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Article

Spatial-Temporal Evolution and Driving Forces of Cultivated Land Based on the PLUS Model: A Case Study of Haikou City, 1980–2020

by
Xiaofu Lin
and
Hui Fu
*
College of Forestry, Hainan University, Haikou 570228, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(21), 14284; https://doi.org/10.3390/su142114284
Submission received: 26 September 2022 / Revised: 19 October 2022 / Accepted: 28 October 2022 / Published: 1 November 2022
(This article belongs to the Special Issue Agricultural Land Change and Soil Degradation)
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Abstract

:
The security of cultivated land is the foundation for stable social and economic development. In recent years, with rapid economic development, urbanization around the world has been accelerating. The demand for urban construction expansion is increasing day by day and lands used for cultivation are being infiltrated by construction, posing a serious threat to food security. This study used the land-use data from Haikou City in 1980, 2000, 2010 and 2020, to generate a transfer matrix, kernel density analysis and landscape pattern index to analyze the spatial-temporal evolution of cultivated land in Haikou. The PLUS model was used to explore potential factors driving land-use evolution. Results show that cultivated land in Haikou was continuously lost from 1980 to 2020 and the area of cultivated land decreased by 7020.58 ha. Loss was most significant during 2010–2020 when cultivated land ascended into construction land in the northern region of the city. Spatial distribution of cultivated land in Haikou is generally characterized by “dense in the southwest and sparse in the northwest”, and the spatial density of cultivated land in the northwest continuously decreased from 1980 to 2020. In the past 40 years, the degree of spatial aggregation for cultivated land in Haikou has decreased and the degree of fragmentation has increased. The primary factors driving changes in spatial-temporal patterns over the past 40 years has been the distance from roads and high-speed railways and the distance from water. During the 40-year timespan, Haikou continued to lose cultivated land areas due to the interaction of social and natural factors such as road traffic and water resources. The juxtaposition between the demand for urban construction lands and the protection of cultivated land has become increasingly evident. Due to the threat cultivated lands are facing in the northern area of Haikou, we suggest future expansion of construction development land should be strictly controlled.

1. Introduction

Food security is a pillar for stable national operations and healthy social development. Ensuring the security of food production is a key issue for the sustainable development of both developing and developed countries [1,2]. Cultivated lands are the main source of food production and the security of cultivated land parallels the security of a country’s food. Due to the large population of China, cultivated land is the key to sustainable development [3,4]. Under accelerated urbanization, the cultivated land of China has undergone changes in the utility of cultivated land-use. The continued fragmentation and decrease of cultivated land have long drawn the attention of the Chinese government. Although a series of cultivated land protection policies have been created since 1978, the protection of cultivated land has still had little effect due to the increased demand for urbanization which has led to the poor management of cultivated land [5,6]. Cultivated land loss is a serious threat to modern society with rapid economic development. Many scholars have carried out a series of studies on cultivated land at various spatial scales in the last few years, including national [7,8,9], provincial [10,11], municipal [12,13] and county scales [14].
Urbanization is often perceived as a sign of regional development and progress. However, the rapid expansion of urban areas also accelerates the loss of cultivated land, leading to a series of problems such as the decline of multiple cropping index (MCI) and ecosystem degradation [15,16]. Revealing the spatial-temporal evolution pattern and driving mechanism of regional cultivated land under the growing pressure of urbanization is important for the generation of cultivated land protection policies and realizing regional sustainable development [17,18]. Many scholars have explored the spatial-temporal patterns variation of cultivated land over specific timescales [19,20,21,22] and often employed the geo-detector model [23,24,25], logistic regression model [26], spatial autoregressive model [27], and other relevant models to further explore the driving factors behind the evolution of cultivated land. The patch-generating land-use simulation (PLUS) model, developed on the basis of the FLUS model, is more advantageous in uncovering the driving forces of land change [28]. The LEAS module in the PLUS model uses the random forest classification algorithm to explore the relationship between each land-use type and multiple driving factors. The algorithm can effectively deal with the spatial autocorrelation and multicollinearity of influencing factors and can also better explain the nonlinear relationship between land use type change and potential driving factors. Therefore, application of the PLUS model to explore factors driving the dynamic change of cultivated land aids in the clarification of changes in cultivated land. Furthermore, this model provides an accurate reference and supports exploration of a sustainable development mode of cultivated land use.
In 2013, China put forward the cooperation initiative of building the New Silk Road Economic Belt and the 21st Century Maritime Silk Road. The Belt and Road is short for the Silk Road Economic Belt and the 21st Century Maritime Silk Road. According to the research of Chen et al. [29], in “Belt and Road Initiative (BRI)” countries, reduction of cultivated land was mainly concentrated in China. As China’s southernmost province, Hainan is the strategic fulcrum of China’s BRI. Haikou is the capital city of Hainan province and relies on the “ecological environment, special economic zone, and international tourism island” and has already become the strategic fulcrum city of the 21st Century Maritime Silk Road. In recent years, the urbanization process of Haikou has been accelerating and the construction of many service facilities and projects in the city is also accelerating. The consequential increase in demand for land drives the outward expansion of construction land and potentially threatens the adjacent cultivated land. This creates challenges for Haikou, which is already limited in cultivated land reserve resources. This study applies a kernel density analysis, land use transfer matrix and landscape pattern index to analyze the evolution of spatial-temporal evolution of cultivated land in Haikou during 1980–2020. Here, we explore the driving factors affecting the evolution of cultivated land by using the PLUS model. We aim to provide a scientific reference for the future development of cultivated land conservation strategies and related policies in Haikou.

