Yining Zhang，Zhigang Wang*
College of Landscape Architecture and Tourism, Agricultural University of Hebei，China
Email:
Yining Zhang: Wangzhg1956@163.com
Zhigang Wang: Wangzhigang1956
Abstract:
As a densely populated and economically active region in North China, the Shijiazhuang metropolitan area faces complex environmental challenges: high fragmentation of the urban green system, significant pressure on environmental service capacity, and degradation of ecological functions. Natural conditions, economic factors, and governmental guidance constrain the realization of the urban planning vision to a certain extent, leading to a mismatch between current urban landscape planning and the actual needs of citizens. Enhancing the ecosystem services of the landscape pattern while improving residents’ experiences has become an urgent issue.
Focusing on the Shijiazhuang metropolitan area, this study identifies high-risk zones with greater vulnerability within the existing landscape pattern. It also simulates two scenarios—non-intervention and the implementation of specific intervention planning targeting these high-risk zones—to derive strategies that support planning decisions and optimize outcomes for the future sustainability of the urban landscape.
Our study primarily uses remotely sensed data, quantitatively explored through geographic information analysis platforms (e.g., ArcGIS). In the first step, the landscape pattern of the study area is evaluated to identify high-risk areas. We mainly consider two dimensions of evaluation indicators: ecosystem service functions (provisioning services, supporting services, regulating services, etc.) and residents’ living experience (ecological environment quality, ease of living, leisure convenience, etc.). This is based on a comprehensive examination of multiple variables, including land use classification, heat island effect, landscape connectivity, habitat quality, and POI for amenities.
In the second step, we construct a state transition matrix based on historical temporal data to predict future landscape pattern trends in the study area without intervention, using the CA-MARKOV model to adjust the state transition matrix, to identify mismatches between planning and actual needs.
This study contributes valuable insights for urban landscape pattern optimization strategies by examining the effectiveness of high-risk zone identification and specific intervention planning. The findings are expected to provide specific guidance for future urban landscape pattern planning and optimization policies in metropolitan areas with similar conditions.
Keywords: Landscape ecology; urban sustainability; Spatial planning; Ecosystem services
Yining Zhang, Ph.D. candidate, Hebei Agricultural University (1992.2-), Baoding, Hebei Province, China. Research interests: Urban Ecology.
Zhigang Wang, Professor, Hebei Agricultural University (1956.9-), Baoding, Hebei Province, China. Academic discipline: Forestry.
- Introduction
In the ongoing interplay between human society and the natural environment, landscape pattern optimization has been widely employed in ecological protection and construction[1], serving as a crucial means to enhance regional ecological performance. As urban expansion and diffusion continue, urban landscape patterns undergo constant changes in both composition and intensity[2], which, in turn, have profound effects on ecosystems[3].
Metropolitan areas, defined as the urban core along with adjacent communities exhibiting strong socio-economic integration with the core[4], typically use administrative districts and counties as their basic analytical units[5]. The dynamic and unstable nature of urban peripheries introduces structural complexity to metropolitan ecosystems, making rational analysis and optimization of landscape patterns particularly critical[5].
Shijiazhuang, located on the North China Plain, is the capital city of Hebei Province and has been designated by the State Council as a key central city within the Beijing–Tianjin–Hebei region[6]. It enjoys a distinct locational advantage and demonstrates substantial development potential. The rapid expansion of the built-up area has intensified ecological stress, and there remains considerable room for improvement in ecological protection.
Landscape structure refers to the composition and spatial configuration of different landscape types, while the spatial characteristics of these landscape components are collectively referred to as landscape patterns[7][8]. Conducting a quantitative study on the landscape patterns of the Shijiazhuang metropolitan area and developing corresponding optimization strategies is essential for advancing scientific urban planning in the region[9].
This study focuses on the metropolitan area of Shijiazhuang—specifically, Qiaoxi District, Yuhua District, Chang’an District, Xinhua District, Gaocheng District, Luquan District, Luancheng District, and Zhengding District. The study period spans from 2010 to 2020. Utilizing geographic information technologies in conjunction with relevant literature and publicly available government statistical data, the research analyzes the current status, temporal changes, landscape connectivity, and habitat quality of landscape patterns within the Shijiazhuang metropolitan area. The goal is to provide a theoretical foundation for urban planning in Shijiazhuang and offer insights applicable to the landscape optimization of other metropolitan areas.
