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Article

Characteristics of the Distribution of Village Enclosure Forests in the Beijing Plain Area and Influencing Factors

by
Yuan Zhang
,
Erfa Qiu
*,
Chenxuan Wang
,
Zhenkai Sun
and
Jiali Jin
Research Center of Urban Forest of National Forestry and Grassland Administration, Key Laboratory of Tree Breeding and Cultivation, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
*
Author to whom correspondence should be addressed.
Forests 2025, 16(6), 1003; https://doi.org/10.3390/f16061003 (registering DOI)
Submission received: 24 April 2025 / Revised: 10 June 2025 / Accepted: 12 June 2025 / Published: 14 June 2025
(This article belongs to the Section Urban Forestry)
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Abstract

:
Beijing’s plain-region villages face significant shortages of internal green space, yet studies on village enclosure forests as a supplementary green infrastructure to serve rural communities are limited. So, this study examines village enclosure forests in Beijing Plain to address rural forest shortages. Using 2019 aerial imagery (0.5 m resolution) and forest inventory data, we analysed 1271 villages’ 300 m radius forest coverage via ArcGIS Pro. Key findings show (1) overall forest coverage is 45.30%, higher in outer suburbs (OA), traditional villages (TSH), and large villages; (2) functional types are mainly ecological landscape (37.58%) and ecological–economic forests (36.37%); and (3) afforestation projects (Million-Mu Project rounds 1–2) account for 47.37% coverage. Regression analyses reveal human activities as dominant influencers, with cultivated land area (CLA) having the highest explanatory power. Other significant factors (p < 0.05) include distance from commercial residences (DCR), village size (VS), distance from famous historical sites based on developmental zoning, and forest functions to optimize rural habitats.

1. Introduction

Since the launch of the “New Rural Construction” initiative in 2005, China’s rural settlement forest development has been the target of nationwide campaigns, such as “Beautiful Villages” and “Rural Revitalization”, and become a focal research topic [1]. Previous studies on rural settlement forests have explored the beneficial aspects, including the classification [2,3], species selection and arrangement [4], structural and functional characteristics [2], and cultural landscape connotations [5] of village enclosure forests.
Internationally, more studies have explored the complex effects of human activities on vegetation greenness [6]. Lingner et al. found that human activities lead to habitat fragmentation [7]. Ma investigated the impact of village clustering on forest structure [8]. Additionally, numerous studies have demonstrated the negative effects of specific factors such as the proportion of built-up land [9], cropland ratio [10], GDP, and nighttime light intensity [11] on forests. Despite the great importance of abundant vegetation resources, research on the quantity of village forests affected by human activities remains very limited [12]. Nevertheless, the impact of human activities on village enclosure forests remains unclear.
Village enclosure forests refer to linear or clustered structural greenbelts surrounding rural settlements, which serve primarily as village protection while also incorporating economic and recreational functions [3]. As an essential component of rural settlement forests, these enclosed forests predominantly exist as “fengshui woods” in southern China, where they are traditionally regarded as vital protective barriers for villages. In contrast, village enclosure forests in the northern and northwestern plain regions of China function mainly as windbreaks and sand-fixation forests for ecological conservation and play a crucial role in improving rural living environments. With evolving production methods and lifestyles among rural residents, the recreational utilization of these forests has gained increasing attention [13]. They also bring economic benefits through ecotourism and green industries, and hold cultural significance [14].
In the Beijing Plain, since the 1990s, the government has implemented a series of regional ecological restoration projects and carried out multiple afforestation programs to address problems caused by insufficient forest coverage [15]. With the successive completion of several rounds of afforestation projects [16], the implementation of these initiatives has significantly increased the total green space and greatly improved the forest coverage in plain areas [17]. However, within villages, tree coverage, such as courtyard trees, roadside trees, and isolated trees, is mostly limited to single-tree plantings in dotted patterns or linear arrangements due to land constraints. These trees typically occupy small areas [18], which results in fragmented green spaces within villages that still suffer from severely insufficient forest coverage and fail to meet the villagers’ needs. Village enclosure forests refer to large woodland areas adjacent to villages, which usually cover extensive areas with continuous clustered or patchy distributions [19]. Owing to these characteristics, they can more comprehensively fulfil protective, aesthetic, economic, and recreational functions.
Currently, research on the village enclosure forests serving villages and villagers in plain areas is limited. Existing studies have focused mainly on tree species surveys as part of rural settlement forests, with most research remaining at a qualitative level [2]. No reports have specifically examined the distribution and factors influencing village enclosure forests as the main object of study. Studying the differences among different types of village enclosure forests and their potential influencing factors can help improve the living environment in these areas and promote sustainable development.
The distribution of village enclosure forests is influenced by multiple factors, including objective geographical locations and human activities. Additionally, policy guidance and public awareness largely determine their protection and development. Some commonly used statistical methods, such as linear regression, generalized linear regression, Pearson correlation, and redundancy analysis, are not very convenient for exploring the interactions between different factors. Random forest and multiple stepwise regression models can effectively address this issue. This paper aims to define the scope of village enclosure forests. Therefore, methods such as random forest and multiple stepwise regression models were used to conduct a detailed quantitative analysis of the forest coverage, compositional structure, and project sources of village enclosure forests in Beijing’s plain areas and to explore relevant driving mechanisms. The objectives of this study are to (i) reveal the coverage status of village enclosure forests across different village types; (ii) analyse the contribution of internal composition to village enclosure forests; (iii) explore the influence of external environments on village enclosure forests; and (iv) provide a basis for improved and targeted management strategies. This study holds significant importance for improving rural living environments and enhancing residents’ happiness.