2. Materials and Methods

2.1. Study Area

Haikou City (N 19°31′32″–20°04′52″, E 110°07′22″–110°42′32″) is located in the north of Hainan province (Figure 1). Haikou is 62.5 km long from north to south and 60.6 km wide from east to west, with a total area of 3126.83 km2 and a total land area of 2296.83 km2. Total land area accounts for 73.46% of total area. The topography of Haikou is slightly long and heart-shaped and the landform can be divided into three parts: coastal plain, river terrace, hill and lava platform with a gentle topography. The north is primarily categorized as a coastal plain belt, lying low and flat. The area is vast and accounts for 52% of total area. The higher terrain in the southeast and northwest is dominated by hills and lava platforms and accounts for 5% of the total area. Nandu river, the longest river in Hainan island, runs through the middle and has a low and flat coast. The central riverside terrace zone accounts for 43% of the total area. Haikou is located at the edge of the low latitude tropics and belongs to the tropical maritime monsoon climate. The annual average sunshine hours exceed 2000 h, the annual average temperature is 24.4 °C, and the annual average precipitation is 1696.6 mm. The soil is mainly divided into 8 soil classes, 12 subclasses, 43 soil genera and 110 soil species. Haikou has jurisdiction over Xiuying, Longhua, Qiongshan, Meilan districts, and these 4 districts have jurisdiction over 21 street offices and 22 towns. In 2021, the GDP of the city was 205.706 billion yuan. The permanent resident population was 2.908 million and the urban population was 2.4023 million with an urbanization rate of 82.61%.

2.2. Data Sources

Data included basic data and driving factor data. The driving factor can be divided into two categories: natural factor data and socioeconomic factor data (Table 1). The spatial resolution of land-use data (1980, 2000, 2010 and 2020) is 30 m. Utilizing the classification scheme formulated by the Chinese Academy of Sciences, land-use data were reclassified into six types: cultivated land, woodland, grassland, water, construction land, and unused land. Natural data included the factors of digital elevation model (DEM), slope, aspect, average annual temperature, average annual precipitation and distance from water. Socioeconomic data included the factors of population (POP), gross domestic product (GDP), distance from railways, distance from highways, distance from roads (main roads, secondary roads, trunk roads), and distance to government. All data were uniformly processed by ArcGIS software for projection coordinate system and spatial resolution to meet the data requirements of PLUS model operation.

2.3. Methods

2.3.1. Land-Use Transfer Matrix

Markov transfer matrix was derived from the quantitative description of states and state transitions within a certain period. The land-use transfer matrix is an application of the Markov model for land-use change. This model shows the quantitative relationship between each land type between two different time nodes in the form of a matrix. This allows us to visualize the flow direction of each land type between two time nodes and indirectly shows the rate of change of each land type at two time nodes [30,31].

2.3.2. Kernel Density Analysis

Kernel density estimation (KDE) is a statistical method for non-parametric density estimation (NPDE) and can be applied to the study of various fields [32]. Kernel density estimation method was used to estimate the kernel density of cultivated land, which could effectively measure the spatial agglomeration of cultivated land of the whole study area. The higher the value of the kernel density of cultivated land, the higher the distribution density. The calculation formula is as follows:
f n x = 1 nh ∑ i = 1 n k ( x − x i h )
where f n x refers to the kernel density estimate at point x, h refers to the bandwidth, n refers to the number of sample points in a circle with bandwidth, h refers to the radius, k refers to the kernel density function, and x − x i refers to the distance between the point x and the sample point x i .

