1. Introduction
Land use/cover change (LUCC) is central to human development and utilization of the natural environment [
1]. Landscape pattern change affects comprehensive geographical factors such as climate [
2,
3,
4], soil [
5,
6] and water [
7,
8,
9]. Additionally, the value of regional ecosystem services are affected [
10,
11] which threatens the regional ecological environment health and increases the regional risk [
12,
13]. Since the establishment of Saihanba Mechanical Forest Farm in 1962, the landscape pattern has undergone major changes. In order to protect the ecological environment and sustainable development, it is necessary to carry out a risk assessment of its ecological environment. Since its inception in 1962, Saihanba Mechanical Forest Farm has undergone major alterations in its landscape pattern. To safeguard the ecological environment and promote sustainable development, it is imperative to conduct a comprehensive risk assessment of its ecological milieu.
At present, landscape risk assessments locally and abroad mainly focus on rivers [
14,
15,
16,
17] and coasts [
17,
18,
19,
20]. Many scholars have conducted forest landscape risk assessments on climate change [
21], strong winds [
22], regulated floodplains [
23], population [
24], and fire [
25]. Tanja et al. [
26] investigated the ecological risks of the Bohai Sea landscape in China by analyzing pesticide residues in freshwater systems. Hossain et al. [
27] analyzed risk and resilience in Swiss Alpine communities from the perspective of people and environment. Yanjie et al. [
19] evaluated the community-level risks induced by marine ecosystems by fitting different models. Rasoul and R. [
21] proposed that forest management efforts should be made to reduce the ecological risk of forest landscapes. Hua et al. [
25] carried out a time series analysis on wildfire and forest landscape risk from the large-scale spread of the mountain pine beetle (MPB) epidemic, which was found to have a multitude of explanatory variables. At present, research on the ecological risk of forest landscapes is limited. We assess the landscape ecological risk drawing from the landscape pattern of Saihanba Mechanical Forest Farm, and then consider the influence of factors on the comprehensive ecological risk assessment results.
Currently, the research around the ecological risks associated with land uses two types of evaluation models. The first model uses a “source correlation–receptor evaluation–exposure and hazard evaluation–risk characterization” model. For example, scholars like Yanes [
17] and Walker [
15] generated an index system for ecological risk assessment from three aspects: risk source intensity, receptor exposure, and risk effect [
28,
29]. The second model involves direct evaluation of the landscape pattern to evaluate the landscape ecological risk. Scholars such as Ayre [
16] and Dale VH [
14] adopt a landscape ecology perspective, using a landscape ecological index to depict the ecological effect of LUCC change [
18,
30,
31].
Using a geographic detector method to identify risk drivers can better ensure the accuracy of landscape risk assessment and provide scientifically sound suggestions for forest farms. The geodetector, grounded in spatial variance analysis theory, examines the correlation between factor variables and result variables. It assesses the strength of influence of each factor variable on the result variable, discerns the disparities in influence among different factor variables, and determines whether the impact of each factor variable on the result variable is independent or interactive [
32]. Widely applied in diverse domains such as urbanization efficiency [
33,
34], population aging [
35], and medicine [
36], this method has recently found application in ecological research, examining insect diversity [
37], soil heavy metal detection [
38,
39], and the intersection of air quality and social economy [
40,
41,
42,
43]. For example, Liu et al. [
37] quantified the interactive effects of 15 variables on beetle distribution using the geographical detector method, successfully analyzing the associated risk drivers. Xu et al. [
43] used a geographical detector to quantify the driving factors affecting air quality and establish national key functional areas. In Mingrui Li et al.’s research on the Ertix River Basin in Central Asia, they applied geographic detectors and geographically weighted regression to detect landscape risk [
44]. However, there is a dearth of research on the detection of ecological risk factors based on remote sensing technology.
