Spatiotemporal Evolution and Driving Mechanisms of Composite Ecological Sensitivity in the Western Sichuan Plateau, China Based on Multi-Process Coupling Mechanisms

Abstract

The Western Sichuan Plateau, an ecologically critical transition zone between the Qinghai–Tibet Plateau and the Sichuan Basin, is also a typical fragile and sensitive area in China’s ecological security. This study established a multi-process evaluation model using the Spatial Distance Index Method, integrating cluster analysis, Sen–Mann–Kendall trend detection, and OWA-based scenario simulations to assess composite ecological sensitivity dynamics. The optimal geodetector was further applied to quantitatively determine the driving mechanisms underlying these sensitivity dynamics. The research showed the following findings: (1) From 2000 to 2020, the ecological environment of the Western Sichuan Plateau exhibited a phased pattern characterized by significant improvement, partial rebound, and overall stabilization. The composite ecological sensitivity grading index showed a declining trend, indicating a gradual reduction in ecological vulnerability. The effectiveness of ecological restoration projects became evident after 2010, with the area of medium- to high-sensitivity zones decreasing by 24.29% at the regional scale compared to the 2010 baseline. (2) The spatial pattern exhibited a gradient-decreasing characteristic from west to east. Scenario simulations under varying decision-making behaviors prioritized Jiuzhaigou, Xiaojin, Jinchuan, Danba, and Yajiang counties as ecologically critical. (3) Driving force analysis revealed a marked increase in the explanatory power of freeze-thaw erosion, with its q-value rising from 0.49 to 0.80. Moreover, its synergistic effect with landslide disasters spans 74.19% of county-level units. Dominant drivers ranked: annual temperature range (q = 0.32) > distance to faults (q = 0.17) > slope gradient (q = 0.16), revealing a geomorphic-climatic-tectonic interactive mechanism. This study provided methodological innovations and decision-making support for sustainable environmental development in plateau transitional zones.

1. Introduction

Ecological sensitivity refers to the extent to which an ecosystem responds to environmental changes and anthropogenic disturbances, reflecting the susceptibility and potential risk levels associated with regional ecological and environmental issues [1]. Under natural stable conditions, ecosystems maintain their structural and functional integrity through multi-process coupling mechanisms that establish dynamic equilibrium. However, when exogenous disturbances disrupt these coupled relationships, certain ecological processes may exhibit nonlinear abrupt changes, leading to severe ecological and environmental problems [2]. Since the beginning of the 21st century, intensified human activities have increasingly disrupted ecological structures, resulting in frequent natural disasters such as land desertification, soil erosion, landslides, and freeze-thaw erosion. Therefore, research on composite ecological sensitivity and the analysis of regional ecological issues are of great significance for guiding the sustainable development of ecological environments [3].

Currently, ecological sensitivity has become a focal point of research among scholars worldwide. Based on the index system method and elasticity coefficient method, numerous studies have been conducted on ecological sensitivity, employing both subjective weighting methods—such as the analytic hierarchy process (AHP), the Delphi method, and expert scoring, and objective weighting methods—such as the entropy method and principal component analysis (PCA) [4]. Subjective weighting methods are easily influenced by the subjective opinions of researchers, potentially leading to inaccurate weight assignments; objective weighting methods determine the weight allocation based on the difference in index values. Although objective, in actual research, not every factor has the same importance, and the weight allocation results may be contrary to the facts, resulting in distorted evaluation results [5,6]. The Spatial Distance Index Method, grounded in Euclidean distance principles and integrating multiple ecological processes, enables comprehensive and scientifically rigorous characterization of ecological elements. This optimized combined weighting method not only solves the subjectivity of traditional weighting methods to a certain extent, but also simplifies complex ecological processes. Extensive studies conducted by Wei Wei’s team have proved the feasibility of the Spatial Distance Index in the field of ecology [7,8], and the Spatial Distance Index Method has increasingly emerged as a new paradigm for multi-source data fusion research [6]. With the deepening of research, ecological sensitivity research has gradually shifted from single issues to comprehensive research; the time scale has developed from single-year research to long-term series research [4]. However, existing research still has two limitations: (1) single-factor sensitivity analysis is difficult to characterize the synergistic effects of multiple ecological processes; (2) there is a methodological gap between mutation detection and driving force analysis.

The Western Sichuan Plateau, located on the southeastern edge of the Qinghai–Tibet Plateau, possesses rich forest, grassland, and wetland resources, serving as a critical ecological barrier in the upper reaches of the Yangtze and Yellow Rivers. Characterized by a complex geographical environment, the Western Sichuan Plateau exemplifies one of China’s most ecologically vulnerable regions. Continuous tectonic collisions between the Indian and Eurasian plates have resulted in frequent major earthquakes and catastrophic landslides. In addition, substantial altitudinal variation and pronounced climatic changes have led to distinct vegetation zonation. The region is highly susceptible to ecological disasters such as land desertification, soil erosion, landslides, and freeze-thaw erosion. Consequently, elucidating the spatiotemporal evolution of ecological sensitivity and identifying its driving mechanisms are essential for promoting the sustainable development of the Western Sichuan Plateau. Due to its highly dynamic and heterogeneous geographical setting, single-factor sensitivity analyses are insufficient to accurately characterize the ecological processes of the region. In this study, four typical ecological disasters were integrated to construct a dynamic assessment model of composite sensitivity for the period 2000–2020. Spatiotemporal evolution was analyzed using Sen’s slope estimation in conjunction with the Mann–Kendall mutation test. Furthermore, scenarios of composite ecological sensitivity under different decision-making behaviors were simulated. An optimized geographical detector model, combined with identified temporal mutation points, was employed to build a dual-level dynamic driving mechanism analysis framework. This framework reveals the ecodisaster interaction mechanism and identifies key driving factors from the environmental background dimension. By adopting this dual ’process–pattern’ analytical perspective, the proposed approach significantly enhances the interpretive depth of driving force analyses compared to conventional geographical detectors. The research outcomes provide crucial decision-making support for the construction of ecological security along the Western Sichuan Plateau and promote the sustainable development of ecological environments in the transitional mountain–plateau regions.