- Research Content
2.1 Overview of the Study Area
Shijiazhuang is located in the south-central part of Hebei Province, between 37°27′–38°47′N and 113°30′–115°20′E[10]. It serves as a key passenger and freight transportation hub in northern China and is one of the core cities in the Beijing–Tianjin–Hebei urban agglomeration. The total administrative area of Shijiazhuang covers 14,530 km², with a built-up area of 338.16 km². The study area, which encompasses the city’s urban core, spans 2,668.06 km².
Situated at the junction of the Shanxi Platform and the Bohai Depression, the terrain of Shijiazhuang generally slopes from west to east. The western part of the city lies in the low mountain and hilly region of the central Taihang Mountains, while the eastern portion consists of the alluvial plain of the Hutuo River[11]. Elevation ranges from 81.5 meters in the northwest to 64.3 meters in the southeast, reflecting a gradient of higher elevation in the northwest descending toward the southeast. Shijiazhuang experiences a continental climate and features diverse land resource types.
In recent years, the city has comprehensively implemented a sustainable development strategy, integrating the governance of mountains, water, air, and urban systems. Emphasizing ecological restoration and resource conservation, Shijiazhuang has advanced holistic management of natural elements—including mountains, rivers, forests, farmlands, lakes, grasslands, and deserts[12]—to enhance its ecosystem service functions and support long-term environmental sustainability[13].
2.2 Technical Framework
The technical framework of this study is illustrated in Figure 2-1.
Figure 2-1 Technical Workflow Diagram
This study utilizes high-quality Level-4 Landsat series satellite remote sensing imagery from the years 2010 and 2020 for the study area. The imagery is first subjected to standard preprocessing operations, followed by interpretation using ArcGIS.
A supervised classification approach is applied to classify the remote sensing images. The classification of landscape types follows the Land Use and Land Cover Classification System developed by the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, which is widely adopted in domestic land use remote sensing studies. Upon completion of supervised classification, the data is further processed in ENVI, and landscape type distribution maps are generated using ArcGIS to visualize the landscape patterns across the study area.
Subsequently, landscape pattern changes between the two years are compared and analyzed. Based on the derived landscape pattern data, the Morphological Spatial Pattern Analysis (MSPA) model is employed to assess landscape connectivity, including fragmentation levels, edge effects, and other structural metrics.
Finally, an evaluation of habitat degradation in the Shijiazhuang metropolitan area is conducted by integrating the landscape pattern and connectivity analysis results.
- Results
3.1 Analysis of Landscape Structure Changes
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Based on administrative boundaries, remote sensing image processing was conducted for eight districts and counties within the Shijiazhuang metropolitan area. Referring to the Land Use and Land Cover Classification System developed by the Chinese Academy of Sciences, the land use landscape types within the study area were classified into six categories: cropland, forest, grassland, water bodies, built-up land, unused land, and others.
Figure 3-1. Landscape Type Distribution 2010-2010
In terms of spatial distribution, cropland landscapes encircle the built-up land across the study area, interspersed with patches of forest, grassland, and water bodies. Built-up land exhibits a core-periphery radiative distribution pattern, with forest predominantly concentrated in the western part of the study region. The spatial extent of water bodies, unused land, and other land types is relatively small.
The most significant changes occurred in the built-up land category. Built-up areas have expanded primarily at the expense of cropland. The largest built-up land patch corresponds to the central urban core, which has become the dominant landscape type. The pattern across various districts indicates an increasingly prominent trend toward urban agglomeration, with a marked increase in the number and size of built-up land patches.
The spatial distribution of water bodies clearly reflects the structure of the urban water network and the locations of major hydraulic engineering projects. Cropland is mainly distributed in the northern, eastern, and southern parts of the study area, forming a surrounding zone around the urban core.
