, Ci is the area percentage of land use type i, Ai presents the grade index of land use type i, and n refers to the number of land use types. According to the influence degree of human activities, land use can be divided into four levels, in which bare areas were assigned a factor value of 1, forest, grassland, and wetland were assigned a 2, farmland was assigned a value of 3, and construction land was assigned a 4.

Land Use Dynamic Degree

The land use dynamic degree is mainly used to characterize the transfer rate of land use change. It can not only reflect the land use change intensity on the whole but also facilitate the hot spots of land use change at different spatial scales. We use the change rate of a single land use type to quantitatively describe the velocity of regional land use change and the regional distinction of a given land use type change [51]. The formulation is as follows:
K = ( U b − U a ) / U a ∗ 1 / T ∗ 100 %
where U a and U b are the number of a certain type of land use at the beginning of the study and the end of the study period; K is the dynamic attitude of a certain land use type during the research period; T is the research period, and the value of K is the annual change rate of a certain land use type.

Comprehensive Land Use Dynamic Degree

The comprehensive land use dynamic degree may be represented as:
LC = [ ∑ i = 1 n Δ L U i − j 2 ∑ i = 1 n L U i ] ∗ 1 T ∗ 100 %
where Δ L U i − j is the absolute value of the region where the i-th land use type changes to the non-i-th land use type for the duration of the monitoring period, L U i is the beginning time of monitoring the region of land use type I, and T is the length of the monitoring duration [52].

2.3.2. Random Forest Model

The Random Forest (RF) Model uses bootstrap sampling technology to extract N samples from N original sample sets, selects K attributes from all attributes, and selects the first-class segmentation attribute as the node to create a decision tree [53]. N choice timber is trained by using the bagging algorithm, and the end result is acquired by way of voting on the modeling results of all decision trees. RF has a robust generalization and steady overall performance [54]. The Random Forest package in R was used to construct the RF model. The importance of the features is sorted primarily based on the RF model. In addition, feature importance refers to the contribution rates of the variables. The larger the value, the more vital it is. In the RF model, feature importance normally refers to relative importance.
The random forest consists of a series of decision tree models {h(x, θ k ), k = 1, 2…}, where h( · ) represents the decision tree model and x is the input direction. The quantity and parameter set { θ k } are independent and identically distributed random vectors, and θ k determines the growth process of a single tree; The final output of RF is obtained by a simple majority voting method (for classification) or the simple average of single tree output results (for regression). Predictive variables can be numeric or categorical, and the variables need not be converted [44,45,55].

3. Spatial Variations of Land Use Change

3.1. Overall Evolution Trend of Land Use Change

From 2000 to 2020, the region and aerial proportion of construction land in the study region expanded continually (Table 2, Figure 2). In particular, construction land area extended by 353.00 hm2 in 2000–2010 and extended by 2064.74 hm2 in 2010–2020. The proportion of construction land to total land area multiplied from 2.72% in 2000 to 4.40% in 2020. The general consistency of the area changes in the two periods suggests that they are notably reliable and can replicate the general regional trend of the land use change [56]. In addition, the natural landscape areas, such as grasslands, forests, and wetlands, decreased. Among them, the grassland area decreased over the two periods by 172,804.14 hm2 and 29,607.39 hm2, respectively. The proportion of grasslands reduced from 8.36% in 2000 to 6.95% in 2020.
The construction land in each region demonstrated a substantial growth trend, with the largest increase in the Central Coastal Area (Figure 3). In addition, farmland trends varied by region. Specifically, the Red River Delta confirmed a trend of continuous reduction in farmland, with the area reducing by 75,374.46 hm2, whilst in the Central Coastal Area, farmland reduced after a sharp increase. From 2000 to 2010, farmland area improved by 210,746.16 hm2, and from 2010 to 2020, it decreased by 40,712.85 hm2. In the Mekong River Basin, farmland first increased and then decreased sharply. During 2000–2010, farmland area improved by 580.5 hm2, whilst during 2010–2020, it diminished by 73,788.21 hm2.

3.2. Regional Difference of Land Use Change

3.2.1. Difference in Change Rate

As can be seen from the comprehensive land use dynamic degree (Figure 4), the rate of change in each subregion was remarkably distinct. In the past 20 years, the areas with the greatest rate of change were in the Central Coastal Area, whereas the areas with the smallest change rate were in the Mekong River Delta. Specifically, from 2000 to 2010, the region with the greatest rate of change was the Central Coastal Area, where the comprehensive dynamic attitude index was 3.24 × 10−3. The region with the smallest rate of change was the Mekong River Delta, with a comprehensive dynamic attitude index of 5.10 × 10−4. From 2010 to 2020, the Mekong River Delta had the largest rate of change with a comprehensive dynamic attitude index of 1.85 × 10−3. The Red River Delta had the smallest rate of change with a comprehensive dynamic attitude index of 1.54 × 10−3. Notably, the variation ranges of each region were also distinct. The region with the largest variation range was the Central Coastal Area, where the comprehensive dynamic attitude index decreased by 48.95%.

3.2.2. Difference of Development Intensity

As can be seen from the comprehensive land use index (Figure 5), the land development intensity differed by subregion. Among them, the Mekong River Delta had the highest development intensity, with an average comprehensive land use index of 291.64 and construction land increasing by 2.012%. This was followed by the Red River Delta, with an average index of 238.99 and an increase in construction land of 1.739%. The lowest development intensity was in the Central Coastal Area, with an average index of 236.22 and an increase in construction land of 1.453%.

