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Predicting trends in land use/cover change (LUCC) and assessing future ecological security (ES) can help effectively balance regional ecological conservation and economic development. In this study, a land use simulation model was used to predict the spatial-temporal variation in land use in Kashgar in 2030 under three scenarios (inertial development, agricultural land protection, and forest and rangeland conservation). Subsequently, by combining the pressure–state–response model and predicted land use, the ES of the Kashgar region in 2000, 2010, and 2020 were evaluated. We found that (1) only agricultural land and built-up land in the Kashgar region increased from 2000–2020 by 4174.79 and 434.44 km2, respectively. (2) The area of the region belonging to a generally safe level or above decreased from 38.36% in 2000 to 36.89% in 2010 and then increased to 37.71% in 2020. (3) In 2030, the ES status under the three scenarios will be improved, among which the agricultural land protection scenario corresponds to the best ES status. This research is valuable for deeply understanding the interaction mechanism between LUCC and ES in typical artificial oases in arid areas of the Kashgar region and sustaining relatively stable internal structures and socioeconomic activities in the arid oasis of the Kashgar region.
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1. Introduction
Currently, environmental problems such as global warming, water scarcity, deteriorating air quality, marine pollution, and the drastic decline in biodiversity are emerging, with the global ecosystem being seriously disturbed and degraded [1]. In this context, ecological security (ES) has received widespread attention from government decision-making departments and academics [2]. ES emphasizes maintaining the interaction between organisms and their environment, and its fous is to maintain the normal ecological operations [3]. Scholars have conducted considerable ES evaluation works and have achieved fruitful results from different research scales, objects, and methods [4]. The research scale involves urban agglomeration [5], watersheds [6,7], and cities [8]. The research objects include forests [9,10], land [11,12], and landscapes [13]. The research methods include pressure–state–response (PSR) models [14], ecological footprints [15,16,17], and ecosystem service values [18,19]. Among these methods, the good performance of the PSR model makes it widely used in ES evaluation at different spatial scales. From the perspective of the organic integration of socioeconomics and the environment, the PSR model shows the causal relationship between various factors in the ecosystem of humans and nature. The model can not only accurately reflect the relationship among natural, economic and social factors of ecosystem security, but it can also provide a logical basis for the construction of ES indicators [20]. At present, scholars are concerned about how to incorporate systematic and dynamic indicators into the PSR framework for ES evaluation [21]. An increasing number of studies are experimenting in this regard. For example, Das et al. [22], Grecchi et al. [23], and Ghosh et al. [24] used the PSR model to effectively explore the ES assessment results, and the applicability of the model was well verified.
Scholars’ evaluations of the current situation of ES have made great achievements. The spatial pattern of regional surface characteristics changed by LUCC also shows the temporal and spatial changes in regional ES from the side [25], which can effectively assess the impact of LUCC on ES [26]. However, how will future LUCC affect regional ES? How to improve ES by optimizing the land use pattern still needs to be investigated in depth. In view of this, strategies for simulating future LUCC and evaluating ES by setting scenario models have emerged [21]. The basis of these strategies is to use reliable models to make reasonable and accurate predictions of future land use. Some land use prediction models, including CA-Markov [27,28], Conversion of Land Use and its Effects at Small regional extent [29], and future land use simulation [30,31], have been developed. Moreover, the patch-generating land use simulation (PLUS) model retains the advantages of the adaptive inertial competition and wheel competition mechanisms of existing models [32,33]. Effective determination of the development potential of each land use type through the random forest algorithm shows good performance in land use simulation [34,35].
Most of the research focuses on nonarid oases in developed coastal economies [8,17], while there are few studies focusing on typical artificial oasis areas with relatively backward economies in arid regions [4,36]. Therefore, the impact of LUCC interaction within the ecological environment on oasis cities in arid areas is ignored. Oases are important places for relatively stable internal structures and socioeconomic activities in arid regions, and their survival and development depend on surface rivers [37]. The Kashgar region, as an oasis city in an arid area, is experiencing an increase in land use intensity, and the imbalance of ecological carrying capacity, such as resource shortage, environmental pollution and water pollution, poses severe challenges to the fragile ecological environment of the Kashgar region, so it is imperative to study the ecological environment of the Kashgar region. Second, the Kashgar region is an important window for China’s opening to the outside world, and its ES is related to the sustainable socio-economic development of the region. The establishment of an ES evaluation index system in the Kashgar region for ES assessment and prediction is helpful to understand the interaction mechanism between the LUCC and ES for oases in arid areas.
2. Materials and Methods
2.1. Study Area
The Kashgar region (35°20′–40°18′ N and 73°20′–79°57′ E) is located in the middle of the Eurasian continent, southwest of Xinjiang, China. The average elevation is 1289 m, the highest altitude is 8560 m, and the lowest altitude is 532 m. The climate is characterized by dry and cold winters and dry and hot summers. The topographic trend is lower in the northeast and higher in the southwest (Figure 1). Large areas of glaciers and permanent snow cover are distributed in Tashkurgan County. Deserts and gobi are widely distributed, with low vegetation coverage, low rainfall, high evaporation, and a harsh ecological environment.
2.2. Data Sources
In this study, the data used by the PSR model and the PLUS model are illustrated. The coordinate system of the data used in this study was WGS-84. The PSR model requires land use data (2000, 2010, 2020) with a spatial resolution of 30 m from the Resource and Environmental Science and Data Center, Chinese Academy of Science (
2.3. Methods
2.3.1. PLUS Model and Calibration
The land types in the study area for all three years included six types of land: agricultural land, forest, rangeland, water areas, urban or built-up land, and barren land. The land use data of 2010 and 2020 were input into the PLUS model to extract the land use changes of the two periods. The driving force of land use change was mainly influenced by natural and socioeconomic factors [35]. Among the natural factors, topographic and climatic factors influence the distribution and transformation probability of land use types; among the socioeconomic factors, human activities mainly concentrate on areas with production advantages, such as economy, population, and transportation. According to the actual situation of the Kashgar region and the availability of data, a total of 17 driving factors of the digital elevation model (DEM) were selected to determine the suitability probability of six land use types: slope; topographic relief; NDVI; temperature; precipitation; dryness; soil type; soil erosion type; population density; GDP; distance from primary, secondary, tertiary, and trunk roads; distance from railways; and distance from water. Based on the 2010 land use data, six suitability probabilities were entered, the 2020 land use distribution map was predicted, and the Kappa value was calculated as 0.96. This model simulation effect is significant and can be applied to the spatial simulation of land use change in the Kashgar region area.
2.3.2. Scenario Simulation
In the PLUS model, three scenarios (inertial development, agricultural land protection, and forest and rangeland conservation) were set to simulate the Kashgar region’s LUCC status in 2030 (Table 1). Scenario I: The inertial development scenario only needs to consider the original transformation of land use types under inertia in the Kashgar area and does not consider the constraint impact of planning policies on land use change. Scenario II: In the agricultural land protection scenario, agricultural land is restricted from being transferred out. Scenario III: In the forest and rangeland protection scenario, the conversion of forest, rangeland, and water areas for other uses is restricted, and the transformation of land use types to forest, rangeland, and water area is accelerated.
2.3.3. ES Index System Construction
The index system was constructed using the PSR model, which was based on previous relevant studies and combined with the specific environmental conditions in the arid region of Northwest China (Figure 2). Seven indicators were selected to characterize the impact of human activities and the pressure of these activities on the ecological environment (P). They were GDP, population density, fertilizer inputs per unit of agricultural land, land use degree, human disturbance index (HDI), per capita food production, and per capita agricultural land area. In addition, 11 indicators, namely, slope, topographic relief, NDVI, NDVI stability, Shannon’s diversity index (SHDI), ecosystem service value [44], ecosystem resilience [45], temperature, precipitation, precipitation stability, and proportion of water areas, were used to characterize the regional environmental state (S). Per capita GDP and the regional development index (RDI) were used to delineate response indicators (R).
In the ES index system of the Kashgar region, fertilizer inputs per unit of agricultural land in 2030 were not applicable and were replaced with the corresponding data in 2020. Slope and topographic relief used 2020 data. The land use degree, HDI, per capita food production, per capita agricultural land area, SHDI, ecosystem service value, ecosystem resilience, proportion of water areas, and RDI data came from the land use simulation data under three scenarios. The temperature, precipitation, and precipitation stability data were derived from the 2021–2100 China 1 km-resolution multiscenario and multimode monthly precipitation and temperature dataset of the National Tibetan Plateau Data Center. The NDVI was derived from the average of the 2000–2020 growing seasons. The GDP, population density, and per capita GDP data were simulated values based on time series analysis.
Among the indicators, land use degree, HDI, RDI, SHDI, ecosystem resilience, and ecosystem service value were calculated based on the land use types. NDVI stability and precipitation stability were calculated based on the coefficient of variation [46].
2.3.4. ES Evaluation
The calculation method of index weights in this paper is the entropy weight method, so the introduction of the ecological safety index (ESI) is closer to reality. The entropy weight method assigns indicators by comparing the information differences between indicators, which is more objective because it can effectively avoid the interference of human factors in solving the weights and has been widely used in multi-index evaluation systems [47].
Range standardization: The evaluation indicators in this paper have both positive and negative indicators, and different directional indicators have different standardized treatment methods. Equations (1) and (2) are:
(1)
(2)
where Yij is the standardized value, Xij is the original value of the indicator, Xmaxj is the maximum value, and Xminj is the minimum value.The information entropy of the indicator is calculated as follows (Equation (3)):
(3)
where and n is the year.The weight of the metric is calculated as follows (Equation (4)):
(4)
where Wj is the indicator’s weight and m is the number of indicators.The comprehensive index of ES in the Kashgar region was calculated by the weighted sum of each index, which was used to characterize the comprehensive level of regional land ES. ESI assesses the degree of ES of an area [48] in a range of values [0, 1]. Equation (5) is:
(5)
where ESI is the ecological safety index, Pi is the standardized value of the indicator, and Wi is the indicator’s weight.The natural breakpoint method was used to classify the ESI into five levels (Table 2): unsafe, relatively unsafe, generally safe, relatively safe, and safe [49]. Using the ecological rank criteria predicted through the simulation of different scenarios in 2000, 2010, and 2030, in line with the natural breakpoint rank criteria in 2020, can make the evaluation results comparable.
3. Results and Analysis
3.1. Dynamic Changes in LUCC from 2000 to 2020
In 2000, 2010, and 2020, the proportions of barren land in the main land use types in the Kashgar region were 53.75%, 52.80%, and 52.44%, respectively. During this period, the areas of barren land, forest, rangeland, and water areas showed a downward trend, whereas the areas of agricultural land and urban or built-up land increased. Specifically, the agricultural land area increased by 4174.79 km2, and the urban or built-up land area increased by 434.44 km2. The rangeland and barren land decreased by 1797.75 km2 and 1515.78 km2, respectively. From the perspective of the area change rate, from 2000 to 2020, the variation in forestland in the Kashgar region was the largest, with a change rate of −59.15%, followed by urban or built-up land, with a change rate of 55.69% (Table 3).
3.2. Scenario Simulation Results
The overall pattern of land use in the inertial development scenario, agricultural land protection scenario, and forest and rangeland conservation scenario is the same, and these scenarios will continue the general pattern of land distribution in 2020. Under the inertial development scenario (Figure 3a), the land use transformation in the Kashgar region mainly includes forest, rangeland, and water areas, and a large amount of urban or built-up land will be transformed into agricultural land and urban or built-up land. Under the agricultural land protection scenario (Figure 3b), the agricultural land area will increase by up to 15,179.50 km2, whereas the urban or built-up area will decrease. Under the forest and rangeland protection scenario (Figure 3c), the forest, rangeland, and water areas will increase to 1060.30 km2, 33,044.48 km2, and 4965.14 km2, respectively.
Under all three scenarios, agricultural land will show an increasing trend, and the largest increase in the agricultural land protection scenario was 13.67%. However, the water area and barren land will exhibit a slight decreasing trend. Urban or built-up land will have the largest increase in area under the inertial development scenario. Forestland and rangeland will be basically maintained under the forest and rangeland protection scenario (Figure 4).
3.3. Changes in ESI in 2000–2020
The ESI of the Kashgar region decreased from 2000 to 2010 and increased from 2010 to 2020, but the fluctuation difference was not obvious (Figure 5). The average ESI in 2000, 2010, and 2020 was 0.1618, 0.1548, and 0.1626, respectively. Spatially, the high-value areas of the ESI were in three areas: Kashgar–Shule, Jiashi–Yuepu Lake–Yingisha, and Makati–Shache–Zepu–Yecheng. These high-value areas had low altitude, small undulations, extensive agricultural land, good vegetation growth conditions, and great ES. The desert in the northeast and unused areas in the southwest had low values; where the vegetation was sparse, the hydrothermal conditions were poor, the desertification stress was greatly affected, and the ES was weak. From 2000 to 2020, the ESI of the Kashgar region showed that the low-value areas were basically unchanged spatially, while the high-value areas increased slightly with the expansion of agricultural land, water areas, forest, and rangeland.
3.4. Changes in ES Levels from 2000 to 2020
The area in the Kashgar region at unsafe and relatively unsafe levels increased from 60.77% in 2000 to 64.19% in 2010 and decreased to 63.39% in 2020. The area at a generally safe level decreased from 22.80% in 2000 to 20.71% in 2010 and continued to decrease to 19.45% in 2020. The area at safe and relatively safe levels declined from 16.43% in 2000 to 15.10% in 2010 and rose to 17.16% by 2020 (Figure 6).
The ES of the Kashgar region is in a relatively fragile state and is vulnerable to external interference, and the ecological function is unstable. According to the hierarchy, the safe areas are concentrated in the areas of the Kashgar region, which is agricultural land (Figure 7). These areas are low in altitude, the terrain is small and undulating, and the land use type is mostly agricultural land with good water areas and soil conditions, so the ES level is high. The relatively safe areas are mainly water areas and urban or built-up land areas. Most of these areas are oases with water area sources and relatively high ES levels. The land use types in generally safe areas are mainly rangeland and forestland, distributed in the periphery of oases and desert-interlaced zones vulnerable to land degradation and desertification. The types of land use in relatively unsafe areas and unsafe areas are mainly barren land and mountainous rangeland, which have harsh natural environments, large undulating mountainous terrain, sparse vegetation in high-altitude areas, frequent geological disasters, and fragile ecological environments.
3.5. Prediction of ES Evaluation Results
Compared with the 2000–2020 data, the ESI in the Kashgar region under the three considered scenarios in 2030 will increase, and the ES status will improve gradually (Figure 8). The proportion of area under different safety levels will change. Only 57% of the areas under the agricultural land protection scenario will be at unsafe and relatively unsafe levels, while 65% of the areas under the inertial development scenario and the forest and rangeland protection scenario will be at these levels. The areas under the inertial development scenario and the forest and rangeland protection scenario will account for 16% of the generally safe level, and the agricultural land protection scenario will account for 20%. The areas in the safe and relatively safe levels will be the largest in the agricultural land protection scenario. Between 2020 and 2030, the proportion of areas at the relatively unsafe level will decrease, and the harsh environment will improve gradually. The areas at the relatively safe level will increase, and the environment of generally safe areas will gradually be enhanced.
4. Discussion
In recent years, many domestic scholars have explored the ES status of the eastern regions of China, such as the Beijing–Tianjin–Hebei urban agglomeration [50], the Pearl River Delta [51], and the arid areas of Northwest China, such as Urumqi [52], Bortala [53], and the Altay region [54]. The above results show that the spatial variation distribution of ES is consistent with the temporal variation in LUCC [1]. In the normal eastern region, the eco-security zone is mainly in areas with a low degree of human impact. The research results of Cui et al. [21] in non-arid regions show that the areas with high ecological safety levels are mainly in mountainous areas with low impact of human activities, such as forests and waters; the low ecological safety areas are mainly in areas with high arable land utilization and urbanization. However, the artificial oasis in the northwestern arid region is the opposite of the normal eastern region. For example, Song et al. [55] concluded that the agricultural land area in the Aral Reclamation Area increased from 1990–2019 and that the barren land area decreased. Among them, the ecologically fragile desert areas were transformed into artificial oasis areas with stable ecosystems due to the increasing demand for agricultural production, which greatly reduced the ecological risk in these areas; that is, the ES continues to improve, which is the main influencing factor. In this study, due to the huge impact of agricultural land and urban or built-up land, arid areas are greatly affected by human transformation. Most of the areas with high values of ES are distributed in the artificial oases in Kashgar, while most of the low-ES areas are concentrated in desert and mountain areas. The area of the Kashgar region comprising artificial oases belonging to the safe level decreased from 10,153.01 km2 in 2000 to 9532.75 km2 in 2010 and then increased to 12,366.85 km2 in 2020. The area comprising desert and mountainous areas belonging to the unsafe level increased from 40,903.09 km2 in 2000 to 43,936.77 km2 in 2010 and then decreased to 40,639.32 km2 in 2020.