Table 3-1 Area Table of Landscape Types in 2010 and 2020
| Landscape Type | 2010 | 2020 | ||
| Area/km2 | Proportion/% | Area/km2 | Proportion/% | |
| Cropland | 1400.84 | 52.50 | 917.79 | 34.40 |
| Forest | 374.79 | 14.05 | 660.31 | 24.75 |
| Grassland | 14.47 | 0.54 | 28.08 | 1.05 |
| Built-up land | 761.44 | 28.54 | 970.79 | 36.39 |
| Water bodies | 52.73 | 1.98 | 55.92 | 2.10 |
| Unused land and Others | 63.79 | 2.39 | 35.18 | 1.32 |
In terms of area statistics, cropland represented the largest landscape type within the study area. However, from 2010 to 2020, its area showed a continuous decline, reflecting the process of urban expansion. Over the ten-year period, the cropland area decreased from 1,400.84 km² to 917.79 km², resulting in a total loss of 483.05 km². This significant reduction is directly attributable to the rapid urbanization occurring in the region.
Built-up area ranked second in extent among all landscape types, and its area steadily increased over the study period, indicating a clear pattern of urban expansion. From 2010 to 2020, the built-up area grew from 761.44 km² to 970.79 km², representing an increase of 209.35 km². Its proportion of the total study area rose from 28.54% to 36.39%, demonstrating a consistent upward trend in urban land use.
Forest occupied a substantial share of the landscape structure. Between 2010 and 2020, forest area fluctuated but exhibited an overall increase, reflecting both urban development and the results of afforestation efforts. Over the decade, the forest area expanded from 374.79 km² to 660.31 km², a net increase of 285.52 km². The proportion of forest land grew from 14.05% to 24.75%, consistent with Shijiazhuang’s policies promoting forest city construction.
Unused land and other land types exhibited fluctuations in area throughout the study period, indicating adjustments in land-use planning. In 2010, these categories accounted for 63.79 km², or 2.39% of the total study area. By 2020, this area had decreased to 35.18 km², comprising 1.32% of the total. Despite periodic variations, the overall trend was a decline, aligned with increasing urbanization and land conversion.
Water bodies remained among the smallest landscape types in terms of area and exhibited relative stability from 2010 to 2020. In 2010, water bodies covered 52.73 km² (1.98% of the total area), and by 2020, this had increased slightly to 55.92 km² (2.10%). The area of water bodies remained essentially unchanged over the decade.
The area of grassland fluctuated but generally increased during the study period, reflecting the dynamic nature of urban land use and ecological construction. In 2010, grassland covered 14.47 km² (0.54%), and by 2020, it had expanded to 28.08 km² (1.05%). This upward trend is closely tied to ongoing ecological urban development initiatives.
3.2 Landscape connectivity analysis
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Landscape connectivity data for the study area in 2010 and 2020 were obtained using the Morphological Spatial Pattern Analysis (MSPA) model, as illustrated in Figure 3-2.
Figure 3-2. Landscape Connectivity Analysis 2010–2020
In terms of spatial distribution, Core areas were primarily concentrated in the ecologically well-preserved mountainous regions in the western part of the study area, where they formed contiguous and compact patches. Islet components, representing isolated landscape fragments, were densely scattered in the northwestern part of the region. Branch elements exhibited a strong alignment along major water systems, indicating the role of hydrological features in shaping ecological corridors.
Between 2010 and 2020, the proportion of Core areas decreased, while the proportion of Islet areas increased. This shift is likely attributable to intensified landscape fragmentation associated with urban expansion. Simultaneously, the proportions of Bridge and Perforation areas increased, which may reflect the effects of recent urban planning strategies focused on green infrastructure development and the integration of ecological networks. Additionally, Edge and Loop components showed spatial adjustments, indicating the ongoing dynamics of landscape structural transitions under urbanization pressure.