3.2.3. Difference of Distribution Characteristics

The predominant land use types in the Red River Delta are forest, farmland, and construction land. Among them, forest is mainly distributed in the southwest and northeast, whilst farmland and construction land are more prominent in coastal areas. The most notable characteristics of land use change in this region are the transformation of farmland into forest land and the transformation of farmland into construction land, which correspond to two hot spots on the southwest coast and the central coast, respectively. The Mekong River Delta is covered by a large area of farmland, with scattered construction land, forests, wetlands, and water bodies. The most prominent characteristic of land use change in this area is that farmland was converted into construction land, which corresponds to the area alongside the river and the estuary. The main land use types in the central coastal areas were forest land, farmland, and construction land. Among them, farmland and construction land were specifically distributed close to coastal areas. The most prominent feature of this area was the growth of construction land alongside the coastal region (Figure 6).

4. Factors Impacting Land Use Change

4.1. Identification of Impact Factors

The natural background conditions and human factors represented by these factors had a direct or indirect influence on regional land use evolution. To explore the impact factors that led to spatial variations in land use, we chose 15 factors closely associated to the spatial evolution of coastal land use based upon measurable and available data (Table 3). The natural background prerequisites and human factors represented by these factors had a direct or indirect impact on regional land use evolution.
Among them, elevation and slope are the explanatory variables of natural background conditions. Because the elevation of the study area fluctuates, the ground elevation and slope have a direct impact on the development of construction land [57].
Geographical factors are essential spatial factors that play a vital function in guiding and constraining the growth of construction land. It is an essential factor that determines the direction, scale, intensity, and speed of expansion of construction land [58]. Distance to a major road, distance to a waterway, distance to a railway, distance to a coastline, and distance to a river are the explanatory variables of geographical factors chosen because of the particularity of the coastal area.
The influence of social and economic factors (human factors), particularly the continuous advancement of urbanization and industrialization, is the primary factor inflicting the continuous expansion of construction land [59]. The distance to a city center, distance to a township, distance to an industrial park, the provincial industrialization index, and the provincial FDI have universal significance, which can better replicate the role of human factors [60].
From 2000 to 2020 (Figure 7), the top 5 factors driving construction land expansion were FDI, industrialization index, population, distance to town, and distance to city center, which shows that construction land expansion is sensitive to globalization, industrialization, and urbanization. Factors such as elevation, slope, distance to railway, and distance to waterway ranked lower, indicating that natural and traffic factors had less impact on the growth of construction land. Unlike previous studies, this study discovered that globalization, urbanization, and industrialization, which are the three most important human activities, ranked high in relative importance, indicating that human activities were still a dominant factor affecting the expansion of coastal construction land [61,62,63,64]. In Section 4.2, we analyze these three types of influencing factors in more detail. Factors such as the distance to rivers, coastal zones, and main roads are relatively important; however, factors such as the distance to waterways and railways are unimportant and are associated to the geographical conditions of the region. There are few railways and waterways in the study area. Therefore, transportation is more dependent on accessible roads and rivers. The relative importance of elevation and slope was low, which suggests that the effect of natural conditions on the growth of construction land was minimal.

4.2. Influence Mechanism of Impact Factors

Based on the above analysis, we further analyzed the mechanism impacting land use change from the perspectives of urbanization and population growth, industrialization and park construction, globalization, and FDI.

4.2.1. Urbanization and Population Growth

The continuous advancement and deepening of urbanization in Vietnam have driven the employment transfer direction. The continuous migration of rural populations from inland to coastal areas has resulted in the coastal region of Vietnam becoming one of the most densely populated areas in Southeast Asia. According to official data, Vietnam had a population of almost 97 million in 2019, of which about 50% was concentrated alongside the coastal area [65]. The fast boom of the population alongside the coastal area accelerated the development of housing construction, trade, and service industries, which in turn further promoted the process of urbanization. The promotion of urbanization required more urban areas and advanced infrastructure construction, which improved the rigid demand for construction land and further promoted the scope and speed of construction land expansion. Among them, the migration of rural surplus labor to big cities led to an increasing population in big cities, expanding the areal extent of construction land around big cities such as Hejing, Qinghe, and Haiphong City (Figure 8).

4.2.2. Industrialization and Industrial Park Construction

After the implementation of reforms and opening up in 1986, Vietnam’s industrialization made high-quality progress, and the percentage of secondary industries continues to rise. With the development of industry, industrial parks, as the main space carrier for the development of industrialization, play an increasingly indispensable role in the economic and social development of Vietnam [66]. In 1992, the first industrial area was built. After 20 years of development, by the end of 2018, Vietnam had established 326 industrial parks (Figure 9). With the continuous development of industrialization and the massive construction of coastal industrial parks, the demand for construction land is continuously increasing, which efficaciously promotes the expansion of coastal construction land. Currently, the total area of natural land cover is about 93,000 ha, which includes 64,000 ha of industrial land, accounting for about 68% of the total land area. Additionally, 250 parks had been established, with a land area of 68,000 ha. The remaining 76 parks, with a land area of 25,000 ha, are undergoing land acquisition, compensation, and infrastructure construction.

4.2.3. Globalization and Foreign Direct Investment

The socioeconomic development of Vietnam is largely influenced by globalization. The impact of globalization on Vietnam is reflected in FDI. Since 2013, Vietnam has attracted many FDI projects, with actual foreign investment reaching USD 20.38 billion in 2019. FDI, as the impact factor of the interaction between socioeconomic factors and land use, mainly affects the process of urbanization and industrialization, thus affecting land use change. Foreign investors have created many employment opportunities, directly or indirectly, by creating investments, mergers, and acquisitions. These employment opportunities promote population migration from rural to urban areas, thus promoting the development of urbanization. With the development of urbanization, construction land has been expanding from the city centers to the peripheral areas, which presents the scale expansion effect in space. Furthermore, because the technical level of foreign inventors is significantly higher than that of local enterprises, advanced production technology and management concepts have been introduced into Vietnam. Through industrial management, it will have a technology spillover effect on upstream and downstream industries, which will promote local technological progress. The advancement of technical levels directly affects the industrialization process in Vietnam. With the development of industrialization, various industrial parks have begun to rise and develop, which directly affects the transformation of land use types and promotes the expansion of construction land (Figure 10).

