Study on the Evolution of the Mountain Ecological Environment Based on Remote Sensing and Water Footprints: Case Study of Northwestern Sichuan, Southwest China

Abstract

The unique geographical conditions in southwestern China lead to a fragile ecological balance and uneven geographical distribution of water resources. This study takes the Aba Tibetan and Qiang Autonomous Prefecture as its research subject, investigating the connection between water resources and the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture, which is of paramount importance. Given this, the current study constructs the remote sensing ecological index and water footprint for the period spanning from 2000 through 2020, analyses the coordination status of the two by using the coupling coordination degree, and then combines it with a detector to determine the primary drivers impacting the state of the ecological environment. The outcomes reveal that: (1) The ecological environment of the Aba Tibetan and Qiang Autonomous Prefecture gradually improved from 2000 through 2020, and the excellent ecological environment area observed within the study’s scope is primarily focused in the eastern part. The poor ecological environment area is focused mainly in the northwestern part within the study’s scope. (2) The total water footprint of the Aba Tibetan and Qiang Autonomous Prefecture has increased year by year, with agricultural water consumption comprising an immense 78% proportion, dominating the total water footprint. (3) The coupled coordination degree of the remote sensing ecological index and the water footprint shows a rising trend in general, turning from a barely coordinated stage to an intermediate coordinated stage. (4) The geodetector results show that all factor interactions were dual-factor enhancement or nonlinear enhancement, and the WET, NDBSI and NDVI factors contributed the most. The above results can provide important guidance for utilizing water resources and protecting the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture.

1. Introduction

As an indispensable element of the earth’s ecosystem, the rational utilization of water resources is crucial for maintaining ecological balance. During the last several decades, with the intensified pace of global urbanization and industrialization, water resource shortage [1,2] and ecological degradation [3,4,5] have become major global challenges, especially in China, where the problem is particularly serious. The ecological environment and water resources are mutually influential and interdependent. On the one hand, the degradation of the ecological environment will exacerbate the water scarcity issue even further; on the other hand, unsustainable extraction and inefficient use of water resources can give rise to ecological concerns, including water contamination [6], wetland degradation [7,8], and the reduction of biodiversity [9,10], forming a vicious circle. Serving as a crucial ecological shield within the headwaters of the Yangtze and Yellow Rivers, in the southwest of China, the rational utilization of the ecological environment and water resources is of paramount importance in maintaining the ecological balance of the region and even of the whole country.

The issue of the relation between the ecological environment and water scarcity is coming to the fore with greater prominence, and research on the ecological environment and water resources has become an academic “hotspot”. Previous studies on the ecological environment are mainly characterized by single indicators, such as the normalized difference vegetation index (NDVI) [11,12,13]; enhanced vegetation index (EVI) [14] and the land surface temperature (LST) [15,16]. However, the ecological environment arises from the integration of diverse components—single indicators do not represent the overall ecological environment [17]. Therefore, the ecological index (EI) [18] has appeared, but it needs a long time interval for assessment and the data are difficult to quantify, and the weighting of the analytic hierarchy process (AHP) [19] is easily influenced by human factors. Therefore, to ensure the results are more authentic and accurate, the remote sensing ecological index (RSEI) [20] has appeared, which contains four indicators of greenness, humidity, dryness and heat. The results are determined based on principal component analysis (PCA) of covariance; the larger the value of the RSEI is, the better the ecological environmental quality (EEQ) [21]. The RSEI, through qualitative and quantitative analysis of the ecological environment, can more accurately reflect the current status of the ecological environment. At this stage, the RSEI is widely used in watersheds [22,23,24], along roads [25,26], cities [27,28] and other areas. Confronting the intricacies of water resource management, the limitations of traditional research methods, such as categorical data envelopment analysis (DEA) [29,30], the indicator evaluation method [31,32], etc., have been gradually exposed, although they have achieved some results in promoting the rational use of water resources. For example, DEA may be limited by the rapid growth in the number of decision-making units when dealing with large-scale datasets, resulting in diminished computational efficiency and heightened intricacy in results interpretation. The method of establishing an indicator system for quantitative evaluation, despite the advantages in standardization and comparability, requires urgent resolution of such issues as how to select indicators scientifically and reasonably, how to ensure the comprehensiveness and accuracy of the data, as well as how to avoid the interference of subjective factors. In 2002, Hoekstra [33] and others put forward the “Water Footprint” concept, which denotes overall water consumption produced within a specified timeframe by a particular country or region to generate goods and services, including blue water, green water and gray water components. The water footprint emphasizes the quantification of water resource utilization from a consumption-based perspective, which can reflect the water utilization situation more comprehensively. Since then, the concept of water footprint has garnered extensive application in various fields, such as agriculture [34,35,36], industry [37,38] and other fields. Therefore, in this study, the RSEI and water footprint are used to characterize the EEQ and water resource utilization.