2. Materials and Methods

2.1. Overview of the Study Area

Beijing is located between 39°26′~41°03′ N and 115°25′~117°30′ E and covers an area of 16,410 km2. The climate is characterized as warm temperate semihumid and semiarid monsoon and features hot and rainy summers, cold and dry winters, and short spring and autumn seasons. The annual average temperature ranges from 11 to 12 °C, with average annual precipitation ranging from 600 to 800 mm. The average annual sunshine duration is 2000–2800 h. The zonal soil is predominantly cinnamon soil and is mostly neutral to slightly alkaline [16,20]. Located at the junction of the North China Plain and the Taihang and Yanshan Mountains, Beijing exhibits distinct natural geographical patterns, with an overall terrain that is high in the northwest and low in the southeast and is composed of two major geomorphological units: mountains and plains.
The Beijing Plain area covers 6338 km2, accounting for 38.62% of the city’s total area. The region features diverse natural environments and rich vegetation types with relatively complex floristic components. Angiosperms are primarily represented by Leguminosae, Compositae, Gramineae, and Rosaceae, followed by Liliaceae, Cruciferae, Umbelliferae, and Cyperaceae. The plain’s vegetation is dominated by artificial forests, which include shelter forests, urban greening forests, and economic forests, whereas the natural vegetation types are primarily warm temperate deciduous broad-leaved forests. There are 1271 villages located on the plain, representing 52.59% of Beijing’s total village count (Figure 1). The plain area encompasses Beijing’s main urban zone and parts of suburban regions, including most of the capital’s core functional zones, urban expansion areas, and new urban development districts, as well as a small portion of urban ecological conservation zones. It serves as the core development area of Beijing and is a major population-bearing region for the city.

2.2. Data Source and Preprocessing

In this study, the following fundamental data sources were utilized: atmospheric-corrected 0.5 m resolution aerial remote sensing imagery of Beijing (2019), the second-class forest resource inventory database of Beijing (2019), and Beijing’s administrative division boundaries and plain/mountainous area boundaries.
On the basis of previous theoretical research, 17 natural factors and human factors were selected to represent the explanatory power for village enclosure forests. The data sources are shown in Table 1.

2.3. Research Methods

2.3.1. Selection of the Study Area and Image Interpretation

The selection scope of village enclosure forests is based on the concept of “accessibility”, which refers to the ease of connection between two locations and reflects the potential for social interaction between areas. Locations with high accessibility can provide many opportunities for recreational services [21]. A “walkable service area” is defined as a circular area centred on the entrance/exit of public open spaces, with the walkable distance as its radius, excluding portions blocked by urban transportation barriers [22]. Generally, a 5 min walk is considered a comfortable distance. Therefore, in this study, a 5 min walkable service area (calculated at a walking speed of 5 km/h, equivalent to 417 m) is adopted [23]. Considering the grid-like road network structure, a 300 m radius is used to represent the 5 min walkable service area [24].
The study area contains a total of 1271 villages. On the basis of the ArcGIS 10.2 platform and with the use of administrative villages as units and village residential construction land boundary lines as source locations, a 300 m accessibility buffer zone was generated outwards as the spatial boundary for village enclosure forests under the premise of a good overlay of image data. Within this area, the canopy interpretation work for village enclosure forests surrounding rural residential areas was completed, along with the classification of forest stand types and other attributes of the village enclosure forests. Ultimately, a vector-format data layer of forest canopy coverage was generated (Figure 2).

2.3.2. Classification of Villages

On the basis of Beijing’s urban development direction and the “Beijing Urban Master Plan (2016–2035)”, the outermost ecological barrier of Beijing’s urban area (the boundary of the second green belt isolation zone) was taken as the suburban boundary [25]. The area within 1 km outside the 6th Ring Road was defined as the suburban area (SA), whereas the plain areas beyond 1 km outside the 6th Ring Road, including the Yanqing Plain (YQ), were classified as the outer suburban area (OA).
With the gradual urbanization of rural settlements, modern new-type villages distinct from the traditional “single-story house” patterns have emerged [26]. Therefore, on the basis of whether residential construction types exhibit apartment-style buildings or community-style layouts, village construction types were classified into traditional single-story house (TSH) villages and commercial residential-type (CR) villages.
On the basis of the scale of village residential construction, villages were classified through cluster analysis into small villages (SV) with areas < 165,397 m2, medium villages (MV) ranging from 165,397 to 207,410 m2, and large villages (LV) with areas >207,410 m2.

2.3.3. Classification of the Functions and Engineering Sources of Perimeter Forests

Using the ArcGIS platform and its analytical tools, the 2019 second-class forest subcompartment spatial data of the Beijing Plain were processed to classify the purposes and project sources of forest stands.
The village enclosure forests within the study area were categorized into three functional types. These include ecological landscape forests (scenic spots, geological parks, wetland parks, heritage parks, forest parks, country parks, etc.), ecological shelterbelts (protective greenbelts along highways, railways, power transmission facilities, sanitation facilities, nature reserves, water source protection zones, wetland reserves, public welfare forest water protection forests, ecological restoration areas, biodiversity habitats, etc.), and ecological–economic forests (timber forests, orchards, other economic forests, fast-growing and high-yield timber forests, nursery stocks, etc.)
On the basis of the total afforestation project construction data, three major construction projects were selected for classification in descending order of scale: the New Round Million-Mu Plain Afforestation Project, the Plains Million-Mu Afforestation Project, and the Green Belt Project.