5. Discussion

This section further discusses the mechanism of different factors on the spatial differentiation of land use in the coastal areas of Vietnam. First, we found that land use in the coastal areas of Vietnam showed a trend of continuous and rapid increase in construction land and had observable spatial variation characteristics. This observation was comparable to the existing research on hot spots with rapid development [67,68,69,70]. Specifically, the research results of this study are similar to these of other regions in Vietnam, other countries in Southeast Asia, the South China Sea, and Asia. These areas are the same as Vietnam in terms of geographical position and stage of economic development, so they also exhibit comparable development trends in land use change. The identification results of impact factors exhibit that the joint actions of urbanization, industrialization, and globalization have led to the rapid growth of construction land in the study area, which is consistent with the present research, though the influence of common human factors such as population and location factors on land use has been widely validated [71,72,73,74,75,76,77]. However, this study especially examined the influence mechanism of human factors closely associated to globalization, urbanization, and industrialization, and analyzed the coupling mechanisms among them.
Second, coastal zones face serious land shortages. Land reclamation has become more frequent because of its low price and ease of use. As the primary approach of utilising the sea, land reclamation is prevalent in many countries, such as the Netherlands, Japan, and South Korea [78,79,80]. Countries in Southeast Asia also have a long record of land reclamation. Since the 1950s, Malaysia has been conducting coastal reclamation projects, most of which are concentrated alongside the coast of the Malacca Strait [81,82]. In Vietnam, Thailand, and Indonesia, large areas of mangroves have been used for aquaculture [83,84]. Concurrently, land reclamation has caused serious environmental problems, such as the reduction in fishery resources, sediment deposition, and wetland degradation [85].
Third, through our field investigation, we also found that the expansion of construction land caused by reclamation along the coastal area was remarkable. Moderate reclamation can give the marine environment time to recover and can generate economic profits. However, sea reclamation at a fast pace may cause serious damage to the environment [70].

6. Conclusions and Policy Implications

6.1. Conclusions

Taking the coastal province of Vietnam as the study area, we used data from the years 2000, 2010, and 2020 in the GlobeLand30 2020 database to analyze the spatial variations of land use change and its impact factors in coastal areas in Vietnam. The main conclusions are as follows.
(1) Overall, from 2000 to 2020, the area of construction land accelerated continually along with the proportion of construction land. The proportion of construction land multiplied from 2.72% in 2000 to 4.40% in 2020. Natural landscape areas, such as grasslands, forests, and wetlands, decreased. Construction land in each subregion showed a substantial growth trend, with the biggest areal increase in construction land in the Central Coastal Area. In addition, farmland trends varied by region.
(2) There are also observable spatial variation characteristics in the study area, which are embodied in three aspects. First, the rate of land use change in each subregion was remarkably distinct, with the greatest rate of change in the Central Coastal Area. Second, land development intensity differed significantly by region, with the highest development intensity in the Mekong River Delta. Third, the distribution characteristics of each subregion also varied.
(3) To further explore the factors impacting land use evolution, we chose 15 factors closely related to the spatial evolution of coastal construction land and constructed a random forest model. The research results confirmed that the major factors were FDI, industrialization index, and population. Finally, we analyzed the mechanisms influencing the above factors from the views of urbanization and population growth, industrialization and park construction, globalization, and FDI, which can properly describe the relationships between the impact factors and the spatial variations of land use.

6.2. Policy Implications

Therefore, we propose the following countermeasures. First, appropriate legislation must be formulated to promote coastal development and protection. At present, there are some issues in Vietnam’s governance, such as doubtful obligations and low coordination efficiency of local regulation enforcement agencies, which lead to the restriction of administrative power and the concentration of power in the comprehensive governance of coastal areas. In the future, the management of coastal areas must be supported by corresponding legal systems. Therefore, governments need to formulate a sound coastal management law system to supervise and regulate the resource development activities in coastal areas [86].
Second, the ecological cost compensation mechanism for land reclamation should be improved. This means that it is necessary to formulate legal guidelines and regulations related to ecological compensation for land reclamation, recuperation, governance, deposits for reclamation, and the ecological environment financial system. In addition, government departments should also formulate corresponding ecological environment construction funds to continuously improve and optimize the current financial expenditure structure to further increase the ecological compensation for land reclamation [87,88].
Third, the supervision of land reclamation should be strengthened. Relevant departments should work out a strict tracking and monitoring system for land reclamation projects and effectively monitor the implementation process of the entire project through spot checks. Concurrently, the relevant government departments should constantly improve the contents of the management system to provide a good guarantee of the rationality of the entire workflow of land reclamation.
Section Note:
1. Data sources: GlobeLand30 2020 (http://218.244.250.80/ accessed on 5 September 2022)
2. Data sources: World Pop (https://www.worldpop.org/ accessed on 5 September 2022)
3. Data sources: Natural Earth. Free vector and raster map data @ naturalearthdata.com.
4. Data sources: Open street map (https://www.openstreetmap.org/ accessed on 5 September 2022)
5. Data sources: Open Development Vietnam (https://vietnam.opendevelopmentmekong.net/ accessed on 5 September 2022)

Author Contributions

Conceptualization, Y.L.; methodology, J.Z.; validation, J.Z. and S.L.; formal analysis, Y.L.; investigation, Y.L.; resources, Y.L. and J.Z.; data curation, J.Z. and S.L.; writing—original draft preparation, Y.L. and J.Z.; writing—review and editing, Y.L. and S.L.; visualization, J.Z. and S.L.; supervision, Y.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Natural Science Foundation of China, grant numbers 41871114 and 42271180.

