1. Introduction
Land use and land cover change (LUCC) is an important factor altering ecosystems and the natural balance at regional and global scales and has intensified in recent decades due to human interventions [
1,
2,
3]. Under the general trend of global urbanization, LUCC occurs frequently and manifests as the conversion from natural covers to artificial surfaces, especially in developing countries [
4,
5,
6]. It breaks the balance of the Earth’s system and has led to a series of subsequent effects, such as land degradation, climate change, eco-environment deterioration, biodiversity loss, food security, etc. [
7,
8,
9,
10]. Thus, how to rationally manage limited land resources to balance socio-economic development and ecological needs is at the core of sustainable development.
Land use simulations illuminate the dynamic mechanism of land systems and anthropogenic–natural impacts, by qualitatively or quantitatively modeling land use conversions in the past and using the underlying driving forces to forecast future land use demand and spatial allocation at various temporal or spatial scales [
11,
12,
13]. To effectively model LUCCs, numerous land use simulation models have been put forward in the past decades. According to the working scheme, they can be summarized into three main kinds: the quantitative prediction model, spatial simulation model, and coupling model of quantitative prediction and spatial simulation [
3,
14].
The quantitative prediction model mainly focuses on land use demand estimation to give a possible area for each land use in the future, but ignores the spatial distributions of various land uses for land allocation guidance. The representative methods include the logistic regression (LR) model [
15,
16], system dynamic (SD) model [
17,
18], Markov chain model [
19,
20], and so on. The spatial simulation model pays more attention to the interactions between geographical units and spatial drivers to estimate the growth probability of different land uses in each geographical unit, with the cellular automata (CA) model [
21,
22], conversion of land use and its effects (CLUE) model [
23,
24], and agent-based model [
25,
26] as typical representations. Such methods directly provide land allocations, but with a limited ability to model global influencing factors, like policy, economy, etc. The coupling model takes full advantages of the above two methods to give more accurate and comprehensive simulation results and has been a common strategy in land use simulation [
27,
28]. The typical examples are the CA–Markov model [
29,
30], CA–SD model [
31,
32], and Dyna–CLUE–Markov model [
33,
34]. Among them, the CA–Markov model combines the advantages of the CA and Markov chain models to simultaneously predict the spatial pattern and temporal trend and is suitable for the complex change simulation of multiple land uses, which has been widely applied to LUCC analysis [
35,
36].
The scenario simulation based on the above models gives reasonable forecasting consequences of land use demand and spatial allocation in the future, which enables strategical land use optimization and scientific decision-making [
37,
38]. In the past years, extensive LUCC simulation research projects under certain scenarios have been carried out at various scales, which greatly improves the understanding of the comprehensive influences of anthropogenic processes and policy formulation on land system evolution. For instance, Han et al. combined CLUE-S and Markov models to conduct a development-and-protection scenario simulation for Beijing during the period between 2010 and 2020 and found that the conversion from arable land to build-up land was the major feature, especially for mountainous areas [
39]. Zou et al. explored a sustainable land use model in Nan’an City through land use conflict (LUC) management, with seven conflict levels and 16 LUC zones considered, to draw a conclusion that differentiated strategies might be adopted to relieve the conflicts in various LUC zones [
40]. In addition, Yang et al. investigated LUCCs in the Beijing–Tianjin–Hebei region during the period between 2000 and 2015 and projected its future land use pattern in 2030 based on the Dyna–CLUE model under three tailored scenarios, with the results indicating that the ecology security scenario is optimal for regional planning [
41].
China has undergone significant urbanization in the past decades, with urbanization accompanied by intense expansion [
42,
43,
44,
45]. Since the 1990s, a large amount of manpower and resources have concentrated into the coastal metropolitan cities, which accelerates the development of big cities like Beijing and Shanghai, in addition to promoting the rapid urbanization of municipalities directly under the central government and sub-provincial cities [
42]. With favorable policies, superior geographical locations, and resource advantages, a rapid rise of marginal cities occurs [
43]. The process of urbanization in China is closely related to economic development, which is not only consistent with the process of population migration but is also the result of economic structure transformation and development. Therefore, as a result of urbanization, city functions have been constantly strengthened, with the gaps between them gradually decreased and the development of urban agglomerations becoming an important symbol of China’s urbanization process [
44]. The core–periphery spatial layout of urban agglomerations becomes more and more obvious, with core cities strengthened and coexisting with the rapid rise of periphery cities [
45].