Table 3-2 Changes in Area of Landscape Connectivity Indicators in the Study Area 2010- 2020
| Category | 2010 | 2020 | ||
| Area/km2 | Proportion/% | Area/km2 | Proportion/% | |
| Core | 248.43 | 9.31 | 158.31 | 5.93 |
| Islet | 16.70 | 0.63 | 161.01 | 6.03 |
| Perforation | 9.18 | 0.34 | 6.94 | 0.26 |
| Edge | 141.82 | 5.32 | 103.12 | 3.87 |
| Loop | 1.97 | 0.07 | 20.43 | 0.77 |
| Bridge | 9.17 | 0.34 | 110.23 | 4.13 |
| Branch | 11.38 | 0.43 | 89.37 | 3.35 |
As shown in Table 3-2, the overall landscape connectivity of the study area in 2020 experienced a slight improvement compared to 2010. However, the area of Core zones decreased significantly. The primary components of landscape connectivity consisted of Core, Islet, Edge, and Bridge classes. According to Table 4-2, the total area of the seven MSPA categories accounted for 24.34% of the entire study region. Among them, Islet, Core, and Bridge classes occupied the largest proportions, at 6.03%, 5.93%, and 4.13%, respectively. The shares of Edge and Branch were slightly lower, at 3.87% and 3.35%, respectively, while Loop and Perforation classes contributed the smallest proportions, at only 0.77% and 0.26%, respectively.
3.3 Habitat Degradation Assessment
The assessment of habitat degradation was conducted using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The InVEST framework is a widely applied, comprehensive ecosystem service valuation tool capable of spatially visualizing and quantifying changes in ecosystem service functions under specified environmental data and threat variables.
In this study, the InVEST model was implemented within ArcGIS to evaluate habitat degradation for the years 2010 and 2020. Landscape classification data were first rasterized and encoded as follows: 1 – Cropland, 2 – Forest, 3 – Grassland, 4 – Built-up area, 5 – Water bodies, 6 – Barren land.
In the InVEST framework, the more sensitive a habitat type is to a specific degradation factor, the lower its resistance to disturbance and the poorer its habitat quality. Based on model recommendations and relevant literature, sensitivity values for each land type to degradation stressors were determined.
Table 3-3. Sensitivity of Habitat Types to Degradation Factors
| ID | Habitat type | Suitability | Farmland | Construction land | Unused and Other land |
| 1 | Cropland | 0 | 0 | 0 | 0 |
| 2 | Forest | 1 | 0.5 | 0.8 | 1 |
| 3 | Grassland | 1 | 0 | 0.8 | 1 |
| 4 | Built-up land | 0 | 0 | 0 | 0 |
| 5 | Water bodies | 1 | 0.3 | 0.8 | 1 |
| 6 | Unused land and Others | 0 | 0 | 0 | 0 |
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The habitat degradation analysis map of the study area for the period 2010–2020 is presented in Figure 3-3.
Figure 3-3. Habitat Degradation Analysis 2010–2020
From a spatial perspective, the habitat degradation pattern within the study area exhibits a clear structural distribution, with regions of relatively low degradation dominating the landscape. Overall, the degree of habitat degradation follows a spatial gradient characterized by higher degradation levels concentrated around the periphery of the urban core, showing a radiating decline in intensity from the central built-up areas outward. Scattered among these mildly degraded zones are pockets of more severe degradation, primarily located along the transition zones between mountainous and plain areas as well as around major water systems. The outermost regions are generally composed of moderately to slightly degraded areas.
Table 3-4. Zonal Area Statistics of Habitat Degradation Levels
| Category | 2010 | 2020 | ||
| Area/km2 | Proportion/% | Area/km2 | Proportion/% | |
| Slight habitat degradation | 2254.78 | 84.51 | 1960.08 | 73.46 |
| Moderate habitat degradation | 146.72 | 5.50 | 238.12 | 8.92 |
| Severe habitat degradation | 105.23 | 3.94 | 257.72 | 9.66 |
| Extreme habitat degradation | 160.30 | 6.01 | 212.14 | 7.95 |
From the perspective of spatial distribution of degradation severity, in 2010, the area categorized as experiencing slight habitat degradation in the study region accounted for 2,254.78 km², representing 84.51% of the total study area. The proportions of areas experiencing moderate, severe, and extreme habitat degradation were all below 10%, indicating a generally low level of habitat degradation. By 2020, the area of slight habitat degradation had decreased to 1,960.08 km², accounting for 73.46% of the study area. Although the proportions of moderately, severely, and extremely degraded areas remained below 10%, the overall level of habitat degradation increased compared to 2010.