Data Availability Statement

Not applicable.

Acknowledgments

We appreciate the insightful comments and suggestions of the editor and reviewers.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Foley, J.A.; DeFries, R.; Asner, G.P.; Barford, C.; Bonan, G.; Carpenter, S.R.; Chapin, F.S.; Coe, M.T.; Daily, G.C.; Gibbs, H.K.; et al. Global consequences of land use. Science 2005, 309, 570–574. [Google Scholar] [CrossRef] [Green Version]
  2. Reid, W.V.; Chen, D.; Goldfarb, L.; Hackmann, H.; Lee, Y.T.; Mokhele, K.; Ostrom, E.; Raivio, K.; Rockström, J.; Schellnhuber, H.J. Earth system science for global sustainability: Grand challenges. Science 2010, 330, 916–917. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Liu, R.; Gao, M.; Xie, F. Simulation and Analysis of Land Use Patterns Based on Logistic Regression Model—A Case Study of Turpan. Res. Soil Water Conserv. 2009, 16, 74–78. [Google Scholar]
  4. Ma, W.; Zhang, Y.; Shi, P.; Xing, Q. Review of research on land use and land cover change in coastal zone. Prog. Geogr. 2008, 27, 87–94. [Google Scholar]
  5. De Sherbinin, A.; Levy, M.; Adamo, S.; Macmanus, K.; Yetman, G.; Mara, V.; Razafindrazay, L.; Goodrich, B.; Srebotnjak, T.; Aichele, C. Migration and risk: Net migration in marginal ecosystems and hazardous areas. Environ. Res. Lett. 2012, 7, 189–190. [Google Scholar] [CrossRef] [Green Version]
  6. Yin, X.; Li, P.; Feng, Z.; You, Z.; Xiao, C. Characteristics of spatio-temporal differences and evolution types of Vietnamese population from 2000 to 2019. World Reg. Stud. 2022, 31, 941–952. [Google Scholar]
  7. Di, X.; Hou, X.; Wang, Y.; Wu, L. Spatial-temporal characteristics of land use intensity of coastal zone in China during 2000–2010. Chin. Geogr. Sci. 2015, 25, 51–61. [Google Scholar] [CrossRef]
  8. Mcgranahan, G.; Balk, D.; Anderson, B. The rising tide: Assessing the risks of climate change and human settlements in low elevation coastal zones. Environ. Urban 2007, 19, 17–37. [Google Scholar] [CrossRef]
  9. Pascual-Aguilar, J.; Andreu, V.; Gimeno-Garcia, E.; Pico, Y. Current anthropogenic pressures on agro-ecological protected coastal wetlands. Sci. Total Environ. 2015, 503, 190–199. [Google Scholar] [CrossRef] [PubMed]
  10. Zone, L.; Kremer, H.H.; Tissier, M.; Burbridge, P.R.; Talaue-Mcmanus, L.; Rabalais, N.N.; Parslow, J.; Crossland, C.J.; Young, B. Land-ocean interactions in the coastal zone: Science plan and implementation strategy. Environ. Policy Collect. 2005, 20, 1262–1268. [Google Scholar]
  11. Nguyen, H.H. The relation of coastal mangrove changes and adjacent land-use: A review in southeast asia and Kien Giang, Vietnam. Ocean Coast. Manag. 2014, 90, 1–10. [Google Scholar] [CrossRef]
  12. Thu, P.M.; Populus, J. Status and changes of mangrove forest in Mekong Delta: Case study in Tra Vinh, Vietnam. Estuar. Coast. Shelf Sci. 2007, 71, 98–109. [Google Scholar] [CrossRef]
  13. Liang, Y.; Li, S.; Zeng, J.; Wu, T. Examining the impact of multidimensional accessibility on regional poverty in Laos. Appl. Geogr. 2022, 148, 102789. [Google Scholar] [CrossRef]
  14. Yu, X.; Yang, G. The advances and problems of land use and land cover change research in China. Prog. Geogr. 2002, 21, 51–57. [Google Scholar]
  15. Hadley, D. Land use and the coastal zone. Land Use Policy 2009, 26, 198–203. [Google Scholar] [CrossRef]
  16. Kyhk, N.; Fagerholm, N.; Asseid, B.S.; Mzee, A.J. Dynamic land use and land cover changes and their effect on forest resources in a coastal village of Matemwe, Zanzibar, Tanzania. Land Use Policy 2011, 28, 26–37. [Google Scholar] [CrossRef]
  17. Mattheus, C.R.; Rodriguez, A.B.; Mckee, B.; Currin, C.A. Impact of land-use change and hard structures on the evolution of fringing marsh shorelines. Estuar. Coast. Shelf Sci. 2010, 88, 365–376. [Google Scholar] [CrossRef]
  18. Teixido, A.L.; Quintanilla, L.G.; Carreno, F.; Gutierrez, D. Impacts of changes in land use and fragmentation patterns on Atlantic coastal forests in northern Spain. J. Environ. Manag. 2010, 91, 879–886. [Google Scholar] [CrossRef] [PubMed]
  19. Han, L.; Hou, X.; Zhu, M.; Yu, N.; Gao, M. Study on the temporal-spatial characters of land-use change in the costal zone of America in the latter half of the 20th century. World Reg. Stud. 2010, 19, 42–52. [Google Scholar]
  20. Muttitanon, W.; Tripathi, N.K. Land use/land cover changes in the coastal zone of Ban Don Bay, Thailand using Landsat 5 TM data. Int. J. Remote Sens. 2005, 26, 2311–2323. [Google Scholar] [CrossRef]
  21. Ou, W.; Yang, G.; Li, H.; Yu, X. Spatio-temporal variation and driving forces of landscape patterns in the coastal zone of Yancheng, Jiangsu. Sci. Geogr. Sin. 2004, 24, 610–615. [Google Scholar]