We selected the Aba Tibetan and Qiang Autonomous Prefecture in northwest Sichuan Province as the research area for this study. The Aba Tibetan and Qiang Autonomous Prefecture has a complex topography, harsh climatic conditions, and a sensitive natural environment [39], and confronts significant challenges, including soil erosion and the decline of forest ecological functionalities. Although water resources in the Aba Tibetan and Qiang Autonomous Prefecture are abundant, they are unevenly distributed, exacerbating the difficulty of water resource management. Since 2015, under the requirement of deepening the reform of the ecological civilization system in the Aba Tibetan and Qiang Autonomous Prefecture, strengthening ecological protection and rationally utilizing water resources has become an important topic. Most of the previous studies on the Aba Tibetan and Qiang Autonomous Prefecture have focused on geological disasters [40,41], the ecological environment [42,43], etc., and have seldom analyzed the connection between water resource utilization and the ecological environment from the viewpoint of the water footprint. To summarize, this study selected MODIS image data, integrated utilizing the Google Earth Engine (GEE) platform, to assess the temporal and spatial variations in the EEQ in the Aba Tibetan and Qiang Autonomous Prefecture from 2000 to 2020 and explored the coupling and coordination relationship between the ecological environment and the water footprint, and then explored the main driving factors through the geographical detector. The findings of this research offer valuable insights and serve as a significant guide for ecological environmental protection and rational utilization of water resources in the Aba Tibetan and Qiang Autonomous Prefecture and similar areas.

2. Materials and Methods

2.1. Study Area

The Aba Tibetan and Qiang Autonomous Prefecture, or the Aba Prefecture for short, is located in the northwestern part of Sichuan Province, adjacent to the Chengdu Plain, with an overall area of 84,242 km². As shown in Figure 1, the Aba Prefecture is located on the southeast edge of the Tibetan Plateau, with the overall outline of a typical plateau and a high terrain, with an average elevation of more than 3000 m; the highest elevation at the prefecture is the main peak of the Four Girls’ Mountains, which is as high as 6247.8 m. The climate type of the Aba Prefecture is mainly a mountainous monsoon climate, with an average annual temperature of around 9 °C, precipitation between 600 mm and 700 mm, with precipitation mainly concentrated in May–October, with more precipitation within the central and eastern sectors of the Aba Prefecture, but overall, less precipitation, uneven spatial distribution, and insufficient per capita water resources. Concurrently, grassland degradation and the expansion of sandy areas have led to a very fragile ecological environment as well.

2.2. Data Source

The data selected for this study are detailed in Table 1. The MODIS series composite data are detailed in Appendix A, Table A1 and Table A2. In order to ensure that the overall trend of the data is maintained and to avoid a reduction in the amount of data, given the relatively small amount of missing data, we decided to use mean substitution to fill in the missing data [44].

2.3. Methods

The study’s organizational framework encompasses five distinct sections (Figure 2), starting with calculation of the RSEI, followed by estimation of the water footprint, followed by analysis of the spatiotemporal variations of the EEQ, in addition to assessment of the coupled and coordinated assessment of the RSEI and the water footprint, and finally, exploration of the ecological drivers.

2.3.1. RSEI Model

In this study, May to September, when the vegetation grows most luxuriantly, was chosen as the study period for the RSEI, which consists of four indexes, namely, greenness, humidity, dryness and heat, coupled to reflect the advantages and disadvantages of the EEQ. Based on the high temporal resolution and global coverage characteristics of the MODIS data, they are able to support long time series of EEQ monitoring, in which GEE is extracted by the MOD13A1 dataset for greenness, the MOD09A dataset for humidity and dryness, and the MOD11A2 dataset for heat. The specific calculation method of each indicator is shown in reference [45], and due to the inconsistency of the scale of each indicator, it is necessary to normalize the four indicators [46] to eliminate the influence of the scale on the results. The formula is as follows:

(1)$X_{\mathrm{i}}={\displaystyle \frac{X-X_{\min }}{X_{\max }-X_{\min }}}$

where X_i denotes the value after normalization; X denotes the value before being normalized, and X_max, X_min denote the maximum and minimum values. The four indicators were calculated and normalized through the GEE platform, and the eigenvectors and eigenvalues of the four indicators were calculated utilizing principal component analysis; then, the contribution rate of each indicator was derived on this basis. According to Table 2, the NDVI and the wetness index (WET) in 2000, 2005, 2015 and 2020 have positive values, which implies a beneficial effect on the environment, and the LST and normalized difference built-up index (NDBSI) have negative values, which implies a detrimental impact on the environment. Of these, the NDVI and WET values were negative and the LST and NDBSI values were positive in 2010, which were restored by 1-PC1 conversion. The five-year contribution rates from 2000 to 2020 were lower, but all of them could truly reflect the ecological conditions [47].

In this study, to limit the impact of subjective factors on the results’ weighting, a principal component analysis was applied to analyze the four indicators in a coupled way. PCA is a commonly used data dimensionality reduction technique, which is mainly used to convert high-dimensional data into low-dimensional data and to extract the most important features in the data, ignoring noisy or redundant features, so as to enhance the expressive ability of the data and the generalization ability of the model, while retaining the main information in the original data as much as possible. The initial RSEI value was calculated by the first principal component and then normalized to obtain the final RSEI. The formula is as follows:

(2)$RSEI={\displaystyle \frac{RSE{I}_0-RSE{I}_{0-\min }}{RSE{I}_{0-\max }-RSE{I}_{0-\min }}}$

where RSEI_0–max and RSEI_0–min, respectively, denote the maximum and minimum values. RSEI ∈ [0, 1] and RSEI closer to 1 indicate better EEQ, and vice versa, worse EEQ. Based on previous studies, the values were categorized into five classes ranging from [0, 0.2) (poor) to [0.8, 1] (excellent) at intervals of 0.2.