The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is one of the most open and prosperous zones in China and is the focus of the world [
46]. However, it has been perplexed by excessive urbanization, with frequent landscape changes occurring, which induces serious land use problems and hinders its sustainability [
47,
48]. In recent years, large amounts of research have focused on urbanization and relevant LUCCs in the GBA. For example, Zhang et al. compared the urbanization of several mega urban agglomerations in China from 1980 to 2015 and found that the GBA has the highest proportions for urban land (8.03%) and urban impervious surface (75.16%) [
49]. Wu et al. investigated the relation between urban sprawl and ecological changes in the GBA during the period between 2000 and 2018 based on nightlight data, and the results indicated that both urbanization and the eco-environment had a circular structure, and their coupling level became larger [
50]. Wang et al. evaluated the ecological service changes induced by LUCCs in the GBA during the period between 1980 and 2018, and found an extensive loss of ecological service value of CNY 40.5 billion, with the major loss caused by water reduction [
51]. Yang et al. explored the forest reduction induced by urbanization in the GBA and found a large forest loss of 4046 km
2 during the period between 1987 and 2017, with 25.60% caused by urbanization, especially for Dongguan, Guangzhou, and Shenzhen [
52]. Moreover, Chen et al. employed the FLUS model to simulate local climate zone (LCZ) changes from 2020 to 2100 and the results showed that different shared socio-economic pathways (SSPs) would lead to different LCZ changes, with different cities exhibiting different LCZ patterns [
53]. Accordingly, many policies were formulated to relieve these problems. Specifically, Guangdong province encouraged the reclamation of unsuitable land into farmland after 2011, with some vacated land used for industrial city construction in areas suitable for transformation [
54,
55]. Agricultural land received more attention, with farmland areas more effectively protected through the optimization of the structure of farmland [
54,
55]. Furthermore, the Guangdong provincial government issued the “Three Lines and One List for Environmental Zone Management Plan”, requiring the strict implementation of an environmental protection red line, an environmental quality bottom line, resource utilization limitations, and an environmental access list [
56,
57], which promoted the maintenance of regional ecological stability.
However, most past research in the GBA mainly focused on the macro change trend and pattern of typical land uses in the past and the subsequent influences. In-depth and systematic explorations of the future dynamics of land systems in the GBA under different development modes are still rare; this leads to an insufficient understanding of the impacts of anthropogenic–natural forces and policies on land system evolution [
58,
59,
60]. Accordingly, there are large challenges and uncertainties for land use optimization and rational decision-making to promote regional sustainability.
For example, the extensive pursuit of rapid economic development may be beneficial for urban agglomeration construction, but it may threaten food security and ecological security [
61,
62,
63]. Additionally, the distribution of urban functional areas, ecological protection areas, etc., will affect the regional sustainability to a large extent [
61,
62]. Furthermore, with the changes in climate, environment, population, economy, and social preferences, the land use management modes and policies will undergo changes. How to make reasonable policies is a big problem [
63]. Generally, without an in-depth understanding of future land use dynamics, all these issues cannot be solved.
In view of this, the purpose of this research is to quantitatively explore and understand the historical LUCCs in the GBA from 2005 to 2015 and forecast its future land use pattern in the short, medium, and long-term future, i.e., 2030, 2050, and 2070, with a CA–Markov model integrating multiple anthropogenic–natural forces, under three typical tailored scenarios, i.e., urban development (UD), cropland protection (CP), and ecology security (ES). Then, based on the simulated results, we comprehensively analyze the preferences and comparative advantages of various scenarios, to give an early warning for future land use risks, which can be applied to land use management and policy formulation to promote the sustainable development of the GBA.