2. Materials and Methods

2.1. Research Area

The Western Sichuan Plateau is geographically located approximately between 27.59° 34.31° N and 97.36° 104.62° E, encompassing the Ganzi Tibetan Autonomous Prefecture and the Aba Tibetan and Qiang Autonomous Prefecture (Figure 1). As part of the southeastern edge of the Qinghai–Tibet Plateau and the Hengduan Mountains, the region lies at the transitional zone between the Sichuan Basin and the Qinghai–Tibet Plateau. The high terrain of the Western Sichuan Plateau is primarily characterized by plateaus and high mountain valleys. The significant elevation of the Western Sichuan Plateau is due to the profound compressive effects resulting from the continuous collision between the Indian subcontinent and Asia. The stark contrast in elevation between the Western Sichuan Plateau and the eastern Sichuan Basin is attributed to the former’s vast and loose sedimentary rocks and destructed, weak basement [9], while the latter possesses a rigid cratonic basement with an oceanic plateau kernel [10]. Climatically, the region falls within the cold temperate zone of the plateau, serving as a transitional area between continental monsoon and plateau monsoon climates, with pronounced vertical climatic variations due to its complex topography [11]. Additionally, the region exhibits diverse vegetation types, though their distribution is uneven. Due to various natural constraints, the ecosystem of the Western Sichuan Plateau has weak resistance to disturbances. Over the long term, the ecological environment has been severely degraded by global climate change and human activities such as excessive deforestation and overgrazing, compromising the ecological barrier function of the Sichuan Basin.

2.2. Data Sources and Processing

The data used in this study span the period from 2000 to 2020 and encompass 11 categories, including natural, environmental, and socio-economic data. The specific data are listed in

Table 1. Data Source (NDVI: Normalized Difference Vegetation Index).

Types of Sensitivity	Name	Data Source	Data Accuracy
Land desertification	NDVI	Resources and Environmental Sciences and Data Center, Chinese Academy of Sciences	1000 m
		(https://www.resdc.cn/) (accessed on 1 April 2024)
Soil erosion	Soil erodibility factor	National Earth System Science Data Center	1000 m
		(https://www.geodata.cn/) (accessed on 28 March 2024)
	Slope length and steepness	National Earth System Science Data Center	1000 m
		(https://www.geodata.cn/) (accessed on 28 March 2024)
	Annual precipitation	Resources and Environmental Sciences and Data Center, Chinese Academy of Sciences	1000 m
		(https://www.resdc.cn/) (accessed on 28 March 2024)
	Land cover	See paper [12]	30 m
Landslide Disaster	Lithology	Resources and Environmental Sciences and Data Center, Chinese Academy of Sciences	1000 m
		(https://www.resdc.cn/) (accessed on 2 April 2024)
	Elevation (slope, aspect)	Geospatial Data Cloud	30 m
		(https://www.gscloud.cn/) (accessed on 2 April 2024)
	River	OpenStreetMap
		(https://www.openstreetmap.org) (accessed on 3 April 2024)
	Fault	China Geological Survey Geological Cloud
		(https://geocloud.cgs.gov.cn/) (accessed on 4 April 2024)
Freeze-thaw erosion	Temperature	NASA	1000 m
		(https://ladsweb.modaps.eosdis.nasa.gov) (accessed on 7 April 2024)

.

The slope and aspect data were generated using the slope and aspect analysis tools in ArcGIS 10.8. The distances to rivers and faults were calculated using the Euclidean distance tool. In this study, monthly average maximum and minimum temperatures were extracted from remote sensing data. Preprocessing steps, including cloud removal, cropping, and mosaicking, were performed on the Google Earth Engine (GEE) platform. The annual temperature range was then derived using raster calculations.

All data underwent geographic alignment, projection rasterization, and resampling to standardize the processing. The coordinate system was unified to WGS_1984, and the spatial resolution was standardized to 1000 m × 1000 m.

2.3. Research Methods

This study integrates multi-source spatial datasets to establish a spatiotemporal continuous database from 2000 to 2020 through spatial registration and standardization processing. A four-dimensional assessment system for composite ecological sensitivity is established based on the Spatial Distance Index Method. Sen’s slope estimation and the Mann–Kendall mutation test are utilized to achieve spatiotemporal trend analysis and mutation point identification. The OWA algorithm is introduced to simulate composite ecological sensitivity under various decision-making behaviors. Based on the critical temporal mutation point in 2010, a dual-level dynamic geographical detector model is constructed (Figure 2).

2.3.1. Composite Ecological Sensitivity Model Construction

1.

Land Desertification Sensitivity Index

Desertification refers to land degradation in arid, semi-arid, and dry sub-humid areas caused by various factors, including climate change and human activities [13]. Vegetation coverage directly reflects the growth status of regional vegetation and serves as a critical feature indicator for monitoring desertification. Following previous studies, this paper approximates vegetation coverage using the Normalized Difference Vegetation Index (NDVI) [14] to calculate the land desertification index for the Western Sichuan Plateau. The specific formula is as follows:

$$V_{FC}={\displaystyle \frac{NDVI-NDV{I}_{min}}{NDV{I}_{max}-NDV{I}_{min}}}$$

(1)

In the formula: $V_{FC}$ denotes vegetation fractional coverage; $NDVI$ represents the normalized difference vegetation index of the target pixel; $NDV{I}_{max}$ and $NDV{I}_{min}$ correspond to the maximum and minimum values of the normalized difference vegetation index within the study area, respectively.

2.

Soil Erosion Sensitivity Index

Soil erosion refers to the destruction and loss of soil and water resources caused by external forces such as hydraulic, wind, and gravitational actions under natural conditions and human activities [15]. This study employs a modified Universal Soil Loss Equation (USLE) to quantitatively assess soil erosion sensitivity in the Western Sichuan Plateau. The rainfall erosivity factor was calculated in accordance with the Guidelines for Soil Loss Calculation in Construction Projects (SL 773-2018). The land cover factor was estimated using NDVI data of the Western Sichuan Plateau through the method proposed by Cai Chongfa [16], and the conservation practice factor was determined by integrating land use types with reference to existing studies [17,18], as shown in

Table 2. P value of land use type in western Sichuan Plateau.

Land Type	P	Land Type	P
Farmland	0.25	Snowfield	0.2
Forest	1	Bare Land	0.4
Shrubland	1	Impervious surface	0.1
Grassland	0.9	Wetlands	0.1
Waters	0

. The annual soil loss is calculated using Equation (2).

$$SESI=R\times K\times LS\times C\times P$$

(2)

In the formula, $SESI$ denotes the Soil Erosion Sensitivity Index; R represents the rainfall erosivity factor; K indicates the soil erodibility factor; $LS$ corresponds to the slope length-steepness factor; C stands for the land cover factor; P refers to the artificial measures factor.

3.

Landslide Disaster Sensitivity Index

The rivers and mountains of the western Sichuan Plateau are widely distributed, and the geological structure is fragile, making it highly susceptible to landslide disasters. Based on previous studies [19,20] and the unique natural environmental characteristics of the Western Sichuan Plateau, this study employs eight indicators: elevation, slope gradient, slope aspect, annual average precipitation, vegetation cover, distance to rivers, distance to faults, and lithology—to assess landslide sensitivity. Using the Analytic Hierarchy Process (AHP) combined with the entropy weight method, the weights of these indicators were determined as follows: 0.128, 0.138, 0.120, 0.200, 0.144, 0.150, 0.086, and 0.128, respectively.