The use of PLUS for land use prediction simulation is not only conducive to better mining of the driving factors of LUCC [56], but the high-resolution changes in land use can be better represented by simulation. The model is beneficial to optimizing the land use model under multiple scenarios in the Kashgar region [35]. The research indicates that ES in the Kashgar region will not change significantly by 2030, which also reflects the status of NDVI stability and ecosystem service value as the main control factors dominate in the overall distribution characteristics of ES in the Kashgar region. Among the three scenarios, the agricultural land protection scenario corresponds to the best ES status, and the area belonging to the general safe level or above is 39,533.94 km2. This finding confirms that the ES of the oasis itself is the core for maintaining the arid region in Northwest China, and the continuous protection of the oasis and the prevention of soil salinization are important guarantees for the ES of the Kashgar region. At the same time, the natural forest and rangeland ecosystems can prevent and guarantee potential soil erosion and desertification in the southern mountainous areas and then make significant contributions to combating desertification and decreasing ES of the oases in the entire study area [57].
The ecological environment of oasis areas is expected to continue to improve with the recent implementation of policies such as land greening and rural revitalization [58], as well as the continuous improvement in living conditions. However, in consideration of the poor ecological environmental conditions in desert and mountainous areas, coupled with the constraints of local funds and water area resources, there is great uncertainty in improving the poor ES [59]. Management decision-making is required to start, as well as expand upon, ecological restoration from the perspective of integrating mountains, water, forests, lakes, grasses, and sands; scientifically compile medium- to long-term ecological restoration plans; and actively strive for national and regional matching funds. Decision-makers can implement precise policies and practice key project governance on the ecological conditions to continuously improve the environmental performance of the entire Kasgar region [60]. The land use simulation model still contains uncertainties in land use prediction due to the required analysis of factors that hardly regularize spatialization, such as policies. At the same time, the case study based on an ES assessment of a land use simulation proposed in this paper needs further improvement in the constructed index system to address the difficulty of obtaining and quantifying some data [61].
5. Conclusions
The ES status in the Kashgar region is generally safe, the areas with good ES are in the oases, and the local ES conditions in mountainous and desert areas are relatively fragile. From 2000 to 2020, the ES status in the Kashgar region showed a trend of degradation first and gradual improvement afterwards to a generally safe level or higher, decreasing from 38.36% in 2000 to 36.89% in 2010 and then increasing to 37.71% in 2020. The spatial characteristics demonstrate that the ecologically safe area of the Kashgar region was roughly the same as the distribution area of agricultural land. Because the natural environment of the arid region in the northwest is harsh, the oasis area of human activities is highly consistent with the ES area. From 2000 to 2020, due to the increase in forest and rangeland types in the internal area of the oasis, the ES area was dominated by the internal distribution of the oasis. In addition, the degree of ES had generally the same spatial distribution characteristics as taking the center of the oasis as the core and gradually decreasing the degree of ES to the periphery of the oasis. A comparison of the area of land use types under the three scenarios in the Kashgar region in 2030 and the area in 2020 (Figure 4) shows that the area to be transferred into agricultural land will be the largest. Compared with the 2020 data, the ESI will generally increase, and the ES areas will expand and exhibit a change process of “generally safe–relatively safe–safe.” These three scenarios have a certain role in promoting ecosystem security, food security, homeland security, and water area security in the Kashgar region. In particular, the safety zone of the agricultural land protection scenario is the largest, which is expected to form the highest ES status.