4. Results and Analysis
4.1. Land System Changes from 2005 to 2015
Over the period of 2005–2015, the GBA underwent an intensive land use conversion under the background of urbanization (
Table 2), with approximately 8.5% (4760 km
2) of the jurisdiction experiencing land dynamics, which led to an evident land use structure change (
Figure 4). The build-up land had an enormous transfer-in area of 2022.20 km
2, which was converted from the other land uses, especially for cropland (1071.02 km
2), forest land (566.9 km
2), and water area (355.82 km
2), but with a small transfer-out area of 580.47 km
2. It contributed to an excessive urban expansion with a net increment of 1441.73 km
2 (23.47%), more than the sum of the area of Hong Kong and Macao, which led to prominent land use contradictions. As a consequence, cropland suffered from the largest loss of 570.77 km
2 (4.38%), followed by forest land, with a loss of 526.05 km
2 (1.76%), and water area, with a loss of 429.89 km
2 (10.88%). Grassland had a certain growth of 91.7 km
2 (8.46%), which mainly profited by the transfer-in area from forest land.
As illustrated by the Sankey diagrams (
Figure 5), at the flow-out end, cropland sustained the largest gross loss of 1732.43 km
2, with the majority converted to build-up land and part converted to forest land and water areas. Forest land and water areas sustained the second and third largest gross loss of 1159.42 km
2 and 968.19 km
2, which were mostly converted to build-up land and cropland. Additionally, the conversion from forest land to grassland was also significant, which mainly accounted for the increment of grassland. At the flow-in end, build-up land obtained the largest gain of 2022.20 km
2, followed by cropland, with 1161.66 km
2, forest land, with 633.37 km
2, and water area, with 538.3 km
2. However, it should be noted that compared with the gross loss, only build-up land and grassland presented a positive increment during the past decade.
4.2. Simulation Validation
The CA–Markov model was validated over the period between 2005 and 2015. From the visualization map (
Figure 6) and the corresponding quantitative evaluations (
Table 3), it can be found that a relatively good simulation effect was obtained for 2015. Generally, the simulated result was basically in line with the reference map, whether from the whole land use pattern or spatial detail perspectives. The PA and UA of most land uses were over 85%, with the rest also over 70%, which suggested both the omission rate and commission rate were at a low level. In general, a high OA of 90.27%, Kappa of 0.8495, and FoM of 89.14% were obtained, with a low total error (T) of 587,686, which suggested the vast majority of geographical units and changes were correctly predicted. Therefore, the simulated result had a high consistency with the reference data [
80,
89].
More specifically, the prediction of forest land and cropland was accurate, according to the relatively high PAs and UAs, i.e., 90.41% and 89.19%, and 98.11% and 89.45%, which indicate less omissions and commissions. As part of the grassland and unused land were not successfully forecasted, the omission rate had a certain rise, leading to a decline in PAs, i.e., 78.88% and 77.33%, but these were still at an acceptable level for practical applications. For build-up land and water areas, due to the misprediction of some cells, the commission rate was relatively high, which leads to a certain decline in UAs, i.e., 73.14% and 81.30%.
A spatial consistency map (
Figure 7) for each land use was also given to indicate the position consistency between the simulated result and the reference map. In general, the simulation has a high consistency with the reference data for each use. Especially for forest land, the vast majority of geographical units were correctly forecasted. The simulation of grassland and unused land was also at an acceptable level, with relatively less inconsistences. For build-up land, the simulation was good in the central areas of the urban agglomeration, with inconsistences mainly scattered in the northwest and southwest of the GBA as well as the suburban areas. For cropland and water areas, the overall consistency was at a high level, with inconsistences mainly concentrated in the junction of Zhuhai and Zhongshan City, i.e., Doumen and Lianzhou Town. The reason may be that the main industry in these areas was aquaculture, with a large number of ponds embedded in arable land, which easily causes confusion between cropland and water areas.
Overall, the CA–Markov model has a good performance for land use simulation under complex circumstances and can be applied well to future multi-scenario simulations in the GBA.
4.3. Multiple Scenario Simulations for Future Land Use Pattern
In order to explore the effects of different development modes and policies on LUCCs, land use patterns of the GBA in the short, medium, and long-term future, i.e., 2030, 2050, and 2070, were simulated with the validated CA–Markov model under UD, CP, and ES scenarios, respectively (
Figure 8,
Figure 9 and
Figure 10). As illustrated in the figures, there were significant differences in the future land use structure between various scenarios, with the discrepancy magnified over time.
Specifically, it can be observed that urbanization will be the main melody for all three scenarios in the next decades, which is very obvious in the main body of the urban agglomeration, such as Shenzhen, Guangzhou, Foshan, Dongguan, and Zhongshan City. The urbanization expands from inside to outside and gradually connects into pieces, forming an increasing compact structure. Meanwhile, the marginal cities, such as Zhaoqing, Jiangmen, and Huizhou, also show a rapid growth on the original basis. In general, the newly added build-up areas are mainly distributed along both banks of the rivers, forming a typical belt-like expansion. However, the urbanization degree and LUCC pattern are quite different between various scenarios.
Under the UD scenario, the build-up land expands outwardly in a rapid and free manner, at the expenditure of any surrounding land use types. Accordingly, abundant cropland and forest areas are occupied, especially for the regions around cities, which leads to an immense reduction of arable land and woodland. By contrast, the forest loss is more significant, since most areas of the GBA are covered by woodland. This phenomenon will be more evident in 2050 and 2070 with the intensification of urbanization, which brings about a great challenge to the sustainability of the GBA.
Under the CP scenario, urbanization is obviously slowed down, with agricultural production and food security as primary objectives. The encroachment of cropland is significantly relieved in permanent farmland regions around cities, with cultivated land in other regions further enlarged under land reclamation activities, especially for the northwest and the south of the GBA, which is in accord with cropland protection policies and accounts for a certain increment of the total amount of cultivated land. Naturally, forest land has been the major consumptive land source of urbanization to meet the needs of urban development, with an increasing loss in the next decades.
Under the ES scenario, the ecological lands, in terms of forest, grassland, and water areas, are well protected without significant reduction, compared with that in 2015, which indicates an ecologically friendly development mode in the future. In the northwest and the northeast of the GBA, forest land and grassland have a certain increment, benefitting from ecological constructions and afforestation/greening projects. The urbanization becomes moderate under the restriction of ecological protection policies, with the occupation of ecological lands significantly reduced, which effectively relieves the contradictions between socio-economic development and ecological service. As the major expenditure of urbanization, cropland has an obvious reduction.
To gain further insight into the scenario differences, the land use details of three typical scenes in the medium term, i.e., 2050, are compared (
Figure 11). It can be clearly observed that LUCCs differ a lot among the three scenarios. The UD scenario brings about a much more intensive urban sprawl under the principle of urban priority, with original discrete build-up areas connected into a piece and expanding outwardly in a rapid manner. The build-up land encroaches its surrounding land unscrupulously, which leads to large fragmentation to cropland and forest land. By comparison, the CP and ES scenarios restrict the free growth of build-up land to protect their preferred land use, with urbanization significantly relieved. More specifically, the CP scenario tries to avoid the expenditure of cropland, with forest land as the major land source of urbanization, as shown in the top left corner and middle lower part of
Figure 11(a2), the left part of
Figure 11(b2), and the lower right corner of
Figure 11(c2). The EP scenario focuses on the protection of ecological lands, with urban sprawl mainly occurring in cropland regions. Consequently, it can be noted that the distribution of ecological lands, i.e., forest land, grassland, and water areas, have small changes compared with that in 2015.
4.4. Future Land System Dynamics under Multiple Scenarios
To better understand the influences of different scenario modes on the future land system evolution in the GBA, the differences in land allocation and dynamic patterns were further explored (
Table 4 and
Figure 12). It can be clearly observed that the future land dynamics will be quite different between the scenarios.
Under the UD scenario, the build-up area shows an excessive growth, profiting by priority policies, and significantly exceeds that under the other two scenarios. As shown in
Table 4, The build-up area reaches 10,337.74 km
2 in 2030 and increases 2752.60 km
2 (36.29%) compared with that in 2015, with 1285.35 km
2 and 1490.01 km
2 more than the CP and EP scenarios. Additionally, it exceeds the planned area of 9938.20 km
2 in 2020, which demonstrates the effectiveness of this scenario for promoting urban development. Furthermore, urbanization will be accelerated during the periods between 2030 and 2050 and 2050 and 2070, with an increment of 3853.60 km
2 and 4311.40 km
2, respectively, leading to a gross gain of 10,917.60 km
2 (143.94%) during the period between 2015 and 2070. Accordingly, a large number of forests and croplands are occupied, with a gross loss of 11,187.09 km
2 (38.13%) and 500.39 km
2 (4.01%), which further exacerbates land contradictions and hinders regional sustainable development. It can be noted that forest land accounts for the main land source of urbanization and undergoes a continuous reduction. Water areas also decrease in the next decades, with a total loss of 389.93 km
2 (11.08%). Grassland continues an increasing trend as in 2005–2015, with an overall increment of 1165.32 km
2 (99.07%).
Under the CP scenario, cropland presents an obvious and constant growth, driven by cropland protection policies and land reclamation activities, forming a sharp contrast with that under the other two scenarios. As in
Table 4, the gross area of cropland reaches 13,082.65 km
2 in 2030, with an increment of 1191.26 km
2 (10.02%), compared with that in 2015. It is up to 14,102.63 km
2 and 15,377.63 km
2 in 2050 and 2070, respectively, with a general gain of 2909.87 km
2 (23.34%) during the period between 2015 and 2070. On the other hand, build-up land still presents an increasing trend in the next decades, but with a much smaller magnitude, with a total increment of 7203.44 km
2 (94.97%). As a consequence, forest land constantly decreases, with a significant loss of 10,679.76 km
2 (36.40%). For water area and grassland, these types have similar dynamic trends as in the UD scenario, with a total loss of 488.07 km
2 (13.86%) and a total gain of 1059.81 km
2 (90.10%), respectively.
Under the ES scenario, ecological lands, including forest land, grassland, and water areas, show obvious advantages to ecological lands under the UD and CP scenarios, benefitting from ecology protection policies and afforestation/greening projects. As in
Table 4, the build-up land increases at a mild rate, with a moderate increment of 5561.37 km
2 (73.32%) during the period between 2015 and 2070. Although forest land has certain reduction under urbanization, the magnitude is significantly reduced, with a gross loss of only 3417.72 km
2 (11.65%). The forest area is retained at 28,659.44 km
2 in 2030 and achieves the planned goal in 2020 of 28,405.62 km
2. For 2050 and 2070, it still remains at a high level of 27,859.89 km
2 and 25,923.37 km
2, without significant reduction, which suggests the effectiveness of this scenario in balancing socio-economic development and ecology security. As opposed to the other two scenarios, water resources are preserved under this scenario, and have a positive growth of 0.34 km
2. Grassland also presents a certain superiority, with a larger gross gain of 1382.36 km
2 (117.53%). Cropland inevitably decreases in a large magnitude as a consequence of urbanization, with a gross loss of 3521.77 km
2 (28.25%). However, the holding area of cropland in 2070 is 8945.99 km
2, far beyond the restricted basic farmland area of 7141.18 km
2 in 2020, which certainly guarantees regional agricultural production and food security [
90,
91,
92].
4.5. The Effect of Land System Changes under Multiple Scenarios
In order to more deeply learn the differences and comparative advantages of various scenarios, in this section, the effect of land system changes concerning three major aspects, i.e., urban expansion, grain yield, and ecology quality, are comprehensively explored.
4.5.1. Urban Expansion Analysis
To better understand the urbanization characteristics of the GBA under various scenarios, three different quantitative metrics were utilized to indicate the internal pattern over two typical periods, i.e., 2030–2050 and 2050–2070 (
Figure 13).
Generally, it can be observed that the UD scenario leads to much larger values of all three indices for most cities. More specifically, the developed cities, like Shenzhen, Zhuhai, and Hong Kong, which are affected by having a smaller jurisdiction and high-density construction, present a larger expansion intensity in both periods. Especially for Macao, it shows a significant expansion during the period between 2030 and 2050. However, restricted by the limited space, urbanization in Macao tends to be saturated in 2050–2070, with a very small expansion intensity. For the marginal cities with other major functions, like Zhaoqing and Huizhou, the broad jurisdiction makes urban sprawl inconspicuous and leads to a small expansion intensity.
On the other hand, Zhaoqing, Jiangmen, and Huizhou show a much larger expansion dynamicity compared with the developed cites, as a result of a smaller foundation area and high-intensity expansion. Meanwhile, it can be found that these three cities contribute most to the total urbanization of the GBA in both periods, accounting for more than half under the UD and CP scenarios during the period between 2030 and 2050 and under all three scenarios during the period between 2050 and 2070. Additionally, the contribution of Guangzhou and Foshan is also significant, especially under the ES scenario. In summary, the combination of further strengthening the core urban agglomeration and rapidly developing the marginal cities of the GBA is in accord with the general law of urbanization in China.
4.5.2. Grain Yield Estimation
To understand the impacts of different scenario modes on agricultural production, both the total grain yield of the GBA and the regional output of 11 internal cities under multiple scenarios in 2030, 2050, and 2070 were compared (
Table 5 and
Figure 14).
It can be found that the CP scenario has an obvious superiority in grain yield in comparison to the other two scenarios, with an increasing total grain yield, which indicates its effectiveness in ensuring food security. The total grain yield of the GBA reaches 8,072,893.5 tons, with an increment of 17.54% during the period between 2030 and 2070, accounting for 1.21% of the national grain yield according to the reports in 2020.
The distribution of the grain yield among 11 internal cities is basically same for different scenarios and different years, with the main grain producing areas, such as Zhaoqing, Jiangmen, and Huizhou, having the largest contribution, followed by Guangzhou and Foshan. The sum of grain production in Zhaoqing, Jiangmen, and Huizhou accounts for more than 64% of the total output for all cases, with Jiangmen contributing the most. The developed cities, like Hong Kong, Macau, Shenzhen, Zhuhai, and Zhongshan, have a much lower grain yield, as they are highly industrialized with little room for agricultural development. On the other hand, it can be noted that under the CP scenario, the grain yield gradually increases in Zhaoqing, Jiangmen, and Huizhou, but is kept stable in the other cities.
4.5.3. Ecology Quality Evaluation
To compare the subsequent effects of LUCCs on the eco-environment under different scenarios, the total ecology quality index of the GBA in 2030, 2050, and 2070 was evaluated (
Table 6), with regional ecology quality indices of 11 internal cities also provided (
Figure 15).
Affected by urbanization, the total ecology quality of the GBA decreases for all three scenarios, with the UD scenario suffering from the fastest decline. Comparatively speaking, the ES scenario has the best ecology quality in all three years and shows a significant superiority to the other two scenarios. It maintains a basic stability for the eco-environment, with little degradation in the next decades. The total ecology quality of the GBA is retained at a high-level of 5.1725 in 2070 under the ES scenario, but it is reduced below 5 in 2050 under the UD and CP scenarios, with an obvious backwardness of 0.91 and 0.85 in 2070. The reason is that, driven by specific development orientations, i.e., the emphasis on cultivated land [
93] and urban development priority [
94], more and more ecological lands are encroached upon; woodland has the greatest ecological contribution, resulting in the continuous degradation of the eco-environment.
Similarly, the distribution of ecology quality is basically same for the different scenarios. With the passage of time, the gap between the ES and the other two scenarios becomes more and more obvious for most cities. Naturally, the marginal cities with high ecological land coverage, like Zhaoqing, Jiangmen, and Huizhou, have a better ecology quality. In contrast, the inner cities of the urban agglomeration, which are dominated by an impervious layer, have a much lower ecology quality, such as Zhuhai, Zhongshan, Shenzhen, Dongguan, and Macao. However, Hong Kong and Guangzhou are exceptions. Benefiting from a high vegetation coverage, these two cities maintain a high ecological value with the developed economy, whose development mode provides a good reference for others.