4.

Freeze-thaw erosion sensitivity index

Freeze-thaw erosion predominantly occurs in high-latitude and high-altitude regions. This process involves phase transitions of water within rock masses, where volumetric changes driven by temperature fluctuations, combined with the differential thermal expansion and contraction properties of distinct rock layers, trigger mechanical fracturing of rocks. Subsequent erosion is exacerbated by external forces such as wind, water flow, and gravitational action, leading to soil and water loss [21]. Key evaluation indicators for freeze-thaw erosion include annual temperature range, annual precipitation, slope gradient, slope aspect, and vegetation cover. Based on the environmental characteristics of the Western Sichuan Plateau, the Analytic Hierarchy Process (AHP) method was employed to determine the weight coefficients of these indicators as follows: 0.543 (annual temperature range), 0.117 (annual precipitation), 0.263 (slope gradient), 0.049 (slope aspect), and 0.028 (vegetation coverage).

5.

Composite Ecological Sensitivity Index

This study employs the Spatial Distance Index Method to calculate composite ecological sensitivity. The Spatial Distance Index is a comprehensive index method based on Euclidean distance theory [22]. Euclidean distance is primarily used to measure the absolute distance between points in multidimensional space. The basic principle is to define a worst point in multidimensional space and then calculate the Euclidean distance from each point to the worst point; the farther the distance from the worst point, the larger the value obtained [23]. This study normalizes four ecological sensitivity indices and calculates the distance from other points in four-dimensional space to the minimum value, representing the ecological environment quality of the western Sichuan Plateau; the farther the distance, the lower the composite ecological sensitivity and the better the ecological environment quality. The Spatial Distance Index method comprehensively considers the complex and variable ecological environment of the western Sichuan Plateau, coupling multiple ecological processes, making it a suitable method for evaluating ecological sensitivity in the western Sichuan Plateau. The formula for the Composite Ecological Sensitivity Index is as follows:

$$CESI=\sqrt{{\left(LDSI-LDS{I}_{max}\right)}^2+{\left(SESI-SES{I}_{max}\right)}^2+{\left(LSI-LS{I}_{max}\right)}^2+{\left(FESI-FES{I}_{max}\right)}^2}$$

(3)

where, $CESI$ is the Composite Ecological Sensitivity Index; $LDSI$ is the Land Desertification Sensitivity Index; $SESI$ is the Soil Erosion Sensitivity Index; $LSI$ is the Landslide Sensitivity Index; $FESI$ is the Freeze-Thaw Sensitivity Index. The subscript max indicates the maximum value. The ecological sensitivity classification standards for the western Sichuan Plateau are shown in

Table 3. Classification Standards for Ecological Sensitivity in the Western Sichuan Plateau.

Level	LDSI	SESI	LSI	FESI	CESI
Insensitive	0.80–1.00	0.00–0.24	0.18–0.40	0.14–0.29	4.11–4.32
Light	0.60–0.80	0.24–0.49	0.40–0.47	0.29–0.36	3.87–4.11
Moderate	0.40–0.60	0.49–0.65	0.47–0.53	0.36–0.41	3.52–3.87
Intense	0.20–0.40	0.65–0.83	0.53–0.58	0.41–0.47	2.95–3.52
Extreme	0.00–0.20	0.83–1.00	0.58–0.82	0.47–0.69	0.63–2.95

LDSI: the Land Desertification Sensitivity Index; SESI: the Soil Erosion Sensitivity Index; LSI: the Landslide Sensitivity Index; FESI: the Freeze-Thaw Sensitivity Index. CESI: the Composite Ecological Sensitivity Index.

.

2.3.2. Spatiotemporal Analysis of Composite Ecological Sensitivity

1.

Mann–Kendall Abrupt Change Detection Method

The Mann–Kendall mutation test is a non-parametric statistical test. Compared to traditional regression analysis methods, this method has the advantages of not requiring data to follow a normal distribution, not requiring the trend to be linear, and being unaffected by outliers and missing values. It is suitable for trend analysis of long-term time series data [24]. Therefore, it is widely used to analyze trends in various environmental and climate variables, and it has advantages in accuracy compared to other methods [25,26]. We derived the Composite Ecological Sensitivity Grading Index (CESI) by summing classification levels weighted by their areal proportions. Subsequently, the Mann–Kendall (MK) test was implemented in MATLAB 2024b to quantify temporal trends in sensitivity dynamics. The calculation formula for the MK test is as follows:

For a time series $x_i$ with n samples, construct a sequence:

$$S_k=\sum _{\mathrm{i}=1}^k{r}_i,k=2,3,\dots n$$

(4)

$$r_i=\left\{\begin{array}{cc}1\hfill & x_i>x_j\hfill \\ 0\hfill & x_i\le x_j\hfill \end{array}\right.$$

(5)

$x_i$ represents the composite ecological sensitivity grading index at time point i, and the order sequence $S_k$ is the cumulative number of values at the i-th moment that are greater than the values at the j-th moment. It is easy to know that when k = 1, $S_1$ = 0. Under the assumption that the time series are random and independent, the statistic is defined as:

$$U{F}_K={\displaystyle \frac{S_K-E\left(S_K\right)}{\sqrt{Var\left(S_K\right)}}}$$

(6)

where $U{F}_k$ follows a standard normal distribution, with $U{F}_1=0$. $E\left(S_k\right)$, $Var\left(S_k\right)$ are the mean and variance of the cumulative number $S_k$. When $x_1,x_2,\dots ,x_n$ are independent and have the same continuous distribution, they can be calculated as follows:

$$E\left(S_K\right)={\displaystyle \frac{K(K-1)}{4}}$$

(7)

$$Var\left(S_K\right)={\displaystyle \frac{n(n-1)(2n+5)}{72}}$$

(8)

In the formula, for a given significance level $\alpha =0.10$, if $U{F}_k>0$, the original series shows an upward trend; if $U{F}_k<0$, the original series shows a downward trend. The reverse time series is defined as $U{B}_k$. When $U{F}_k$ and $U{B}_k$ exceed the critical line, it indicates a significant upward or downward trend. If the two curves intersect between the critical lines, the intersection point corresponds to the time when the mutation begins. Typically, the MK mutation test combined with the sliding t-test can more accurately identify mutation points [27,28].

2.

