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Article

Analysis of Habitat Quality Changes in Mountainous Areas Using the PLUS Model and Construction of a Dynamic Restoration Framework for Ecological Security Patterns: A Case Study of Golog Tibetan Autonomous Prefecture, Qinghai Province, China

by
Zihan Dong
1,2,†,
Haodong Liu
3,†,
Hua Liu
4,
Yongfu Chen
1,2,
Xinru Fu
1,2,
Yang Zhang
1,2,
Jiajia Xia
1,2,
Zhiwei Zhang
5 and
Qiao Chen
1,2,*
1
Institute of Forest Resource Information Techniques Chinese, Academy of Forestry, Beijing 100091, China
2
Key Laboratory of Forest Management and Growth Modelling, National Forestry Grassland Administration, Beijing 100091, China
3
China Aero Geophysical Survey and Remote Sensing Center for Natural Resoures, Beijing 100083, China
4
School of Forestry & Lanscape Architecture, Anhui Agricultural University, Hefei 230036, China
5
Resources and Enviroment College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Land 2025, 14(8), 1509; https://doi.org/10.3390/land14081509
Submission received: 23 May 2025 / Revised: 26 June 2025 / Accepted: 18 July 2025 / Published: 22 July 2025
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Abstract

The intensifying global climate warming caused by human activities poses severe challenges to ecosystem stability. Constructing an ecological security pattern can identify ecological land supply and an effective spatial distribution baseline and provide a scientific basis for safeguarding regional ecological security. This study analyzes land-use data from 2000 to 2020 for Golog Tibetan Autonomous Prefecture. The PLUS model was utilized to project land-use potential for the year 2030. The InVEST model was employed to conduct a comprehensive assessment of habitat quality in the study area for both 2020 and 2030, thereby pinpointing ecological sources. Critical ecological restoration zones were delineated by identifying ecological corridors, pinch points, and barrier points through the application of the Minimum Cumulative Resistance model and circuit theory. By comparing ecological security patterns (ESPs) in 2020 and 2030, we proposed a dynamic restoration framework and optimization recommendations based on habitat quality changes and ESPs. The results indicate significant land-use changes in the eastern part of Golog Tibetan Autonomous Prefecture from 2020 to 2030, with large-scale conversion of grasslands into bare land, farmland, and artificial surfaces. The ecological security pattern is threatened by risks like the deterioration of habitat quality, diminished ecological sources as well as pinch points, and growing barrier points. Optimizing the layout of ecological resources, strengthening barrier zone restoration and pinch point protection, and improving habitat connectivity are urgent priorities to ensure regional ecological security. This study offers a scientific foundation for the harmonization of ecological protection and economic development and the policy development and execution of relevant departments.

1. Introduction

Human survival and development depend on natural resources and the environment. Rapid economic development, a hallmark of human societal progress, has inevitably driven high-intensity human activities such as infrastructure construction and industrial mining, leading to numerous ecological and environmental issues, such as climate change, declining ecosystem services, and habitat loss. These human activities have radically transformed land-use patterns and spatial configurations [1,2]. Over the next decade, the Kunming–Montreal Global Biodiversity Framework (Kunming–Montreal GBF), which was adopted in Montreal, is designed to guide efforts to conserve biodiversity to stop and turn around biodiversity loss [3,4]. To promote sustainable development, safeguarding the integrity in structure and function of natural ecosystems has become a worldwide concern [5,6].
Over the past few years, the Tibet region has witnessed an average annual temperature increase of 0.32 °C per decade. This has triggered glacier recession, enhanced freeze–thaw processes, and subsequently given rise to issues like grassland deterioration and land desertification [7,8]. Many scholars have employed remote sensing technologies to study land-use changes [9,10], including forecasting future land-use changes, evaluating land-use habitat quality, and constructing ecological security patterns [11,12,13]. Given the long-term impacts of some driving factors such as policy implementation and human activities, it is necessary to predict and analyze the spatial distribution of land use influenced by these drivers [14]. To analyze the regional hydrological conditions, long-integrated land-use scenario forecasting with climate simulation, showing that the joint changes in land use and climate substantially impact the water supply [15]. Zhou upgraded and optimized the regional land-use patterns by analyzing the relationship between future land-use change and biological carbon storage [16]. Studies have shown that land-use changes significantly impact regional environmental development [17] and ecosystem service value [18]. To better assess and predict these changes, researchers have developed various land-use and land-cover change models. The Cellular Automata (CA) model, introduced in the mid-20th century, was one of the earliest tools for forecasting future land-use spatial distribution but has limitations in analyzing driving factors of land-use changes and simulation accuracy [19]. Subsequent models, such as CLUE-S [20], FLUS [21], and PLUS [22], were built and refined based on the CA model. Islam et al. [23] used the CLUE-S model to simulate and predict regional land-use changes and proposed restoration and protection measures. Hou et al. [24] combined System Dynamics (SD) with the Future Land-Use Simulation (FLUS) model to simulate and analyze land-use dynamics in China’s coastal zones under different scenarios. Liang et al. [25] compared the PLUS model with other CA models using Wuhan as a case study, finding that the PLUS model achieves higher simulation accuracy and better reflects real landscape pattern indicators. Currently, most studies have relatively focused on using the PLUS model for land-use scenario predictions [26,27].
Ecological security is the bedrock of sustainable development. By analyzing land-use types, the InVEST model mainly carries out quantitative assessments of values of ecosystem functions and is broadly utilized in assessments of habitat quality [28]. Ecological security patterns, as sustainable landscape frameworks, are considered effective spatial pathways for protecting ecological security and maintaining socio-economic development [29]. Consisting of networks of points, lines, and areas, these configurations offer a straightforward, insightful perspective for grasping the ecological dynamics of landscape patterns [30]. Previous studies have indicated that the approach to building ecological security patterns has turned into a fundamental framework, that is, “pinpointing ecological sources—constructing resistance surfaces—delineating ecological corridors—determining ecological security patterns”. By analyzing the present situation of regional ecological security, it offers policy suggestions for improvement of the ecological security patterns [31,32]. With the continuous development of ecosystem service value theory and quantitative models, more scholars commonly use the MCR (minimal cumulative resistance) model [33] and circuit theory [34] to construct ecological security patterns, significantly enhancing the scientific validity of ecological security pattern construction. More scholars have gradually shifted their focus to the exploration of ecological processes, conducting comprehensive analyses and evaluations of the integrated service capacity of ecosystems, such as habitat quality and dynamic changes in ecological security patterns [35,36].
China places a high priority on the protection and sustainable use of terrestrial ecosystems in land planning. For example, when establishing ecological protection boundaries, important ecosystems such as forests, wetlands, and grasslands need to be incorporated into the protection range, and development and building activities should be rigorously limited to protect biodiversity. Located in the southeast of Qinghai Province, China, Golog Tibetan Autonomous Prefecture, a representative high-altitude mountainous area, has established a stable management system. How to effectively regulate the ecological balance in mountainous areas and to promote the stable development and sustained rapid economic growth of mountainous areas has become a pressing issue to be addressed. To promote regional ecological security and sustainable development, carrying out forecasting of regional land use, building a framework for ecological security, examining changes, and offering recommendations for restoration are of crucial significance. This study focuses on Golog Tibetan Autonomous Prefecture as the study area. Based on three phases of land-use type maps from 2000 to 2020, the PLUS model is employed to simulate and predict land-use changes for 2030. Using the InVEST model, land-use data from 2020 to 2030 are analyzed, threat factors are selected and assigned weights, and the sensitivity of each habitat type to the threat factors and its habitat suitability are determined. The spatiotemporal impacts of land-use changes on habitat quality in Golog Tibetan Autonomous Prefecture are then explored. At the same time, high-quality habitat areas (0.8–1) are identified as ecological sources, and resistance factors relevant to the region’s ecological processes are selected to generate resistance surfaces through weighted overlay. Based on circuit theory, ecological corridors, barriers, and pinch points are identified, and an ecological security pattern for the prefecture is ultimately constructed.