2.4. Data Processing

A linear regression model was employed to analyse the factors influencing the internal composition structure of forest coverage around villages and explore its relationships with various functional types and project sources. The model was used to calculate the degree of contribution (R2) of each indicator to forest coverage, and significance tests were conducted. “Relaimpo” quantifies the relative contribution of internal factors to village enclosure forest coverage.
A random forest regression model was used to evaluate the external influencing factors (Figure 3). First, with Python-3.12.0 software, Pearson correlation analysis was conducted using the Pandas and Seaborn Libraries to examine the correlations between independent variables, and a correlation heatmap was generated. To prevent bias caused by multicollinearity among explanatory variables, factors with an absolute Pearson correlation coefficient > 0.7 were excluded [27]. Additionally, the random forest regression model from the Scikit-learn library in Python-3.12.0 was employed to assess the relative importance of the influencing factors. Next, a multiple stepwise regression model was applied to evaluate the interactive effects of all influencing factors on the coverage and evenness of village enclosure forests. Separate full models were constructed for the influencing factors related to coverage and evenness. Finally, the absolute values of the model’s complete average coefficients were taken to represent the effect of each influencing factor. The sum of the absolute values of all the influencing factors’ coefficients constituted the total effect, and their ratios represented the contribution of each factor [28].

3. Results

3.1. Analysis of the Spatial Heterogeneity in Village Enclosure Forest Distribution Across the Beijing Plain Area

3.1.1. Analysis of Regional Variability in Forest Cover

Statistical analysis of forest coverage in the 300 m buffer zones surrounding the 1271 villages in the Beijing Plain area revealed a total buffer area of 155,329.82 hm2, with the forest coverage area reaching 70,348.49 hm2. This yielded an overall forest coverage rate of 45.30%.
From the perspective of urban development zoning (Table 2, Figure 4), the forest coverage rate initially tended to increase but then decreased when radiating outwards from the city centre. The outer suburban area (OA) of the Beijing Plain region presented the highest forest coverage rate around villages (47.13%) (p < 0.05). The suburban area (SA) (42.69%) presented greater forest coverage than did the Yanqing Plain area (YQ) (40.34%), although the difference was not statistically significant. This may be because the Yanqing Plain (YQ) is located within the suburban area of Yanqing District, which results in lower coverage than that in the outer suburban areas. With respect to village construction types, traditional single-story house (TSH) villages in Beijing’s plain area had significantly greater surrounding forest coverage (48.07%) than did commercial residential-type (CR) villages (42.72%) (p < 0.05). In terms of village size, the forest coverage rate around large villages (LV) (47.78%) was significantly higher than that around small villages (SV) (44.85%) (p < 0.05).

3.1.2. Analysis of Regional Differences in Functional Types of Village-Enclosing Forests

An analysis of village enclosure forest types across Beijing revealed that ecological landscape forests, ecological shelterbelts, and ecological economic forests account for 37.58%, 25.33%, and 36.37%, respectively, of the total canopy coverage area in the study region. This demonstrates that ecological landscape forests and ecological–economic forests are the predominant types.
In terms of urban development zones, both suburban areas (SAs) and Yanqing Plain (YQ) areas present significantly lower contributions from ecological economic forests than from ecological landscape forests and ecological shelterbelts (p < 0.05). In terms of village scale, the villages of all construction types and size categories in the Beijing Plain area follow the overall functional distribution trend of village enclosure forests. However, no significant differences were observed between traditional single-story house (TSH) villages and large villages (LV) (Table 3, Figure 5).

3.1.3. Analysis of Regional Differences in the Sources of Village-Enclosing Forests

An analysis of the construction project sources for village enclosure forests across Beijing reveals that the New Round Million-Mu Afforestation Project, the Plains Million-Mu Afforestation Project, and the Green Belt Project account for 12.39%, 34.98%, and 1.07%, respectively, of the total forest coverage area in the study region.
A comparison of construction projects with regard to different types of village enclosure forests (Figure 6) shows that all village types are composed predominantly of both new and previous Million-Mu plain afforestation projects, with the Plains Million-Mu Afforestation Project accounting for the highest proportion, followed by the New Round Million-Mu Afforestation Project, while other afforestation projects contribute far less than these two (p < 0.05). In terms of urban development zones, both rounds of Million-Mu afforestation projects and the Green Belt Project demonstrate significantly higher contribution rates in suburban areas (SA) of the Beijing Plain region than in the outer suburban (OA) and Yanqing Plain (YQ) areas (p < 0.05). From the perspective of residential types, the first-phase Plains Million-Mu Afforestation Project shows significantly higher contribution rates in traditional single-story house (TSH) villages than in commercial residential-type (CR) villages (p < 0.05). In terms of village scale, the proportions of these projects are generally similar across different village sizes in the Beijing Plain area (Table 4).