3.4 Analysis of the Heat Island Effect
This paper studies the distribution of urban heat islands in the research area by using the atmospheric correction method and obtains the heat island distribution maps of the research area in 2010 and 2020. The surface temperature inversion images are segmented into five grades: high-temperature area, sub-high-temperature area, medium-temperature area, sub-medium-temperature area, and low-temperature area by the natural breaks method. The area of each zone is calculated by reclassifying the number of pixels at each temperature level and then converting the pixel size into area.
From the spatial distribution perspective (see Figures 3-4), by comparing the spatial distribution of heat islands in different years, it can be found that in 2010, the heat island distribution in Shijiazhuang was mainly concentrated in the central urban area and the range was not large. In 2020, the central urban area began to show a significant heat island effect. By comparing the topography of Shijiazhuang, it can be known that the western part of the research area is mostly mountainous with good vegetation coverage, including Xishan Forest Park, etc. The temperature is lower than that of the surrounding non-green areas, making it possible for low-temperature areas to exist. High-temperature areas are mostly distributed in densely built-up areas. By comparing the heat island distribution map with the landscape pattern distribution map, it is easy to find that the high-temperature areas are closely related to the built-up land in space. Built-up land has a hard surface, dense human activities, and a concentration of industrial activities, all of which can lead to the intensification of heat islands. In terms of spatial distribution, the heat island effect has always been in a relatively obvious state, with the area of the low-temperature zone being relatively small.
Figure 3-4. Heat island distribution Analysis 2010–2020
During the period from 2010 to 2020, the areas of the high-temperature zone and the sub-high-temperature zone fluctuated and increased (Tab.3-5); the proportions of the sub-moderate-temperature zone and the moderate-temperature zone remained relatively stable, with only a small increase; the area of the low-temperature zone fluctuated and decreased. Overall, the heat island effect in the study area has become increasingly prominent. From 2010 to 2020, the proportion of the high-temperature zone increased from 3.63% to 6.39%, and the area increased from 96.74 km2 to 170.42 km2; the proportion of the sub-high-temperature zone increased from 11.98% to 17.14%; the proportion of the moderate-temperature zone increased from 18.70% to 19.12%; the proportion of the sub-moderate-temperature zone decreased from 33.41% to 29.74%; and the proportion of the low-temperature zone increased from 32.29% to 27.61%.
Tab.3-5 Statistical table of urban heat island category area in Shijiazhuang metropolitan area 2010-2020
3.5 CA-Markov model prediction
The CA-Markov model is a combination of CA and Markov models. It has powerful functions in predicting changes in spatiotemporal patterns and is widely applied in fields such as geography, urban planning, and ecology, improving the accuracy of land use spatiotemporal evolution information and possessing strong scientificity and practicability. During the prediction process, a transition probability matrix needs to be established to describe the mutual conversion relationships between different categories. The transition matrix is a matrix that describes the conversion probabilities between different landscape types, and it can calculate the probability of each grid cell transitioning from one landscape type to another, and fill these probabilities into the transition matrix.
Based on the transfer rate matrix and predicted values from 2010 to 2014, the predicted situation of the landscape structure in 2017 was calculated, and a comparative analysis was conducted with the actual situation. The kappa coefficient was calculated and analyzed, which was 0.846, indicating that the model was applicable. Then, based on the transfer rate matrix and predicted values from 2017 to 2020, the predicted situation of the landscape structure in the Shijiazhuang metropolitan area in 2030 under the natural development model was calculated, and a comparative analysis was conducted with the landscape pattern situation in 2020.
The land use transfer rate matrix was obtained, as shown in Table 3-6.