Due to differences in software, the calculated results may vary. In some software, the calculated NDVI and WET values may be negative, while the NDBSI and LST values may be positive [47]. When NDVI and WET are positive, the initial RSEI₀ is obtained through principal component analysis using Equation (3). Conversely, if NDVI and WET are negative, a transformation using Equation (4) (i.e., 1-PC₁) is applied, followed by principal component analysis to obtain the initial RSEI₀. The definition of RSEI₀ is as follows:

(3)$RSE{I}_0=P{C}_1[f(NDVI,WET,NDBSI,LST)] (NDVI,WET>0; NDBSI,LST<0)$

(4)$RSE{I}_0=1-P{C}_1[f(NDVI,WET,NDBSI,LST)] (NDVI,WET<0; NDBSI,LST>0)$

where f is PCA, a powerful multivariate data analysis technique. It automatically and objectively determines the weight proportions of each indicator based on the data characteristics and the contribution of four key indicators to the composition of the principal components, effectively reducing the subjective biases that may arise from manually setting weights.

2.3.2. Water Footprint Modelling

The water footprint serves as a quantitative measure to evaluate the cumulative water consumption of a country, region or individual [33]. It can be divided into two main calculation methods: top-down and bottom-up. Taking into account the ease of access and applicability of the data, the investigation here opts for a bottom-up methodology [48,49] and calculation method to estimate the water footprint more accurately and comprehensively, relying on the quality of the consumption data. The formula is as follows:

(5)$WF=I+E$

(6)$I=W_a+W_i+W_d+W_e-W_{ep}+W_{cont}$

(7)$W_a=P_v\times P$

(8)$E=W_{ip}-W_{ip-ep}$

where WF denotes the total water footprint; I denotes the internal water footprint; E denotes the external water footprint; W_a, W_i, W_d, W_e, W_ep and W_cont denote the agricultural water footprint, industrial water footprint, daily life water footprint, ecological water footprint, export water footprint and gray water footprint, respectively. The industrial, daily life and ecological water footprints are expressed as the industrial water consumption, domestic water consumption and ecological water consumption, respectively. P_v denotes the virtual water content per unit of product; P denotes the product output. The virtual water content of agriculture was obtained referring to previous related studies [50], as shown in Table 3. W_ip(W_ep) expressed as the water consumption of CNY 10,000 GDP × total import (export). W_(ip−ep) indicates the water footprint of imported goods re-converted to exported goods, which is ignored due to the difficulty in obtaining data.

The gray water footprint represents the quantity of water necessary to dilute a polluted water source back to an acceptable or natural concentration level [51]; due to the limitation of data, only the gray water footprint of the chemical oxygen demand (COD) in industrial effluent was calculated in this study. The calculation method is as follows:

(9)$W_{cont}=L/(C_{\max }-C_{nat})$

where W_cont denotes the gray water footprint, L denotes the emissions of pollutants (kg/a), C_max denotes the maximum safe concentration threshold (kg/m³), C_nat denotes the normal concentration of natural water (kg/m³); C_max uses the concentration of pollutants set in Class V of the environmental quality standards for surface water in China (GB3838-2002) [52], which is taken to be 40 mg/L. C_nat takes a value of 0.

2.3.3. Coupled Coordination Models

The coupling level indicates the intensity of interplay among two or more entities. The coupling degree correlates positively with the force intensity between objects; inversely, weaker coupling indicates a lesser force [53]. However, the coupling degree cannot describe the level of coordination between systems; therefore, the coordination degree and cooperation between systems are reflected through the degree of coordination [54], with the following formula:

(10)$C=2{[(U_1\times U_2)/{(U_1+U_2)}^2]}^{{\displaystyle \frac{1}{2}}}$

(11)$T=\alpha U_1+\beta U_2$

(12)$D={(C\times T)}^{{\displaystyle \frac{1}{2}}}$

where U₁ denotes the combined water footprint value and U₂ denotes the RSEI value. C ∈ [0, 1], C the closer to 1, the correlation intensifies between the two. T denotes the degree of coordination; α, β denote the coefficient to be determined, and both are considered equally important in this study, that is α = β = 0.5. D denotes the coupling coordination degree; D ∈ [0, 1] and D closer to 1 indicate a higher degree of coupling coordination. According to previous related research [55], the coupling coordination degree is categorized into four major categories and ten subcategories, as shown in Table 4.

2.3.4. Geodetectors

Geodetectors analyze spatial patterns to identify variations in single variables and correlations between variables through their spatial coupling [56]. Geodetectors encompass factors, interaction, risk and ecological detections. This study employs factor and interaction detections. Factor detection is calculated as follows:

(13)$q=1-{\displaystyle \frac{{\displaystyle \sum _{h=1}^L{N}_h{\partial }_h^2}}{N{\partial }^2}}$

where L denotes the number of categories or strata of the independent variable Y or the dependent variable X indicator; N denotes the number of image elements in a particular stratum; ${\partial }^2$ and ${\partial }_h^2$ denote the variance of the Y values in the whole stratum and h stratum, respectively. Factor detection is used to explore the effect of the independent variable X on the spatial distribution of the dependent variable Y. It is expressed as a q value [57]; the larger the explanatory power of the independent variable Y, the greater q.