2. Overview of the Study Area and Data Sources

2.1. Overview of the Study Area

Golog Tibetan Autonomous Prefecture is located in the eastern Qinghai–Tibet Plateau (Figure 1) and forms part of the Yellow River source region. The study area has a total area of 74,200 km2, whose elevation ranges from 2846 to 6293 m. With ample sunlight and water resources but moderately low heat, the prefecture features a typical plateau continental climate. The mean annual temperature is −4 °C, with no consistent frost-free period throughout the year [37].

2.2. Data Sources

The data used in this study primarily include three phases of land-use data from 2000, 2010, and 2020 (30 m resolution) and 11 quantifiable driving factor datasets (Table 1) [38,39]. Download global land-use cover data for 2000–2020 (https://www.webmap.cn, accessed on 28 August 2023). The study area’s land was classified into nine categories based on land-use data: cultivated land, forestland, grassland, scrubland, wetland, water, artificial surface, nudation, and glaciers and permanent snow. Based on the relevant data and actual development situation of the study area, and considering the availability, quantifiability, and comprehensiveness of data, eleven driving factors were selected, including four socio-economic factors (population density, GDP, distance to roads, and distance to settlements) and seven climate and environmental factors (distance to water bodies, soil type, average annual evaporation, average annual precipitation, average annual temperature, DEM, and slope). Population density, GDP, soil type, average annual evaporation, average annual precipitation, and average annual temperature data were sourced from the Resource and Environmental Science Data Platform (http://www.resdc.cn, accessed on 20 September 2024). Road network and settlement distribution data were obtained from the National Catalogue Service For Geographic Information (https://www.webmap.cn, accessed on 20 September 2024). DEM data were sourced from Geospatial Data Cloud (https://www.gscloud.cn, accessed on 21 September 2024). Euclidean distances to roads, settlements, and water bodies were calculated using spatial processing tools in ArcGIS 10.7 (ESRI, Redlands, CA, USA).

3. Research Methods

3.1. Land-Use Prediction

3.1.1. PLUS Model

The PLUS model (V1.4) was a land-use simulation model developed by the HMSCIL@CUG Laboratory inhina, which integrates the rule framework of Land-use Expansion Analysis Strategy (LEAS) and the Cellular Automata model of Random Seed (CARS) [40,41].
The following process was used for accuracy validation and for predicting land use in Golog Tibetan Autonomous Prefecture for 2030: (1) extracting land expansion areas from 2000 to 2010 using the LEAS module; (2) predicting land-use demand with the Markov chain method; (3) determining domain weight parameters and simulating the 2020 land-use pattern using the CARS module; and (4) deterministic accuracy validation: comparing the simulated results with actual 2020 land-use data, calculating the Kappa coefficient and FOM index with the validation module to assess the model’s accuracy, and using the validated parameters to repeat the above steps to predict land-use data for 2030.
To analyze land-use changes in Golog Tibetan Autonomous Prefecture in 2030, a spatial cost transfer matrix and neighborhood weights were established based on actual land use and spatial distribution in the study area. The land-use cost transfer matrix contains only binary values, where 1 indicates that the transition of this land type to other land types is allowed, and 0 indicates that such transition is not allowed. Neighborhood weights, ranging from 0 to 1, represent the expansion intensity of each land-use type [42]. They assist in decision-making by considering the neighborhood effects generated by different land-use types, with higher values indicating stronger neighborhood influence and greater expansion potential [43]. By adjusting the neighborhood weights and comparing the simulation accuracy under different parameters, the parameters that yield the highest accuracy are selected. The cost transfer matrix (Table 2) and neighborhood weights (Table 3) are provided below.

3.1.2. Markov Model

In the last century, Russian scholar Markov proposed a model with strong quantitative predictive capability based on pixel scale, which became known as the Markov model. The Markov model is used in the PLUS model to simulate and predict the pixel number of each land type in the study area in the future, which is lacking in the common spatial model [44]. This model allows for quantitative prediction, which is lacking in ordinary spatial models, and plays an auxiliary role in the PLUS model prediction. The Markov model calculates the probability matrix of land-use transition based on the initial and final periods of land use. Based on these data, mathematical expressions are used to predict the future number of pixels, as shown in the following formula:
S t = S t + 1 × P i j
P i j = P 11 … P 1 n ⋮ ⋱ ⋮ P n 1 … P m n
P i j ∈ [ 0 , 1 )   and   ∑ n = 1 n P i j = 1 ( i , j = 1 , 2 , … , n )
where St represents the status of a land-use type at time t, and St+1 represents the status of the same land-use type at time t + 1. The probability matrix of land-use transition is derived from Pij, where the value of Pij is calculated based on the land-use status of the previous two periods within the prediction period. It represents the probability of transitioning from land-use type i to land-use type j, and n is the number of land-use types.