This study focuses on the Saihanba Mechanical Forest Farm as the research area. Since the establishment of the forest farm, the vegetation cover grade has improved, leading to further restoration of the ecological environment and consequential significant changes in the landscape pattern. To capture the diverse developmental stages of the study area, Landsat remote sensing images from 1987, 1997, 2001, 2013, and 2020 were selected. The support vector machine method was applied to generate the landscape classification map, and field survey data were utilized for validation. The NDVI was employed to obtain the vegetation index, while the landscape ecological risk index was utilized to investigate the spatial and temporal distribution characteristics of landscape ecological risk. The spatial autocorrelation method was used to assess landscape spatial correlation characteristics, and the geographical detector method was employed to explore the driving factors of landscape ecological risk. The findings derived from this research can furnish a scientific foundation for land use planning and forest management rooted in ecological security considerations.
2. Materials and Methods
2.1. Study Area
The Saihanba Mechanical Forest Farm is located in the semi-arid and semi-humid zone (
Figure 1), which is positioned at the intersection of the Yinshan Mountains, the Daxing’an Mountains, and the Hunshandake Sandy Land. It falls within the transitional area both below and above the dam, marking the juncture of forest–grassland and arid–semi-arid regions. The topography is characterized by a north-to-south slope, creating a distinctive landform with higher elevations in the north and lower elevations in the south. The altitude ranges from 1010 to 1940 m, with an average annual temperature of −1.2 °C, an average annual sunshine duration of 2548.7 h, and an average annual precipitation of approximately 452.2 mm, primarily concentrated in the months of June to September.
The Saihanba Mechanical Forest Farm has strategically embraced the concept of the “four banks” of forests as a pivotal strategy for advancing high-quality development in forestry and grassland management. This approach serves as a valuable model for fostering integrated protection and systematic governance across diverse ecosystems, including mountains, rivers, forests, farmland, lakes, and grasslands. Leveraging the synergistic benefits of the forest “four reservoirs” concept, the farm aims to maximize high-level advantages, thereby promoting development and enhancing the well-being of the local population. Presently, the predominant forest resources consist of artificially cultivated pure forests, with key tree species including Larix principis-rupprechtii, Pinus sylvestris var. mongolica, Picea asperata, Betula platyphylla, among others.
In history, the Saihanba area was a hunting paddock of the Qing government, with a good natural environment and many trees. Due to a lack of finance, a large number of trees were cut down and the forest coverage rate decreased sharply. Subsequently, the Japanese invasion of China caused a serious fire, and the destruction of forest resources became more serious. After the founding of the People’s Republic of China, it began to pay attention to ecological construction.
Currently, the total value of Saihanba Forest Farm forest resources has reached CNY 20.2 billion, the total value of forest resources is about USD 2.8 billion, driving the local social income of more than USD 80 million every year. Saihanba will build a global ecological civilization and green development demonstration zone in the next, striving to create higher social and economic benefits.
The establishment of Saihanba Mechanical Forest Farm has alleviated the ecological crisis in the Beijing–Tianjin–Hebei region and even the entire north region of China. But while the external crisis has been alleviated, problems such as single tree species and fragmentation of patches have appeared inside the forest farm, and the landscape risk is unknown.
2.2. Data Source
The Landsat remote sensing images from 1987, 1997, 2001, 2013, and 2020 used in this paper were acquired from the United States Geological Survey (
https://glovis.usgs.gov/). Analysis of the images was conducted using ENVI 5.1 software. The support vector machine classification method was applied to categorized the preprocessed images. To align with the research objectives, the images were segmented into five distinct classes: forest, grassland, wetland, sandy land, and construction land (
Figure 2). To ensure the accuracy of the classification, a confusion matrix was established, and verification results indicated that the kappa coefficients for the interpreted land use maps during the five periods consistently exceeded 0.76, meeting the accuracy standards for medium-resolution remote sensing images.
Precipitation, soil type, DEM, vegetation cover grade, air temperature, slope aspect, slope, and subcompartment accumulation were selected from the second-class survey data of subcompartment in Saihanba Mechanical Forest Farm.