  22. Wang, F.; Zhou, B.; Jiang, Z. Land use/cover change study of coastal area in Zhejiang Province in the past decade by remote sensing monitoring. Bull. Sci. Technol. 2007, 23, 332–336+341. [Google Scholar]
  23. Zuo, L.; Xu, J.; Zhang, Z.; Wen, Q.; Liu, B.; Zhao, X.; Yi, L. Spatial-temporal land use change and landscape response in Bohai Sea coastal zone area. J. Remote Sens. 2011, 15, 604–620. [Google Scholar]
  24. Ellis, E.A.; Navarro-Martínez, A.; García-Ortega, M. Drivers of forest cover transitions in the Selva Maya, Mexico: Integrating regional and community scales for landscape assessment. Land Degrad. Dev. 2021, 32, 3122–3141. [Google Scholar] [CrossRef]
  25. Hepcan, C.C.; Turan, I.A.; OZkan, M.B. Monitoring land use change in the eme coastal zone, Turkey using aerial photographs and satellite imaging. Land Degrad. Dev. 2011, 22, 326–333. [Google Scholar] [CrossRef]
  26. Shalaby, A.; Tateishi, R. Remote sensing and GIS for mapping and monitoring land cover and land-use changes in the Northwestern coastal zone of Egypt. Appl. Geogr. 2007, 27, 28–41. [Google Scholar] [CrossRef]
  27. Shahbudin, S.; Zuhairi, A.; Kamaruzzaman, B.Y. Impact of coastal development on mangrove cover in Kilim river, Langkawi Island, Malaysia. J. For. Res. 2012, 23, 185–190. [Google Scholar] [CrossRef]
  28. Liang, Y.; Zeng, J.; Sun, W.; Zhou, K.; Zhou, Z. Expansion of construction land along the motorway in rapidly developing areas in Cambodia. Land Use Policy 2021, 109, 105691. [Google Scholar] [CrossRef]
  29. Veldkamp, A.; Fresco, L.O. Reconstructing land use drivers and their spatial scale dependence for Costa Rica (1973 and 1984). Agric. Syst. 1997, 55, 19–43. [Google Scholar] [CrossRef]
  30. Gao, Y.; Su, F.; Sun, X.; Yang, X.; Xue, Z.; Zhang, D. A study on driving forces of land use change of Guangdong Province coastal zone and islands in recent 20 a. Acta Oceanol. Sin. 2011, 33, 95–103. [Google Scholar]
  31. Tan, M.; Li, X.; Hui, X.; Lu, C. Urban land expansion and arable land loss in China—A case study of Beijing-Tianjin-Hebei region. Land Use Policy 2005, 22, 187–196. [Google Scholar] [CrossRef]
  32. Azadi, H.; Ho, P.; Hasfiati, L. Agricultural land conversion drivers: A comparison between less developed, developing and developed countries. Land Degrad. Dev. 2011, 22, 596–604. [Google Scholar] [CrossRef]
  33. Murali, R.M.; Kumar, P. Implications of sea level rise scenarios on land use /land cover classes of the coastal zones of Cochin, India. J. Environ. Manag. 2015, 148, 124–133. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Firman, T. Rural to urban land conversion in Indonesia during boom and bust periods. Land Use Policy 2000, 17, 13–20. [Google Scholar] [CrossRef]
  35. Fazal, S. The need for preserving farmland. A case study from a predominantly agrarian economy (India). Landsc. Urban Plan. 2001, 55, 1–13. [Google Scholar] [CrossRef]
  36. Jayanthi, M.; Duraisamy, M.; Thirumurthy, S.; Samynathan, M.; Muralidhar, M. Dynamics of land-use changes and their future trends using spatial analysis and the CA-Markov model—A case-study with a special emphasis on aquaculture development in India. Land Degrad. Dev. 2021, 32, 2563–2579. [Google Scholar] [CrossRef]
  37. Hu, X.; Xu, L.; Zhang, S. Land use pattern of Dalian City, Liaoning Province of northeast China based on CA-Markov model and multi-objective optimization. Chin. J. Appl. Ecol. 2013, 24, 1652–1660. [Google Scholar]
  38. Li, X.; Xu, L.; Jang, E. Multi-objective optimization of land use structure in western Jilin province based on system dynamics. Trans. Chin. Soc. Agric. Eng. (Trans. CSAE) 2013, 29, 247–254+294. [Google Scholar]
  39. Rindfuss, R.R.; Walsh, S.J.; Turner, B.L.; Fox, J.; Mishra, V. Developing a science of land change: Challenges and methodological issues. Proc. Natl. Acad. Sci. USA 2004, 101, 13976–13981. [Google Scholar] [CrossRef] [Green Version]
  40. Meiyappan, P.; Dalton, M.; O’Neill, B.C.; Jain, A.K. Spatial modeling of agricultural land use change at global scale. Ecol. Model. 2014, 291, 152–174. [Google Scholar] [CrossRef] [Green Version]
  41. Liu, K.; Li, Y.; Wu, Q.; Shen, J. Driving force analysis of land use change in the developed area based on Probit regression model: A case study of Nanjing City, China. Chin. J. Appl. Ecol. 2015, 26, 2131–2138. [Google Scholar]
  42. Carella, E.; Orusa, T.; Viani, A.; Meloni, D.; Borgogno-Mondino, E.; Orusa, R. An integrated, tentative remote-sensing approach based on NDVI entropy to model canine distemper virus in wildlife and to prompt science-based management policies. Animals 2022, 12, 1049. [Google Scholar] [CrossRef] [PubMed]