Cluster Analysis

This study employs the Global Moran’s I to measure the global spatial correlation of desertification sensitivity, soil erosion sensitivity, landslide sensitivity, freeze-thaw erosion sensitivity, and composite ecological sensitivity in the Western Sichuan Plateau, with significance at the 0.01 level. The global spatial correlation values are 0.86, 0.57, 0.67, 0.84, and 0.74, respectively. Subsequently, cluster analysis in ArcGIS10.8 was applied to detect five types of ecological sensitivity patterns in spatial distribution: high-high clusters (high-value aggregation zones), low-low clusters (low-value aggregation zones), isolated sensitive patches (high-low clusters), low-sensitivity cores within high-sensitivity areas (low-high clusters), and randomly distributed mixed zones (not significant). This approach reveals the local spatial autocorrelation characteristics of ecological sensitivity.

3.

Sen+Mann–Kendall Trend Analysis Method

The Theil–Sen Median method has been widely used in trend analysis in fields such as climate, hydrology, and ecological environments. This method selects the median of the slopes of all lines between paired points, thereby better fitting the line to the sampled points on the plane [29]. The combination of Sen slope estimation and the Mann–Kendall (MK) trend test is suitable for analyzing long-term time series data trends. This study applied Sen’s slope integrated with the Mann–Kendall trend significance test in MATLAB 2024b to quantify the trend of composite ecological sensitivity from 2000 to 2020. The Sen slope is calculated to determine whether the trend is increasing or decreasing. When the Sen slope $>0$, it indicates a growth trend; when the Sen slope $=0$, it indicates no change; when the Sen slope $<0$, it indicates a declining trend. Simultaneously, the MK trend test is used to detect the significance level: when $\left|Z\right|>2.56$, it indicates a highly significant change; when $1.96<\left|Z\right|\le 2.56$, it indicates a significant change; when $1.65<\left|Z\right|\le 1.96$, it indicates a slightly significant change; and when $\left|Z\right|\le 1.65$, it indicates no significant change. The combination of Theil–Sen and Mann-Kendall methods has certain advantages in determining trends in long-term time series data. It has strong resistance to data errors and is a robust non-parametric statistical trend calculation method [30,31,32]. This method can effectively measure the trend and fluctuation range of composite ecological sensitivity from 2000 to 2020.

4.

OWA multi-scenario simulation

The OWA algorithm is currently often used for multi-scenario simulations of trade-offs in ecosystems [33,34]. The weights of each ecosystem are determined by the OWA algorithm, including the calculation of criterion weights and order weights. In this paper, the criterion layer weights are obtained through the CRITIC weighting method, with the weights for soil erosion sensitivity, landslide disaster sensitivity, desertification sensitivity, and freeze-thaw erosion sensitivity being 0.0541, 0.1273, 0.1574, and 0.1396, respectively. Subsequently, ordered weighted averaging weights were derived and synthesized in MATLAB 2024b based on the indicators’ attribute values (

Table 4. OWA order weight calculation results.

Order Weight	$\mathit{\alpha }=0.0001$	$\mathit{\alpha }$ = 0.1	$\mathit{\alpha }$ = 0.5	$\mathit{\alpha }$ = 0.8	$\mathit{\alpha }$ = 1	$\mathit{\alpha }$ = 1.2	$\mathit{\alpha }$ = 2	$\mathit{\alpha }$ = 10	$\mathit{\alpha }$ = 10,000
$W_1$	1.00	0.87	0.50	0.33	0.25	0.19	0.06	0.00	0.00
$W_2$	0.00	0.06	0.21	0.24	0.25	0.25	0.19	0.00	0.00
$W_3$	0.00	0.04	0.16	0.22	0.25	0.27	0.31	0.06	0.00
$W_4$	0.00	0.03	0.13	0.21	0.25	0.29	0.44	0.94	1.00
Risk attitude	Most optimistic	Optimism	Slightly optimistic		No preference		Slightly pessimistic	Pessimistic	Most pessimistic

). By systematically modulating different risk coefficients and trade-off values, evaluation outcomes across diverse decision-making scenarios were generated in ArcGIS 10.8 [35]. The calculation formula is as follows:

$$V_j={\left[\sum _{k=1}^j{w}_k\right]}^{\alpha }-{\left[\sum _{k=1}^{j-1}{w}_k\right]}^{\alpha }$$

(9)

In the formula, $V_j$ represents the order weight, $V_j\in \left[0,1\right],j=1,2\dots n,{\displaystyle \sum _{j=1}^n}{V}_j=1$; $\alpha$ is the decision risk coefficient, $\alpha \in \left(0,\infty \right)$, When $\alpha$ > 1, the greater the factor weight, the more pessimistic the researcher is, and the ecological risk tends to increase. When $\alpha$ = 1, it indicates no preference. When $\alpha$ < 1, the smaller the factor weight, the more optimistic the researcher is, and the landscape ecological risk tends to decrease. This study sets nine different levels of risk coefficients: $\alpha$ = 0.0001, $\alpha$ = 0.1, $\alpha$ = 0.5, $\alpha$ = 0.8, $\alpha$ = 1, $\alpha$ = 1.2, $\alpha$ = 2, $\alpha$ = 10, and $\alpha$ = 1000 for simulation; $W_k$ is the importance level of the index, and the $W_k$ formula is as follows:

$$w_k={\displaystyle \frac{n-r_k+1}{{\displaystyle \sum _{j=1}^k}\left(n-r_j+1\right)}}\left(k=1,2,\dots n\right)$$

(10)

where, $r_k$ assigns values to the importance of the indicators in sequence according to the size of the indicator values, with the maximum value assigned 1, the second largest value assigned 2, and the minimum value n, where n is the number of indicators in the criterion layer. The OWA multi-criteria evaluation formula based on criterion layer weights and order layer weights is as follows:

$$OW{A}_i=\sum _{j=1}^n\left({\displaystyle \frac{u_j{v}_j}{{\displaystyle \sum _{j=1}^n}{u}_j{v}_j}}\right)z_{ij}$$

(11)

where $u_j$ is the criterion weight; $v_j$ is the order weight; $z_{ij}$ is the attribute value of the j index of the i pixel that has been standardized and hierarchically assigned.

5.

Optimal Parameter Geodetector

The optimal parameter geographic detector improves the capability of the geographic detector model through spatial discretization and spatial scale optimization. Compared to traditional geographic detectors, it can more accurately and effectively extract and interpret the geographic characteristics of variables. It can be flexibly applied to global and regional spatial analysis of various types of spatial data, providing a comprehensive solution for spatial stratified heterogeneity analysis [36,37]. In this study, the GD package in RStudio 2024 is used to calculate the optimal discretization method and classification number of different driving factors, quantifying the explanatory power of single factors on the spatial variation of ecological sensitivity using q values. Subsequently, the interaction detector is used to detect the interaction strength between factors.

3. Results

3.1. Single Ecological Sensitivity Characteristic

3.1.1. Land Desertification Sensitivity

During the 21-year period, the proportions of land in the Western Sichuan Plateau classified as insensitive, light sensitive, moderate sensitive, intense sensitive, and extreme sensitive to desertification are 43.88%, 42.94%, 9.76%, 2.65%, and 0.76%, respectively (see Supplementary Materials). The desertification assessment results indicate a significant gradient evolution characteristic of desertification sensitivity in the Western Sichuan Plateau. This is mainly due to the gradual transition of vegetation cover from forest and shrubland to grassland with increasing altitude, leading to a decrease in vegetation cover and an increase in land desertification sensitivity [38,39]. Among them, the extreme sensitive and intense sensitive areas exhibit a “dual-core” spatial lock-in pattern (Figure 3), stably distributed in the arid river valleys of the Hengduan Mountains in the southwest (Daocheng County, Batang County, Derong County, and Litang County) and the alpine desert zone in the northwest (Shiqu County). The sensitivity transition zone follows the altitude control law, with high and low sensitivity areas mixed in the central 2500–4000 m altitude zone, corresponding to the ecologically fragile interface of the shrub-grass transition zone. In the northeastern Zoige Plateau, areas such as Hongyuan County and Zoige County form ecological resilience units with good vegetation cover.

3.1.2. Soil Erosion Sensitivity

The proportions of land in the Western Sichuan Plateau classified as insensitive, light sensitive, moderate sensitive, intense sensitive, and extreme sensitive to soil erosion are 69.53%, 17.36%, 9.17%, 3.28%, and 0.67%, respectively (see Supplementary Materials). The results indicate a gradient differentiation pattern of “stable base-fragile corridor” for soil erosion sensitivity in the Western Sichuan Plateau (Figure 4). Approximately 86.89% of the study area is classified as low-risk zones, predominantly located in Aba, Hongyuan, and Zoigê counties within the Zoigê Plateau. The ecological resilience of these regions can be attributed to the synergistic interaction between high vegetation coverage in alpine meadows and the gentle topography. In contrast, the moderate to extreme sensitivity areas, accounting for 13.11%, form three major fragile corridors: Wenchuan County, Mao County, and Li County in the Longmenshan fault zone; Jiulong County and Kangding County in the middle reaches of the Yalong River; and Batang County, Derong County, and Baiyu County in the Jinsha River basin. These regions are located in areas of intense tectonic activity, with frequent earthquakes, fractured rock masses, and widespread mountain hazards such as landslides, collapses, and debris flows. Under the influence of precipitation, these areas are highly prone to severe soil erosion [40].

3.1.3. Landslide Disaster Sensitivity

The entire Western Sichuan Plateau is prone to landslides. The proportions of insensitive, light sensitive, moderate sensitive, intense sensitive, and extreme sensitive areas to landslide disasters are 8.90%, 21.81%, 29.25%, 26.17%, and 13.88%, respectively (see Supplementary Materials). The low and high landslide susceptibility zones are vertically interlaced across the Western Sichuan Plateau. The northwestern Shiqu County and the northeastern Hongyuan County and Ruoergai County are low-susceptibility clusters, where landslide geological disasters are unlikely to occur. The line from Mao County, Wenchuan County, and Songpan County; the line from Seda County, Luhuo County, Daofu County, Danba County, and Luding County; the line from Ganzi County, Xinglong County, and Litang County; and the line from Dege County, Baiyu County, Batang County, and Derong County are high-susceptibility clusters, where landslide geological disasters are frequent (Figure 5). The lines from Seda County to Luding County and from Dege County to Derong County are significant river disaster zones, where river lateral erosion and downcutting significantly impact slope stability, leading to landslide geological disasters [41,42]. The line from Mao County to Songpan County is a post-earthquake damaged mountain disaster zone, located near the Longmenshan Fault Zone, where joints and fissures are well-developed, the surface soil layer is loose, and tectonic activities are intense, making landslides highly likely [43].

3.1.4. Freeze-Thaw Erosion Sensitivity Index

The proportions of insensitive, light sensitive, moderate sensitive, intense sensitive, and extreme sensitive areas to the total area of the Western Sichuan Plateau were 11.96%, 20.13%, 26.49%, 27.65% and 13.76%, in that order (see Supplementary Materials). The freeze-thaw erosion sensitivity gradually increases from east to west across the Western Sichuan Plateau. The area east of the line from Maerkang City to Jiulong County is a low-sensitivity cluster, while the area west of this line is a high-sensitivity cluster (Figure 6). The northern section of the Hengduan Mountains (Shiqu County, Ganzi County), the eastern foothills of the Daxue Mountain Range (Kangding City), and the southern foothills of the Shaluli Mountains (Litang County, Daocheng County) experience the most severe freeze-thaw erosion. These regions are characterized by high altitude, low oxygen, intense weathering, and frequent freeze-thaw cycles.

3.2. Characteristics of Composite Ecological Sensitivity

3.2.1. Spatial Characteristics of Composite Ecological Sensitivity

The research results indicate that over the 21-year period, the area proportions of composite ecological sensitivity levels in the study area exhibit a gradient distribution pattern: insensitive areas (70.04%) > light sensitive areas (16.87%) > moderate sensitive areas (9.03%) > intense sensitive areas (3.36%) > extreme sensitive areas (0.71%) (see Supplementary Materials). The ecological environment of the study area demonstrates a phased characteristic of “significant improvement—local rebound—overall stabilization” over time. However, the spatial pattern of composite ecological sensitivity in the study area shows significant temporal stability, with the spatial distribution patterns of the 21 observation nodes maintaining high consistency. The spatial trend decreases from northwest to southeast, with high-value areas of composite ecological sensitivity concentrated in the eastern Aba Tibetan and Qiang Autonomous Prefecture (Figure 7).

3.2.2. Spatiotemporal Evolution and Prediction of Composite Ecological Sensitivity

1.

Temporal Evolution

Figure 8 shows composite ecological sensitivity classification index fluctuation trend line of the Western Sichuan Plateau based on linear regression fitting. From 2000 to 2020, the composite ecological sensitivity classification index of the study area exhibited a fluctuating downward trend, with an average annual rate of change of −0.01. The mean value of the composite ecological sensitivity classification index was 1.55, with a standard error of 0.46. Overall, the ecological quality was relatively high, and the ecological environment continued to improve.