3.1.3. Model Validation

To verify the model’s accuracy, land-use expansion data from 2000–2010 were extracted. Using the Markov chain model, the land-use demand for 2020 was predicted. The “Verification” module of the PLUS model was utilized to contrast the “simulated land-use map” with the “actual land-use map”. To evaluate the PLUS model’s simulation accuracy, the general accuracy, Kappa coefficient, and Figure of Merit (FoM) coefficient were utilized. The general accuracy and Kappa coefficient were computed by creating an error matrix of raster cells for the simulation and actual results. For details on the operational procedures, refer to the PLUS Model User Manual. Kappa values are generally divided into five categories: 0–0.2 (slight), 0.21–0.40 (fair), 0.41–0.60 (moderate), 0.61–0.80 (substantial), and 0.81–1 (almost perfect) [45]. When the changed area represents only a small portion of the study area, the Kappa coefficient provides limited information. Therefore, the FoM coefficient is constructed to help determine simulation accuracy, and it outperforms the Kappa coefficient in evaluating simulation accuracy. The formula for the FoM coefficient is as follows [46]:
F o M = B A + B + C + D
where A represents the error area where land use actually changed but was simulated as a constant; B represents the common area of change between the actual map and the simulation; C represents areas where both the actual and simulated maps show changes, but with different land-use types; and D represents areas that did not change in the actual map but changed during the simulation process. The FoM coefficient ranges from 0 to 1, with a higher value indicating a more reliable simulation result. However, most actual validation results are usually below 0.3.

3.2. Construction of Ecological Security Patterns

Ecological security patterns are important indicators for evaluating the health and integrity of ecosystems [47]. Constructing these patterns facilitates effective regional ecological protection, balancing ecological protection with economic development, and maintaining ecosystem service functions [48]. Ecological security patterns consist of an ecological network composed of ecological sources, ecological resistance surfaces, ecological corridors, and ecological key nodes [49]. This study constructed ecological security patterns through the following steps: (1) identification of ecological sources; (2) construction of ecological resistance surfaces; (3) extraction of ecological corridors; and (4) identification of ecological strategic nodes for ecological restoration, including pinch points and barrier points, based on circuit theory [50,51].

3.2.1. Identification of Ecological Sources Using the InVEST Model

Ecological sources are the highest quality and most stable habitat patches within an ecosystem. They play multiple roles, such as maintaining ecosystem stability, promoting ecological processes, and providing ecosystem services, making them the foundation for establishing ecological security patterns [52]. To identify ecological sources, we use the InVEST model (3.14.1) to simulate the material quality and value of the ecosystem service system under different land cover scenarios. This model can quantitatively assess habitat quality from a biodiversity perspective and visualize the evaluation results [53].
In this study, forests, shrubs, grasslands, water bodies (glaciers), and wetlands were selected as ecological land, while construction land, cropland, and barren land were considered sources of habitat threats. Different parameters of the model were determined by referencing existing studies [54,55].
The Habitat Quality module in the InVEST model assumes that habitat quality is a continuous variable ranging from 0 to 1, with values closer to 1 indicating better habitat quality. Using the natural break method, the habitat quality index was classified into five levels: poor (0–0.2), relatively poor (0.2–0.4), average (0.4–0.6), relatively good (0.6–0.8), and good (0.8–1) [56]. Patches with a habitat quality of 0–0.8 and an area greater than 50 km2 were identified as ecological sources in the study area. The calculation formula is as follows:
Q x J = H j 1 − D x j z D x j z + k z
where Qxj is the habitat quality index; Hj is the habitat suitability of land-use type j; Dxj is the habitat degradation degree of grid cell x for land-use type j; k is the half-saturation constant; and z is a scaling parameter. The model requires four input data types—the current land-use type raster data, threat source raster data, threat source CSV scale, and the CSV scale of land-use type sensitivity to each ecological threat source. The model’s operational parameters are set by referencing existing research literature [57,58] and the user guide and by assigning values to the parameters in the two CSV scales based on the actual situation of Golog Tibetan Autonomous Prefecture and recommendations from relevant experts (Table 4 and Table 5).

3.2.2. Constructing Ecological Resistance Surface Based on the Minimum Cumulative Resistance (MCR) Model

The utilization of the environment by species can be viewed as a process of spatial coverage and competition management, which must be achieved by overcoming corresponding resistances. The resistance surface also reflects the trends of species and ecological flow diffusion [59]. Since species encounter certain resistances when moving through different types of landscapes, constructing a resistance surface has become a fundamental component of overcoming resistance in species diffusion paths.
The Minimum Cumulative Resistance (MCR) model, also known as the minimum cost distance model, was first introduced by Knaapen in 1992. It primarily studies species diffusion processes and has since been applied in different types of research in natural ecology and the humanities [60]. The MCR model has excellent adaptability and scalability in horizontal spatial expansion analysis.
Traditional resistance surfaces are typically assigned values based on land-use data as a single indicator, and the resulting resistance surfaces cannot accurately simulate the distribution of ecological resistance. Therefore, this study constructs a comprehensive resistance surface based on land-use data, assigning values to factors such as distance from roads, distance from settlements, distance to water, DEM, slope, land-use type, and habitat quality. Each resistance factor is converted into raster attribute data with a resolution of 30 m. Using the Kriging interpolation method in the ArcGIS Raster Calculator tool, the ecological resistance of the study area is accurately simulated. The parameters for each factor are set with reference to existing studies (Table 6). The formula is as follows:
M = f m i n ∑ j = m i = m   D i j R i
where M is the minimum cumulative resistance value; f is the positive correlation between cumulative resistance values and ecological processes; Dij is the spatial distance from landscape unit i to source j; Ri is the resistance coefficient of landscape unit i to the target unit’s diffusion; and the influence factor weight dimension is 1.