3.2. Analysis of the Factors Influencing the Distribution of Village Enclosure Forests in the Beijing Plain Area

3.2.1. Analysis of the Factors Influencing the Composition Structure of Village Enclosure Forests

The linear regression analysis results for the composition of village enclosure forests and forest areas (Figure 7) reveals that among the sources of village enclosure forests, the relationship between the Green Belt Project and the forest coverage rate is not significant (p > 0.05), whereas the New Round Million-Mu Afforestation Project and the Plains Million-Mu Afforestation Project show highly significant relationships (p < 0.01). Among the functional compositions of village enclosure forests, the areas of ecological landscape forests, ecological shelterbelts, and ecological economic forests all exhibit highly significant relationships with the forest coverage rate around villages (p < 0.01).
The results indicate that the forest coverage rate around villages is significantly correlated with both the area of various functional types of village enclosure forests and the implementation scale of the new and previous Million-Mu Plain Afforestation Projects, while it has no significant relationship with other afforestation project (Table 5).
The relative contributions of key influencing factors on village enclosure forest coverage were quantitatively assessed using the “Relaimpo” package (Figure 8). The analysis revealed that the Plains Million-Mu Afforestation Project exhibited the most substantial relative contribution (28.8%), followed by ecological landscape forests (26.9%) and ecological economic forests (24.9%), demonstrating their statistically significant impacts on coverage expansion.

3.2.2. Analysis of External Influencing Factors on Village Enclosure Forests

According to the random forest model analysis results (Figure 9), the key factors most significantly influencing village enclosure forest coverage are as follows: among the human factors, CLA emerges as the primary determinant, followed by DCR and NDV; among the natural factors, AMT constitutes the dominant influence, with ANP and EL being secondary contributors.
In this study, a random forest regression model was employed to assess the relative importance of influencing factors, and Pearson correlation analysis, for eliminating variables with strong multicollinearity. The remaining factors showed significant differences after collinearity was addressed, and an indicator system comprising 11 influencing factors for village enclosure forest coverage was formed (Figure 10). Among the external factors affecting forest coverage around villages, human factors demonstrated higher relative contribution rates than did natural factors. CLA had the highest explanatory power for forest coverage, with a negative correlation (p < 0.01). DCR, VS, DFHS, and VRA were also statistically significant (p < 0.01), followed by NDV and DDC (p < 0.05). However, among the natural factors, EL and ANP were not significantly correlated with village enclosure forest coverage. Forest coverage in the Beijing Plain area is influenced by multiple elements but is more closely related to social and human activities than to natural factors.

4. Discussion

4.1. Differences in Forest Coverage Around Villages in the Beijing Plain Area

Forest canopy coverage is the most intuitive indicator for measuring the quantity and extent of trees [29]. The study of the forest canopy coverage around villages is highly important for understanding the current status and development potential of rural greening. In the Beijing Plain area, the overall forest coverage around villages is 45.30%, which is relatively favourable compared with existing research on forest coverage within villages [26,30] and urban communities in Beijing [31].
Influenced by urban development stages and regional characteristics, significant differences exist in forest coverage across different areas [32]. Villages in outer suburban areas exhibit greater forest coverage than other regions. This finding is consistent with studies on overall Beijing, rural interiors [21], residential areas [28], and parks [33]. This is attributed to urbanization-driven land-use changes, where original woodlands are converted into construction land or farmland. Additionally, villages close to cities face increased development pressure, which leads to the compression of ecological space and a decline in forest coverage. In contrast, the Yanqing Plain, which is located within an ecological conservation zone, has lower forest coverage than the central plain area. This may be because the Yanqing Plain, surrounded by mountains, relies on the ecological functions of the mountainous regions. To ensure sufficient green space in the central plain, great emphasis is placed on forest coverage around villages. Compared with other village types, traditional single-story house villages demonstrate greater forest coverage, contrary to the findings of some previous studies [26]. Some scholars argue that newly developed villages, benefiting from systematic planning, exhibit better forest canopy coverage than traditional rural settlements do. This discrepancy arises because the present study focuses on external forest coverage, whereas prior research has analysed internal greening characteristics and highlighted the improved internal greening in new villages while overlooking external development. Large villages have greater forest coverage than small villages do. In large villages, concentrated populations and relatively active economic activities often lead to increased emphasis on and investment in enclosed forests, which results in greater coverage. In contrast, small villages tend to have low forest coverage due to their limited resources and management capacity.

4.2. The Relationship Between the Internal Composition Structure and Village Enclosure Forests

The forest coverage around villages shows an increasing trend with the continuous growth of various functional compositions of village enclosure forests. Compared with ecological shelterbelts (25.33%), ecological landscape forests (37.58%) and ecological–economic forests (36.37%) account for a greater proportion of village enclosure forests and play a dominant role in their composition. These two types of forests have a more positive impact on forest coverage around villages. This provides strong evidence for future ecological planning and construction of villages.
The New Round Million-Mu Afforestation Project and the Plains Million-Mu Afforestation Project also represent a relatively large proportion of various types of village enclosure forests. The forest coverage in rural peripheral areas is driven primarily by these two rounds of afforestation projects [34], which reflects Beijing’s emphasis on these initiatives. Linear regression analysis reveals a significant relationship between both afforestation projects and the forest coverage around villages. The implementation of these projects has substantially increased forest coverage in rural areas. Without consideration of the influence of other factors on land use, the afforestation projects have expanded the forest area in the plain region by 9.8%, particularly in peripheral rural areas. These projects have effectively addressed the uneven distribution of forest resources in the Beijing Plain area [35].

4.3. The Influence of the External Environment on Village Enclosure Forests

The analysis of the relationships among human factors, natural factors, and village enclosure forests in the Beijing Plain area reveals that human factors constitute a crucial pathway for improving the canopy coverage of village enclosure forests in the Beijing Plain area and aligns with previous research findings on urban built-up area greening rates [36]. In complex social contexts, human activities have a particularly pronounced impact on village enclosure forests. While most prior studies have qualitatively described the importance of the surrounding farmland area for forest coverage distribution [37]—in studies conducted in Sri Lanka, the expansion of agricultural land has led to the shrinkage and fragmentation of forest land [38]—this study quantitatively validates this perspective for the first time. Among the human factors, the CLA has significant explanatory power for the distribution of forest canopy coverage in the study area. Against the backdrop of advancing the protection of farmland redlines and blue–green spaces, many villages face challenges related to land-use changes. How to balance the legally protected status of farmland and forested land in the future remains a critical issue for further discussion.
To determine which factors have the greatest impact on forest coverage, various spatial variables were tested to reveal the complex relationships between human environmental factors and forest coverage. Notably, forest coverage around villages was significantly correlated with only DDC among the relationships between the distance of a village to the city, district, and town centres. This indicates that in the construction of greenery around villages, the influence of individual administrative divisions outweighs that of Beijing as a whole. Each administrative division can formulate precise and effective plans to improve forest coverage on the basis of its specific regional characteristics and economic development needs; planning implemented at the level of each village can be more targeted than Beijing’s overall planning. Saunders [39] explored the drivers of forest coverage changes at the community level in Perth, Western Australia, on the basis of socioeconomic characteristics and urban form metrics. Their results revealed a positive correlation between socioeconomic status and forest coverage, which contrasts with the findings of this study regarding the influence of DCR and DDC. In rural areas farther from economic centres, forest coverage around villages is greater because, in the more urbanized Beijing plain area, land close to economic centres is more likely to be allocated for construction than for forests. Mockrin [40] reported that forest coverage was consistently negatively correlated with road density but positively correlated with undeveloped areas, which aligns with the results of this study. The urbanization process has adversely affected forest coverage around villages, as road construction often prioritizes transportation convenience and economic development over forest conservation, which squeezes the space available for trees in land allocation. A higher proportion of construction land, cultivated land, GDP, and the quantity of night-time lights are associated with greater negative impacts on forests, which is consistent with the findings of this study [8]. The development of transportation infrastructure will inevitably damage the trees along the roadside [41]. Sun Quan [42] discussed the impact of tourism development on surrounding green environments and suggested that tourist attractions promote greenery in their vicinity. However, the findings of this study on forest coverage around villages differ. Here, DFHS tends to have high forest canopy coverage. This may be because most large A-grade scenic spots in Beijing’s plain area are located in urban or suburban areas, which attract much tourist traffic. Surrounding areas often undergo land-use adjustments for public facilities, such as hotels, restaurants, and parking lots, which leads to the transformation of traditional villages into commercial tourist zones or residential areas and threatens the quantity and quality of village enclosure forests. Studies have also confirmed that the Miao people have a strong preference for clustered settlement and proactive planning during site selection, leading to the tendency of Miao villages to agglomerate. Human activities in these villages have consequently induced changes in landscape patterns, thereby influencing forest structure [38]. Additionally, Li Tong [25] reported that VS is positively correlated with forest coverage, a phenomenon that also applies to village enclosure forests in the Beijing Plain area. However, their observation that the clustering degree of rural settlements is negatively correlated with forest coverage contradicts this study’s results. This discrepancy arises because their research focused on internal village forest distribution, whereas this study addresses external forest coverage. Dispersed rural settlements often provide much growing space for enclosed forests, whereas clustered settlements may cause the continuity of forest canopy coverage to be fragmented to some extent.
The relationship between natural factors and the distribution pattern of village enclosure forests in the Beijing Plain area has relatively low explanatory power among the variables (p < 0.05). This is primarily because, in the development history of enclosed forests in northern regions, anthropogenic selection diminishes the role of natural elements. Additionally, climate variations at the urban scale are minimal [43]. Consequently, research on the distribution patterns of forest coverage in northern enclosed forests should focus more on anthropogenic factors than on natural influences.

4.4. Implications and Recommendations for the Management of Village Enclosure Forests in the Beijing Plain Area

Under the current rural development context, the issue of insufficient forest coverage within villages has become increasingly prominent. Most land inside villages is allocated for residential construction and various infrastructures, which leaves extremely limited space for tree planting and results in severely low internal forest coverage [18]. In this situation, villagers’ aspirations and demands for a green ecological environment are not met. The presence of village enclosure forests compensates for the low internal forest coverage caused by land use constraints and may increase the overall forest coverage of rural settlement forests by 36.1%. This approach aligns well with villagers’ usage patterns and behavioural needs and enables quality improvement transformations on the basis of existing peripheral vegetation.
The functional composition of village enclosure forests provides guidance for their future enhancement and transformation. Ecological landscape forests can be further developed into scenic recreational forests to meet the sightseeing and leisure needs of both villagers and surrounding tourists. During their development, a rational layout and meticulous design based on landscape aesthetic principles can enhance the natural beauty around villages and support residents’ daily recreational use. Ecological–economic forests may be transformed into sightseeing orchards or other high-efficiency economic forests depending on specific circumstances, which can increase their recreational and economic value to support rural revitalization. Ecological shelterbelts can serve as ecological reserve resources or be developed into scenic forests to enrich distant views. This approach aims to create forest communities that combine ecological and economic benefits while incorporating recreational functions and aesthetic value for local villagers’ use.
This study precisely classifies villages by analysing various driving factors affecting the coverage of village enclosure forests. Villages can be categorized on the basis of the surrounding farmland area (CLA) and distance from famous historical sites (DFHS) as farmland-type villages and tourism-type villages. They can be grouped by neighbourhood distance (NDV) as clustered villages and dispersed villages or classified according to village size (VS), distance to district centres (DDC), village road area (VRA), and other factors. Under circumstances where future forest areas change minimally due to policy constraints, priority should be given to villages with low forest coverage for quality improvement initiatives. The further evaluation of landscape quality characteristics, such as forest stand structure and the visual morphology of enclosed forests, in different types of villages can support the proposal of targeted enhancement recommendations that align with villagers’ needs.

5. Conclusions

The findings reveal a 45.30% average forest coverage rate, with significant spatial variations tied to village typology and location. The predominance of ecological landscape (37.58%) and ecological–economic forests (36.37%), coupled with the substantial impact of the Million-Mu Afforestation Project (contributing 47.37% coverage), highlights the successful implementation of targeted forestation policies. Our multivariate analysis identifies human activities as the primary driver of coverage patterns, particularly through agricultural land-use (CLA) and settlement characteristics (DCR, VS). These insights underscore the importance of developing differentiated forest management strategies that account for (i) village spatial classification (village proximity or settlement type), (ii) functional forest composition, and (iii) localized human–environment interactions. The demonstrated approach provides a replicable framework for optimizing rural green infrastructure in rapidly urbanizing regions, balancing ecological and socioeconomic objectives through evidence-based spatial planning. In the future, the construction of village enclosure forests should not merely focus on increasing coverage area. Instead, it should aim to establish stable and diverse plant communities, forming forest ecosystems that can be utilized by local villagers while integrating ecological benefits, recreational functions, and aesthetic value. Currently, research on the construction techniques, evaluation methods, and management practices for village enclosure forests remains insufficient. On the basis of meeting the quantitative characteristics of villagers, how village enclosure forests can fulfil the villagers’ demands for green spaces warrants further in-depth study. Researchers should assess the landscape quality features of village enclosure forests, such as stand structure and visual morphology, and investigate the actual needs of villagers for green spaces, in order to propose more tailored suggestions for improvement and upgrading. The goal is to ensure that afforestation is not only visible but also tangible.

Author Contributions

Y.Z. analysed data and drafted the manuscript. Y.Z. and C.W. participated in collecting the experiment data. E.Q. was involved in the planning of this study and designing of the work. The remaining authors contributed to refining the ideas, carrying out additional analyses, and finalizing this paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to the confidentiality agreement involved.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Plain village sampling points.
Figure 1. Plain village sampling points.
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Figure 2. Interpretation reference maps of village enclosure forests.
Figure 2. Interpretation reference maps of village enclosure forests.
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Figure 3. Methodological framework of the random forest regression model. Note: see Table 1 for the meaning of EL, AMT, ANP, DWB, DCC, DDC, DTC, NDV, GDP, PD, VRA, VISA, DCR, DIP, DFHS, VS, and CLA. (***: p < 0.001; **: p < 0.01; *: p < 0.05).
Figure 3. Methodological framework of the random forest regression model. Note: see Table 1 for the meaning of EL, AMT, ANP, DWB, DCC, DDC, DTC, NDV, GDP, PD, VRA, VISA, DCR, DIP, DFHS, VS, and CLA. (***: p < 0.001; **: p < 0.01; *: p < 0.05).
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Figure 4. Forest coverage rates (mean ± S.E.) across location types (a), residential types (b), and size types (c). (OA: the outer suburban area, SA: the suburban area, YQ: the Yanqing Plain, TSH: traditional single-story house village, CR: commercial residential-type village, LV: large village, MV: medium village, SV: small village). Note: different letters indicate significant differences (p < 0.05) among different types of villages.
Figure 4. Forest coverage rates (mean ± S.E.) across location types (a), residential types (b), and size types (c). (OA: the outer suburban area, SA: the suburban area, YQ: the Yanqing Plain, TSH: traditional single-story house village, CR: commercial residential-type village, LV: large village, MV: medium village, SV: small village). Note: different letters indicate significant differences (p < 0.05) among different types of villages.
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Figure 5. Contribution rates (mean ± S.E.) of different functional types in location types (a), residential types (b), and size types (c). (OA: the outer suburban area, SA: the suburban area, YQ: the Yanqing Plain, TSH: traditional single-story house village, CR: commercial residential-type village, LV: large village, MV: medium village, SV: small village). Note: different uppercase letters indicate significant differences (p < 0.05) among different functional types within the same village category. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same functional type.
Figure 5. Contribution rates (mean ± S.E.) of different functional types in location types (a), residential types (b), and size types (c). (OA: the outer suburban area, SA: the suburban area, YQ: the Yanqing Plain, TSH: traditional single-story house village, CR: commercial residential-type village, LV: large village, MV: medium village, SV: small village). Note: different uppercase letters indicate significant differences (p < 0.05) among different functional types within the same village category. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same functional type.
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Figure 6. Contribution (mean ± S.E.) of different construction source types in location types (a), residential types (b), and size types (c). (OA: the outer suburban area, SA: the suburban area, YQ: the Yanqing Plain, TSH: traditional single-story house village, CR: commercial residential-type village, LV: large village, MV: medium village, SV: small village). Note: Different uppercase letters indicate significant differences (p < 0.05) among different sources within the same village type. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same source.
Figure 6. Contribution (mean ± S.E.) of different construction source types in location types (a), residential types (b), and size types (c). (OA: the outer suburban area, SA: the suburban area, YQ: the Yanqing Plain, TSH: traditional single-story house village, CR: commercial residential-type village, LV: large village, MV: medium village, SV: small village). Note: Different uppercase letters indicate significant differences (p < 0.05) among different sources within the same village type. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same source.
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Figure 7. Analysis of the factors influencing the composition structure of village enclosure forest coverage.
Figure 7. Analysis of the factors influencing the composition structure of village enclosure forest coverage.
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Figure 8. Relative contribution rates of key factors to the impact on village enclosure forest coverage.
Figure 8. Relative contribution rates of key factors to the impact on village enclosure forest coverage.
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Figure 9. Heatmap of the correlation (a) and the random forest regression model (b) between coverage rates in village enclosure forests. Note: see Table 1 for the meaning of EL, AMT, ANP, DWB, DCC, DDC, DTC, NDV, GDP, PD, VRA, VISA, DCR, DIP, DFHS, VS, and CLA.( *: p < 0.05; ns: p > 0.05).
Figure 9. Heatmap of the correlation (a) and the random forest regression model (b) between coverage rates in village enclosure forests. Note: see Table 1 for the meaning of EL, AMT, ANP, DWB, DCC, DDC, DTC, NDV, GDP, PD, VRA, VISA, DCR, DIP, DFHS, VS, and CLA.( *: p < 0.05; ns: p > 0.05).
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Figure 10. Contribution rates of influencing factors to village enclosure forest coverage. Note: see Table 1 for the meaning of EL, ANP, DCC, DDC, DTC, NDV, VRA, DCR, DFHS, VS, and CLA. Asterisks represent the significance of the coefficients (***: p < 0.001; **: p < 0.01; *: p < 0.05).
Figure 10. Contribution rates of influencing factors to village enclosure forest coverage. Note: see Table 1 for the meaning of EL, ANP, DCC, DDC, DTC, NDV, VRA, DCR, DFHS, VS, and CLA. Asterisks represent the significance of the coefficients (***: p < 0.001; **: p < 0.01; *: p < 0.05).
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Table 1. Descriptions of variables used to the experimental study.
Table 1. Descriptions of variables used to the experimental study.
FactorExplanationResolutionSource
ELElevation12.5 mGeospatial Data Cloud platform of the Chinese Academy of Sciences
(http://www.gscloud.cn/ (accessed on 1 January 2023))
AMTAnnual mean temperature1 kmWorldClim (1970–2000)
(https://worldclim.org/ (accessed on 1 January 2023))