  43. Meiyappan, P.; Roy, P.S.; Sharma, Y.; Ramachandran, R.M.; Joshi, P.K.; Defries, R.S.; Jain, A.K. Dynamics and determinants of land change in India: Integrating satellite data with village socioeconomics. Reg. Environ. Chang. 2017, 17, 753–766. [Google Scholar] [CrossRef] [PubMed]
  44. Kong, X.; Wang, K.; Chen, H.; Zhang, J.; Zhang, M.Y. Canonical Correspondence Analysis of land-use change and socio-economic development in Hechi Prefecture, Guangxi Province. J. Nat. Resour. 2007, 22, 131–140. [Google Scholar]
  45. Wang, P.; Song, G. Land use pattern change and influential factors analysis of Songnen Plain in 1979–2015. Trans. Chin. Soc. Agric. Eng. 2018, 34, 256–264. [Google Scholar]
  46. Li, L.; Liu, X.; Ou, J.; Niu, N. Spatio-temporal changes and mechanism analysis of urban 3D expansion based on random forest model. Geogr. Geo-Inf. Sci. 2019, 35, 53–60. [Google Scholar]
  47. Chen, K.; Liu, K.; Liu, L.; Zhu, Y. Urban expansion simulation by random-forest-based cellular automata: A case study of Foshan City. Prog. Geogr. 2015, 34, 937–946. [Google Scholar]
  48. Tufekcioglu, A.; Tufekcioglu, T.; Gezer, E.D.; Sariyildiz, T. Land-use effect on distributions of as, cr and cu in soils adjacent to cca-treated utility poles in artvin and rize, Turkey. Fresenius Environ. Bull. 2009, 18, 571–577. [Google Scholar]
  49. Chai, L.; Ma, C.; Xu, G.; Liu, M.; Xu, Y. Energetic and exergetic performances simulating of GSHP greenhouse cooling system in Northern China. IFIP AICT 2012, 370, 74–84. [Google Scholar]
  50. Zhuang, D.; Liu, J. Study on the model of regional differentiation of land use degree in China. J. Nat. Resour. 1997, 12, 10–16. [Google Scholar]
  51. Wang, X.; Bao, Y. Study on the methods of land use dynamic change research. Prog. Geogr. 1999, 18, 83–89. [Google Scholar]
  52. Liu, J. Macro Survey and Dynamic Research on Remote Sensing of Resources and Environment in China; Press of University of Science and Technology of China: Hefei, China, 1996. [Google Scholar]
  53. Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
  54. Lujan-Moreno, G.A.; Howard, P.R.; Rojas, O.G.; Montgomery, D.C. Design of experiments and response surface methodology to tune machine learning hyperparameters, with a random forest case-study. Expert Syst. Appl. 2018, 109, 195–205. [Google Scholar] [CrossRef]
  55. Kandaswamy, K.K.; Chou, K.C.; Martinetz, T.; Möller, S.; Suganthan, P.N.; Sridharan, S.; Pugalenthi, G. AFP-Pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties. J. Theor. Biol. 2011, 270, 56–62. [Google Scholar] [CrossRef] [PubMed]
  56. Zhu, J.; Cui, B.; Yao, H.; Dong, S. Landuse changes and expansion effects caused by road construction in longitudinal range-gorge region. J. Nat. Resour. 2006, 4, 507–515. [Google Scholar]
  57. Ren, X.; Lin, X.; Zhang, H.; Zhang, W. The spatial evaluation of urban residential suitability in Dalian. Geograp HICAL Res. 2008, 27, 683–692. [Google Scholar]
  58. Ye, Y.; Zhang, H.; Liu, K.; Wu, Q. Impact of site factors on expansion of construction land: A case study in the Pearl River Delta. Prog. Geogr. 2010, 29, 1433–1441. [Google Scholar]
  59. Zhang, W.; Wang, C.; Xue, D. Urbanization base of city-land use expansion in the Zhujiang River Delta. J. Nat. Resour. 2003, 18, 575–582. [Google Scholar]
  60. Liang, C.; Wang, N. Driving factor analysis of construction land changes in coastal economic zone based on logistic regression: A case study of Dalian new urban. Sci. Geogr. Sin. 2014, 34, 556–562. [Google Scholar]
  61. Hauser, L.T.; Nguyen Vu, G.; Nguyen, B.A.; Dade, E.; Nguyen, H.M.; Nguyen, T.T.Q.; Le, T.Q.; Vu, L.H.; Tong, A.T.H.; Pham, H.V. Uncovering the spatio-temporal dynamics of land cover change and fragmentation of mangroves in the Ca Mau peninsula, Vietnam using multi-temporal SPOT satellite imagery (2004–2013). Appl. Geogr. 2017, 86, 197–207. [Google Scholar] [CrossRef]
  62. Tran, T.V.; Tran, D.X.; Nguyen, H.; Latorre-Carmona, P.; Myint, S.W. Characterising spatiotemporal vegetation variations using LANDSAT time-series and Hurst exponent index in the Mekong River Delta. Land Degrad. Dev. 2021, 32, 3507–3523. [Google Scholar] [CrossRef]
  63. Van, T.T.; Wilson, N.; Thanh-Tung, H.; Quisthoudt, K.; Quang-Minh, V.; Xuan-Tuan, L.; Dahdouh-Guebas, F.; Koedam, N. Changes in mangrove vegetation area and character in a war and land use change affected region of Vietnam (Mui Ca Mau) over six decades. Acta Oecologica 2015, 63, 71–81. [Google Scholar] [CrossRef] [Green Version]