3.2.3. Constructing Ecological Corridors and Ecological Strategic Nodes Using Circuit Theory

Ecological corridors, as areas with low cumulative resistance between ecological source areas, can serve as primary channels for species migration, ecological information, and energy flow in ecosystems. Between ecological source patches, they can also serve as linkage corridors, boosting the robustness of landscape configurations [61]. Ecological corridors are frequently made up of valuable ecological landforms, such as grasslands, forests, and aquatic areas [62]. In this study, we use the Linkage Pathways module in the Linkage Mapper tool to identify ecological corridors. This module extracts the least-cost path based on the MCR model, which is considered the optimal ecological corridor for species dispersion in the landscape [63]. After several iterations, it was found that when the cumulative resistance threshold is set at 10 K, the width of the ecological corridors mainly ranges from 1000 to 1500 m, meeting the needs of most species for the width of the ecological corridors for migration and diffusion. In this study, a 10 K threshold is uniformly used to delineate the spatial scope of the ecological corridors.
Strategic ecological nodes, also known as “stepping stones”, include pinch points and barriers and are crucial for regional ecological sustainability [64]. Ecological pinch points play a significant role in connectivity and serve as critical nodes for interlinking ecological sources. On the other hand, ecological barriers hinder species migration, and their elimination is crucial for enhancing ecological resilience and preserving ecological connectivity [63]. To detect the ecological pinch points and barrier points in the study area, the Pinchpoint and Barrier Mappers components of Linkage Mapper were utilized.

3.3. Technical Framework Diagram

This study analyzes the dynamic changes in habitat quality and ecological security patterns in Golog Tibetan Autonomous Prefecture from 2020 to 2030 by coupling the PLUS model, InVEST model, and circuit theory, and proposes corresponding restoration recommendations. The research framework is shown in Figure 2. It mainly includes the following: (1) using the PLUS model to simulate land-use types in 2030 and analyze the land-use change trends in Golog Tibetan Autonomous Prefecture from 2020 to 2030; (2) using the InVEST model to analyze habitat quality changes in Golog Tibetan Autonomous Prefecture from 2020 to 2030 and identify ecological source areas; (3) from 2020 to 2030, the ecological security framework of the study area was developed using the Minimum Cumulative Resistance (MCR) model and circuit theory; and (4) the variations over time in ecological quality and ecological security frameworks were thoroughly examined to pinpoint priority restoration zones in the study area.

4. Results and Analysis

4.1. Validation of PLUS Model Simulation Results

Based on land-use data from Golog Tibetan Autonomous Prefecture in 2000 and 2010, and considering land-use type growth probabilities and the parameter settings in the CARS module, a simulation of the 2020 land-use pattern of Golog Tibetan Autonomous Prefecture was obtained. The closer the Kappa coefficient is to 1, the higher the model accuracy. If the value exceeds 0.81, the model’s simulation accuracy is deemed satisfactory. The simulation accuracy validation results show an overall accuracy of 96.4%, a Kappa coefficient of 0.824, and a FoM coefficient of 0.256. These results indicate that the PLUS model is suitable for simulating the land-use patterns in Golog Tibetan Autonomous Prefecture.

4.2. Land-Use Pattern Changes in Golog Tibetan Autonomous Prefecture from 2020 to 2030

The land-use system, comprising various land-use types, is a complete system. The primary land-use type in Golog Tibetan Autonomous Prefecture is grassland, and in higher-altitude areas, bare land occupies a relatively large proportion (Figure 3). Our analysis of land-use changes in Golog Tibetan Autonomous Prefecture for 2020 and 2030 shows increases in the areas of cultivated land, wetland, water, artificial surface, nudation, and glaciers and permanent snow, with the largest increase observed in nudation, which grew by 1330.72 km2. The areas of forestland, grassland, and scrubland decreased, with grassland experiencing the largest reduction of 1569.11 km2 and a total land-use transfer area of 1571 km2. Analysis of the land-use change matrix indicates that most of the reduction in grassland was converted to nudation, with the remaining portion moving to wetland, artificial surface, and agricultural land (Figure 4).

4.3. Spatiotemporal Evolution of Habitat Quality in Golog Tibetan Autonomous Prefecture from 2020 to 2030

The habitat quality of the study area was measured on a continuous scale from 0 to 1 using the Habitat Quality module in the InVEST model. The nearer the value is to 1, the higher the ecological quality in the study area, the lower the extent of human activity impact, and the greater the ecological benefits demonstrated by the land. Using the InVEST model, the habitat quality distribution maps for 2020 and 2030 in Golog Tibetan Autonomous Prefecture were obtained (Figure 5). To more intuitively represent the spatial distribution of habitat quality, the distribution maps were classified into five levels using the Natural Breaks method in ArcGIS: very poor (0–0.2), poor (0.2–0.4), medium (0.4–0.6), good (0.6–0.8), and excellent (0.8–1) [65].
In terms of spatial pattern, the overall habitat quality of Golog Tibetan Autonomous Prefecture shows a distribution pattern of lower quality in the northeast and higher quality in the southwest, with the overall habitat quality being at a relatively high level. We found that areas with poorer habitat quality are mainly concentrated in the high-altitude mountainous wastelands and flat artificial surfaces. The eastern regions at higher altitudes have a cold and dry climate, lower biodiversity, and are unsuitable for vegetation growth, which easily leads to soil erosion, thus resulting in poorer habitat quality. In contrast, the flatter southern areas consist mostly of cultivated land and built-up areas, where human activities are more frequent, causing greater disturbance and destruction to the ecological environment, leading to easier ecological degradation. In terms of temporal and spatial patterns, from 2020 to 2030, the habitat quality of Golog Tibetan Autonomous Prefecture gradually decreases, with the area of low habitat quality (0–0.6) increasing by 14.02%, while the area of excellent quality (0.8–1) decreases by 2.88% (Table 7). The predicted results show that by 2030, due to climate change and human factors, the areas of barren land and artificial surfaces in the northeast of the prefecture will increase, significantly reducing the habitat quality of the study area. Therefore, it is necessary to plan land-use areas reasonably and improve habitat quality.