To deeply analyze the temporal characteristics of the evolution of composite ecological sensitivity in the Western Sichuan Plateau, this study conducted a Mann–Kendall mutation test on the composite ecological sensitivity classification index from 2000 to 2020. The results indicate that since 2000, $U{F}_K$ values have been less than 0, showing a downward trend, confirming the persistence of the decline in ecological sensitivity levels. However, the decline in composite ecological sensitivity has phased characteristics: an insignificant decline from 2000 to 2005, a significant decline from 2005 to 2007, a return to an insignificant decline from 2007 to 2013, and a significant decline trend after 2013. Within the 90% confidence interval, $U{F}_K$ and $U{B}_K$ intersected in 2010, and the sliding t-test confirmed that 2010 is a mutation point in the evolution of composite ecological sensitivity (Figure 9).

2.

Spatial evolution and multi-scenario simulation

A pixel-by-pixel analysis of the composite ecological sensitivity in the Western Sichuan Plateau from 2000 to 2020 shows an overall improvement in the ecological environment. The deteriorated areas account for 2.38% of the Western Sichuan Plateau, the improved areas account for 95.52%, and the unchanged areas account for 2.10% (Figure 10). The areas of significant deterioration, slight deterioration, and insignificant deterioration cover $5.538\times {10}^3$ m², concentrated in Songpan County, Heishui County, Li County, Xiaojin County, and Maerkang City near the Chengdu Plain. The areas of slight improvement, significant improvement, and highly significant improvement cover $5.052\times {10}^4$ m², widely distributed across the entire Ganzi Tibetan Autonomous Prefecture near the Qinghai–Tibet Plateau.

The Western Sichuan Plateau is ecologically fragile, and the future pattern of the ecological environment will mainly depend on the intensity of human exploitation, which is closely related to national regulatory policies. When decision-makers hold an extremely optimistic attitude towards the ecological environment ($\alpha$ = 0.0001 or $\alpha$ = 0.1), the ecological environment quality of the entire study area is excellent, with almost no ecologically sensitive areas, generally in an unrealistic extreme protection state that prohibits all human development activities. As the risk coefficient continues to rise and decision-makers’ attitudes become more pessimistic, the impact of high-weight indicators on the ecological environment expands, leading to the expansion of ecologically sensitive areas. When decision-makers hold a pessimistic attitude towards the ecological environment ($\alpha \ge 10$), almost the entire Western Sichuan Plateau becomes a high-sensitive area, showing a state of rampant development with no concern for the ecological environment, severely detached from reality. According to existing research results, it is believed that when the $\alpha$ coefficient is between 0.8 and 1.2, scenario simulations are more realistic [44,45]. Therefore, this paper mainly presents the ecological scenarios under these three risk coefficients (Figure 11) ($\alpha$ = 0.8, 1, and 1.2). When $\alpha$ = 0.8, decision-makers adopt “sustainable-oriented” decision-making behavior, with a composite ecological sensitivity classification index of 1.40; when $\alpha$ = 1, decision-makers aim to maintain the status quo, with a composite ecological sensitivity classification index of 1.45, an increase of 3.30% compared to the “sustainable-oriented” scenario, and a decline in ecological environment quality; when $\alpha$ = 1.2, decision-makers believe that economic development should be prioritized, with a composite ecological sensitivity classification index of 1.57, an increase of 11.96% compared to the “sustainable-oriented” scenario, and a significant deterioration in ecological environment quality. Scenario simulations under the three policy orientations all show that Jiuzhaigou County, Xiaojin County, Jinchuan County, Danba County, and Yajiang County have high composite ecological sensitivity and should be given focused attention.

3.2.3. Driving Mechanisms of Composite Ecological Sensitivity

• Analysis of Dominant Ecological Disasters

Based on the Parameter Optimal Geographic Detector, the study systematically reveals the spatiotemporal evolution patterns of the driving mechanisms of ecological disasters in the Western Sichuan Plateau (Figure 12). Using 2010 as a boundary, a phased diagnosis of ecological disasters dominated by 31 counties was conducted, revealing significant spatiotemporal reconstruction characteristics of the dominant factors of ecological disasters. In 2010, the counties dominated by landslide disasters, freeze-thaw erosion, soil erosion, and land desertification were 14, 9, 5, and 3, respectively, showing a spatial pattern of “large clusters, small dispersions”. The counties dominated by landslides accounted for 45.16%, concentrated in Ganzi County, Xinlong County, and Litang County near the Xianshuihe Fault Zone, and Songpan County, Heishui County, and Li County near the Songpan-Jiuzhaigou Fault Zone and Longmenshan Fault Zone. The dominant factor of composite ecological sensitivity in the upper reaches of the Dadu River, such as Rangtang County and Maerkang City, is freeze-thaw erosion; in the lower reaches of the Dadu River, such as Daofu County and Kangding City, soil erosion has the strongest explanatory power. In 2020, the counties dominated by freeze-thaw erosion, land desertification, and landslide disasters were 25, 4, and 2, respectively. The freeze-thaw erosion gradually expanded in the mid-low mountain areas of the Western Sichuan Plateau, with the dominant counties increasing to 80.65%.

The ecological disasters in the Western Sichuan Plateau exhibit significant interactions, meaning that these ecological disasters are not independent. They interact, primarily manifesting as bi-factor enhancement or nonlinear enhancement. Compared to nonlinear enhancement, bi-factor enhancement has a greater explanatory power for the spatial differentiation of composite ecological sensitivity in the Western Sichuan Plateau. From Figure 12a, it can be seen that in 2010, the explanatory power of landslides combined with other factors showed significant bi-factor enhancement. The top three interaction factors in terms of explanatory power among the 31 counties were landslide disasters ∩ freeze-thaw erosion, landslide disasters ∩ desertification, and landslide disasters ∩ soil erosion. From Figure 12b, it can be seen that in 2020, the explanatory power of freeze-thaw erosion combined with other factors showed significant bi-factor enhancement. The top three interaction factors in terms of explanatory power among the 31 counties were: freeze-thaw erosion ∩ landslide disasters, freeze-thaw erosion ∩ land desertification, and freeze-thaw erosion ∩ soil erosion. Over the past 21 years, the areas where landslide disasters ∩ freeze-thaw erosion are distributed most widely, covering 74.19% of the counties (Figure 13). The q value of landslide disasters ∩ freeze-thaw erosion is the highest at 0.85, indicating the strongest explanatory power for composite ecological sensitivity in Western Sichuan. Freeze-thaw erosion and landslides are the focus of ecological environment management in the Western Sichuan Plateau.

2.