  64. Yoon, H.H.; Chun, S.S.; Hong, S.H. Rapid change in sedimentary facies from wave-to tide-dominated Macrotidal Flat in the Sinduri Bay, West Coast of Korea. J. Coast. Res. 2020, 95, 728–732. [Google Scholar] [CrossRef]
  65. Petrişor, A.I.; Hamma, W.; Nguyen, H.D.; Randazzo, G.; Muzirafuti, A.; Stan, M.I.; Ianoş, I. Degradation of coastlines under the pressure of urbanization and tourism: Evidence on the change of land systems from Europe, Asia and Africa. Land 2020, 9, 275. [Google Scholar] [CrossRef]
  66. Hu, X.; Wang, X.; Zhao, S. Spatial pattern and industrial development characteristics of the industrial zone in Vietnam. Trop. Geogr. 2019, 39, 889–900. [Google Scholar]
  67. Lam-Dao, N.; Pham-Bach, V.; Nguyen-Thanh, M.; Pham-Thi, M.-T.; Hoang-phi, P. Change detection of land use and riverbank in Mekong Delta, Vietnam using time series remotely sensed data. J. Resour. Ecol. 2011, 2, 370–374. [Google Scholar]
  68. Nguyen, H.H.; McAlpine, C.; Pullar, D.; Leisz, S.J.; Galina, G. Drivers of coastal shoreline change: Case study of Hon Dat Coast, Kien Giang, Vietnam. Environ. Manag. 2015, 55, 1093–1108. [Google Scholar] [CrossRef] [PubMed]
  69. Nguyen, T.P.; Parnell, K.E. Coastal land use planning in Ben Tre, Vietnam: Constraints and recommendations. Heliyon 2019, 5, e01487. [Google Scholar] [CrossRef] [PubMed]
  70. Zhang, J.; Su, F.; Ding, Z. Sea reclamation status of countries around the South China sea from 1975 to 2010. Sustainability 2017, 9, 878. [Google Scholar] [CrossRef] [Green Version]
  71. Du, G.; Kuang, W.; Meng, F. Spatiotemporal pattern and driving forces of land use/cover change in Brazil. Prog. Geogr. 2015, 34, 73–82. [Google Scholar]
  72. Li, Q.; Lu, R.; Li, G. Research progress on regional land-use change along national border and its interactive relationship with frontier trade development. China Land Sci. 2015, 29, 16–23. [Google Scholar]
  73. Zhang, H.; Deng, W.; Wang, Y. Analyses on the spatio-temporal evolution and driving factors of land use in India during 2000—2020. World Reg. Stud. 2022, 31, 786–799. [Google Scholar]
  74. Oueslati, W.; Alvanides, S.; Garrod, G. Determinants of urban sprawl in European cities. Work. Pap. 2015, 52, 1594–1614. [Google Scholar] [CrossRef] [Green Version]
  75. Liang, Y.; Zeng, J.; Kuik, C.C.; Zhou, Z.; Zhou, K. Policy transfer and scale reconstruction of China’s overseas industrial parks: A case study of the Malaysia-China Kuantan Industrial Park. J. Geogr. Sci. 2021, 31, 733–746. [Google Scholar] [CrossRef]
  76. Weilenmann, B.; Seidl, I.; Schulz, T. The socio-economic determinants of urban sprawl between 1980 and 2010 in Switzerland. Landsc. Urban Plan. 2017, 157, 468–482. [Google Scholar] [CrossRef]
  77. Zheng, R.; Dong, Y.; Chen, S. Comparative study on the change of land use between Mainland and Taiwan: A case study of Quanzhou and Gaoxiong City. World Reg. Stud. 2005, 14, 70–77. [Google Scholar]
  78. Fang, S.; Jia, X.; Qian, Q.; Cui, J.; Cagle, G.; Hou, A. Reclamation history and development intensity determine soil and vegetation characteristics on developed coasts. Sci. Total Environ. 2017, 586, 1263–1271. [Google Scholar] [CrossRef]
  79. Hoeksema, R.J. Three stages in the history of land reclamation in the Netherlands. Irrig. Drain. 2007, 56, S113–S126. [Google Scholar] [CrossRef]
  80. Kawagoe, T. Agricultural Land Reform in Postwar Japan: Experiences and Issues; World Bank Publications: Tokyo, Japan, 1999; Volume 2111. [Google Scholar]
  81. Ghazali, N.H.M. Coastal erosion and reclamation in Malaysia. Aquat. Ecosyst. Health Manag. 2006, 9, 37–247. [Google Scholar] [CrossRef]
  82. Ishak, N.S.A.; Yusup, M.; Arshad, A.F.; Abdullah, Y.A. Review on the significance of local plan for coastal reclamation development: The case of Malacca, Malaysia, in: MATEC Web of Conferences. EDP Sci. 2016, 66, 50. [Google Scholar]
  83. Giri, C.; Zhu, Z.; Tieszen, L.L.; Singh, A.; Gillette, S.; Kelmelis, J.A. Mangrove forest distributions and dynamics (19752005) of the tsunami-affected region of Asia. J. Biogeogr. 2008, 35, 519–528. [Google Scholar] [CrossRef]
  84. Nurkin, B. Degradation of mangrove forests in South Sulawesi, Indonesia. Hydrobiologia 1994, 285, 271–276. [Google Scholar] [CrossRef]
  85. Jickells, T.D.; Andrews, J.E.; Parkes, D.J. Direct and indirect effects of estuarine reclamation on nutrient and metal fluxes in the global coastal zone. Aquat. Geochem. 2016, 22, 337–348. [Google Scholar] [CrossRef]
  86. Fan, J.; Wang, Q.; Wang, Y.; Chen, D.; Zhou, K. Assessment of coastal development policy based on simulating a sustainable land-use scenario for Liaoning Coastal Zone in China. Land Degrad. Dev. 2018, 29, 2390–2402. [Google Scholar] [CrossRef]