Interaction detections examine how the interplay between X1 and X2 enhances or diminishes Y’s explanatory strength

(1) If q(X1∩X2) < Min[q(X1),q(X2)], it indicates a nonlinear weakening state;

(2) If [Min[q(X1),q(X2)] < q(X1∩X2) < Max[q(X1),q(X2)], it indicates a single-factor nonlinear weakening state;

(3) If q(X1∩X2) > Max[q(X1),q(X2)], it indicates a dual-factor enhancement state;

(4) If q(X1∩X2) > q(X1) + q(X2), it indicates a nonlinear enhancement state;

(5) If q(X1∩X2) = q(X1) + q(X2), it indicates an independence state.

In this study, RSEI was used as the dependent variable. Greenness (NDVI), humidity (WET), heat (LST), dryness (NDBSI), population density (PD), gross domestic product (GDP), land use data (LUD), DEM, and ASPECT were used as the independent variables. The nine drivers were categorized into three groups by their attributes: modelling variables (WET, NDVI, LST, NDBSI); social factors (PD, GDP, LUD); and natural factors (DEM, ASPECT), which were then unified in terms of projected coordinates and resolution and reclassified according to quartiles, and finally, 5 km × 5 km sampling points were established for sampling analysis.

3. Results

3.1. Analysis of EEQ Dynamics

The average ecological environment of the Aba Prefecture is in the stage of gradual improvement, with specific values of 0.49, 0.55, 0.49, 0.51 and 0.59 in order from 2000 to 2020. Figure 3 illustrates the evolutionary trajectory of the NDVI demonstrating an upward trend, then decreasing, then recovering, with some ups and downs, but a positive growth trend in general. The WET also exhibits an upward trend, then decreasing and then increasing, and climbs to a maximum value of 0.54 in 2020. Meanwhile, due to the complex and variable topographic conditions of the Aba Prefecture and the persistent human influence on the environment, especially the intensification of the phenomenon of land desertification, the LST exhibits a marked upward trend, consistently climbing from 0.45 in 2000 to 0.57 in 2020, with an increase of up to 21%. However, the NDBSI shows a decreasing trend, but the decrease is small, showing relative stability.

In conjunction with Figure 4, it is evident that the overall EEQ in the Aba Prefecture shows significant geographic differentiation. High RSEI values are concentrated in the east and gradually diminish to the northwest, with high values mainly clustered in Jiuzhaigou County, Mao County and Li County. The low values are mainly concentrated in Ruoerge County, Hongyuan County and Aba County. In 2000, 32% of the Aba Prefecture had excellent or good ecological quality; these areas were predominantly in the east and southeast of the Aba Prefecture. In 2005, the ecological status of the Aba Prefecture showed a positive change, especially in the reduction of the poor and fair areas, whereby most of these areas have transitioned to moderate ecological quality, the most significant change occurring in the northwestern Aba Prefecture. At the same time, the area of excellent and good grade areas changed to 9.6% and 26.6%, respectively. However, by 2010, the positive trend turned around, with a more significant decline in the excellent and moderate grade areas, which decreased by 7.7% and 9.9%, respectively, while the area of fair grades bucked the trend and increased by 14.3%. Since 2015, the excellent graded areas in the Aba Prefecture have begun to steadily rebound, reaching 10.5% by 2020. Nonetheless, the main ecologically degraded areas are still concentrated in the northwestern Aba Prefecture, with 1.7% and 13.1% of the area in poor and fair grades, respectively.

Figure 5 provides a better understanding of the changes in the horizontal direction of the EEQ, which was mainly focused on the transformation between fair, moderate and good grades throughout the study period. In detail, the EEQ trended upwards, with areas in the fair and moderate grades transforming mainly to moderate and good from 2000 to 2005; the EEQ declined in relative terms, with areas in the excellent, good and moderate grades transforming mainly to good, moderate and fair grades. The fair grades significantly expanded from 2005 to 2010, and the EEQ continued its upward trend from 2010 to 2020. The areas with excellent and good grades increased, and the areas with fair grades significantly decreased.

The spatial analysis reveals the changes in the EEQ in the Aba Prefecture over time. It is divided into three categories: the deterioration zone (decreasing rank), the invariability zone (maintaining the same rank) and the improvement zone (increasing rank). As shown in Figure 6, the invariability and improvement zones were widely distributed, representing 42.6% and 42.3% of the total, respectively, while the improvement zones mainly benefited the western, northern and central Aba Prefecture during the period 2000–2005. In contrast, the deterioration zones accounted for 15.1% but were mainly concentrated in areas such as the southeastern Aba Prefecture. However, the Aba Prefecture experienced significant challenges to its EEQ from 2005 to 2010. The deterioration zone expanded dramatically by nearly three times in size to 44% over the previous period. At the same time, the improvement zone shrank dramatically to 15.3%. Moving into the 2010–2015 period, the EEQ changes in the Aba Prefecture tend to be more complex. Although the deterioration zone has decreased, it is still concentrated in the southern and central areas of the Aba Prefecture, accounting for 16.5%. Meanwhile, the improvement in environmental quality becomes more obvious during this period, the invariability zone is more than half of the area, and the EEQ change in the Aba Prefecture shows a new pattern by 2015–2020. The deterioration zone gradually shifted eastward, focusing on the eastern part of the Aba Prefecture; while not a high percentage, the geographic concentration should not be ignored. The improvement zones showed significant growth and dominated, with a share of 45.6%. In contrast, the share of zones with invariability zone environmental quality is slightly lower, at 42.8%.

