3.1. Land-Use Change Characteristics
The land-use types in the Hung River Valley region are diverse and structurally complex. Dividing the land-use types in the study area into cropland, woodland, grassland, water, construction land, and bareland, the five-phase landscape type distribution map shows (
Figure 2) that the land-use types in the study area are mainly woodland, grassland, and arable land, of which woodland and grassland both account for more than 32% of the total area of the study area, followed by the area of arable land, which accounts for about 20% of the total area of the region. Overall, the woodland, grassland, and arable land cover 90% of the total area of the study area and have a greater impact on the overall landscape, while the proportion of construction land, water, and bare land is smaller, accounting for about 10% of the total area of the study area. From 2000 to 2020, the area of arable land decreased, and the area of water bodies continued to increase; from 2000 to 2020, the area of arable land transferred out was the largest, and the proportion of construction land increased the most.
To fully understand the structural characteristics of land-use changes in the Hung River Valley during this period, a land-use transfer matrix was constructed to calculate the number of mutual land-use transfers from 2000 to 2020 (
Table 3). As shown in the table, from 2000 to 2005, land-use shifts mainly occurred between arable land, grassland, and forest land, with a larger amount of arable land shifting to grassland and construction land. During the period 2010–2015, the trend of the previous period continued, with arable land always being the source of inflow of construction land and grassland, while the inflow and outflow of grassland and forest land were basically the same. From 2015 to 2020, the interconversion of various land uses made the inflow and outflow basically the same.
The land-use changes in the Hung River Valley in the past 20 years are mainly influenced by policies and urban expansion. In ecological protection policies, agricultural and livestock production and urbanisation, industrialisation, and other factors under the comprehensive effect of the Hung River Valley grassland, arable land, and construction land change dramatically. Each landscape flows between each other. Since 2002, Qinghai Province has fully implemented the policy of returning farmland to forest and grass, coupled with the establishment of many nature reserves. Arable land is in a net outflow situation, manifested in the expansion of grassland scale. At the same time, the Hung River Valley is an important axis of economic development in Qinghai Province, with intense human activity, high levels of urbanisation and industrialisation, and a continuous increase in the demand for construction land, the main source of which is the occupation of arable land.
3.2. Analysis of the Evolution of the Landscape Pattern Features
Since the magnitudes of different indicators are different, to facilitate comparison, the sampling of Z is standardized, as are all landscape pattern index values. As can be seen in
Figure 3, NP and PD showed an increasing trend between 2000 and 2005, which is related to the partial conversion of arable land in the study area into forest land and grassland, and a large amount of arable land outflow, causing arable land fragmentation. After 2005, PD and NP gradually decreased, combined with the actual situation of the Hung River Valley. It can be seen that urban construction land encroachment on arable land, where there are many construction enclaves, merged with the original construction land, the main reason for the decline in PD and NP, in addition to the demolition and merging of rural settlements. LPI and AREA_MN increased slightly between 2000 and 2005 and fluctuated little after 2005, while DIVISION decreased slightly, indicating that the overall landscape pattern of the Hung River Valley showed a clustering trend after 2005.
The ED and LSI decreased slightly from 2000 to 2020, indicating that the overall landscape shape of the study area tends to be clustered, with the shape changing from complex to simple. conTAG increased relatively over the 20-year period, indicating that the overall landscape connectivity in the study area has increased. The Shannon Diversity Index (SHDI) decreased, and landscape heterogeneity diminished; the Shannon Evenness Index (SHEI) shows a decreasing trend, and the landscape type dominant over the overall landscape in the study area increased.
Changes in habitat quality are an indirect reflection of changes in different land types. To reveal in more detail the relationship between landscape changes in the study area and their impact on habitat quality, the characteristics of changes in different land types between 2000 and 2020 were calculated using Fragstats 4.2 (
Figure 4). Grassland and woodland are important landscapes regarding the quality of habitat, with a decrease in the number of patches (NP), a decrease in density (PD), an increase in the mean patch area (AREA_MN), and a decrease in ED, LSI, and DIVISION from 2005–2020, indicating an overall clustering of landscape patterns and an increase in disturbance resistance in grassland. It is particularly important to note that the patches of built-up land in the study area decreased, and density decreased over the 20-year period, but ED increased slightly, and LSI and DIVISION remained largely unchanged, indicating that the landscape pattern in some areas became dispersed, which may be related to the extensive expansion of towns in Xining.
3.3. Spatial and Temporal Variation of Habitat Quality
The Habitat Quality Index (HQI) reflects the fragmentation of habitat patches in the study area, on the one hand, and the ability of habitat patches to resist the threat of habitat degradation brought about by human activities. On the other hand, its value is a continuous value between 0 and 1: the closer to 1, the better the habitat quality, indicating that biodiversity is better maintained. The raster areas of different classes and their percentages were counted (
Table 4). To more accurately portray the evolution of habitat quality, using the Re-classify tool of the ArcGIS 10.6 software platform, habitat quality was classified into five levels: very low (0–0.2), low (0.2–0.4), medium (0.4–0.6), high (0.6–0.8), and very high (0.8–1), based on the actual situation in the Hung River Valley and with reference to existing studies [
13,
18].
From 2000 to 2020, the overall habitat quality showed an upward trend, and the global average habitat indexes were 0.656, 0.657, 0.661, 0.661, and 0.662, respectively. The main reason for this is that around 2000, Qinghai Province, based on its development orientation, successively carried out a series of ecological protection and restoration work, e.g., returning farmland to forest, returning farmland to grassland, and constructing nature reserves, which promoted the transformation of some medium- and high-grade habitat patches to higher-grade habitat patches.
On a spatial scale (
Figure 5), the quality of habitats in the concentrated areas of woodland, grassland, and watersheds is high, while the quality of habitats on arable land, construction land, and bare land is low. The whole area is dominated by habitat patches of excellent grade. Habitat quality in the north, east, and west districts of Xining is low compared to other areas, due to the deteriorating ecological conditions in the central areas of Xining as a result of increasingly intense human activities. Among all areas, the habitat quality indexes of Datong County, Guide County, Mutual Aid County, Menyuan County, and Zunhua County all exceed 0.7, with Guide County being the highest, maintaining a level of 0.9; Hualong County, Jianzha County, Ping’an County, and Minhe County all show an increasing trend, with Hualong County being the highest and Minhe County being relatively low; the socio-economic development of Huangyuan County and Huanzhong County is strongly affected by the radiation of Xining City, and the social and economic development has a greater impact on the ecological environment.
3.4. Hotspot Analysis of the Spatial Distribution of Habitat Quality
3.4.1. Overall Clustering Characteristics
To explore the spatial differentiation characteristics of habitat quality in the Hung River Valley in more detail, the study area was divided into 2328 4 × 4 km grids using grid analysis. The mean values of habitat quality in 2000, 2005, 2010, 2015, and 2020 were extracted from 18 counties and cities in the Hung River Valley based on grid scale, and the ArcGIS 10.6 platform was used to calculate the spatial clustering of habitat quality in the study area from 2000 to 2020.
The Moran’s I calculations for the five periods of 2000, 2005, 2010, 2015, and 2020 showed that the Z scores of the five periods were all above 75 and much higher than 2.58, and the
p-values passed the 1% significance test, indicating that the spatial distribution of habitat quality values in the Hung River Valley was not random at a 99.9% confidence level and that there was significant spatial correlation (
Table 5).
The Moran’s I index for all five periods was greater than 0.65, showing a significant pattern of aggregation, i.e., high values of habitat quality clustered in space, and low values tended to be adjacent to each other. Since 2000, the aggregation effect of habitat quality in the Hung River Valley has been increasing on the whole; however, from 2015 to 2020, the aggregation effect has been on the decline, mainly because the areas with high habitat quality are affected by the expansion of urban land, eroding the original woodland, grassland, and other ecological landscapes and causing habitat fragmentation. The development of a large amount of urban land has led to an increasingly widespread distribution of areas with a low habitat quality. This is shown in
Table 5.
3.4.2. Local Agglomeration Characteristics
The global spatial autocorrelation can only reflect whether there are agglomerative features in the study area as a whole and cannot clarify the location distribution of agglomerative features. Based on the ArcGIS 10.6 platform, a hotspot analysis was conducted based on a grid, and cold spots and hotspots with a confidence level above 90% were selected to reflect the distribution of high- and low-value habitat quality clusters in the Hung River Valley (
Figure 6).
During 2000–2020, the Hung River Valley habitat quality changes show obvious regional differences “Guide-Ledu”, south of the line of the habitat quality index, including Ping’an County, Ledu District, Tongren County, and Guide County, generally improved. This is mainly due to the implementation of ecological protection policies, e.g., returning farmland to forest and grass, and establishing establishment many nature reserves, scenic spots, forest parks, and geoparks, such as the Mengda Nature Reserve and the Sanjiangyuan Nature Reserve. The overall habitat quality north of the “Guide-Ledu” linkage has a significant spatial aggregation effect, but the change effect is not obvious, and there are cold spots in Xining City and surrounding counties. The cold spot area is concentrated in Xining City, which is the political and economic centre of Qinghai Province and the gathering area of arable land, and the intense production and construction and agricultural activities have interfered with the ecological environment, resulting in the clustering and distribution of low habitats in the area. Mutual Aid County and Ping’an County are adjacent to Xining. Mutual Aid County has the largest population and the most intense human activities in Haidong, thus showing a secondary cold point concentration distribution, and the habitat cold point rose and then declined between 2000 and 2020, dropping to the lowest point in 2015 with the worst habitat quality.
3.5. Spatial Response of Habitat Quality to Urbanisation
The spatial pattern of habitat reflects that human activities and natural elements are important influencing factors of spatial differentiation of habitat quality. Among these factors, human activity status has gradually become the main independent variable of habitat quality. Natural factors, such as slope, average annual rainfall, average annual temperature, and elevation, and urbanisation process elements such as population density, gross domestic product, and the night light index, are selected as independent variables. The optimal model was selected by a comparative analysis of GWR and ordinary least squares (OLS). The coefficient of variance expansion is a measure of the severity of multiple (multiple) collinearities in a multiple linear regression model. It represents the ratio of the variance of the regression coefficient estimator to the variance, assuming that the independent variables are not linearly correlated. Usually, 10 is used as the judgment boundary. When VIF < 10, there is no multicollinearity; when 10 ≤ VIF < 100, there is strong multicollinearity; when VIF ≥ 100, there is severe multicollinearity.
The results show that the VIF of the natural and socio-economic factors in the OLS model is less than 10, and there is no covariance between the variables, which satisfies the requirements of the explanatory variables. The explanatory power of the OLS model for habitat quality was less than 50%, and the goodness of fit of the GWR model was significantly higher than that of the OLS model in all five time sections, with its explanatory power reaching more than 90%. Meanwhile, the Sigma and AICc in the GWR model were lower than those of the OLS model, indicating that the GWR model had better explanatory power for the factors influencing habitat quality and its model accuracy was better (
Table 6 and
Table 7).
Slope and elevation are important natural factors influencing habitat quality. However, due to the interaction of human activities, the correlation between habitat quality and natural factors such as slope and elevation is complicated. As can be seen in
Figure 7, slope, elevation, and habitat quality generally show a positive relationship, with the positive and negative effects of slope on habitat being relatively complexly distributed within a geographical area. The positive correlation between slope and habitat is mainly concentrated in mountainous and hilly areas and areas with continuous construction land, and the positive correlation between height and slope is distributed in discontinuous bands. The central region is flat. Human activities are relatively frequent, and the natural ecological space is encroached upon by construction land, resulting in serious habitat degradation. Some of the hilly areas are affected by human development and construction activities, and the habitats are damaged to a certain extent, while the green areas and water areas in the plains are better protected by ecological protection, and the overall level of habitats is higher; under the influence of human activities, the slope of the area is negatively correlated with habitat quality. The areas with a high positive correlation between elevation and habitat quality are mainly concentrated in the hilly areas, with a cluster and circle pattern of distribution. In general, natural factors such as slope and elevation play an important role in the overall pattern of habitat distribution. With higher slope and elevation, socio-economic activities are generally less frequent in the area, and the disturbance factors to the ecosystem are smaller, so the impact on habitat quality is relatively small. In plain areas, where human activities are frequent, socio-economic factors have a more prominent impact on habitat quality than do natural factors.
Urbanisation factors such as population density, gross domestic product, and the night-time light index have a more significant negative correlation with habitat quality (
Figure 6). The negative correlation between population density and habitat quality is most significant and widely distributed: During the study period, the negative influence of the northern and central zones was further expanded, because the central zone was influenced by the economic radiation of Xining, and the towns were developed significantly, with a relatively obvious population growth, which increased the pressure on the ecological carrying capacity of the surrounding areas. The high density of economic activities also contributed to the fragmentation of the landscape pattern and the encroachment of arable land and construction land on ecological land.
The regression coefficients of the economic impact on habitat show that the negative impact in the study area is less intense. The reason for this is that the study area is affected by the policy of “returning farmland to forest” and “returning farmland to grass” in Qinghai Province, and the GDP output value of mainly arable land is low, so the negative impact on habitat is weak. The northern and southern parts of the city have seen rapid economic development, and the ecological environment has been significantly disturbed by economic activities. The night-time light index characterises the indirect disturbance effect of urban socio-economic development and high intensity human activities on the ecosystem: In 2000, the positively correlated areas were mainly scattered in clusters in the plains, while the negatively correlated areas were widely distributed, with the most intense negative impact in the mountainous hills. Between 2000 and 2010, the areas with intense negative correlation and the positively correlated areas both decreased. However, the negative correlation dominated the region, and the trend from the mountains to the plains was stronger and weaker. Between 2010 and 2020, the areas with strong negative correlations expanded again, which was related more to urban development. Overall, the agglomeration of factors in the urbanisation process is an important driver of regional habitat quality change, and the spatial heterogeneity of the impact of socio-economic factors on habitat quality is more significant as the rate of urbanisation accelerates.