Spatiotemporal Changes in The Urban Landscape Pattern and Driving Forces of LUCC Characteristics in The Urban Agglomeration on The Northern Slope of The Tianshan Mountains from 1995 to 2018

Abstract

Analyses of landscape patterns, analyses of land-use/land-cover evolution characteristics, and a driving force analysis during the expansion of urban agglomerations can assist urban agglomerations in solving ecological and environmental problems; moreover, these tools can provide a reference for urban land-use structure optimization and urban landscape planning. In this study, the rapid urbanization and urban agglomeration of small- and medium-sized cities were evaluated; specifically, the urban agglomeration of the northern slopes of the Tianshan Mountains (UANSTM) was assessed. Using multi-period (1995, 2000, 2005, 2010, 2015, 2018) land-use data and statistical yearbook data, we calculated the landscape index and comprehensive land-use degree index using the Moran index and geographic probe methods. We studied the expansion patterns and spatial distribution patterns of urban land and quantitatively explored the influence mechanisms of natural and socioeconomic factors on the degree of land use to clarify the characteristics and driving forces of land-use evolution. The results show the following: the area of urban land continued to increase between 1995 and 2018; the dominance of the landscape indexes within each time period changed with urban development; and intra-urban landscape heterogeneity is gradually increasing. In terms of the spatial and temporal distribution of the degree of land use, the expansion of urban agglomeration is centered on the city of Urumqi, spreading towards the cities of Changji and Shihezi; the rest of the counties and cities are fast urban-expansion zones. Under the influence of multiple source factors, the expansion of the UANSTM depends on the distribution of oases, which is mainly influenced by the distribution of vegetation and the density of the river network and can only rely on the ecological carrying capacity of oases for production and life. The results of the study can provide a basis for decision-making processes surrounding the future layout of the UANSTM ecological environment.

1. Introduction

Cities are the most direct carriers of land-use/-cover change (LUCC) [1], and urbanization is an inevitable result of global economic development—cities are regarded as the future environment for all human beings [2]. Rapid urbanization is bound to produce urban ecological problems such as ecosystem decline, biodiversity reduction, and urban heat island effect, which directly or indirectly affect the quality of urban habitat and sustainable urban development [3]. The emergence of these problems is closely related to land-use type conversion and landscape pattern changes in the region [4]. In this process of rapid urbanization, the contradiction between the expansion of urban land and limited land resources—between rapid urbanization and the ecological environment—is gradually emerging, especially in developing countries. Therefore, analyzing the urban expansion and landscape pattern changes and driving forces can help to both solve these urban problems and provide a scientific basis for the optimization and upgrade of urban land-use structures.

Regarding the analysis of urban landscape pattern changes, scholars have conducted a large number of studies globally. Research on landscape patterns began in the 1950s, focusing on the characteristics and driving mechanisms of spatial and temporal changes in landscape patterns [5] and the prediction of landscape patterns [6]. With the rapid development of remote sensing and geo-information systems, the combination of the two methods has become an effective research method for analyzing changes in landscape patterns, mainly including dynamic monitoring and landscape index analysis—such explorations have changed in nature from qualitative research to quantitative research [7]. Domestic research on landscape patterns started relatively late, but the results are fruitful. The relevant literature mainly focuses on the following aspects: The analysis and simulation research of landscape pattern evolution, using the CA–Markov model and the FLUS model, has been performed to simulate future land-use conditions, allowing the prediction of landscape patterns [8,9]. Secondly, researchers have combined landscape patterns and ecological risk models and have built an ecological network with the help of the least-resistance model to allow the optimization of landscape pattern formulation and ecological risk assessments in a given region [10]. Thirdly, researchers have analyzed the driving factors of landscape patterns using geographic detectors, grey correlation analyses, and other methods to gain insight into the driving factors that affect the evolution of the landscape pattern in the region [11,12]; however, due to the different geographical locations of the study areas, there are huge differences in the driver databases chosen in these studies [13]. Based on the above research results at home and abroad, we can see that current research mainly focuses on regional units, such as watersheds, forests, wetlands, and cities; meanwhile, fewer analyses have been performed on the landscape pattern evolution characteristics and driving forces of urban agglomerations in arid zones. Inspired by the above related studies, we consider the landscape pattern evolution characteristics of oasis urban agglomerations in arid zones: What are their driving forces? What are the links between landscape pattern evolution and land-use intensity in terms of spatial divergence patterns? Therefore, we selected the urban agglomeration on the northern slope of the Tianshan Mountains, in the arid region of Northwestern Xinjiang, China, as our research object; here, we analyzed the evolution characteristics and driving forces of its landscape pattern.

In recent years, human activities have gradually increased in the degree of disturbance for the ecological environment and the intensity of land use; thereby, the regional landscape pattern has changed, and the stable development of the ecological environment has been affected [14]. As China’s economic and social development center gradually shifts to the west, small- and medium-sized cities have gradually grown into the mainstay of promoting national economic growth and urbanization [15]. These small- and medium- sized cities have gradually developed into urban agglomerations. However, due to the constraints of geographical location, development scale, transportation conditions, resources and environment, and economic development level, the urbanization process of small- and medium- sized urban agglomerations may show different characteristics from those of large urban agglomerations [16]. Recent studies have mainly focused on large and mature urban agglomerations, such as the Yangtze River Delta urban agglomeration [17], the Beijing-Tianjin-Hebei urban agglomeration [18], and the Pearl River Delta urban agglomeration [19]; in these studies, insufficient attention has been paid to the rapidly urbanizing small- and medium-sized urban agglomerations. The Tianshan North Slope City agglomeration, located in the northern Tianshan Mountains in Xinjiang, Northwest China, is the most economically developed region in the northern Tianshan Mountains and is one of the most important nodal city clusters for the development of Western China. In the critical period of comprehensive urbanization in Xinjiang, the study on the landscape pattern and land-use intensity of urban agglomerations on the northern slope of the Tianshan Mountains not only enhances the mechanisms of the expansion pattern and the mechanisms of small- and medium-sized urban agglomerations, but also has guiding significance for the territorial spatial planning of small- and medium-sized urban agglomerations that are gradually developing in similar regions. Therefore, we took the urban agglomeration on the northern slope of Tianshan Mountains as the study area; we used multi-period land-use data and applied landscape pattern analysis, spatial autocorrelation analysis, and geographic probe methods to study landscape morphology and pattern changes within the urban agglomeration; finally, we quantitatively explored multiple factors in the dynamic changes of landscape fragmentation and their driving factors.

2. Study Area and Data Source

2.1. Study Area

Located at the northern foot of the Tianshan Mountains in Xinjiang, the urban agglomeration on the northern slope of the Tianshan Mountains (UANSTM) is the most economically developed area in Xinjiang, China. Taking Urumqi City, Shihezi City, and Karamay City as the central axis, the UANSTM includes Urumqi City, Changji City, Fukang City, Hutubi County, Manas County, Shihezi City, Shawan County, Wusu City, Kuitun City, and Karamay City (Figure 1). The economic belt on the northern slope of the Tianshan Mountains covers an area of about 95,400 km², with a population of about 4.58 million, accounting for 23.3% of the total population of Xinjiang [20]. The UANSTM is located in the southeast and northwest, with an altitude of 400–600 m, and it is the most densely covered oasis area in Xinjiang. The economic belt on the UANSTM will become the main area of Xinjiang’s new urbanization construction and the strategic hub area for promoting economic development, which makes it an important engine for promoting better and faster development in the new era of Xinjiang.

2.2. Data Sources and Pre-Processing

In this study, we used 1995–2018 land-use data with a resolution of 30 m from the Resource and Environmental Science Data Center of the Chinese Academy of Sciences. The original 25 land-use types were reclassified into urban areas, cropland, forest, grassland, water, and unused areas (Table 1). The meteorological data used comprise the annual average precipitation, annual average temperature, monthly precipitation, sunshine hours, evaporation, and daily average wind speed from 1981–2015 in Urumqi. Digital elevation model (DEM) data were obtained from the geospatial data cloud. Socioeconomic data were obtained from the statistical yearbooks of Xinjiang Uygur Autonomous Region from 1990 to 2018.

3. Research methods

3.1. Dynamic Degree of Land Use

The dynamic degree of land use refers to the quantitative change in a certain land-use type in a certain period of time in the study area [21]. The formula is as follows:

(1)$K=\frac{U_b-U_a}{U_a}\times \frac{1}{T}\times 100\%\%$

where K is the dynamic degree of land use; U_a and U_b are the areas of a certain land type at the beginning and end of the study, respectively; and T is length.

3.2. Landscape Pattern Analysis

The landscape index is a simple quantitative index which contains highly concentrated information about the landscape pattern and reflects the characteristics of its structure and spatial allocation [22]. We selected the patch density (PD), edge density (ED), and largest patch index (LPI) to represent the characteristics of the individual landscape units; the Shannon diversity index (SHDI) was used to characterize the overall landscape diversity (Table 2).

3.3. Degree of Comprehensive Land Use

The degree of comprehensive land use is the degree of human disturbance to the land ecosystem [23,24]. According to the comprehensive analysis method [25], the land use was divided into four levels according to the equilibrium state of the natural land complex, and it was given as an index (Table 3). The change in the degree of land use reflects the development of the land use. The formula is as follows:

(2)$\Delta L_{b-a}=L_b-L_a=100\times \left({\displaystyle \sum }_{i=1}^n{A}_i\times C_b-{\displaystyle \sum }_{i=1}^n{A}_i\times C_a\right)$

where L_a and L_b are the comprehensive indexes of the land-use degree at times a and b, respectively; ΔL_b–a is its variation; C_a and C_b are the area proportions of a certain land type at times a and b, respectively; A_i is the grading index; and n is the grading number of the degree of land use. If ΔL_b–a > 0, the land use is in a period of development; otherwise, it is in a period of adjustment or decline.

3.4. Spatial Autocorrelation Analysis

A spatial autocorrelation analysis can be used to explain the aggregation characteristics of the landscape, and it can be divided into global spatial autocorrelation and local spatial autocorrelation [26]. Moran’s I is used to measure the degree of spatial autocorrelation. Anselin proposed a bivariate spatial autocorrelation analysis to explain the correlations between the spatial variables and other variables in adjacent areas [27]. The calculation formula is:

(3)$I={\displaystyle \sum }_{i=1}^n{\displaystyle \sum }_{j=1}^n{W}_{ij}\left(x_i-x\right)/S^2{\displaystyle \sum }_i{\displaystyle \sum }_j{w}_{ij}$

where n is the total number of patches; X_i and X_j are the surface temperature grades of patches i and j, respectively; W_ij is the spatial weight matrix; x is the average value of all of the patches; W_ij is the weight of patches i and j; and S is the standard deviation.

A local autocorrelation analysis was used to analyze the spatial correlation between the variables in the local area. The Lisa distribution map based on the Z-test can directly show the aggregation and differentiation characteristics of the variables in the local area. The calculation formula is as follows:

(4)$I=\left(x_i-\stackrel{-}{x}\right){\displaystyle \sum }_{i=1}^n{W}_{ij}\left(x_j-\stackrel{-}{x}\right)/S^2$

where I is the autocorrelation value of the local space.

3.5. Driving Force Analysis

Geographic Detector is a spatial analysis model with four levels: risk, factor, ecology, and interaction [28]. Among them, factor detection is mainly used to identify the independent variables that affect the dependent variables, while interactive detection is used to judge whether the effects of the different independent variables are enhanced or weakened when they act on the dependent variables at the same time [29] (Table 4).

4. Results

4.1. Temporal and Spatial Changes in Land Use Types

Land use in UANSTM produced significant changes in time and space over the period 1995–2018. In general, the area of cropland and urban land continues to increase; the area of water, forest, and grassland decreases year by year; the area of unused land does not change much. From the viewpoint of counties and cities, urban land in all regions showed an increasing trend, among which the urban land area in Urumqi grew the fastest, with an average annual growth of 24.08 km². The cropland area showed a decreasing trend only in Urumqi and Shihezi, while the remaining nine regions showed an increasing trend. Forest, grassland, and water areas all show decreases, but the trend is gradually leveling off.

4.2. Analysis on the Dynamic Degree of Land Use

As can be seen from Figure 2 the area of urban land in UANSTM increased significantly from 1995 to 2018, followed by cropland, with increases of 23.55% and 13.75%, respectively—these are the two most dramatic types of land-use changes in the UANSTM. Additionally, the areas of forest, grassland, and water showed a decreasing state year by year. From each time period, it can be seen that the area of urban land and cropland grew the fastest from 2005 to 2010, with an average annual growth rate of 9.17% and 6.26%, respectively—these are much higher than in other time periods. There was a slow increase in water area from 2010 to 2018 (Figure 3).

For the cities and counties in the UANSTM, the urban area of each region showed an increasing trend in all time periods, with the average annual growth rate of urban land in Urumqi peaking at 26.28% between 2000 and 2005. However, the change in cropland varies among cities and counties. Kuitun City, Wusu City, Manas County, Hutubi County, and Fukang City all showed a continuous growth trend in cropland area in all time periods, with Kuitun City having the highest average annual growth rate of 26.69% from 2005 to 2010 (Figure 4).

4.3. Land-Use Transfer Matrix

As we can see in Figure 5, the 11 regions in the UANSTM experienced more frequent land-use conversions over the 23-year period, and the continuous urbanization process led to a deepening fragmentation in the study area, affecting and changing the surrounding natural landscape. During the study period, the four land types of grassland, cropland, unused land, and urban land shifted more significantly, while forest and water areas shifted less significantly. Although the mutual transfer of water and forest areas with other land types was less obvious, there was still a small transfer of grassland to water area and forest land.

Table 5 allows us to see in more detail that there was a large transfer out as well as a large transfer in of land use within the UANSTM during this 23-year period. Among them, among the areas of urban land and cropland with the most significant changes, grassland and water areas were the main input types, with 5884.9 km² and 1700.4 km² transferred, respectively; cropland, with grassland as the main input type, showed an input area of 5988.4 km². Since the UANSTM regions are located on top of the same arid zone oasis, there is a similarity in their land-use type transfer; the urban land in these 11 regions is dominated by cropland and grassland transfer, and cropland is also dominated by grassland input.

4.4. Landscape Pattern Analysis

The landscape pattern index of the landscape level can clearly reflect the landscape characteristics of the whole region. The landscape characteristics of urban land use changed significantly with the change of land use in UANSTM during 1995–2018. As shown in Figure 6, the PD, ED, and LPI of urban land all increased to varying degrees from 1995 to 2018, with the LPI showing the largest increase. This group of trend changes shows that the townscape of UANSTM tends to be fragmented, and the increase in LPI indicates that the inner city is gradually clustering, but because the urbanization of other counties and cities is still in the development stage, the growth of the number of urban landscape patches is accompanied by the increase in patch density, which shows that the number of patches increases with a greater landscape density, and the fragmentation of the landscape increases, indicating that the inner city landscape is becoming more complex. It can be seen that, over the past 23 years, the urban lands of the UANSTM have shown a trend of increasing landscape fragmentation, landscape heterogeneity, and complex patch shapes.

4.5. Degree of Comprehensive Land Use

The degree of comprehensive land use in the UANSTM increased during the study period, with significant differences in different directions and regions (Figure 7). During these 23 years, the spatial distribution of the degree of land use comprehensively spread to the north, northeast, northwest, and southeast, respectively. The expansion was mainly to the north, but intensive growth also occurred within the cities.

The spatial and temporal distribution of the expansion of comprehensive land use in UANSTM is different; the key expansion areas are concentrated in Urumqi, Karamay, and Changji, and the urban land in these three cities shows a large-scale expansion trend. This type of land in Shihezi and Fukang cities is also in a state of rapid expansion, which is driven by economic development. Among them, Urumqi, Changji, and Shihezi cities have urban land as the dominant expansion type, while other areas are in the middle stage of urbanization development. Therefore, the distribution of areas with a high value of comprehensive land use is more scattered, and they show more reorganization stages of urban land use.

4.5.1. Spatial Clustering of the Degree of Comprehensive Land Use

The UANSTM‘s degree of comprehensive land use, assessed using the global Moran’s I, was calculated by the ArcGIS software. The results show that Moran’s I was positive for the period 1995–2018, with 0.304 and 0.188, respectively (Figure 8), indicating that there is a high positive spatial correlation and significant spatial aggregation of the comprehensive land-use degree in the study area. The univariate local spatial autocorrelation LISA aggregation plot of land-use extent over the entire study period was also drawn based on the z-value test results (Figure 9). Five types of spatial association are suggested as follows: high-high clustering type, high-low outlier type, low-high outlier type, low-low clustering type, and not significant. The analysis of the regional spatial agglomeration characteristics concluded that, in 1995, the high-high clusters were mainly distributed in the central part of the study area in a band-like manner, and the high-high clusters in 2018 showed a piecewise distribution with an increase in the distribution range compared with 1995. The low-low clusters during these 23 years were mainly distributed in the north and south sides of the study area, mainly because these two parts were mainly composed of bare land with low vegetation cover and poor ecological quality, which led to a decrease in land use.

4.5.2. Analysis of Driving Factors based on Geographic Detector

From the results of the factor detection (Table 6), it can be seen that the explanatory power of different factors on the spatial and temporal distribution of the degree of comprehensive land use from 1995 to 2018 is, in descending order, as follows: NDVI > river density > temperature > sunshine hours > DEM > precipitation > slope > population > GDP. It can be seen that the most dominant factor affecting the spatial and temporal distribution of the degree of comprehensive land use in UANSTM is NDVI, indicating that the distribution of NDVI and river density becomes the biggest threat source of land-use/land-cover changes in the UANSTM. This also confirms that the UANSTM, as a typical oasis city agglomeration in an arid zone, can only rely on the oasis for development—urban development has certain limitations.

From the results of the interaction detection (Figure 10), it can be seen that—taking the results of 2018 as an example (the results of the remaining years have the same mode of action)—the interactions of all the factors mutually reinforce each other, indicating that there is a clear correlation and coordination among them. Among the factors, river density and NDVI together provided a strong explanation. This indicates that the influence of human activities on oasis production is lower than that of other factors, and the contribution of natural factors is significantly higher than that of social and economic factors.

5. Discussions

5.1. Spatial Distribution of Landscape Patterns and LUCC Changing Trend

We used PD, ED, LPI, and SHID to evaluate the landscape patterns in the UANSTM. It was found that the dominance of these four indexes changed with the different stages of urban development. From the perspective of landscape pattern, the study area showed a trend of deepening landscape fragmentation with the development of urbanization; to a certain extent, this finding supports the hypothesis that urban landscape heterogeneity, fragmentation, and complexity are presented within the UANSTM [30] and indicates a close relationship between urban expansion and landscape pattern. This is consistent with the results of other scholars in this study area [31,32]. With regard to land use/land cover, the present research results are consistent with those of other scholars [33]: Urumqi, Changji, and Shihezi were found to be at the center of the urban expansion of the UANSTM, with leading roles in urban development. As the center of the urban agglomeration, Urumqi is the “leader” of the UANSTM. Urumqi’s urban expansion pattern is mainly a marginal outward growth pattern, which is conducive to the strengthening of its core city functions, fully utilizing its role as an economic, financial, technological innovation and transport hub in Xinjiang. This is consistent with the urban development pattern of “one core, two axes and multiple ethnic groups” in the “Fourteenth Five Year Plan”. The pattern of urban expansion involved in the study is consistent with the national and government policy planning.

There are differences in the data sources used and in the time frame for the data; however, in general, the direction of urban expansion is largely consistent. The UANSTM is slightly different from other mega-city clusters in China, and its geographical location lends it special characteristics. It has both shortcomings and strengths of its own, with different structures and functions within the urban agglomerations, and the radiating capacity of the core cities does not fully generate certain advantages [34]. However, studies in some large cities (e.g., Shanghai, Beijing, etc.) have shown that rapid economic growth significantly changes the landscape pattern of cities [35,36]; our study demonstrates this also, while finding that the economic contribution has a phased effect.

5.2. Influencing Factors of Urban Expansion

The urban expansion of the UANSTM is due to a number of factors. A review of the relevant literature reveals that, firstly, the topography of the UANSTM restricts the direction of development—the Tian Shan Mountain Range lies to the south and is undulating and unsuitable for human production and living; thus, the direction of urban expansion and development is mainly towards the northern plains. Secondly, in recent years, the development of UANSTM has mainly focused on regional integration policies, which have radiated outwards from Urumqi—such as the Urumqi-Chanji integration and military integration [37]—promoting close integration and joint development between Urumqi and the northern and northeastern cities of Changji, Shihezi, Wujiaqu, and Fukang. Additionally, in 2007, the expansion of the administrative area of Urumqi to the northeast facilitated the continued construction of the built-up area to the north. Finally, the findings of some scholars also indicate that the urban road system also has a certain traction effect on urban expansion [38,39]. For example, the S111 provincial road, the Lian-Huo highway, the Tu-Wu highway, the Wu-Chang highway, and the G216 national highway have played roles in promoting the traction development of urban construction land in the northwest, northeast, and southeast. UANSTM, as a typical oasis city agglomeration in an arid zone, is influenced by both natural and human factors. The ecosystem is very fragile, the relationship between man and land is extremely sensitive, and the development of the city depends on the distribution of the oasis, which comprises water only; this makes it impossible for UANSTM to develop into a mega-city cluster, and it can only rely on the ecological carrying capacity of the oasis to gather and live.

5.3. Research Limitations and Future Work

Any geographical element has a scale effect [40], and landscape patterns and urban sprawl are no exception. This suggests that differences across time and space can result in different outcomes. In this study, 11 counties and cities north of the Tianshan Mountains were selected as the study area, while other studies on the UANSTM have included the Turpan region, located in the eastern part of the Tianshan Mountains, in the study area [33,34]. Therefore, there is a difference in scope selection between this study and the results of other scholars, and the Turpan region will be considered for inclusion in the study area for future exploration. Another drawback is that the study only examines the period 1995–2018, and only analyses the past landscape pattern and urban expansion in the study area, without using specific models to project future land-use conditions, which is one of our future research directions.

6. Conclusions

In summary, this study takes the UANSTM as the study area, combines land-use data, and calculates the landscape pattern index to explore the land-use change pattern and spatial characteristics of the landscape pattern of the UANSTM. The results indicate a gradual increase in intra-urban landscape heterogeneity over the study period. Urban land and cropland were the most significant land-use types that changed. This shows that urbanization is accelerating in this region, while safeguarding the amount of cropland to meet the needs of population growth. This result is feasible and reasonable. We calculated the land-use intensity of the UANSTM over a 23-year period and analyzed the spatial correlation of the index, finding a high positive spatial correlation and significant spatial aggregation in the degree of land use. In the analysis of the driving forces, it was found that UANSTM, as an oasis city cluster in the arid zone, can only rely on the oasis for its development, and that urban development is restrictive. Therefore, ecological protection should be used as the basis for future development to maintain the ecological security of the oasis and build a firm ecological security barrier for the construction of the urban cluster on the northern slopes of the Tianshan Mountains.

Author Contributions

Writing—original draft preparation, Y.Z.; methodology, H.L. and A.K.; software, P.G.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available in article.

Acknowledgments

We thank the four anonymous reviewers for their constructive comments and suggestions that have helped to improve the original manuscript. Thanks also to the editorial staff.

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Overview map of the study area.

Figure 2. Changes in land use during 1995–2018.

Figure 3. Land-use dynamic degree for the entire study area.

Figure 4. Land-use dynamics of each county and city.

Figure 4. Land-use dynamics of each county and city.

Figure 5. Land-use transfer matrix for 1995–2018.

Figure 6. Landscape pattern index analysis. (a) is Patch Density (PD); (b) is Edge Density (ED); (c) is Largest Patch Index (LPI); and (d) is Shannon Diversity Index (SHDI).)

Figure 7. Distribution of the degree of comprehensive land use from 1995 to 2018.

Figure 8. Moran’s index of comprehensive land use from (a) 1995 to (b) 2018.

Figure 9. LISA clustering map of the land-use degree from 1995 to 2018.

Figure 10. Interactive detection results for the urban agglomeration on the northern slope of the Tianshan Mountains in 2018. Note: * indicates non-linear enhancement; + indicates two-factor enhancement; no marker indicates no significant difference.

Table 1

Land-use types.

Primary Type	Secondary Type
Name	Number	Name
Cropland	11	Cultivated land
	12	Dry land
Forest	21	Has woodland
	22	Bush forest
	23	Sparse woodland
	24	Other woodland
Grassland	31	High coverage grassland
	32	Medium coverage grassland
	33	Low coverage grassland
Water	41	Canal
	42	Lake
	43	Reservoir pond
	44	Permanent glacier snow
	45	Tidal flat
	46	Beach
Urban	51	Urban land
	52	Rural settlement
	53	Other construction land
Unused	61	Sand
	62	Gobi Desert
	63	Saline-alkali land
	64	Wetlands
	65	Bare land
	66	Bare rock
	67	Other
	99	Sea

Table 2

The meaning of the selected landscape index.

Landscape Metrics	Unit	Meaning
Patch density	pcs/km2	The larger the value, the greater the number of patches
Edge density	Meter/km2	The larger the value, the greater the edge length of the patches
Largest patch index	-	The larger the value, the larger the proportion of the largest patches to the landscape area
Shannon diversity index	-	The larger the value, the greater the patches’ heterogeneity

Table 3

Land-use types and classification.

Land-Use Type	Unused Land	Forest Land, Grassland, and Water Bodies	Cultivated Land	Urban Land
Grading index	1	2	3	4

Table 4

Results of the two-factor interaction types.

Judgment Basis	Interaction Type
q(X1∩X2) < Min(q(X1), q(X2))	Nonlinear weakening
Min(q(X1), q(X2)) < q(X1∩X2) < Max(q(X1), q(X2)	Single-factor nonlinear weakening
q(X1∩X2) > Max(q(X1), q(X2)	Two-factor enhancement
q(X1∩X2) = q(X1) + q(X2)	Independent
q(X1∩X2) > q(X1) + q(X2)	Nonlinear enhancement

Table 5

Details of the land-use transfer matrix for 1995–2018.

	1995	2018
		Grassland	Cropland	Urban	Forest	Water	Urban	Total
UANSTM	Grassland	27,130.2	5988.4	666.2	574.1	301.3	5884.9	40,545.2
	Cropland	830.3	9076.9	781.5	24.8	59.7	50.3	10,823.4
	Urban	71.1	271.1	628	1.8	2.3	107.9	1082.2
	Forest	2101.4	557	68.9	1156.4	36.4	202.2	4122.3
	Water	372	39.5	20.4	0.8	964.5	1700.4	3097.6
	Urban	3845.3	1873.2	210.3	27.8	292.9	20,387.8	26,637.2
	Total	34,350.3	17,806	2375.4	1785.6	1657	28,333.5	86,307.9
Urumqi	Grassland	11,662.8	305.8	526.4	234.9	107.1	2687.5	15,524.6
	Cropland	468.5	1666.9	414.3	12.5	12	18	2592.3
	Urban	18.6	43.7	481.4	3.1	0.5	5.8	553.1
	Forest	583.4	55.3	69.2	514.4	6.9	8	1237.2
	Water	83.1	1.9	7.9	0	180.5	455.4	728.9
	Urban	1305	98	115.5	9.1	116.2	4847.4	6491.3
Changji	Grassland	5575.1	500.8	81.7	104.1	43.9	488.4	6793.9
	Cropland	191.4	1786.3	223.6		15	17	2233.5
	Urban	8.7	41.3	46.2		0.7		97
	Forest	752.6	59.1	1.4	277.9		19.2	1110.1
	Water	56.2	39.8	17	0.1	102.8	251.7	467.5
	Urban	349.4	434.3	1.8		7.3	3946.3	4739
Shihezi	Grassland	130	33.3	31.3	0	0.4	0.5	195.4
	Cropland	12.3	448	126.4	4.5	3.4	3.1	597.7
	Urban	0.3	5.9	60.4				66.6
	Forest	0.3	4.8	6.3		0.6		12
	Water	9.2	3	1.7		2.9	0.5	17.3
	Urban	0.5	0.4	0.1		0.1	0.2	1.3
Karamay	Grassland	2922.4	1033.8	150.5	1.2	93.8	523.4	4725.1
	Cropland	66.8	643.5	35.3	4.5	1.7	9.1	761
	Urban	50.9	30.3	200.9		2.4	189.7	474.1
	Forest	280.6	149.9	23.3	0.5	8.1	140.1	602.5
	Water	8.5	0.4	0.2		69.3	3.5	81.9
	Urban	1604.5	458.9	169.8	0.3	61.3	6726.3	9021.1
Fukang	Grassland	3486.2	578.7	99.4	101.1	19.4	5376	9660.8
	Cropland	39.1	752.6	38.8		10.6	3.3	844.3
	Urban	11.2	56.9	65.1		0.4	22.6	156.3
	Forest	155.7	99.2	3.5	163.8	3.7	0.4	426.3
	Water	5.1	3.8	0.5	0.2	80.7	31.3	121.7
	Urban	720.6	426.4	53.9	3	38.1	4221.7	5463.8
Hutubi	Grassland	6394.3	1669.9	48.8	173.1	33.1	135	8454.2
	Cropland	204.2	2054.6	116.6	1.4	35	6.8	2418.6
	Urban	8.3	64.5	50.4	0.1	0	0.1	123.4
	Forest	440.4	69.3	3.7	379.6	1.1	10.3	904.3
	Water	184.4	6	6.2	0.6	73.9	176.8	448
	Urban	668.8	224.1	30.8	0.1	25.4	5223.5	6172.7
Manas	Grassland	3942.7	1486.6	68.9	190.2	32.8	415.5	6136.7
	Cropland	150.9	2748.1	132.8	8.7	13.4	16.5	3070.5
	Urban	17.9	86.7	72.2	0	0.2	0.1	177.2
	Forest	352.1	93.2	3.9	252.7	10	13.5	725.5
	Water	87.7	4	4.6		212.7	236.6	545.7
	Urban	231.2	291.2	3.8	2.9	107	6637.7	7273.9
Wujiaqu	Grassland	140.8	27.3	4.9		7.8	4	184.9
	Cropland	74.2	280.6	47.4		0.7	8.3	411.2
	Urban	4.8	6.7	13.9		0	0.3	25.7
	Forest	2.6	3.3	2.4		0	0.8	9.1
	Water	1.1	2.5	0.3		14.1	3.4	21.3
	Urban	72.3	5.6	5.3		1.4	6.9	91.6
Wusu	Grassland	10,274.1	2652.8	80.2	220.0	113.1	1319.8	14,660.1
	Cropland	144.3	2597.8	146.4	3.1	10.2	3.3	2905.1
	Urban	1.5	48.4	44.5		0.1	0.6	95.1
	Forest	933.8	164.3	5.1	353.7	20.5	157.4	1634.8
	Water	129.7	6.7	0.2	0.2	441.5	775.9	1354.1
	Urban	2052.3	430.0	20.9	31.2	166.1	4740.7	7441.1
Shawan	Grassland	7014.2	2395.1	72.6	68	113.7	543.2	10,206.9
	Cropland	165.3	4242.6	162.9	13.2	10.8	4.9	4599.6
	Urban	13.5	132.8	136.9	0.1	0.2	0	283.6
	Forest	541.2	372.8	11.4	267.4	20.5	47.5	1260.8
	Water	136.9	5.6	0.6	0.4	654.9	1,297	2095.5
	Urban	410.4	1301.1	7.6	7.6	40.3	4050.4	5817.3
Kuitun	Grassland	654.4	1043.7	121.2	7	16.9	1.2	1844.4
	Cropland	24.2	252.3	28.1	0.2	2.4	0.2	307.5
	Urban		5.7	38.4				44.1
	Forest	6.1	21.9	1.4		0.2		29.6
	Water	7.9	0.5	0.1		3.2		11.7
	Urban	23.2	22.4	0.5			11.5	57.5

Table 6

Detection results of the land-use interannual factors.

Factors	Q
	1995	2000	2005	2010	2015	2018
DEM	0.041	0.053	0.089	0.045	0.062	0.062
GDP	0.047	0.020	0.008	0.012	0.011	0.011
NDVI	0.422	0.023	0.067	0.595	0.590	0.590
River density	0.274	0.032	0.059	0.384	0.374	0.374
Precipitation	0.142	0.020	0.021	0.053	0.050	0.050
Population	0.022	0.036	0.003	0.010	0.013	0.013
Sunshine hours	0.023	0.025	0.018	0.093	0.086	0.086
Temperature	0.078	0.039	0.009	0.122	0.119	0.119
Slope	0.011	0.005	0.036	0.014	0.019	0.019

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