3.2. Water Footprint Components

In Table 5, the total water footprint of the Aba Prefecture for the five years 2000, 2005, 2010, 2015 and 2020 showed an upward trend, increasing from 1.057 billion m³ in 2000 to 1.715 billion m³ in 2020. It is worth noting that during the data collection process of this study, it was found that aquatic products data were missing for the year 2010, and in order to maintain the integrity of the data and the continuity of the analysis, the mean substitution method was used to make reasonable substitutions for the aquatic products data for that year. The agricultural water footprint of the Aba Prefecture exhibited an upward movement. The agricultural water footprint accounts for 78% of the total water footprint, and it is highly correlated with the trend in the overall water footprint. Its growth rate is also the fastest, rising significantly from the initial 0.846 billion m³ to 1.604 billion m³. The industrial water footprint shows a rising trend in general, but the rise is small, from only 0.011 billion m³ in 2000 to 0.028 billion m³ in 2020. In terms of the water footprint of daily life, although there was a slight decline in 2005 and 2015, the overall trend is upward. In terms of the ecological water footprint, the overall water consumption is relatively small. With regard to the import and export water footprint, the overall total exports of the Aba Prefecture is greater than the total amount of imports from 2000 to 2020, and therefore, has been maintained in a state of “surplus”. On average, the export water footprint is about twice as large as the import water footprint. As a whole, the proportion of the import and export water footprints in the total water footprint is still low; in terms of the gray water footprint, the Aba Prefecture’s gray water footprint decreased from 0.161 billion m³ in 2000 to 0.006 billion m³ in 2020, a decrease of 96%.

3.3. Dynamic Assessment of Coupled Coordination Relationships

Based on the analysis of Figure 7 and Table 6, in the last two decades, the coupling degree of the Aba Prefecture’s water footprint and the EEQ showed consistency with the coupling coordination degree, and both exhibited an upward movement. From the coupling degree indicator, the growth in the coupling degree in this period is relatively flat and consistently maintained at a very high level. From the coupling coordination degree level, the coupling coordination degree of the Aba Prefecture is still in the transitional reconciliation zone in the early 2000s, which is manifested in the coupling and coordination relationship between the water footprint and the EEQ being relatively small and the degree of interdependence being limited. With time, in 2005, the coupling coordination degree of the Aba Prefecture has stepped into the new stage of a coordinated development zone. This is specifically expressed in the state of primary coordination, with the link between the water footprint and the EEQ becoming increasingly pronounced, with a significantly enhanced reciprocal influence. The coupling coordination degree declined to the barely coordinated stage in the transition reconciliation zone in 2010. The coupling coordination degree in the Aba Prefecture reached the intermediate level of coordination in coordinated development in 2015 and 2020, and the positive interactions between the water footprint and the EEQ became more and more frequent during this period, which not only improved the order of the system but also contributed to the overall leap in the capacity of the sustainable development of the region.

3.4. Driving Factor Analysis

3.4.1. Change Trends in Drivers

The DEM factor and aspect factor do not change easily, and the trend in the remaining factors is shown in Figure 8. The overall changes in NDVI are large, and the main changes are concentrated in the northwestern part of the Aba Prefecture. In the last two decades, WET fluctuated less, with a slight downward trend in the southeastern part of the Aba Prefecture and a slight increase in the northwestern part. Between 2000 and 2020, the LST factor pattern changed significantly, showing an overall increase, and this increase was mainly focused on the northwestern area of the study area, in contrast to the southeastern region, where LST was lower. The NDBSI showed an overall decreasing trend, especially in the northwestern part of the Aba Prefecture. The spatial distribution of the population density is less variable, but the cumulative effect of specific locations is becoming more pronounced, showing a trend of increasing concentration. The GDP changes are more pronounced, increasing from CNY 5.43 million in 2000 to CNY 35.28 million in 2020, and showing a patchy distribution, with particularly rapid growth in the southeastern portion of the Aba Prefecture. In terms of land use data, the Aba Prefecture has maintained a high degree of stability, with grassland and forest as the main cover types.

3.4.2. Factor Detection Analysis

In Figure 9, the impact of each driver on the RSEI in the study area varied across time and all passed the significance test (p < 0.01). The WET factor had the greatest influence on the spatial divergence of the RSEI in the Aba Prefecture from 2000 to 2020, with the largest q value of 0.84. The NDBSI and NDVI factors followed, but the contribution increased or decreased at different times. The contribution of each factor to the RSEI in 2000 was ranked from largest to smallest as follows: WET, NDBSI, NDVI, LUD, DEM, GDP, LST, PD, and ASPECT. Among them, the top three contributors had q-values of 0.84, 0.77 and 0.74, respectively. This indicates that WET, NDBSI and NDVI were the main factors influencing the RSEI of the Aba Prefecture in 2000. Compared with 2000, all factors showed a decreasing trend in 2005, and their contribution to the RSEI was weakened, but their dominant contributing factor was still WET. The contribution rate of most factors increased significantly in 2010, with the WET and NDBSI factors having the most significant influence on the RSEI. In 2015, the contribution rate of WET reached a maximum of 0.8, followed by NDBSI and NDVI at 0.79 and 0.74, respectively. In 2020, there is a small decrease in the explanatory ability of factors, and the contribution of WET is still the largest with 0.71. Overall, WET, NDBSI and NDVI are the main factors influencing RSEI.

3.4.3. Interactive Detection Analysis

Since the WET and LST factors reflect the precipitation and temperature factors, the precipitation and temperature are not used as driving factors in this study. As shown in Figure 10, the interaction detection showed that the interaction of all factors exhibited significant dual-factor enhancement or nonlinear enhancement characteristics, and the effect of dual-factor enhancement was prominent. In particular, the dual-factor enhancement of NDVI and WET peaked in 2000 (0.961), which indicated that NDVI and WET had a very strong joint influence on the ecological status. In the last two decades, WET, NDBSI and NDVI became the three most significant factors influencing the RSEI, and their interactions with other environmental factors all showed high contributions. Specifically, the interaction effects of WET, NDBSI and NDVI with other factors exceeded the threshold of 0.7 in both 2000 and 2015. In other years, the interaction between WET and NDBSI remained strong despite fluctuations in the interaction strengths of the factors. In 2005, 2010 and 2020, the interaction effects of WET and NDBSI were maintained above 0.7. Once again, WET, NDBSI and NDVI proved to be the main controlling factors of the RSEI in the Aba Prefecture.

4. Discussion

4.1. Spatial and Temporal Patterns of the RSEI

Between 2000 and 2020, the EEQ of the Aba Prefecture has generally maintained a relatively stable trend, demonstrating a certain degree of resilience. In terms of EEQ classification, the Mao, Li and Jiuzhaigou counties in the eastern part of the Aba Prefecture constitute the core of the EEQ zone, which happens to be located amid the Minjiang River Grand Canyon, and benefits from its unique geographic location (the confluence of the Sichuan Basin and the Qinghai-Tibet Plateau) with abundant precipitation. It has long unswervingly implemented the policy of ecological environmental protection, and through a series of powerful initiatives, such as clearly delineating and strictly enforcing the ecological red line, vigorously strengthening the construction and management of nature reserves [58], and actively promoting green and sustainable development models, such as eco-agriculture and eco-tourism [59], it has successfully preserved and enhanced the ecological superiority and pristine natural state of these areas. According to authoritative data released by the Aba Prefecture government, the Aba Prefecture has achieved a significant increase in forest coverage, steadily growing from 52.4% in 2010 to more than 60% in 2022; at the same time, the area of soil erosion has been effectively reduced by 30%, and the quality of the ecological environment has been significantly improved. The vigorous development of eco-tourism and the in-depth practice of green agriculture have brought tangible economic benefits to local farmers, with per capita income approaching CNY 20,000 per year. In contrast, the northwestern part of the Aba Prefecture, including Aba County, Hongyuan County and Ruoergai County, faces more severe ecological challenges. This region is deeply embedded in the Tibetan Plateau and its margins, and the complex and variable climatic conditions have become the primary factor constraining ecological development, which not only restricts the growth of vegetation but also weakens the ecosystem’s ability to recover itself. This, together with the persistent problems of wetland degradation, soil salinization, sanding, and rodent, insect, and poisonous weed infestation [10], has further weakened the vegetative cover of the region, reduced the function of water containment, and exacerbated the risk of soil erosion, thus making the EEQ relatively poor. Notably, the distribution pattern of the EEQ in the Aba Prefecture is consistent with the distribution of vegetation types, with areas of excellent EEQ often covered by dense forests, while areas of relatively poor EEQ are mostly dominated by grasslands. Existing studies show that different vegetation types contribute differently to the quality of the ecosystem, with forest vegetation playing an important role in maintaining water and soil and regulating the climate, and steppe vegetation being relatively weak in some ecological functions [60]. At the same time, the study period in the Aba Prefecture is the period of the most luxuriant vegetation growth, when grazing activities are more frequent, exacerbating the downward trend in its ecological quality. In addition, the spatial distribution pattern of the EEQ and precipitation showed a high degree of similarity. The southeastern part of the Aba Prefecture is characterized by high vegetation cover due to abundant precipitation, thus guaranteeing the premium status of the ecological environment; by contrast, in the northwestern part of the area, the EEQ is relatively inferior due to the relatively low precipitation [61,62]. In summary, the Aba Prefecture shows distinct geographical differences in ecological environmental protection, and the ecological advantages of the eastern part need to continue to be consolidated, while the northwestern part needs to take more active and effective measures to cope with the dual challenges of harsh natural conditions and anthropogenic activities and to promote ecological restoration and sustainable development.