  87. Parnwell, M.J.G. Eco-localism and the shaping of sustainable social and natural environments in North-East Thailand. Land Degrad. Dev. 2006, 17, 183–195. [Google Scholar] [CrossRef]
  88. Zhang, X.; Cao, Z.; Zhang, R.; Han, Y.; Li, X. Evaluation and prediction of water resource carrying capacity in eco-economic zone of Yellow River Delta. World Reg. Stud. 2022, 31, 549–560. [Google Scholar]
Land 11 01751 g001 550
Figure 1. The location map of the study area.
Figure 1. The location map of the study area.
Land 11 01751 g001
Land 11 01751 g002 550
Figure 2. Land use change of study area.
Figure 2. Land use change of study area.
Land 11 01751 g002
Land 11 01751 g003a 550Land 11 01751 g003b 550
Figure 3. The land use change of subregions during 2000–2020 ((A): Red River Delta; (B): Central coastal area; (C): Mekong River Delta;).
Figure 3. The land use change of subregions during 2000–2020 ((A): Red River Delta; (B): Central coastal area; (C): Mekong River Delta;).
Land 11 01751 g003aLand 11 01751 g003b
Land 11 01751 g004 550
Figure 4. The land use dynamic degree of subregions.
Figure 4. The land use dynamic degree of subregions.
Land 11 01751 g004
Land 11 01751 g005 550
Figure 5. The development intensity of subregions (a) and changes in the proportion of construction land to the total area (b).
Figure 5. The development intensity of subregions (a) and changes in the proportion of construction land to the total area (b).
Land 11 01751 g005
Land 11 01751 g006 550
Figure 6. The land use change of subregions in 2000–2020.
Figure 6. The land use change of subregions in 2000–2020.
Land 11 01751 g006
Land 11 01751 g007 550
Figure 7. The importance of driving factors from 2000 to 2020.
Figure 7. The importance of driving factors from 2000 to 2020.
Land 11 01751 g007
Land 11 01751 g008 550
Figure 8. Relationship between population growth and expansion of construction land.
Figure 8. Relationship between population growth and expansion of construction land.
Land 11 01751 g008
Land 11 01751 g009 550
Figure 9. Distribution of coastal industrial parks.
Figure 9. Distribution of coastal industrial parks.
Land 11 01751 g009
Land 11 01751 g010 550
Figure 10. Coupling mechanism of globalization, urbanization and industrialization.
Figure 10. Coupling mechanism of globalization, urbanization and industrialization.
Land 11 01751 g010
Table
Table 1. Land use/cover classification system transformation.
Table 1. Land use/cover classification system transformation.
CodeLandcover ClassCodeLandcover Class
10Farmland50Wetland
20Forest60Water bodies
30Grassland80Construction land
40Shrubland90Bare land
Table
Table 2. Land-use status of the study area in 2000, 2010, and 2020 (hm2).
Table 2. Land-use status of the study area in 2000, 2010, and 2020 (hm2).
Type200020102020
Farmland6,317,896.56,471,823.686,339,347.64
Forest5,904,898.925,934,586.235,887,351.26
Grassland1,204,677.991,031,873.851,002,266.46
Shrubland26,969.9429,186.126,731.26
Wetland195,389.55186,418.53162,846.72
Water bodies337,717.8287,861.85315,380.07
Construction land392,150.07427,449.78633,923.37
Bare land22,536.922,541.3133,981.12
Farmland6,317,896.56,471,823.686,339,347.64
Table
Table 3. Impact factors index system.
Table 3. Impact factors index system.
VariableType
Dependent variableChanges construction land (2000–2010)0 or 1
Changes construction land (2010–2020)0 or 1
Natural backgroundElevationContinuous
SlopeContinuous
Geographical factorDistance to coastlineContinuous
Distance to the riverContinuous
Distance to the major roadContinuous
Distance to the waterwayContinuous
Distance to the railwayContinuous
Human factorsDistance to the city centerContinuous
Distance to the townshipContinuous
Distance to the industrial parkContinuous
Provincial industrialization indexContinuous
Provincial foreign direct investment dataContinuous
PopulationContinuous
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Liang, Y.; Zeng, J.; Li, S. Examining the Spatial Variations of Land Use Change and Its Impact Factors in a Coastal Area in Vietnam. Land 2022, 11, 1751. https://doi.org/10.3390/land11101751

AMA Style

Liang Y, Zeng J, Li S. Examining the Spatial Variations of Land Use Change and Its Impact Factors in a Coastal Area in Vietnam. Land. 2022; 11(10):1751. https://doi.org/10.3390/land11101751

Chicago/Turabian Style

Liang, Yutian, Jiaqi Zeng, and Shangqian Li. 2022. "Examining the Spatial Variations of Land Use Change and Its Impact Factors in a Coastal Area in Vietnam" Land 11, no. 10: 1751. https://doi.org/10.3390/land11101751

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop