1. Introduction
Land Use/Land Cover (LULC) change is one of the fundamental concerns in global environmental change and sustainable development. Rapid worldwide population growth accompanied by economic activities causing urban agglomeration and subsequent construction land expansion has led to rapid LULC changes [
1,
2,
3]. In connection to urban expansion, the less attention given to other LULC categories, particularly in developing countries, have resulted in various environmental consequences [
4,
5]. For example, it has been observed that the conversion of LULC into agricultural land and urban areas is destructive to various Ecosystem services (ESs), such as entertaining scenes [
6], genetic resources and nutrient cycling [
7,
8,
9], erosion control and climate regulation [
10], and water availability and soil fertility [
11]. The consequences of these changes result in the degradation of ESs, which is the aggregate of ecosystem goods (such as food) and services (such as waste assimilation), that represent the benefits human populations derive, directly or indirectly, from ecosystem functions [
12]. These impacts make the quantification of Ecosystem Service Values (ESVs) essential to raise awareness [
13], develop decision making for the distribution of scarce resources among conflicting demands [
14], incorporate ESs into the socioeconomic and marketing systems [
15], formulate policy [
16] and stimulate the conservation of ecosystems that deliver the most valuable services in support of human well-being [
17].
Following the pioneering works of Costanza et al. [
18], who estimated global ESVs by suggesting a list of ESV coefficients for different biomes, the evaluation of ESVs and their changes has received broad attention [
15,
16,
19]. Since then, to support mitigations of local degradation and global change problems, the interest in the valuation of ESs has grown rapidly in research and policy making communities. In particular, the dynamics of ESVs in response to changes in LULC have been widely considered in various academic fields [
20,
21,
22,
23]. For instance, in northwest China [
12], LULC changes driven mainly by the expansion of oasis agriculture, significantly impact ESVs and the functions of the Yanqi basin, by causing land degradation and changes in aquatic environment. The authors of [
14] estimated that changes in ESVs were the result of LULC dynamics in the Ethiopian highland area over decades of time. The authors showed agricultural land is expanded at the expense of natural vegetated areas with high ESVs. Moreover, the authors of [
22] considered changes in ESs using the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model in northern Thailand in response to LULC changes, mainly caused by the expansion of built-up areas and increases in rubber plantation cultivation, at the cost of natural environment. To understand and evaluate the consequences of these changes in the long run, the availability of reliable and adequate information on LULC change over time is becoming increasingly necessary [
23].
As considered by the authors of [
24], for sustainable development after evaluating how the land is used at the present time, an assessment of the future demand is needed, as are steps to guarantee the adequacy of the future supply. Thus, to answer the question as to how land use may change in the future, a modeling approach is thought to be a valuable tool. In recent years, researchers in various academic areas, ranging from those who favor modeling [
25,
26,
27], to those concerned with the causes and consequences of LULC dynamics [
28,
29], have been greatly attracted to the issues of LULC changes. To simulate possible LULC changes for future change detection, a large set of working models has been produced by the community studying LULC [
30]. Numerous kinds of models, such as Cellular Automata (CA) [
23], Markov chain [
1], Agent-based [
31] and CLUE [
26], have been developed for the prediction of LULC change. It has been suggested that a multidisciplinary model that combines elements of various modeling techniques could be extremely important in developing projections of future LULC change [
1].
In this research, for the purpose of monitoring the current LULC management practices and assessing the effects of LULC on ESVs in the future, we focus on the forecasting of future LULC changes. This can be explored using a CA–Markov model. It has been widely cited that the CA–Markov model is the most effective method for modeling the probability of spatiotemporal change in LULC along with a geographic information system [
32,
33]. In the CA–Markov model, a Markov chain concerned with the temporal change occurring among the different LULC types is constructed, grounded in the probability of change matrices [
34]. A CA model is used to forecast the patterns of spatial changes over time by considering the neighborhood configuration and a transition map of the LULC to be considered [
35]. With the advantage of hybridizing these two methods, the CA–Markov model can achieve better simulation of LULC disparities in both space and quantity [
36,
37]. This feature makes the model a robust LULC change simulation [
34,
35].
Since the economic reform and the open door policy in the late 1970’s, the regional economic developments of China have shown incredible change [
38]. As a result, primarily, many coastal areas of the country have experienced dramatic economic and spatial restructuring, resulting in tremendous LULC change [
39,
40,
41]. Empirical investigations of the coastal areas of China have shown that changes in LULC over the past two decades have been arguably the most widespread in the country’s history [
42], and the process has been more intense than in other areas and continues to worsen [
43]. These impacts lead to continuous deterioration and losses in the ecological values of these fragile environments.
As one of the main coastal areas of China, the characteristics of LULC change in the Su-Xi-Chang region are typical and representative of those resulting from rapid population and economic growth. Because of these changes, severe LULC change and loss of the semi-natural environment and associated ESVs have been experienced in this region [
44]. In their study, the authors of [
41,
42] indicated that there is a gap in detecting the future LULC changes and the associated threat to the ESVs of the Su-Xi-Chang region. Hence, it is important to conduct research to identify the extent and rate of LULC change for the future and to detect the impacts on ESs to sustain the ecological values of the region. Considering these factors, this study is designed with the objectives of (1) simulating and modeling the future distribution of LULC types in the Su-Xi-Chang region based on a CA–Markov model and exploring the change process; and (2) predicting the rate and extent of changes in the ESVs of the region for a future date, 2020, to enable the anticipation of future planning policies that seek to preserve the unique natural characteristics and ecological values of the studied landscape.
4. Conclusions
This study explored a future LULC change simulation using a CA–Markov model in combination with GIS technology and predicted the subsequent changes in ESV by using land use data and the modified ES coefficients of the studied landscape. The validation of our model with the actual data of the base year (2010) shows an overall satisfactory result, revealing that CA–Markov is an appropriate model for predicting future LULC change. The results of the predicted future LULC area changes indicate decreases in farmland, wetland, and water bodies, but increases in construction land, woodland, and grassland. From the temporal patterns of the changes between 2010 and 2020, wetland decreases at a higher rate, followed by farmland and water bodies. Furthermore, the patterns of LULC change over the three decades show that wetland decreased at an average rate of 14%, followed by farmland (13%) and water bodies (2.8%). However, at the expense of these LULC categories, construction land is expanding at a higher average rate (48%) than other LULC types. As a consequence, with the current trend in land management, these three LULC types with high ecological values have been continuously declining, leading to losses in the environmental and ecological values of the region.
The predicted ESV results reveal that each LULC category exhibited different trends of changes, in which the values for farmland, wetland, and water bodies were reduced, whereas the values for woodland, grassland, and construction land showed an increase. In 2020, farmland and water body are the dominant LULC types, providing more than 90% of the total ESV. Over the study period, the total ESV of the Su-Xi-Chang region decreased from 59.6652 billion CNY in 1990 to 52.2737 billion CNY in 2020, mainly because of the loss of farmland, water bodies, and wetland. During the study period, the water body class produced the largest proportion of the total ESV (73%), and combining water body and farmland accounted for more than 90% of the total ESV, indicating that the two LULC categories play a role in providing the highest ESs in the region.
Water supply and waste treatment were the top two ecological functions, accounting for more than 68% of the total, mainly provided by water body and wetland cover types, followed by woodland and farmland. Even though water body and farmland are the dominant LULC classes providing a major share of the ESVs in the Su-Xi-Chang region, these two categories are becoming highly degraded. For sustainable management, a compromise between the current patterns of LULC change and ecological protection must be reached. A reasonable land use plan should be made with an emphasis on controlling construction land (industrial, commercial, residential) encroachment on farmland, wetlands, and water bodies. Furthermore, the rules of ecological protection should be followed in LULC management to preserve ecological resources and benefit society. Thus, decision makers should plan alternative conservation activities to enact improved LULC management practices for sustainable and balanced ecological protection.