2.3.3. Landscape Pattern Index

Landscape patterns are distinguished by the spatial relationships between components and describes composition and configuration. Landscape pattern index (LPI) can quantitatively describe landscape pattern [33]. LPI is an important index in the study of landscape patterns, one which is able to highly condense landscape spatial pattern information and well reflect the structural composition and spatial configuration characteristics of a cultivated landscape. A total of five landscape pattern indices were selected at the overall landscape level to analyze the landscape pattern characteristics of cultivated land in Haikou (Table 2). Characteristics included number of patches (NP), patch density (PD), aggregation index (AI), mean patch fractional dimension (MPFD), and landscape shape index (LSI).

2.3.4. PLUS Model

The patch-generating land-use simulation (PLUS) model is a combination of the rule mining method based on land expansion analysis (LEAS) and the cellular automata model of the multitype random patch seeds (CARS). This method is more advantageous in exploring the potential driving factors behind LULC evolution and the degree of contribution from each driving factor to land type change [28]. In the LEAS module, the model applies random forest classification algorithm (RFC) to the expansion of each land-use type between two time nodes to explore the driving factor degree of contribution to the expansion of land-use type. The multicollinearity problem among multiple drive factors can be solved effectively using this algorithm. The calculation formula is as follows:
P i , k d x = ∑ n = 1 M I = h n x = d M
where P i , k d x refers to probability of occurrence of land type k on cell i, d takes the value of 1 or 0, x refers to a vector composed of several driving factors, I function refers to the indicator function of the set of decision trees, h n x refers to the prediction type of the nth decision tree for x, and M refers to the total number of decision trees.

3. Results

3.1. Spatial-Temporal Evolution Analysis of Cultivated Land

Change in cultivated land area in the city (Table 3) during 40 years decreased from 75,628.12 ha in 1980 to 68,607.54 ha in 2020. This decrease totaled 7020.58 ha and indicated a significant reduction in area. By comparing the changes of cultivated land area during 1980–2000, 2000–2010 and 2010–2020, we can see that over the past 40 years cultivated land showed a trend of “no significant change–small reduction–significant reduction”, with a general trend of reduction.
Cultivated land is inevitably influenced by changes in other land-use types within the municipality. Therefore, to further explore explanations for the large loss of cultivated land in the past 40 years, spatial-temporal transfer of cultivated land should be understood in detail. Land-use data from 1980, 2000, 2010 and 2020 were processed in ArcGIS software to obtain the transfer matrix of land-use types in the three periods of 1980–2000, 2000–2010 and 2010–2020. Based on this, the chordal diagram of cultivated land change (Figure 2) and the table of transfer (Table 4) were drawn.
Combined with the above charts and actual conditions, this study further explored the spatial-temporal evolution of cultivated land in Haikou over the past 40 years and the causes:
(1) No significant shift in the cultivated land area from 1980 to 2000 was observed (Table 4). Only a small area of conversion between cropland and woodland was observed (Figure 2). This phenomenon is consistent with Table 3. Spatially (Figure 3), no visible conversions among cultivated land other land types were identified.
Before 1988, Hainan island was an administrative region under the jurisdiction of Guangdong province. The development policy for Hainan island was still based on an agricultural economy as the core and a micro-light industry as the auxiliary. As the main source of agricultural production, cultivated land cover was the primary land-use type during this time. The independence of Hainan island from Guangdong province in 1988 and the implementation of preferential policies and urbanization in Haikou gradually accelerated with the help of a large number of domestic and foreign funds for the improvement of facility infrastructure [34]. Although overall urbanization had increased, according to the research of Liu et al. [35], the area of construction land on the whole island of Hainan only expanded slightly during 1980–2000, the intensity of human activity remained at a low level of 10.13–10.94%. The infiltration of cultivated land and other land types by construction land was not obvious. Thus, the cultivated land area did not change significantly during this period.
(2) Compared to 1980–2000, cultivated land area showed a relatively evident loss during 2000–2010. The area reduced by 1832.36 ha (Table 3). A total of 4128.75 ha of cultivated land was converted to other land types during this period (Table 4), of which 65.55% and 20.89% were converted to construction land and water area, respectively. Table 3 and Figure 3 combined show the significant loss of cultivated land area in 21 street offices, Haixiu town, Chengxi town and Yanfeng town are related to the expansion of construction land and water area. A total of 1028.61 ha of woodland was converted into cultivated land in Lingshan town and Changliu town (Figure 3), which provides an explanation for the cultivated land area increases in these two towns in Table 3. The spatial-temporal changes in cultivated land were mainly related to a decrease in woodland and the increase in construction land, which is consistent with the research conclusions of Li [36] and Wang et al. [37].
After 1999, as the national economy of Haikou entered a stable development stage. The acceleration of the construction process for major projects, such as an outward-oriented port tourism city and an ecological city, further promoted the increase in demand for construction land. The construction land in the main urban area expanded at the expense of the cultivated land in the surrounding towns, posing a threat to the safety of cultivated land resources. In order to prevent further loss of cultivated land, the General Land Use Plan of Haikou city (1997–2010), which was approved and implemented by the State Council in 1999, strengthened the constraints on the scale of construction land and reinforced the implementation of the compensation system for cultivated land, and some illegal houses in rural settlements were demolished and reclaimed as cultivated land. Furthermore, a study by Liu et al. [35] also showed that human activity intensity increased slowly during this period. Thus, only a small reduction in the area of cultivated land occurred during this period.
(3) The cultivated land in Haikou showed a "significant decrease" during 2010–2020, with a total loss of 5188.14 ha, and the loss of cultivated land in Changliu town, Xixiu town and Lingshan town accounted for 17.25%, 16.49% and 17.64% of the total loss respectively, much higher than the loss in other areas (Table 3). According to Table 4 and Figure 2, the amount of cultivated land transferred to other land types in this period is much higher than that in the previous period, and it mainly flowed to construction land. Spatially (Figure 3), the conversion of cultivated land to construction land is most evident, and these areas are distributed in the form of radiation expansion from the main urban area to the surrounding towns, mainly in Changliu town, Xixiu town, and Lingshan town, indicating that encroachment of construction land on cultivated land may be the main cause of cultivated land loss.
According to Liu et al. [35], the intensity of human activity in Hainan increased rapidly to 12.86% during 2010–2018. In 2010, the construction of Hainan as an international tourism island was officially promoted as a national strategy. In this context, as the capital city of Hainan province, Haikou accelerated the development of tourism and other industries, and the area of the main urban area also expanded rapidly [38]. In 2011, the Overall Urban Planning of Haikou (2011–2020) formally confirmed the construction of two new urban clusters, Jiangdong and Changliu, on the east and west sides of the central cluster of Haikou, respectively, and the development space of the city gradually expanded from the central cluster to the east and west wings. In the same year, the Haikou municipal government moved westward into the Changliu cluster. Since then, the urbanization process of the towns in these two new urban clusters, such as Changliu town, Xixiu town and Lingshan town, has accelerated. Compared with other towns in Haikou, the construction land of the towns in these two clusters has been expanded significantly, which also leads to the great loss of cultivated land resources in these towns.