4.2. Trend Analysis of the Evolution of the Water Footprint

The present research delves into the analysis of the evolution of water utilization dynamics in the Aba Prefecture based on the water footprint. The outcomes reveal that the water footprint fluctuates occasionally between 2000 and 2020 but exhibits a clear increasing trend overall. This trajectory of change may be related to the multiple transformations and adaptations of water use in the Aba Prefecture. In the initial period (2000–2010), the people of the Aba Prefecture relied on their unique grassland resources and the livestock industry flourished; livestock production increased from 123,000 tons in 2000 to 206,000 tons in 2010, an increase of 40 per cent. And in conjunction with the virtual water content in Table 3, as can be seen, the water footprint continued to expand during this period, from 490 million m³ to 860 million m³, which also posed new challenges to the efficiency of water allocation and utilization. Subsequently (2010–2015), the Aba Prefecture’s economy stepped into a critical period of transition. Since the 2008 earthquake, with the strong momentum of disaster recovery and rebuilding and the in-depth execution of the development strategy of “one state, two regions and three homelands”, the traditional industrial system of the Aba Prefecture has gone through profound changes. Mining, chemical and other traditional high water-consuming industries have gradually transformed into high-tech and strategic emerging industries. The COD content of discharged effluent was reduced from 2876 tons in 2010 to 927 tons in 2015, and the graywater footprint was reduced from 0.72 to 0.23, which effectively reduced the grey water footprint, resulting in a relatively flat growth trend of the water footprint in this phase, reflecting the positive impact of industrial structure optimization on water resources management. Finally (2015–2020), the Aba Prefecture is in the 13th Five-Year Plan period, which is also the decisive stage for the realization of a moderately prosperous society. During this period, the accelerated urbanization process, the significant population agglomeration effect, and the requirement for water resources have also climbed, while the production of agricultural products has also increased significantly, which has resulted in a swift resurgence of the water footprint. During this period, the Aba Prefecture, while pursuing economic and social development, also faced new problems of utilization in a sustainable manner and protection of water resources. In summary, the changing trajectory of water resource utilization in the Aba Prefecture is a vivid portrayal of regional water resource management issues. In the future, how to optimize water allocation and use efficiency while ensuring sustainable and healthy economic and social development will be an important topic to be explored and practiced in depth in the Aba Prefecture and in the wider region.

4.3. Evolutionary Analysis of Coupled Coordination

The coupling degree and coupling coordination degree of the water footprint and RSEI in the Aba Prefecture show an upward trend between 2000 and 2020, mainly because the government of the Aba Prefecture has actively implemented the strategy of ecological civilization construction and sustainable development in this period [63,64], increased its efforts in water resources governance and ecological environmental protection, and through the formulation and execution of a series of environmental protection policies and regulations, promoted the rational allocation and efficient use of water resources to minimize water resources waste and pollution. At the same time, it has strengthened the protection and restoration of ecologically fragile areas and improved the stability and self-recovery ability of ecosystems. Secondly, the Aba Prefecture government actively introduced and applied advanced water conservation technologies, sewage treatment technologies and ecological restoration technologies, which have improved water use efficiency and the monitoring capacity of the ecological environment. Concurrently, with economic growth and rising living standards, the Aba Prefecture residents’ awareness of environmental protection has gradually increased [65]. Finally, the Aba Prefecture has focused more on the protection of the ecological environment during economic transformation and upgrading. By optimizing the industrial structure, promoting clean energy and energy-saving and emission reduction technologies and other measures, benign ecological environmental development has occurred.

4.4. Limitations and Prospects

In this study, the GEE platform is utilized to calculate the RSEI of the Aba Prefecture region, but due to the influence of cloud and atmospheric conditions and other factors, there are also problems such as the uneven quality of remote sensing image maps and the poor quality of data in some areas, which will adversely affect the accuracy of the RSEI [66]. In order to solve these problems, the GEE platform, which has a powerful data processing capability, was borrowed to perform median synthesis of the images. For the MODIS data source used in this study, these data have the advantages of high temporal resolution, multiple time scales, and rich data products, but also have the disadvantages of low spatial resolution and susceptibility to cloudiness. In addition, this study only takes water resources as an entry point to explore the connection between water resources and ecological environment. However, the ecological environment is the result of multiple factors [67] and may also be influenced by human activities [68], climate [69] and urbanization [42]. Therefore, the relationship with the ecological environment can be analyzed from a more comprehensive perspective in the future.

5. Conclusions

In the present research, the water footprint is used as an assessment of water resource utilization, and MODIS remote sensing image data are used to calculate the RSEI and its four component indicators. On this basis, a coupled coordination degree model is introduced to deeply analyze the synergistic development between water resources and the ecological environment in the Aba Prefecture between 2000 and 2020. The study shows that:

(1) During the past 20 years, excellent ecological environment areas were primarily focused on the four eastern counties of the Aba Prefecture. Meanwhile, relatively poor ecological environment areas were mostly distributed in the four northwestern counties. In 2005, the percentage of poor ecological environments was very low, at 1.1%; in 2010, the proportion of excellent ecological environment areas was also low, at 2.9%. As a whole, the EEQ of the Aba Prefecture has fluctuated slightly but still demonstrates a clear improvement.

(2) The Aba Prefecture’s total water footprint from 2000 to 2020 is mainly driven by agricultural water demand, and shows significant growth overall, with only a slight decline in 2015 during this period. The total water footprint climbs from 1.057 billion m³ to 1.715 billion m³, a significant increase of 45%, reflecting the sustained growth in water use demand in the Aba Prefecture.

(3) The coupling coordination between the Aba Prefecture’s water footprint and the RSEI continued to rise between 2000 and 2020, steadily progressing from the initial moderate coordination stage to the intermediate coupling coordination level in the high coordination in 2020, and the coupling coordination relationship is gradually improving.

(4) Through the factor detection analysis, all the interactions among factors showed positive effects of dual-factor enhancement or nonlinear enhancement, and the interactions of WET, NDBSI and NDVI with other factors had a marked effect on the RSEI during the last 20 years.