2.3. Methods
2.3.1. Analysis of Temporal and Spatial Changes in Land Use and Vegetation Cover
The SVM classification method revealed significant landscape type changes in 1987–2020. Therefore, the transfer matrix method was used to explore the amount of exchanges of each landscape type over the entire period of 1987–2020, and the NDVI was calculated to see if there was a trend of decreasing vegetation cover classes.
Normalized Vegetation Index
The normalized difference vegetation index (
) is extensively used in the research and application of vegetation cover changes, and has a linear relationship with the vegetation distribution density [
45,
46,
47]. Its formula is:
The notation used in this context defines NIR as the reflection value in the near-infrared band, and RED as the reflection value in the red band. Specifically, in Landsat 2 TM images, these correspond to band 6 for NIR and band 5 for RED. In Landsat 5–8 TM images, the relevant bands are band 4 for NIR and band 3 for RED.
Transfer Matrix Method
The transition matrix model serves to elucidate the initial probability of various stages and the transitions between these stages, thereby allowing the determination of change trends before and after different periods. The formula for this model is:
where
reflects the coverage area for each vegetation grade;
reflects the number of vegetation cover grade types;
and
reflect the vegetation cover grade types at the initiation and commencement of the study period, respectively.
2.3.2. Landscape Ecological Risk Analysis
Division of Landscape Ecological Risk Units
In this study, according to the national grid GIS standard “Geographical Grid” (GB12409-2009) and related research, the grid size was determined based on 2–5 times the average patch area in the study area [
48]. The ArcGIS fishnet creation function was employed to partition the study area into ecological risk units of 2 km × 2 km, and these fishnets were subsequently segmented to generate a total of 382 ecological risk units. Utilizing landscape ecological risk value assignment, the semi-variation function within the Kriging interpolation method was applied for spherical fitting, yielding the landscape ecological risk level map across different periods.
Construction of Landscape Ecological Risk Index
The landscape index, through the comprehensive analysis of multiple indices, enables a quantitative reflection of the landscape pattern and its dynamic changes. In this study, landscape fragmentation, dispersion, dominance, vulnerability, and disturbance were designated as landscape ecological risk indices.
Calculating the landscape disturbance index through the landscape fragmentation index, landscape separation index, and landscape dominance index can reflect the complexity of spatial structure. The landscape vulnerability index reflects the ecological sensitivity of the study area. The higher the vulnerability, the worse the resistance to interference. The landscape loss index is calculated by combining the landscape disturbance index and the landscape vulnerability index. According to the results, the landscape ecological risk index was then calculated (
Table 1).
2.3.3. Spatial Autocorrelation Analysis
Spatial autocorrelation analysis was employed to unveil the spatial correlation characteristics of a specific attribute unit and its neighboring attribute units based on eigenvalues [
52]. It was used to measure the distribution characteristics and interrelationships of spatial risk data.
Global Autocorrelation
The global Moran’s
I index was utilized to assess the spatial correlation of attribute values within a unit across the entire study area.
where
represents the observation value of the
-th area,
is the number of grids, and
is a binary adjacency space weight matrix, which is used to represent the adjacency relationship of spatial objects. When the area
and the area
are adjacent,
= 1; when the area
and the area
are not adjacent,
= 0.
Local Autocorrelation
The local Moran’s
I index was employed to reflect the correlation between the attribute value of a unit and the adjacent spatial units.
The Moran’s I value ranges from −1 to 1. A positive Moran’s I (>0) suggests a positive correlation in the study area, indicating that the attribute values of the study units are convergent. Conversely, a negative Moran’s I (<0) signifies a negative correlation and a dispersed distribution of attribute values. When Moran’s I equals 0, it indicates the absence of spatial correlation.