3.2. Analysis of Spatial Agglomeration of Cultivated Land

This study used a kernel density analysis tool to calculate the spatial distribution of the kernel density of cultivated land in Haikou for four years, 1980, 2000, 2010 and 2020 (Figure 4). In order to facilitate comparative observation, 1980 was used as the base year and was then divided by the cultivated land kernel density value into 6 levels using the natural discontinuity point classification method. Low density zone (0–4.79/km2), medium-low density zone (4.79–11.25/km2), medium density zone (11.25–17.54/km2), medium-high density zone (17.54–23.66/km2), high density zone (23.66–30.12/km2), and extremely high density zone (30.12–42.20/km2). The data of 2000, 2010 and 2020 were based on the grade division of 1980. 4.79, 11.25, 17.54, 23.66, 30.12 and 42.20 were used as the cut-off points for each grade.
In Figure 4, the overall spatial distribution of cultivated land in Haikou shows high density values in most of the southwest and some parts of the northeast, and low density values in the southeast and northwest. The areas with extremely high density and high density were mainly distributed in the towns of Dongshan, Xinpo, Jiuzhou, Jiazi, Lingshan and Shishan, all of which have low altitude and flat terrain. Medium and low-density areas were mainly located in the northwest and southeast of Haikou. The northwest is dominated by an urban area with a concentration of street offices. The two towns of Shishan and Yongxing are at higher elevations. The southeast is dominated by the higher elevation of the towns of Dapo and Sanmenpo. Comparing the spatial distribution of cultivated land kernel density in 1980, 2000, 2010 and 2020, the spatial layout of cultivated land kernel density in Haikou remained generally stable in the period of 1980–2000. During 2000–2010, the range of areas with high density values expanded in Xixiu and Lingshan, and decreased significantly in Haixiu. By 2010–2020, the range of areas with high values distributed in Xixiu, Changliu, Haixiu, Chengxi, Fengxiang Street Office and Lingshan had shrunk significantly.
In general, areas with high values of cultivated land density were mostly located at lower elevations. The density of cultivated land in the northern part of Haikou has changed significantly over the 40-year period and continued reduction of high density areas are distributed around built-up areas in the northern low elevation region. The southwestern part of the city, located at a lower elevation and farther from the urban area, had a higher density and more stable overall variation of cultivated land, indicating that the spatial aggregation of cultivated land in Haikou is related to natural geographical and socio-economic factors.