Analysis of Main Driving Factors

To investigate the main driving factors affecting the ecological environment quality of the Western Sichuan Plateau, this paper uses the 21-year composite ecological sensitivity average as the dependent variable and selects a total of 12 influencing factors, including elevation, precipitation, and temperature difference, as independent variables. The optimal parameter geographical detector is used to analyze their impact on the ecological environment quality changes in the study area. The p-values of the 12 driving factors are all 0.01, indicating that each driving factor sufficiently explains the composite ecological sensitivity, although there are certain differences in the explanatory power of different driving factors. In single-factor detection, the factors were ranked according to the q value: $X_2>X_{10}>X_{11}>X_4>X_1>X_6>X_8>X_{12}>X_9>X_3>X_5>X_7$, indicating that temperature difference, distance to fault, and slope are the dominant factors (

Table 5. Results of Single-Factor Detection Using Optimal Geographical Detector. (NDVI: Normalized Difference Vegetation Index).

Factors	Name	Q Value	Classification Methods	Number of Classifications
$X_2$	Annual Temperature Difference	0.3182	Quantile Classification Method	11
$X_{10}$	Distance to Fault	0.1732	Standard Deviation Classification Method	9
$X_{11}$	Slope	0.1609	Quantile Classification Method	9
$X_4$	Distance to River	0.1354	Standard Deviation Classification Method	9
$X_1$	Lithology	0.1277	Quantile Classification Method	9
$X_6$	Artificial Measures	0.1249	Standard Deviation Classification Method	8
$X_8$	Soil Erodibility	0.1180	Quantile Classification Method	11
$X_{12}$	NDVI	0.1135	Geometric Interval Classification Method	11
$X_9$	Elevation	0.0727	Natural Breaks Classification Method	10
$X_3$	Aspect	0.0154	Quantile Classification Method	11
$X_5$	Annual Precipitation	0.0142	Standard Deviation Classification Method	11
$X_7$	Slope Length	0.0049	Standard Deviation Classification Method	11

). This is related to the unique geographical location of the western Sichuan Plateau. As a transition zone between the Sichuan Basin and the Qinghai–Tibet Plateau, the study area experiences active tectonic movements and dramatic terrain fluctuations, leading to amplified annual temperature differences. This results in widespread seasonal and permanent permafrost on the western Sichuan Plateau, which, through freeze-thaw cycles and rock weathering, accelerates the evolution of sensitivity.

4. Discussion

This study systematically reveals the spatiotemporal differentiation characteristics and driving mechanisms of composite ecological sensitivity on the Western Sichuan Plateau, providing a scientific basis for the sustainable management of the plateau ecosystem. In terms of spatial patterns, the regional composite ecological sensitivity exhibits a significant “low in the east, high in the west” gradient differentiation, which aligns with the findings of Lü Yuanyang’s team in ecological sensitivity assessments [46]. This differentiation pattern stems from the vertical zonality of natural elements: from east to west, as the altitude gradient increases, vegetation types transition from forests and shrublands to grasslands, with vegetation coverage gradually decreasing. Additionally, the western region of the Western Sichuan Plateau, influenced by the strong incision by rivers such as the Jinsha and Yalong Rivers, forms a typical “alpine canyon” geomorphological unit. The coupling effect of steep terrain and fragile soil parent material makes this region prone to soil erosion and landslide disasters. With the gradual intensification of the greenhouse effect, the stability of snow cover in the mid- and low-elevation mountain areas of the Western Sichuan Plateau has deteriorated, leading to frequent freeze-thaw cycles and increased freeze-thaw erosion. The interplay of various natural environmental factors collectively shapes the complex ecological sensitivity pattern of the Western Sichuan Plateau. Monitoring data over the past 21 years show that 2.37% of the area have experienced a worsening trend in composite ecological sensitivity, mainly concentrated in eastern counties, while 95.52% of the areas have shown improvement, primarily in western counties.

An analysis of temporal evolution reveals that the composite ecological sensitivity index exhibited a significant downward trend from 2000 to 2020, corroborating the cumulative effects of regional ecological conservation policies. During the initial implementation period (2000–2005) of ecological projects such as the Natural Forest Protection Program (Tianbao Project) and Grain-for-Green Program (Tui Geng Huan Lin), vegetation restoration primarily occurred in areas of low to medium vegetation coverage, such as cutover areas. However, ecological restoration effects were significantly delayed due to the constraints of the alpine climate. During the policy efficacy phase (2005–2007), vegetation coverage notably rebounded, with the proportion of medium and higher vegetation coverage areas increasing by 17.81% [47,48]. In 2008, the Wenchuan earthquake and its associated secondary disasters caused significant damage to the ecological environment in areas near the epicenter. The threat of soil erosion increased sharply, leading to a 5.75% rise in the average soil erosion sensitivity index. The average values of the other three indices slightly increased. However, the interaction of the four ecological disasters significantly amplified the earthquake’s impact, causing the composite ecological sensitivity classification index to abruptly rise from 1.55 to 1.65, while the maximum composite ecological sensitivity decreased sharply from 3.81 to 3.08. The overall ecological environment quality of the Western Sichuan Plateau has shown fluctuations [49], and extreme disturbances pose a threat to the ecological security of the region. Notably, 2010 marked a mutation point in composite ecological sensitivity, with the average soil erosion sensitivity index sharply decreasing by 15.75%. After 2013, the average soil erosion sensitivity index stabilized below 0.10. In 2010, the average vegetation coverage of the Western Sichuan Plateau reached 0.70 for the first time and increased gradually to 0.75 in subsequent years. The average landslide disaster sensitivity index dropped to 0.50 in 2010, the lowest value in the previous 11 years. Only the average freeze-thaw sensitivity index increased by 1.23% compared to 2009. From 2013 to 2020, the composite ecological sensitivity classification index remained below 1.40, significantly lower than before 2013, indicating enhanced ecological resilience. Over time, the ecological benefits of afforestation, grassland restoration, and sand control projects in the Western Sichuan Plateau have become increasingly evident, and the ecological environment has gradually recovered to pre-earthquake conditions [50]. These developments demonstrate progress toward achieving sustainable development goals. Simulations of the composite ecological sensitivity pattern under three different decision-making behaviors show high spatial consistency with the calculated trend. The ecological environment in areas near the Chengdu Plain, such as Jiuzhaigou County, Xiaojin County, Jinchuan County, and Danba County, remains a concern and calls for the establishment of an ecological risk warning system and the enhancement of dynamic monitoring efforts.