Author Contributions

Conceptualization, P.R.; methodology, L.H., J.X. and P.R.; formal analysis, L.H.; investigation, Z.Z.; resources, J.X. and P.R.; writing—original draft preparation, L.H.; writing—review and editing, J.X. and P.R.; project administration, L.H., J.X. and P.R.; funding acquisition, J.X. and P.R. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

All data can be found on the website provided.

Acknowledgments

The authors would like to thank all reviewers and editors for their valuable comments.

Conflicts of Interest

The author declares no conflict of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Location of the study area.

Figure 2. Technical blueprint.

Figure 3. Average values of indicators in the Aba Prefecture from 2000 to 2020.

Figure 4. Spatial patterns of the RSEI classes.

Figure 5. Diagram of RSEI grade conversion.

Figure 6. Spatial distribution of RSEI class changes.

Figure 7. The trend of coupled coordination of the water footprint and RSEI in the Aba Prefecture from 2000 to 2020.

Figure 8. Analysis of Driving Factors for the Aba Prefecture 2000 and 2020.

Figure 9. Factor detection diagram.

Figure 10. Factor interaction diagram.

Table 1

A listing of data origins.

Data	Type	Times	Data Sources
Water footprint	/	2000–2020	Sichuan Provincial Water Resources Department(https://slt.sc.gov.cn/scsslt/szy/szy.shtml, Accessed on 15 May 2024) Sichuan Provincial Bureau of Statistics(https://tjj.sc.gov.cn/scstjj/c105855/nj.shtml, Accessed on 15 May 2024)
NDVI, WET, NDBSI, LST	raster	2000–2020	Google Earth Engine (https://earthengine.google.com/, Accessed on 24 May 2024)
Average annual precipitation, average annual temperature	raster	2000–2020	GRDC (http://www.gis5g.com/home, Accessed on 20 June 2024)
DEM	raster	/	Geospatial Data Cloud (https://www.gscloud.cn/search, Accessed on 20 June 2024)
ASPECT	raster	/	ArcGIS Extraction by DEM
Land use data	raster	2000–2020	Data published by Professors Jie Yang and Xin Huang of Wuhan University, with a resolution of 30 m

Table 2

The four indicators and contribution rates from 2000 to 2020.

	NDVI	LST	WET	NDBSI	Contribution (%)
2000	0.47	−0.22	0.73	−0.45	61.87
2005	0.55	−0.001	0.7	−0.46	41.08
2010	−0.25	0.38	−0.72	0.53	46.73
2015	0.46	−0.41	0.65	−0.44	65.93
2020	0.48	−0.07	0.71	−0.51	47.42

Table 3

Virtual water content of agricultural products.

Crop Products	Virtual Water Content (m3/kg)	Animal Products	Virtual Water Content (m3/kg)
cereals	1.313	meats	6.7
oilseeds	5.24	dairy	1.9
fruits	0.1	bird eggs	3.55
fruit	1
aquatic product	5

Table 4

Categorizing coupling coordination levels.

Serial Number	Degree of Coupling Coordination	A Major Category of Coordination	Type of Coupled Coordination
1	(0–0.1]	Dislocated recessionary range	extreme disorder
2	(0.1–0.2]		severe disorder
3	(0.2–0.3]		moderate disorder
4	(0.3–0.4]		mild disorder
5	(0.4–0.5]	Transitional reconciliation zone	on the verge of imbalance
6	(0.5–0.6]		barely coordinated
7	(0.6–0.7]	Coordinated development zone	primary coordination
8	(0.7–0.8]		intermediate level of coordination
9	(0.8–0.9]		good coordination
10	(0.9–1]		quality coordination

Table 5

Water footprint of the Aba Prefecture.

	AgriculturalWater Footprint(Billion m3)	IndustrialWater Footprint(Billion m3)	DailyWater Footprint(Billion m3)	EcologicalWaterFootprint(Billion m3)	Imported Water Footprint(Billion m3)	Export WaterFootprint(Billion m3)	GrayWaterFootprint(Billion m3)	Total Water Footprint(Billion m3)
2000	0.846	0.011	0.039	0	0.0009	0.0012	0.161	1.057
2005	1.007	0.016	0.022	0	0.0009	0.0003	0.085	1.135
2010	1.217	0.014	0.063	0	0.0005	0.0006	0.072	1.366
2015	1.282	0.024	0.061	0.007	0.0005	0.0013	0.023	1.396
2020	1.604	0.028	0.075	0.002	0.0004	0.0007	0.006	1.715

Table 6

Coupled coordination index of water footprint and RSEI in the Aba Prefecture.

	Combined Water Footprint	RSEIValue	C (Coupling)	T(Degree of Coordination)	D (Degree of Coupling Coordination)
2000	0.258	0.492	0.95	0.375	0.597
2005	0.277	0.551	0.943	0.414	0.625
2010	0.256	0.492	0.949	0.374	0.596
2015	0.763	0.506	0.979	0.634	0.788
2020	0.541	0.59	0.999	0.535	0.751

Appendix A

Table A1

Composite data sources.

Sources	Datasets	Time Resolution	Spatial Resolution
Google Earth Engine	MOD09A1	8 d	500 m
	MOD11A2	8 d	1 km
	MOD13A1	16 d	500 m

Table A2

Number of synthesized images for the four RSEI metrics, 2000–2020.

	2000	2005	2010	2015	2020
NDVI	10	10	10	10	8
WET	19	20	20	20	16
LST	19	20	20	20	16
NDBSI	19	20	20	20	16

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