3.3. Analysis of Cultivated Land Fragmentation

The fragmentation of cultivated land affects the security of China’s food production and the evolutionary characteristics of cultivated land fragmentation over time need to be evaluated [39,40]. The cultivated land raster data of Haikou in each year were imported into Fragstats software for calculation. The cultivated land landscape pattern indexes from 1980 to 2020 were obtained (Table 5). Table 5 shows that in the 40 years from 1980 to 2020 the number of patches (NP) increased from 463 to 509 and the patch density index (PD) increased from 0.6119 to 0.7416. The aggregation index (AI) decreased from 94.4773 to 93.9836, indicating the degree of fragmentation for cultivated land was gradually increased. The landscape shape index (LSI) and mean patch fractional dimension (MPFD) of the shape index increased from 51.5763 and 1.0951 in 1980 to 53.4699 and 1.0981 in 2020, respectively, indicating that the shape of cultivated land patches tends to be complex and irregular. With accelerated urbanization, cultivated land area decreased and the spatial aggregation continued to decline. Fragmentation of cultivated land in Haikou is evident during 1980–2020.

3.4. Analysis of the Driving Factors for Cultivated Land Evolution

The expansion data of each land type during 1980–2020 were extracted by the PLUS model (Figure 5). From Figure 5, the most apparent expansion of each land type during 1980–2020 is construction land. The expansion area is mainly distributed in the northwestern part of Haikou city, while the number of expansion areas of cultivated land is much less than construction land. To further investigate the driving factors influencing the change of cultivated land in Haikou over the past 40 years, this study imported 12 driving factors (Figure 6) and expansion data for each land type during 1980–2020 were input into the LEAS module of the PLUS model for analysis.
Based on the degree of contribution for each driving factor (Figure 7), the distance from road, high-speed railway, and water were the main driving factors of cultivated land expansion over the past 40 years. When overlaying the cultivated land expansion areas with the three driving factors (Figure 8), the results show cultivated land expansion areas are mainly distributed in areas far from roads, high-speed railway, and water.
The calculation of the construction land expansion area extracted by the PLUS model showed approximately 42.25% of construction land expansion originates from cultivated land. Results suggest cultivated land loss is closely related to the expansion of construction land. Therefore, driving factor analyses of the construction land expansion indirectly reflect the explanation for the decrease of cultivated land. Figure 7 shows that distance from water is the most important driving factor of construction land expansion, followed by distance from roads and distance from high-speed railway. Overlaying areas where cultivated land was converted to construction land with the spatial distribution map of the three driving factors (Figure 8), we can see that cultivated land was converted to construction land in areas closer to roads, high-speed railways and water. This phenomenon of land type conversion was more significant in the northern areas with dense road traffic lines.
Rapid urbanization is usually accompanied by the increase of water consumption, therefore water resources are an important reference factor for selecting new construction sites [41]. According to Zhou et al. [38], land on both sides of the Nandu river in Haikou was heavily developed by people as construction land during 2003–2016. This indicates water resources are an important factor in the expansion of construction land. Road traffic conditions also play a vital role in urbanization. Guo et al.’s study of urbanization in China from 2000 to 2018 showed that transportation contributed 28.3% of the comprehensive level of urbanization (CLU) in the eastern region [42]. In general, in the context of rapid urbanization, construction land occupies a large amount of cultivated land in areas with good transportation conditions and abundant water resources, thus resulting in the loss of cultivated land.

4. Discussion

According to the findings of Liu et al. [43], 71.16% of China’s city construction land expansion during 1990–2020 was attributed to the conversion of cultivated land. Analysis of cultivated land evolution in Haikou during 1980–2020 shows that areas of cultivated land have a trend of continuous decrease, mainly in the northern part of the city where large amounts of land are converted to construction land. Results are consistent with the conclusion of Meng et al. [44].
The trend of cultivated land loss was not obvious before 2000 and the occupation of cultivated land by urban construction land is not obvious in this period. This result is consistent with the findings of Xu et al. [45]. A significant downward trend was only observed during 2000–2020. After analysis of Hainan’s history, the development pattern of Hainan before 1988 was still dominated by agricultural economy. According to the study of Gu et al. [46], after 1988 Hainan’s establishment as a province and as a special economic zone, the rapid development of the real estate industry was promoted, but due to excessive relaxation of the real estate market environment, the resulting real estate bubble was not solved in time and collapsed in 1994. This collapse led to the slow process of urbanization in the following period. Zhang et al.’s study further confirmed that the land-use change (LUC) in Haikou hardly changed during 1995–2000, and they also found an interesting phenomenon—that the LUC in 1990–1995 was larger in 1995–2000—and pointed out that this was most likely related to the Basic Farmland Protection Regulations in 1994 [47]. After 2000, the implementation of a series of development strategies, such as building an export-oriented port city, an international tourist island and a free trade port fundamentally promoted the urbanization of Haikou and accelerated the outward expansion of construction land in the main urban area. This led to a decreasing trend in the area and spatial distribution density of cultivated land and an increase in the landscape fragmentation degree of the cultivated land. These conclusions from this study are consistent with the findings about the change of cultivated land in Haikou in the study of Zhou et al. [38] and Wang et al. [48].
In fact, during 2000–2020 the accelerated expansion of urban construction land and the loss of cultivated land drew the attention of the government. Measures to strengthen cultivated land protection have been clearly proposed in city planning documents. However, results from this study show that, during 2000–2020, the loss of cultivated land did not slow, but instead accelerated. Theoretically, the policies in the new era should be more comprehensive compared with the earlier policies on farmland protection, but in practice these new policies have not even achieved the same effect as the Basic Farmland Protection Regulations in 1994. This phenomenon should be further investigated by the government and scholars and the influence of policy factors on the change of cultivated land should be quantified in future studies.
The analysis results of the PLUS model show that the occupation of areas with convenient transportation conditions and abundant water resources is the main reason for the loss of cultivated land. In 2018, the Hainan provincial government decided to plan and build the Jiangdong New Area in Haikou. This would serve as a key area for the construction of the Hainan Pilot Free Trade Zone. As the population grows gradually and the number of new construction projects increases, the demand for urban construction land is bound to increase further. The impact of economic factors on cultivated land will increase gradually, posing a greater threat to the security of cultivated land resources. In the future land planning of Haikou the protection of cultivated land should be strengthened and the cultivated land compensation system should be strictly implemented. Additionally, the scale of urban construction land should be strictly controlled and the utilization rate of stock land should be improved.
Due to its remote location (the southernmost province in China) and unique geography (an island province), Hainan has poor informational communication with the outside world. Because Hainan was the last province in China to be established, the early urbanization processes were slow and failed to cause significant changes in land-use patterns. Thus, Hainan’s land use has failed to attract the attention of many scholars. Due to the implementation of a series of development strategies after 2000, rapid urbanization has driven an accelerated transformation of land-use patterns in Hainan province. In Haikou especially, the provincial capital city, the expansion of urbanization has threatened the security of cultivated land. The study on cultivated land in Haikou needs further investigation urgently. Many existing studies have focused only on the spatial-temporal evolution of cultivated land, without exploring the potential driving factors behind the evolution. This study introduced a novel land-use change simulation model— the PLUS model—to further explore the driving force of cultivated land evolution. This helps fill the gaps in the lack of cultivated land research in Haikou and improves understanding of the driving forces of land-use change.

5. Conclusions

Using the methods of land-use transfer matrix, kernel density analysis and landscape pattern index, this study explored the characteristics of spatial-temporal pattern change of cultivated land in Haikou city from 1980 to 2020. Additionally, this study further explored the driving factors of spatial-temporal pattern change of cultivated land in the past 40 years by using PLUS model. Results show:
(1)
The loss of cultivated land resources in Haikou has intensified in the past 40 years. From 1980 to 2020, the loss of cultivated land has gradually increased. Spatially, the cultivated land is mainly converted into construction land in the northern built-up area of Haikou and surrounding towns and is related to the continuous expansion of the main urban area.
(2)
The spatial distribution pattern of cultivated land in Haikou shows the characteristics of "dense in the southwest and sparse in the northwest". Over the past 40 years, the density zone in the northern part of Haikou has distinctly changed. The high-density zone in the northwest has been continuously replaced by medium-density zone and low-density zone, leading to a gradual decline in the spatial aggregation of cultivated land.
(3)
The changes in the landscape pattern index show the number and density of cultivated land patches in Haikou increased over the past 40 years. The degree of spatial aggregation has decreased, and the shape of cultivated land has gradually become more complex. Overall, the fragmentation of cultivated land in Haikou has increased.
(4)
The evolution of the spatial-temporal pattern of cultivated lands in Haikou is the result of combined natural and economic factors. From 1980 to 2020, natural and economic factors, such as the location of water resources, transportation and construction land, governed the expansion and encroachment on cultivated land, eventually leading to the reduction of cultivated land area.

Author Contributions

Conceptualization, X.L. and H.F.; methodology, X.L. and H.F.; software, X.L.; formal analysis, X.L.; writing—original draft preparation, X.L.; writing—review and editing, H.F.; supervision, H.F.; funding acquisition, H.F. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Hainan Provincial Natural Science Foundation of China (Grant Nos. 421MS015 and 421QN200).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Study area.
Figure 1. Study area.
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Figure 2. Transfer between other land-use types and cultivated land in Haikou during 1980–2020. The arc of the outer circle divided by nodes in the chord diagram reflects the proportional relationship between the area conversion between cultivated land and other land types (including the amount of transfer in and out); the connecting lines of different colors indicate the flow direction of different land types within the period, and the thickness of the connecting lines is proportional to the size of the transfer volume.
Figure 2. Transfer between other land-use types and cultivated land in Haikou during 1980–2020. The arc of the outer circle divided by nodes in the chord diagram reflects the proportional relationship between the area conversion between cultivated land and other land types (including the amount of transfer in and out); the connecting lines of different colors indicate the flow direction of different land types within the period, and the thickness of the connecting lines is proportional to the size of the transfer volume.
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Figure 3. Spatial conversion of cultivated land in Haikou during 1980–2020.
Figure 3. Spatial conversion of cultivated land in Haikou during 1980–2020.
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Figure 4. Spatial distribution of cultivated land kernel density classes in Haikou during 1980–2020.
Figure 4. Spatial distribution of cultivated land kernel density classes in Haikou during 1980–2020.
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Figure 5. Spatial distribution of expansion of each land type in Haikou during 1980–2020.
Figure 5. Spatial distribution of expansion of each land type in Haikou during 1980–2020.
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Figure 6. Spatial distribution of driving factors.
Figure 6. Spatial distribution of driving factors.
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Figure 7. The contribution of each driving factor. (A) the contribution of each driving factor to cultivated land expansion, (B) the contribution of each drive factor to construction land expansion. A: Distance from road; B: DEM; C: Distance from water; D: Distance from highways; E: Distance from high-speed railway; F: GDP; G: Average annual precipitation; H: Slope; I: Population; J: Aspect; K: Average annual temperature; L: Distance from government.
Figure 7. The contribution of each driving factor. (A) the contribution of each driving factor to cultivated land expansion, (B) the contribution of each drive factor to construction land expansion. A: Distance from road; B: DEM; C: Distance from water; D: Distance from highways; E: Distance from high-speed railway; F: GDP; G: Average annual precipitation; H: Slope; I: Population; J: Aspect; K: Average annual temperature; L: Distance from government.
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Figure 8. Main driving factors of cultivated land expansion in Haikou.
Figure 8. Main driving factors of cultivated land expansion in Haikou.
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Table
Table 1. Data types and sources.
Table 1. Data types and sources.
Data TypeData NameYear(s)Data Sources
Basic dataLand-use data1980, 2000, 2010, 2020Resource and Environment Science and Data Center
(http://www.resdc.cn/authors (accessed on 1 June 2022))
Drive factor data (Natural factors)DEM Geospatial Data Cloud
(http://www.gscloud.cn/authors (accessed on 1 June 2022))
Slope
Aspect
Distance from water2020Resource and Environment Science and Data Center
(http://www.resdc.cn/authors (accessed on 1 June 2022))
Annual average temperature1991–2020Institute of Mountain Hazards and Environment, CAS
Annual average precipitation
Drive factor data (Socioeconomic factors)POP2019Resource and Environment Science and Data Center
(http://www.resdc.cn/authors (accessed on 25 June 2022))
GDP
Distance from government AMAP
(https://lbs.amap.com/authors (accessed on 23 June 2022))
Distance from railway2021OpenStreetMap (https://www.openstreetmap.org/authors (accessed on 21 June 2022))
Distance from highway
Distance from road
Table
Table 2. Various landscape pattern indicators and descriptions.
Table 2. Various landscape pattern indicators and descriptions.
Index NameUnitsRangeIndex Description