The year 2010 marked a mutation point in composite ecological sensitivity for the Western Sichuan Plateau. During this period, not only did ecological quality improve significantly, but the region also underwent a structural shift in dominant ecological stressors—freeze-thaw erosion superseded landslides as the primary environmental threat. Prior to 2010, landslides posed substantial ecological risks across 14 counties, constituting the predominant hazard. Post-2010, freeze-thaw erosion progressively expanded into central and eastern low-mountain counties, with its spatial impact expanding its spatial footprint from 9 to 25 counties. This spatial-temporal pattern aligns closely with Nie Luming’s findings on intra-annual snow cover variability and long-term snow cover trends [51]. The Western Sichuan Plateau, situated within China’s severe freeze-thaw zone, experiences 80–119 annual freeze-thaw cycles, and certain extreme zones exceed 120 cycles (averaging 147 cycles) [52]. Temperature emerges as the paramount driver of freeze-thaw erosion dynamics, directly regulating frost penetration depth and thaw intensity [53]. Regionally, the mean annual temperature has risen at 0.215 °C/decade, with winter warming being particularly pronounced (0.341 °C/decade) [54]. Accelerated greenhouse forcing has amplified freeze-thaw cycle frequency, thereby intensifying erosion.

Freeze-thaw erosion intensifies landslides, desertification, and soil erosion through multiple mechanisms, thereby amplifying regional ecological disaster risks. In alpine steep-slope zones, landslide risks are primarily driven by freeze-induced bedrock fracturing and freeze-thaw-induced mudflows. Frost heaving enlarges existing fissures within the bedrock, generating loose rock accumulations that serve as potential landslide material. Simultaneously, freeze-thaw mudflows shear along potential slip surfaces, disrupting soil structure, reducing shear strength, and triggering shallow landslides. In plateau meadow regions, freeze-thaw-induced topsoil stripping damages grass root systems, resulting in surface exposure and accelerating desertification through the depletion of soil organic matter. The loose sediments generated by freeze-thaw processes are readily transported by seasonal runoff, intensifying gully incision and soil erosion [55,56].

Geographical detector analysis identifies annual temperature difference (q = 0.32), distance from faults (q = 0.17), and slope (q = 0.16) as the primary explanatory factors, elucidating the underlying mechanisms of ecological vulnerability in the Western Sichuan Plateau. In the tectonically active zone along the eastern margin of the Qinghai-Tibet Plateau, annual temperature fluctuations trigger a series of physical processes, including water–ice phase transitions, structural plane softening, and frost wedging. The widespread distribution of faults induces rock mass fracturing in the Western Sichuan Plateau, facilitating significant displacement along fracture surfaces. Substantial annual temperature variations allow meltwater to infiltrate rock masses during warmer periods, softening structural planes and reducing their shear strength. During cooling phases, water freezes and expands, accelerating the rupture of locked rock segments through frost wedging. Steep slopes offer favorable conditions for rock mass detachment and movement along exposed slope surfaces. Following the failure of locked segments, rock masses slide along shear outlets toward free faces, triggering debris flows through the scraping and fragmentation of weathered slopes. The substantial gravitational potential energy propels the resulting debris flows at high velocities, amplifying their erosive impact [57].

The ecological disasters in the Western Sichuan Plateau exhibit significant characteristics of “vertical differentiation and composite enhancement,” forming a dynamic interplay with regional sustainable development capabilities. In light of ecological environmental changes observed since 2010, it is essential to implement tailored and integrated management strategies according to the dominant disaster types and ecological sensitivity levels. In areas sensitive to both landslides and freeze–thaw erosion, slope stability should be reinforced through the installation of anti-slide piles, complemented by heat rods to mitigate sliding forces induced by moisture migration within the active permafrost layer. These interventions should be concurrently implemented with the construction of slope interception and drainage systems to reduce snowmelt infiltration and softening of the sliding zone. Additionally, protective nets should be deployed to capture frost-heaved fragmented rock debris within loose material source zones. In areas where freeze–thaw erosion and desertification are the primary concerns, management should prioritize the restoration of turf layers. This includes transplanting turf to rebuild humus layers damaged by frost heave mounds and planting cold-resistant, sand-fixing vegetation in a grid pattern. Moreover, seasonal rotational grazing should be adopted in degraded grasslands to regulate livestock carrying capacity and promote ecological recovery. In zones characterized by freeze–thaw erosion coupled with soil erosion, shrubs should be planted on vulnerable slopes, and concrete interception walls should be installed at gully heads to prevent collapse caused by freeze–thaw cycles. Grass planting should be promoted to stabilize slopes within active thaw mudflow areas. A comprehensive dynamic monitoring system should be established across all regions, utilizing Beidou-based displacement sensors to capture real-time deformation associated with freeze–thaw processes. Implementing zonal governance strategies tailored to ecological sensitivity and dominant disaster typologies will significantly contribute to advancing the sustainable development of the Western Sichuan Plateau.

One methodological limitation of this study is that the Spatial Distance Index Method may oversimplify the dynamic processes of complex ecosystems. This limitation poses challenges in accurately identifying the thresholds at which individual ecological disasters influence the overall composite ecological sensitivity of the Western Sichuan Plateau. Future research will prioritize fitting dynamic curves for individual ecological sensitivities and quantifying their impact thresholds on composite ecological sensitivity to enhance analytical precision.

5. Conclusions

(1) From 2000 to 2020, the ecological environment of the Western Sichuan Plateau followed a phased trajectory characterized by significant improvement, localized rebound, and overall stabilization. The composite ecological sensitivity index exhibited a general declining trend, reflecting reduced sensitivity levels and indicating a positive trajectory of ecological restoration. During 2000–2007, the benefits of early-stage ecological conservation projects gradually emerged. However, the 2008 Wenchuan Earthquake caused severe ecological disturbances, with 2010 identified as a critical turning point for composite ecological sensitivity. Following the disaster, ecological rehabilitation efforts progressed steadily, ultimately achieving stable and sustained recovery outcomes.

(2) The composite ecological sensitivity of the Western Sichuan Plateau exhibits pronounced spatial heterogeneity, characterized by a “low in the east, high in the west”. Projections indicate that approximately 95.52% of the region will experience ecological improvement. Based on the simulations of the composite ecological sensitivity pattern, it is recommended that ecological protection efforts be prioritized in Jiuzhaigou County, Xiaojin County, Jinchuan County, Danba County, and Yajiang County.

(3) Since 2010, freeze-thaw erosion has played an increasingly significant role in influencing composite ecological sensitivity, progressively expanding from eastern to central and western counties. Over the past two decades, the combined impacts of landslides and freeze-thaw erosion have emerged as the most widespread ecological stressor, affecting nearly 74.19% of counties by 2020. The key driving factors—annual temperature range, distance to faults, and slope gradient—collectively shape the ecological sensitivity in the Western Sichuan Plateau through mechanisms associated with tectonic activity, thermal oscillation, and geomorphic energy dynamics.