Conceptualization, Y.M. and Z.X.; methodology, H.R.; software, H.R.; validation, H.R.; formal analysis, H.R.; investigation, H.R.; resources, H.R., Y.M., and Z.X.; data curation, H.R.; writing—original draft preparation, H.R.; writing—review and editing, Y.M. and Z.X.; visualization, H.R.; supervision, Y.M.; project administration, Y.M.; funding acquisition, Y.M. and Z.X. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The land use, DEM, and population density data come from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. The meteorological data come from the National Tibetan Plateau Science Data Center. NDVI comes from the United States Geological Survey. Statistical data are from the Statistic Bureau of Xinjiang Uygur Autonomous Region. Road data comes from Open Street Map.
The authors declare no conflict of interest.
Footnotes
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Figure 4. Comparison of the proportion of land use under the three scenarios in the Kashgar region in 2020 and 2030.
Figure 8. Comparison of the proportion of ES levels in the Kashgar region under the three scenarios in 2020 and 2030.
Transformation cost matrix in three scenarios during 2030.
| Scenario | Land Use Type | Agricultural Land | Forest | Rangeland | Water Areas | Urban or Built-Up Land | Barren Land |
|---|---|---|---|---|---|---|---|
| I | Agricultural land | 1 | 0 | 1 | 0 | 1 | 0 |
| Forest | 1 | 1 | 1 | 0 | 1 | 1 | |
| Rangeland | 1 | 0 | 1 | 0 | 1 | 0 | |
| Water areas | 0 | 0 | 1 | 1 | 0 | 1 | |
| Urban or built-up land | 1 | 0 | 0 | 0 | 1 | 0 | |
| Barren land | 1 | 0 | 0 | 0 | 1 | 1 | |
| II | Agricultural land | 1 | 0 | 0 | 0 | 0 | 0 |
| Forest | 1 | 1 | 1 | 0 | 0 | 1 | |
| Rangeland | 1 | 0 | 1 | 0 | 0 | 0 | |
| Water areas | 1 | 0 | 1 | 1 | 0 | 1 | |
| Urban or built-up land | 1 | 0 | 0 | 0 | 1 | 0 | |
| Barren land | 1 | 0 | 0 | 0 | 0 | 1 | |
| III | Agricultural land | 1 | 1 | 1 | 1 | 1 | 0 |
| Forest | 0 | 1 | 1 | 1 | 0 | 0 | |
| Rangeland | 0 | 1 | 1 | 1 | 0 | 0 | |
| Water areas | 0 | 1 | 1 | 1 | 0 | 0 | |
| Urban or built-up land | 1 | 1 | 1 | 1 | 1 | 0 | |
| Barren land | 1 | 1 | 1 | 1 | 0 | 1 |
Grading standards for ecological security in the Kashgar region.
| Unsafe | Relatively Unsafe | Generally Safe | Relatively Safe | Safe |
|---|---|---|---|---|
| <0.1151 | 0.1151–0.1626 | 0.1626–0.2293 | 0.2293–0.3080 | >0.3080 |
Schedule of changes in land use area in the Kashgar region from 2000 to 2020.
| Year | Area Changes | Agricultural Land | Forest | Rangeland | Water Areas | Urban or Built-Up Land | Barren Land |
|---|---|---|---|---|---|---|---|
| 2000 | Area/km2 | 9086.59 | 1701.48 | 34,928.96 | 5840.09 | 345.63 | 60,321.413 |
| Proportion | 8.10% | 1.52% | 31.12% | 5.20% | 0.31% | 53.75% | |
| 2010 | Area/km2 | 11,159.61 | 1086.60 | 35,049.76 | 5066.20 | 631.08 | 59,290.69 |
| Proportion | 9.94% | 0.97% | 31.22% | 4.51% | 0.52% | 52.80% | |
| 2020 | Area/km2 | 13,261.38 | 1069.14 | 33,131.21 | 5083.84 | 780.07 | 58,805.63 |
| Proportion | 11.83% | 0.95% | 29.55% | 4.53% | 0.70% | 52.44% | |
| 2000–2020 | Variation area/km2 | 4174.79 | −632.34 | −1797.75 | −756.25 | 434.44 | −1515.78 |
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