ANPAnnual mean rainfall
DWBDistance from water bodies/Processed using Euclidean distance calculation tools in the ArcGIS platform, from Beijing’s 2019 second-class forest resource inventory database
DCCDistance from the city centre
DDCDistance from the district centre
DTCDistance from the town centre
NDVNeighbourhood distance of villages
GDPGross domestic product100 mResources and Environment Science and Data Center of the Chinese Academy of Sciences
(https://www.resdc.cn/ (accessed on 1 January 2023))
PDPopulation density100 mWorldPop (2020)
(https://www.worldpop.org/ (accessed on 1 January 2023))
VRAVillage road area/Road density and impervious surface area information were downloaded from OpenStreetMap (OSM)
VISAVillage impervious surface area
DCRdistance from commercial residences/Point of interest (POI) data coordinates (including famous historical sites, bus stops, medical institutions, commercial hotels, commercial residences, and industrial parks) were collected from Amap (https://www.amap.com/ (accessed on 1 January 2023)) through the API interface and processed using Python programming for coordinate point collection and subsequently, Euclidean distance calculation tools
DIPDistance from industrial parks
DFHSDistance from famous historical sites
VSVillage size/extracted and calculated from
Beijing’s 2019 second-class forest resource inventory database
CLACultivated land area
Table 2. Forest coverage rates (mean ± S.E.) across location types, residential types, and size types. (Note: different letters indicate significant differences (p < 0.05) among different types of villages).
Table 2. Forest coverage rates (mean ± S.E.) across location types, residential types, and size types. (Note: different letters indicate significant differences (p < 0.05) among different types of villages).
TypeMean
location typesthe outer suburban area (OA)42.67 ± 1.36 ab
the suburban area (SA)47.13 ± 0.61 b
the Yanqing Plain (YQ)40.34 ± 2.75 a
residential typestraditional single-story house village (TSH)48.07 ± 0.70 a
commercial residential-type village (CR)42.72 ± 0.87 b
size typeslarge village (LV)47.78 ± 0.98 a
medium village (MV)45.46 ± 0.94 ab
small village (SV)44.85 ± 0.93 b
Table 3. Contribution (mean ± S.E.) rates of different functional types in location types, residential types, and size types. Note: different uppercase letters indicate significant differences (p < 0.05) among different functional types within the same village category. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same functional type.
Table 3. Contribution (mean ± S.E.) rates of different functional types in location types, residential types, and size types. Note: different uppercase letters indicate significant differences (p < 0.05) among different functional types within the same village category. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same functional type.
TypeMean
Ecological Landscape ForestEcological ShelterbeltEcological–Economic Forest
location typesthe outer suburban area (OA)45.76 ± 3.07 Aa27.26 ± 1.53 Bb52.37 ± 3.35 Aa
the suburban area (SA)41.01 ± 3.12 Aa41.31 ± 1.85 Aa27.43 ± 2.77 Bb
the Yanqing Plain (YQ)41.66 ± 3.96 Aa31.29 ± 3.20 ABab22.12 ± 3.13 Bb
residential typestraditional single-story house village (TSH)42.08 ± 4.53 Aa34.45 ± 1.66 Aa42.22 ± 2.97 Aa
commercial residential-type village (CR)46.40 ± 2.89 Aa26.91 ± 1.74 Bb49.60 ± 3.93 Aa
size typeslarge village (LV)36.32 ± 1.41 Ab28.48 ± 1.19 Aa38.56 ± 1.40 Ab
medium village (MV)40.58 ± 3.39 Ab25.85 ± 2.19 Ba45.09 ± 4.76 Ab
small village (SV)61.80 ± 7.28 Aa33.31 ± 3.27 Ba62.69 ± 7.99 Aa
Table 4. Contribution (mean ± S.E.) of different construction source types in location types, residential types, and size types. Note: Different uppercase letters indicate significant differences (p < 0.05) among different sources within the same village type. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same source.
Table 4. Contribution (mean ± S.E.) of different construction source types in location types, residential types, and size types. Note: Different uppercase letters indicate significant differences (p < 0.05) among different sources within the same village type. Different lowercase letters denote significant differences (p < 0.05) among different village types within the same source.
TypeMean
The New Round Million-Mu Afforestation ProjectPlains Million-Mu Afforestation ProjectGreen Belt Project
location typesthe outer suburban area (OA)12.53 ± 0.55 Bb27.98 ± 0.86 Ab0.34 ± 0.12 Cb
the suburban area (SA)15.68 ± 1.25 Ba33.92 ± 2.11 Aa4.31 ± 0.77 Ca
the Yanqing Plain (YQ)12.19 ± 2.16 Bb26.28 ± 3.55 Ab0.00 ± 0.00 Cb
residential typestraditional single-story house village (TSH)13.91 ± 0.84 Bb26.22 ± 1.16 Aa1.58 ± 0.33 Cb
commercial residential-type village (CR)12.45 ± 0.66 Ba30.35 ± 1.34 Aa0.58 ± 0.11 Cb
size typeslarge village (LV)13.84 ± 0.62 Ba29.62 ± 1.22 Aa1.27 ± 0.21 Ca
medium village (MV)11.36 ± 1.45 Ba28.19 ± 2.31 Aa0.86 ± 0.31 Ca
small village (SV)13.47 ± 0.95 Ba27.45 ± 1.27 Aa0.34 ± 0.11 Cb
Table 5. Spatial patterns of the regression model for factors influencing the structural composition of village enclosure forest coverage.
Table 5. Spatial patterns of the regression model for factors influencing the structural composition of village enclosure forest coverage.
VariablesEquationParameters
Ecological landscape forestsy = 38.7 + 0.35 xR2 = 0.13, p < 0.001
Ecological shelterbeltsy = 42.6 + 0.25 xR2 = 0.03. p < 0.001
Ecological–economic forestsy = 41.7 + 0.22 xR2 = 0.04, p < 0.001
the New Round Million-Mu Plain Afforestation Projecty = 42.8 + 0.48 xR2 = 0.05, p < 0.001
Plains Million-Mu Afforestation Projecty = 39.7 + 0.33 xR2 = 0.12, p < 0.001
the Green Belt Projecty = 46.1 + 0.08 xR2 < 0.01, p = 0.66
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Zhang, Y.; Qiu, E.; Wang, C.; Sun, Z.; Jin, J. Characteristics of the Distribution of Village Enclosure Forests in the Beijing Plain Area and Influencing Factors. Forests 2025, 16, 1003. https://doi.org/10.3390/f16061003

AMA Style

Zhang Y, Qiu E, Wang C, Sun Z, Jin J. Characteristics of the Distribution of Village Enclosure Forests in the Beijing Plain Area and Influencing Factors. Forests. 2025; 16(6):1003. https://doi.org/10.3390/f16061003

Chicago/Turabian Style

Zhang, Yuan, Erfa Qiu, Chenxuan Wang, Zhenkai Sun, and Jiali Jin. 2025. "Characteristics of the Distribution of Village Enclosure Forests in the Beijing Plain Area and Influencing Factors" Forests 16, no. 6: 1003. https://doi.org/10.3390/f16061003

APA Style

Zhang, Y., Qiu, E., Wang, C., Sun, Z., & Jin, J. (2025). Characteristics of the Distribution of Village Enclosure Forests in the Beijing Plain Area and Influencing Factors. Forests, 16(6), 1003. https://doi.org/10.3390/f16061003

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