2.3.4. Geographical Detector
The geographical detector model, optimized for parameters, encompasses four functions: factor detector, interaction detector, ecological detector, and risk detector, each capable of handling various types of variables [
53,
54]. It can be used to test the stratified heterogeneity of a single factor; it can also be used to detect a possible causal relationship between two factors by testing the coupling of their spatial distributions.
In this study, the factor detector, interaction detector, and ecological detector were employed to investigate the comprehensive influence of diverse risk factors in the forest landscape on the risk level of the Saihanba forest landscape in 2020.
Factor Detector
Factor detector was used to measure the impact strength of each risk factor on the risk of forest landscapes. The detection method is:
where
is the influence of a certain factor on the spatial distribution of landscape ecological risk, the value range is [−1, 1];
L is the partition number;
is composed of
units;
and
are the unit numbers of layer
and the whole region, respectively.
Ecological Detector
The ecological detector reflects whether the risk factors have significant differences in the spatial distribution of landscape ecological risk, and it is measured by the F test. The formula is as follows:
where
,
represents the sample size in the two risk factor partitions, and the null hypothesis
=
. Rejecting the null hypothesis at the chosen significance level signifies the presence of significant differences between the two factors influencing the spatial distribution of landscape ecological risk.
Interaction Detector
In assessing whether an interaction exists among various risk factors impacting the spatial distribution of landscape ecological risk, we ascertained whether the two factors operate independently or interactively influence the spatial distribution of landscape ecological risk. This determination is achieved through a comparative analysis of the interactions between the two risk factors, encompassing the following five relationships:
If q (A∩B) < Min (q (A), q (B)), it means that the nonlinearity of the two factors is weakened;
If Min (q (A), q (B)) <q (A∩B) < Max (q (A), q (B)), it means that the single-factor nonlinearity is weakened;
If q (A∩B) > Max (q (A), q (B)), it means two-factor enhancement;
If q (A∩B) = q (A) + q (B), it means that the two factors are independent of each other;
If q (A∩B) > q (A) + q (B), it means two-factor nonlinear enhancement.
4. Discussion
4.1. Landscape Pattern and Spatial Scale Change in Landscape Ecological Risk
In this research, we used NDVI and the transition matrix method, and found that the landscape pattern of Saihanba has undergone substantial changes from 1987 to 2020, and the vegetation cover grade area has increased significantly. Among diverse landscape types, there was an upward trajectory in the area of forests and wetlands, while the areas of grassland, sandy land, and construction land exhibited a declining trend.
In 1987, the overall risk was relatively high, and the risk was significantly reduced in 1997. This was related to the decrease in grassland and sand and the substantial increase in forest area from 1987 to 1997. Before 1987, China began extensive afforestation: as an important strategic location in China, a large number of forests were planted in Saihanba. Until 1997, tree growth resulted in increased crown density, so that through remote sensing imagery we found that the landscape changed markedly during this period, and this change was mainly due to the trees that were planted on grasslands and sandy land. Based on the spatial correlation analysis, the changes to landscape risk within the five periods all showed a positive correlation trend. This was consistent with the results of many scholars that changes in land use have a significant effects on landscape ecological risks [
1,
55,
56,
57].
4.2. Evaluation Method of Landscape Ecological Risk Index
The assessment of regional landscape ecological risk primarily relies on landscape pattern index evaluation [
58,
59] and ecosystem service value [
60,
61]. Given the significant changes in the landscape pattern over more than 40 years in the study area, the variation in the landscape ecological risk index is primarily attributed to alterations in the landscape pattern. The landscape ecological risk index exhibited a continuous decrease from 1987 to 2020. Notably, from 2013 to 2020, the high-risk area of landscape ecology remained relatively stable with no noticeable transfers, indicating an inward contraction trend in the high-risk area. However, diverse quantitative methods yielded different outcomes for the landscape ecological risk assessment, and the subjective nature of landscape vulnerability classification impacted the spatial distribution of landscape ecological risk. Consequently, the assessment of landscape ecological risk in this study remains relatively one-sided. Moving forward, it is imperative to further account for the influence of additional factors on landscape ecological risk.