4.4. Construction of the Ecological Security Pattern in Golog Tibetan Autonomous Prefecture

4.4.1. Identification of Ecological Source Area and Construction of Resistance Surface

From the perspective of data availability, according to the results calculated by the habitat quality module of the InVEST model and the representation of ecological sources, the core area with extremely high habitat quality (i.e., a habitat quality index above 0.8) and a patch area greater than 50 km2 is to be selected as the ecological source area of Guoluo Tibetan Autonomous Prefecture. Using the method in Section 3.2.2, comprehensive resistance surfaces for 2020 and 2030 in Guoluo Tibetan Autonomous Prefecture were constructed according to different driving factors (Figure 6).
In 2020, ecological source areas in the study area were spread out extensively, spanning an area of 8154.62 km2 and making up 10.99% of the overall area. By 2030, ecological source areas in the study area were primarily located in the western region, spanning an area of 6349.96 km2 and making up 8.56% of the overall area. The types of land use for ecological sources are predominantly grasslands, water bodies, and forests. The northwestern part of the study area, which has many lakes such as Gyaring Lake, Ngoring Lake, and Winter Gecuo Na Lake, contains larger and more intact ecological sources. From 2020 to 2030, the area of ecological source areas shrank by 1804.66 km2, with the most significant decline concentrated in Maqin County, Gande County, and Jiuzhi County in the eastern part of Golog Tibetan Autonomous Prefecture. This area has witnessed rapid economic development, which is apt to cause environmental deterioration and ecological instability. Therefore, in the next 10 years, concentrating on ecological civilization development in the three counties in the southeast of Golog Tibetan Autonomous Prefecture will be a primary concern.
Economic growth in Golog Tibetan Autonomous Prefecture demonstrates a pattern of lower development in the west and higher development in the east, with progressively rising density of roads and settlements. Altitude gradients show a pattern of being higher in the northwest and lower in the southeast. The Yellow River courses from the northwest to the southeast, traversing the southeastern region where it forms Cenozoic sedimentary formations. Due to river erosion and lake sedimentation, some areas with relatively steep slopes have formed in the south. The central and eastern areas, characterized by lower terrain and slopes, are conducive to population settlement, road construction, and economic activities, which adversely affect ecology, resulting in higher resistance values. Conversely, the western region, with higher altitudes and larger water bodies, experiences fewer anthropogenic influences, resulting in lower resistance values. These elements lead to reduced resistance values in the west and increased resistance values in the east of Golog Tibetan Autonomous Prefecture.

4.4.2. Construction of Ecological Corridors and Ecological Strategic Nodes

Ecological corridors (linear features) and key ecological nodes (areas) were constructed using Linkage Mapper combined with the Circuitscape plugin and analyzed with circuit theory tools. The extent of ecological linkages and the magnitude of constriction points and impediment points were determined with ArcMap. In 2020, 72 ecological corridors were identified in Golog Tibetan Autonomous Prefecture, with a total length of 3187.08 km and the maximum length being 182.36 km. By 2030, 52 ecological corridors were recognized, amounting to 4453.56 km, with the longest corridor spanning 98.11 km. While the aggregate length increased and the corridors became more concentrated in the western region, the number of ecological corridors was reduced by 20 in the next 10 years. The western region is abundant in water bodies, grasslands, and other ecologically beneficial natural resources, which offers optimal conditions for animal migration.
Using the Jenks natural breaks method, strategic ecological nodes, including pinch points and barrier points, were identified (Figure 7). The maximum cumulative current values calculated by the Pinchpoint Mapper module in 2020 and 2030 were 0.228 and 0.374, respectively. Classification was carried out using the natural breaks method. In 2020, 716 ecological pinch points were identified in the study area, covering a total area of 55.56 km2. In 2030, there were 634 ecological pinch points, covering a total area of 34.78 km2. In terms of land types, the ecological pinch points in both 2020 and 2030 were primarily grasslands and water bodies. The identified pinch points were mainly natural water corridors, with higher terrain surrounding them. Species migration was restricted to the lower, narrower areas, with the pinch points exhibiting a narrow, elongated, strip-shaped distribution. Over the next decade, with economic and social development, the area of ecological pinch points will gradually decrease and shift westward by 2030. This indicates that, due to the impact of climate and human factors, the eastern part of the study area has experienced an increase in barren land and artificial surfaces, disrupting the original ecological conditions and leading to a reduction in ecological sources, ecological corridors, and ecological pinch points. Using the Barrier Mapper module in the Linkage Mapper tool, the corridor resistance values were calculated. The maximum resistance values for 2020 and 2030 were 68.39 and 72.07, respectively. Using the natural break method, these values were divided into five categories, with the areas of highest resistance identified as ecological barriers. The total area of ecological barriers in 2020 was 615.3 km2, and in 2030, it increased to 705.62 km2. In terms of distribution, ecological barrier points in 2020 were mainly concentrated in the high-altitude, densely populated eastern regions, but by 2030, they had gradually shifted westward. This indicates significant ecological resistance in the eastern regions of the study area at the current stage, necessitating targeted ecological restoration of the ecological barrier points in this region to improve habitat quality, enhance landscape connectivity, and maintain ecosystem stability.
Analysis of the ecological security framework in Golog Tibetan Autonomous Prefecture from 2020 to 2030 indicates that in the next 10 years, the ecological quality in the eastern part of the study area will decline, habitat fragmentation will increase, and the ecological impediment will become more vulnerable and susceptible to damage (Figure 7c,f). The ecological source zones in the study area show a gradual trend towards concentration in the western part. In the future, greater focus should be placed on the eastern regions of the study area, where the population is larger and urban development is faster. These areas have smaller ecological source zones, greater distances from other ecological source zones, and more obstacle points in the ecological corridors, making them more susceptible to natural, human, and other factors.

5. Discussion

5.1. Comparison with Related Studies

Driven by human activities, the degradation of natural habitats and urban sprawl endanger the structure and makeup of existing ecosystems. Previous studies have indicated that changes in ecological configurations can have diverse effects on the environment [66,67]. Analyzing the spatiotemporal evolution of land resources during periods of rapid economic development and their effects on future landscape patterns can provide scientific recommendations for rational resource use [22,68]. Selecting pertinent indicators for ecological constraints is an essential condition for modeling land-use change. Based on the development planning and ecological protection requirements of Golog Tibetan Autonomous Prefecture, the likely expansion of land-use types was modeled using socioeconomic and climate data as restrictive factors for land-use change. Cellular Automata (CA) models and their derivatives have been extensively utilized to model the spatial and temporal changes of land use under various influencing factors [69,70]. In the end, we chose the PLUS model because its Land Expansion Analysis Strategy (LEAS) addresses the limitations of Transition Analysis Strategy (TAS) and Pattern Analysis Strategy (PAS). Additionally, the model incorporates a Cellular Automaton model with multi-type random patch seeds (CARS), enhancing its capability and accuracy in simulating real-world landscape models [71].
Golog Tibetan Autonomous Prefecture, a critical component of the Qinghai–Tibet Plateau, has a highly fragile ecological environment where slight climate changes and socio-economic activities can disrupt ecosystems. In this study. From 2020 to 2030, the 2.88% reduction in the areas of high-quality habitat in the study area is not only associated with the increase in artificial surfaces but also with gradual deterioration processes such as excessive grazing, soil erosion, and alterations in land cover [37,72]. Since 2000, the enactment of the “Western Development Initiative” and projects like the “West-to-East Gas Transmission Project” have boosted economic and social progress in Golog Tibetan Autonomous Prefecture while affecting habitat quality to different extents. Additionally, the trend of an increasingly arid climate negatively affects the ecological landscape of Golog Tibetan Autonomous Prefecture. Reduced natural runoff limits the development of grazing grasslands. Overgrazing and the conversion of grasslands into farmland have also contributed to the decline in grassland areas. In low-altitude regions, global warming has led to glacier melt and increased precipitation, expanding water bodies. In high-altitude areas, grasslands are prone to degrading into bare land, where glacial meltwater freezes again upon contact with cold air, increasing both glacial and bare land areas. Therefore, measures like controlling overgrazing and limiting urban expansion should be promoted. Specific strategies include establishing urban growth boundaries to constrain urban expansion or implementing land consolidation plans to develop land-use potential.

5.2. Ecological Security Pattern Restoration and Improvement

The “ecological security pattern” refers to a possible spatial pattern of ecosystems within a landscape [73]. Establishing predictive ecological security patterns, comparing patterns across different time periods, and regulating ecological processes to ultimately establish regional ecological security systems are feasible strategies [74]. Current research often focuses on remote sensing-based predictions of ecological threats [75], evaluations of ecological security in vulnerable areas, and the patterns of spatial and temporal variation of ecological security of landscapes [76]; the full range of potential effects of land-use change on habitat quality and ecological security patterns are often overlooked. By combining the PLUS model, InVEST model, and ecological security patterns, we examined ecological changes in the region and put forward recommendations for restoration. Therefore, we combined the PLUS model and InVEST model with the ecological security patterns to examine ecological changes in the region and put forward recommendations for restoration.
Over the next decade, the eastern part of Golog Tibetan Autonomous Prefecture will face greater challenges from climate change and urban expansion. From 2020 to 2030, our analysis of the ecological security framework in the study area reveals that the area of ecological resource utilization (farmland, artificial surfaces) is expected to increase by 80.99 km2, while the area of non-usable ecological land (bare land) will expand by 1330.72 km2. Ecological sources and corridors are mainly made up of water bodies and grasslands, which offer high ecosystem service capabilities and optimal conditions for species migration. The research results are in line with those of Kou J. et al. [42]. The research area points are mostly located in flat, low-altitude areas and are more susceptible to human activities, while the barrier points are more extensive and are usually located in high-altitude or densely populated urban areas. Eliminating these barriers can improve the connectivity of ecological sources [77].
Given Golog Tibetan Autonomous Prefecture’s unique human and geographical conditions, ecological sources and corridors in the eastern region, which are prone to threats from active human activities and steep slopes, require protective measures. Relevant research promotes the gradual improvement of the ecological environment of salt ponds through the construction of ecological projects [78]. Without interventions such as returning farmland to grasslands and forests or slowing urban expansion, habitat quality in this region will gradually decline, and the ecological security pattern will be disrupted [79]. In the future, the ecological sources with better habitat quality will increasingly concentrate in the western part of the prefecture. As a typical high-altitude mountainous region, the study area faces significant challenges in implementing ecological restoration projects. Predicting future land uses can help proactively address potential challenges, minimize human interference in ecological restoration processes, and identify priority areas for ecological protection and restoration in advance [80]. Establish a biodiversity database, share China’s data on biodiversity monitoring, protection, and restoration with international organizations and other countries, and promote global exchanges and cooperation in biodiversity conservation [81]. These efforts are crucial for enhancing regional ecological security and achieving sustainable development [82].
Based on the projected conditions, we propose several recommendations. Firstly, we should prioritize the protection of ecological source areas in the eastern part of Golog Tibetan Autonomous Prefecture to improve habitat quality. Protection should primarily rely on natural restoration, supplemented by artificial measures, adhering to the principles of minimal or no human intervention to enhance ecosystem services and ecological security [83]. For ecological pinch points, focus on restoring and stabilizing the structure, quantity, and functionality of ecosystems to provide suitable environments for species migration, habitation, and reproduction. Given that pinch points are often water bodies and grasslands, land policies in these areas should be flexibly applied to control and prevent soil erosion and forest degradation [84]. Barrier points, which are mostly composed of farmland, artificial surfaces, or complex mountainous terrain, exhibit high ecological resistance. Reducing or eliminating existing barriers can significantly enhance the functional integrity of the ecosystem [85]. The process of protecting and restoring the ecological security pattern should not be confined to the current situation. It is necessary to actively respond to possible problems in the future, such as habitat degradation and environmental damage caused by urban development.

5.3. Limitations and Uncertainties

This study proposes a restoration framework for ecological security patterns based on future land-use changes. However, as a predictive framework, it has certain limitations. Land-use patterns are influenced by various natural and socio-economic factors at different spatial and temporal scales, with uncertain driving factors [86]. Due to data availability and accessibility, only drivers closely related to the study objectives were considered to simulate land-use scenarios, with 2020–2030 resistance surfaces constructed based on 2020 influence factors. Therefore, future research should further explore refining the hierarchical structure of driving indicators, identify major influencing factors, and assign resistance values more precisely to different indicators. The impact of dynamic changes in ecosystem functions on species migration, survival, and reproduction is often unpredictable [87]. Furthermore, due to regional differences in practical conditions, it is difficult to standardize the selection of driving factors, which can only be chosen and assigned values based on real conditions and expert recommendations. Secondly, while this study constructed an ecological security pattern and proposed restoration strategies, it did not integrate regional socio-economic development and coordination among stakeholders (e.g., herders, social organizations, and governments). Future research should focus on establishing standardized driving factors and balancing regional ecological security with socio-economic development to construct an integrated ecological and sustainable development pattern.

6. Conclusions

Creating a comprehensive, evolving ecological security framework allows for a precise mapping of the geographical connections between climate change, intensive human activities, and ecological restoration. This approach promotes resilient ecosystems that balance natural and artificial restoration. Ecological restoration must align with the surrounding environment and future landscape patterns to address existing research gaps in accounting for future landscape changes. This study innovatively integrates the PLUS model, InVEST model, and ecological security patterns to propose a restoration framework based on the dynamic changes in habitat quality and ecological security patterns. By selecting Golog Tibetan Autonomous Prefecture as a representative mountainous region, this study provides valuable references for mountainous ecosystem restoration systems.
Using land-use data, the PLUS model was used to predict land-use scenarios for the study area in 2030 and analyze the land-use transfer trends from 2020 to 2030. The InVEST model was applied to construct the habitat quality pattern for 2020–2030 in Golog Tibetan Autonomous Prefecture, comparing and analyzing changes. Finally, based on habitat quality and driving factors, the ecological security pattern for 2020–2030 was constructed to analyze its dynamic changes, clarify restoration directions, and propose strategies. The results indicated that, in terms of geographical distribution, the overall habitat quality of Guoluo Tibetan Autonomous Region was lower in the east and higher in the northwest. Most of the ecological sources were clustered in the northwest of the study area, and the ecological corridors were longer with Ecological Priority Nodes dispersed but gradually converging to the west. In terms of spatial and temporal pattern, the habitat quality in Guoluo Tibetan Autonomous Prefecture decreased gradually from 2020 to 2030, with the area of low habitat area (0–0.6) increasing by 14.02% and the areas with excellent habitat quality (0.8–1) decreasing by 2.88%. From 2020 to 2030, the ecological source zone showed a declining trend and progressively concentrated in the northwest of the study area, decreasing from 8154.62 km2 to 6349.96 km2. The cumulative length of ecological linkages increased, but the area of ecological constriction points decreased by 22.77 km2 and the area of ecological obstruction points increased by 90.32 km2. Overall, the ecological environment of Guoluo Tibetan Autonomous Prefecture is somewhat vulnerable. Overall, the ecological environment of Golog Tibetan Autonomous Prefecture remains fragile. The eastern region, due to climate change and human factors, faces increased barren and artificial surfaces, threatening its habitat quality and ecological integrity. Strengthened protection and restoration of ecological corridors and pinch points, along with rational land-use planning, are necessary to enhance regional ecosystem security.
This study provides a direction for dynamically adjusting regional ecological protection and restoration based on land-use data, improving our understanding of the relationship between future landscape patterns and ecological restoration, and providing new ideas for multi-tiered restoration measures. This is conducive to achieving sustainable ecological resource utilization and economic development.

Author Contributions

Conceptualization, Z.D.; methodology, Z.D. and H.L. (Haodong Liu); software, Y.C. and H.L. (Hua Liu); validation, Z.D.; formal analysis, H.L. (Haodong Liu); investigation, Y.Z.; resources, H.L. (Haodong Liu); data curation, X.F.; writing—original draft preparation, Z.D.; writing—review and editing, Q.C.; visualization, J.X.; supervision, Z.D.; project administration, Q.C. and Z.Z.; funding acquisition, Q.C. All authors have read and agreed to the published version of the manuscript.”

Funding

This research was funded by the Special Investigation Project for Scientific and Technological Basic Resources of China grant number [2021FY100800] and [2019FY101601-4]. And The APC was funded by [2021FY100800].

Data Availability Statement

Data will be made available on request.

Conflicts of Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Figure 1. Location and elevation of Guoluo Tibetan Autonomous Prefecture.
Figure 1. Location and elevation of Guoluo Tibetan Autonomous Prefecture.
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Figure 2. Technology roadmap.
Figure 2. Technology roadmap.
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Figure 3. Land-use in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030.
Figure 3. Land-use in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030.
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Figure 4. Land-use transfer in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030.
Figure 4. Land-use transfer in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030.
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Figure 5. Habitat quality distribution map of Guoluo Tibetan Autonomous Prefecture from 2000 to 2030.
Figure 5. Habitat quality distribution map of Guoluo Tibetan Autonomous Prefecture from 2000 to 2030.
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Figure 6. Ecological source and resistance surface of Guoluo Tibetan Autonomous Prefecture in 2020 and 2030.
Figure 6. Ecological source and resistance surface of Guoluo Tibetan Autonomous Prefecture in 2020 and 2030.
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Figure 7. Establishment of an ecological security pattern in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030. (a) 2020 Annual Distribution Map of Leakage Current; (b) 2020 Annual Current Distribution Map of Ecological Barrier Points; (c) 2020 Ecological Security Pattern Distribution Map; (d) 2030 Annual Distribution Map of Leakage Current; (e) 2030 Annual Current Distribution Map of Ecological Barrier Points; (f) 2030 Ecological Security Pattern Distribution Map.
Figure 7. Establishment of an ecological security pattern in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030. (a) 2020 Annual Distribution Map of Leakage Current; (b) 2020 Annual Current Distribution Map of Ecological Barrier Points; (c) 2020 Ecological Security Pattern Distribution Map; (d) 2030 Annual Distribution Map of Leakage Current; (e) 2030 Annual Current Distribution Map of Ecological Barrier Points; (f) 2030 Ecological Security Pattern Distribution Map.
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Table 1. Main data used in this study.
Table 1. Main data used in this study.
DataSourceResolution
Land-use dataLand-use data for 2000Globeland 30 (https://www.webmap.cn, accessed on 28 August 2023)30 m × 30 m
Land-use data for 2010Globeland 30 (https://www.webmap.cn, accessed on 28 August 2023)30 m × 30 m
Land-use data for 2020Globeland 30 (https://www.webmap.cn, accessed on 28 August 2023)30 m × 30 m
Driving factor dataSocio-economic dataPopulation densityResource and Environmental Science Data Platform (https://www.resdc.cn, accessed on 20 September 2024)1000 m × 1000 m
GDPResource and Environmental Science Data Platform (https://www.resdc.cn, accessed on 20 September 2024)1000 m × 1000 m
Distance to roadsNational Catalogue Service For Geographic Information (https://www.webmap.cn, accessed on 20 September 2024)-
Distance to settlementsNational Catalogue Service For Geographic Information (https://www.webmap.cn, accessed on 20 September 2024)-
Climate and environmental dataDistance to water bodiesNational Catalogue Service For Geographic Information (https://www.webmap.cn, accessed on 20 September 2024)-
Soil typeResource and Environmental Science Data Platform (https://www.resdc.cn, accessed on 20 September 2024)1000 m × 1000 m
Average annual evaporationResource and Environmental Science Data Platform (https://www.resdc.cn, accessed on 20 September 2024)1000 m × 1000 m
Average annual precipitationResource and Environmental Science Data Platform (https://www.resdc.cn, accessed on 20 September 2024)1000 m × 1000 m
Average annual temperatureResource and Environmental Science Data Platform (https://www.resdc.cn, accessed on 20 September 2024)1000 m × 1000 m
DEMGeospatial Data Cloud (https://www.gscloud.cn, accessed on 21 September 2024)30 m × 30 m
SlopeGenerated by DEM30 m × 30 m
Table 2. Land-use conversion cost matrix. a, b, c, d, e, f, g, h and i represent, respectively, the following land-use types in Guoluo Tibetan Autonomous Prefecture: a for cultivated land; b for forestland; c for grassland; d for scrubland; e for wetland; f for water; g for artificial surface; h for nudation; and i for glaciers and permanent snow.
Table 2. Land-use conversion cost matrix. a, b, c, d, e, f, g, h and i represent, respectively, the following land-use types in Guoluo Tibetan Autonomous Prefecture: a for cultivated land; b for forestland; c for grassland; d for scrubland; e for wetland; f for water; g for artificial surface; h for nudation; and i for glaciers and permanent snow.
abcdefghi
a101100000
b011100000
c111111110
d011110010
e010011000
f000011010
g000000100
h101001011
i000000001
Table 3. Table of neighborhood weights (a, b, c, d, e, f, g, h and i) represent the same land-use types as Table 2).
Table 3. Table of neighborhood weights (a, b, c, d, e, f, g, h and i) represent the same land-use types as Table 2).
abcdefghi
Neighborhood weight0.360.550.90.30.110.10.330.170.33
Table 4. Habitat quality model threat factor parameters.
Table 4. Habitat quality model threat factor parameters.
Threat FactorMaximum Influence Distance/kmWeightSpatial Attenuation Type
Cultivated land101linear
Artificial surface40.6exponential
Nudation80.8linear
Road60.6linear
Table 5. Habitat suitability of different land-use types and sensitivity to threat factors.
Table 5. Habitat suitability of different land-use types and sensitivity to threat factors.
Land-Use TypeHabitat SuitabilityThreat Factor Sensitivity
Cultivated LandArtificial SurfaceNudationRoad
Cultivated land00000
Forestland10.80.80.70.7
Grassland0.80.80.70.70.5
Scrubland0.80.80.40.60.6
Wetland0.70.50.50.70.3
Water0.90.60.50.60.5
Artificial surface00000
Nudation0.20.10.300.5
Glaciers and permanent snow0.60.30.30.50.4
Table 6. Ecological resistance factors and their weights in Animachen Mountain.
Table 6. Ecological resistance factors and their weights in Animachen Mountain.
Resistance FactorsResistance ValueWeight
1007550251
Human influencing factorsDistance from road0–50005000–10,00010,000–15,00015,000–25,000>25,0000.1
Distance from settlements0–10,00010,000–20,00020,000–30,00030,000–40,000>40,0000.1
Natural influencing factorsDistance to water>80006000–80004000–60002000–40000–20000.1
DEM>60006000–50005000–40004000–30000–30000.15
Slope>6045–6030–4515–300–150.15
Land-use typeArtificial surfaceNudation, Cultivated landGlaciers and permanent snowForestland, Grassland, ScrublandWater, Wetland, Forestland0.2
Habitat quality0–0.20.2–0.40.4–0.60.6–0.80.8–10.2
Table 7. Overview of habitat quality at different levels in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030.
Table 7. Overview of habitat quality at different levels in Guoluo Tibetan Autonomous Prefecture from 2020 to 2030.
Habitat QualityHabitat Value20202030
Area/km2Area Proportion/%Area/km2Area Proportion/%
Very poor0–0.23176.674.284558.296.16
Poor0.2–0.41610.942.173162.704.28
Medium0.4–0.61561.742.108971.1012.13
Good0.6–0.858,348.9178.6349,931.5867.49
Excellent0.8–19512.9912.827354.739.94
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MDPI and ACS Style

Dong, Z.; Liu, H.; Liu, H.; Chen, Y.; Fu, X.; Zhang, Y.; Xia, J.; Zhang, Z.; Chen, Q. Analysis of Habitat Quality Changes in Mountainous Areas Using the PLUS Model and Construction of a Dynamic Restoration Framework for Ecological Security Patterns: A Case Study of Golog Tibetan Autonomous Prefecture, Qinghai Province, China. Land 2025, 14, 1509. https://doi.org/10.3390/land14081509

AMA Style

Dong Z, Liu H, Liu H, Chen Y, Fu X, Zhang Y, Xia J, Zhang Z, Chen Q. Analysis of Habitat Quality Changes in Mountainous Areas Using the PLUS Model and Construction of a Dynamic Restoration Framework for Ecological Security Patterns: A Case Study of Golog Tibetan Autonomous Prefecture, Qinghai Province, China. Land. 2025; 14(8):1509. https://doi.org/10.3390/land14081509

Chicago/Turabian Style

Dong, Zihan, Haodong Liu, Hua Liu, Yongfu Chen, Xinru Fu, Yang Zhang, Jiajia Xia, Zhiwei Zhang, and Qiao Chen. 2025. "Analysis of Habitat Quality Changes in Mountainous Areas Using the PLUS Model and Construction of a Dynamic Restoration Framework for Ecological Security Patterns: A Case Study of Golog Tibetan Autonomous Prefecture, Qinghai Province, China" Land 14, no. 8: 1509. https://doi.org/10.3390/land14081509

APA Style

Dong, Z., Liu, H., Liu, H., Chen, Y., Fu, X., Zhang, Y., Xia, J., Zhang, Z., & Chen, Q. (2025). Analysis of Habitat Quality Changes in Mountainous Areas Using the PLUS Model and Construction of a Dynamic Restoration Framework for Ecological Security Patterns: A Case Study of Golog Tibetan Autonomous Prefecture, Qinghai Province, China. Land, 14(8), 1509. https://doi.org/10.3390/land14081509

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