Number of Patches (NP)pcsNP ≥ 1Total number of cultivated land patches in the study area, the value of NP is positively correlated with the degree of cultivated land fragmentation
Patch Density (PD)(pcs/km2)PD > 0The number of patches of cultivated land per unit land area, the higher the PD value, the higher the fragmentation of cultivated land
Aggregation Index (AI)(%)0 < AI ≤ 100Reflecting the degree of aggregation of cultivated land patches in the study area, the higher the AI value, the lower the degree of fragmentation of cultivated land in the region
Mean Patch Fractal Dimension (MPFD) 1 ≤ MPFD ≤ 2Measuring the complexity of patch boundaries, the larger the MPFD value, the more complex the shape of the cultivated land patch boundary
Landscape Shape Index (LSI) LSI ≥ 1Reflecting the complexity of the shape of cultivated land patches, the larger the LSI value, the more complex the shape of the cultivated land patch
Table
Table 3. Area and changes of cultivated land in each street office and town in Haikou (ha).
Table 3. Area and changes of cultivated land in each street office and town in Haikou (ha).
AreaArea of Cultivated LandAmount of Cultivated Land Change
19802000201020201980–20002000–20102010–20201980–2020
21 street offices2072.652072.741281.78740.130.09−790.96−541.65−1332.52
Changliu town1610.531609.721847.02951.78−0.81237.3−895.24−658.75
Xixiu town2060.052060.231913.121057.330.18−147.11−855.79−1002.72
Haixiu town1126.801126.89552.67261.520.09−574.22−291.15−865.28
Shishan town4803.074803.714791.394531.330.64−12.32−260.06−271.74
Yongxing town1706.801705.991750.931699.66−0.8144.94−51.27−7.14
Dongshan town4431.604431.154430.264407.38−0.45−0.89−22.88−24.22
Chengxi town1485.701485.661008.93412.97−0.04−476.73−595.96−1072.73
Longqiao town893.12891.81920.20897.51−1.3128.39−22.694.39
Longquan town1833.921832.301834.531814.40−1.622.23−20.13−19.52
Xinpo town3015.573014.853009.153006.99−0.72−5.7−2.16−8.58
Zuntan town1460.361460.271462.851461.68−0.092.58−1.171.32
Longtang town1246.841246.931243.731243.980.09−3.20.25−2.86
Yunlong town4286.344287.274261.764151.340.93−25.51−110.42−135.00
Hongqi town5515.055514.935468.375356.10−0.12−46.56−112.27−158.95
Jiuzhou town6013.096013.466012.556015.150.37−0.912.602.06
Sanmenpo town4030.254030.323994.433978.690.07−35.89−15.74−51.56
Jiazi town6982.786985.856967.306896.473.07−18.55−70.83−86.31
Dapo town1406.801407.161485.831472.530.3678.67−13.3065.73
Lingshan town4969.364968.555474.564559.16−0.81506.01−915.40−410.20
Yanfeng town4232.344233.603778.803416.101.26−454.8−362.70−816.24
Sanjiang town5126.795126.165024.885018.32−0.63−101.28−6.56−108.47
Dazhipo town5318.315318.495280.645257.020.18−37.85−23.62−61.29
Total75,628.1275,628.0473,795.6868,607.54−0.08−1832.36−5188.14−7020.58
Notes: The cultivated land area data for the “21 street offices” form a collection consisting of the cultivated land area data of Xiuying, Haixiu, Zhongshan, Binhai, Datong, Jinmao, Jinyu, Haiken, Guoxing, Fucheng, Fengxiang, Binjiang, Bailong, Baisha, Boai, Haidian, Lantian, Haifu road, Renmin road, Hepingnan and Xinbu, a total of 21 street offices.
Table
Table 4. Cultivated land transfer in Haikou during 1980–2020 (ha).
Table 4. Cultivated land transfer in Haikou during 1980–2020 (ha).
PeriodTransferWoodlandGrasslandWaterConstruction LandUnused LandTotal
1980–2000Transfer out33.031.620.906.930.1842.66
Transfer in34.291.170.366.480.2742.57
2000–2010Transfer out546.0312.96862.652706.480.634128.75
Transfer in1028.6122.68447.39500.13299.252298.06
2010–2020Transfer out1212.5744.19303.035421.420.906982.11
Transfer in1313.7328.35303.39144.540.091790.10
Table
Table 5. Landscape pattern index of cultivated land in Haikou during 1980–2020.
Table 5. Landscape pattern index of cultivated land in Haikou during 1980–2020.
YearLandscape Pattern Index
NPPDAIMPFDLSI
19804630.611994.47731.095151.5763
20004650.614694.47831.095151.5671
20104590.621794.34391.098652.1578
20205090.741693.98361.098153.4699
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Lin, X.; Fu, H. Spatial-Temporal Evolution and Driving Forces of Cultivated Land Based on the PLUS Model: A Case Study of Haikou City, 1980–2020. Sustainability 2022, 14, 14284. https://doi.org/10.3390/su142114284

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Lin X, Fu H. Spatial-Temporal Evolution and Driving Forces of Cultivated Land Based on the PLUS Model: A Case Study of Haikou City, 1980–2020. Sustainability. 2022; 14(21):14284. https://doi.org/10.3390/su142114284

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Lin, Xiaofu, and Hui Fu. 2022. "Spatial-Temporal Evolution and Driving Forces of Cultivated Land Based on the PLUS Model: A Case Study of Haikou City, 1980–2020" Sustainability 14, no. 21: 14284. https://doi.org/10.3390/su142114284

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