Building on the evaluation of the landscape ecological risk index from 1987 to 2020, a more focused investigation into the risk of forest landscapes in 2020 was conducted. Through a comprehensive analysis of landscape ecological risk in the study area, the vulnerability of the forest landscape was determined based on the risk level associated with each forest landscape, combined with the expert consultation method. The findings revealed that mid-high and high-risk areas were dispersed in the south, northeast, and central parts of the study area, primarily characterized by extensive larch forests and birch forests. This study suggests the need to adjust stand structures within a reasonable range in these areas.
4.3. Analysis of Forest Landscape Risk Drivers and Suggestions for Future Development
Addressing the ecological risk status of the forest landscape in 2020, the geographical detector method was employed to delve into its governing factors. Recognizing that the internal structure of the forest landscape and various geographical factors exert a substantial impact on landscape risk, nine factors with a significant influence on the Saihanba forest landscape were chosen based on the management status of the Saihanba Mechanical Forest Farm. The study revealed that the forest landscape type poses the greatest risk to landscape ecology, aligning with the findings of numerous experts. Simultaneously, this observation is connected to the classification method of landscape vulnerability. Currently, research on landscape ecological risk predominantly focuses on river basins and coastlines, leaving a research gap in the context of forest landscapes [
62]. Therefore, building upon the landscape risk in the study area, this study compared the risk levels of each forest landscape and classified landscape vulnerability based on expert advice.
Soil type, precipitation, forest landscape type, temperature, slope, DEM, and slope direction have a significant impact on forest landscape ecological risk. Among these factors, there are significant differences in soil type and precipitation compared to other factors. Hence, this area should be designated as a pivotal zone for risk prevention to avert an upward trend in risk. Analyzing the factors that have a pronounced impact on the Saihanba Mechanical Forest Farm, including landscape type, slope, slope direction, precipitation, vegetation cover grade, and subcompartment volume, reveals that, apart from fixed geographical factors (slope, slope direction, precipitation), the vegetation cover grade has reached an optimal level, with low-value areas sparsely distributed, mainly found in water areas and construction land. Subcompartment volume is related to the forest landscape type and forest management. Therefore, formulating a reasonable forest management plan and gradually adjusting the forest landscape type, specifically the stand structure, is imperative. Currently, the stand structure is monotonous, necessitating the introduction of diverse tree species. Simultaneously, adhering to the principle of suitable tree species and updating the forest patch configuration, as suggested by this study, not only contributes to mitigating the risk of the forest landscape but also holds significant ecological importance for forest carbon storage and enhancing forest site quality.
Utilizing the landscape ecological risk index and spatial autocorrelation analysis, this study assessed the ecological risk pattern of the Saihanba Mechanical Forest Farm from the viewpoint of landscape spatial structure. Additionally, a geographic detector was employed to analyze risk-driving factors. From the results, the effect of multi-factor combination is not simply one-plus-one, but far greater than the single factor, so it should be fully considered in the management and development of forest farms. Since the establishment of Saihanba, extensive tree planting efforts have significantly improved the ecological environment, resulting in substantial changes to the landscape pattern. Given that landscape pattern alterations inherently impact ecosystem functions, utilizing landscape pattern indices to investigate the ecological risk pattern is both feasible and effective. Consequently, it is recommended that the Saihanba Mechanical Forest Farm maintains and adjusts its existing landscape structure and addresses high-risk areas in alignment with its specific conditions. At the same time, to pay attention to the diversity of forest structure and its spatiotemporal variation characteristics is necessary [
62]. The enhancement of the landscape integration pattern should prioritize natural factors. During resource development, adhering to the principle of ecological priority is crucial. While safeguarding river waters and promoting forestry development, it is imperative to regulate the uncontrolled development of local tourism. Zoning management, aligned with the current state, should be implemented, coupled with the establishment of an effective ecological compensation system.