Tab.3-6 Landscape types transfer rate matrix
| Landscape type | Farm land | Forest land | Grass land | Construction land | Water | Unused and other land |
| Cropland | 0.3690 | 0.3826 | 0.0195 | 0.2079 | 0.0082 | 0.0128 |
| Forest | 0.3701 | 0.3350 | 0.0096 | 0.2665 | 0.0052 | 0.0136 |
| Grassland | 0.0104 | 0.0253 | 0.4514 | 0.0355 | 0.4763 | 0.0012 |
| Built-up land | 0.1286 | 0.2110 | 0.0270 | 0.5939 | 0.0171 | 0.0225 |
| Water bodies | 0.0673 | 0.1173 | 0.0221 | 0.1792 | 0.6080 | 0.0061 |
| Unused land and Others | 0.1418 | 0.1890 | 0.0164 | 0.6253 | 0.0108 | 0.0167 |
Based on this, the IDRISI software can predict the landscape structure characteristics of the study area in 2030.The predicted area data of landscape types in 2030 and the area data of landscape types in 2020 are shown in Table 3-7:
Tab.3-7 Predicted area of landscape types in Shijiazhuang metropolitan area in 2030
| Types | 2020 | 2030 | ||
| Area /km2 | Proportion /% | Area /km2 | Proportion /% | |
| Cropland | 917.79 | 34.4 | 776.37 | 29.1 |
| Forest | 660.31 | 24.75 | 688.85 | 25.82 |
| Grassland | 28.08 | 1.05 | 56.33 | 2.11 |
| Built-up land | 970.79 | 36.39 | 1039.29 | 38.95 |
| Water bodies | 55.92 | 2.1 | 70.31 | 2.64 |
| Unused land and Others | 35.18 | 1.32 | 36.92 | 1.38 |
The area of cultivated land landscape types decreased from 917.79 km2 in 2020 to 776.37 km2, with the proportion decreasing from 34.4% to 29.1%. With the rapid urbanization of the study area, the area of cultivated land landscape types significantly decreased, and more cultivated land was transformed into construction land and forest land.
The area of construction land landscape types rose to the highest value among all types, reflecting a significant urban expansion. The area of construction land landscape types increased from 970.79 km2 to 1039.29 km2 over 10 years, an increase of 68.50 km2, and the proportion increased from 36.39% to 38.95%. With the rapid urbanization of the study area, the area of construction land landscape types continued to increase.
The area of forest land landscape types rose, reflecting the situation of urban development and afforestation achievements. The area of forest land landscape types increased from 660.31 km2 to 688.85 km2 over 10 years; the proportion increased from 24.75% to 25.82%. With the construction of the Forest City of Shijiazhuang, the area of forest land landscape types continued to increase, and the regional ecological construction is expected to be relatively good.
The area of unused and other landscape types fluctuated little, possibly related to changes in urban planning and construction. The area of unused and other landscape types was 35.18 km2 in 2020, accounting for 1.32% of the total area of the study region; the predicted area in 2030 was 36.92 km2, accounting for 1.38% of the study region.
The area of water body landscape types was at a relatively low level among all types, with an area of 55.92 km2 in 2020, accounting for 2.10% of the study region; the predicted area in 2030 was 70.31 km2, accounting for 2.64% of the total area of the study region.
The area of grassland landscape types was 28.08 km2 in 2020, accounting for 1.05% of the study area; it was 16.46 km2 in 2030, accounting for 0.62% of the study area; the predicted area in 2020 was 56.33 km2, accounting for 2.11% of the total area.
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Fig.3-6 Landscape type structure prediction map in Shijiazhuang metropolitan area in 2030
The construction land landscape in the study area has expanded significantly, showing a radial development pattern, which is in line with common urban planning types. The central areas of districts such as Zhengding, Gaocheng, Luancheng, and Luquan have a clear tendency towards organic dispersion. As shown in the above figure, the area of forest land has increased significantly, and its connectivity has improved. The water body landscape has not changed much; the area of cultivated land has decreased, and the overall development situation is predicted to be reasonable.
- Evaluation and Recommendations
4.1 Urban Ecological Assessment
Based on the above spatial analyses, an urban ecological evaluation of the Shijiazhuang metropolitan area reveals that cropland, built-up areas, and forest landscapes occupy the largest proportions among land cover types. Cropland generally encircles the built-up areas, while forested landscapes are primarily concentrated in the western portion of the study region. Urban green space within the built-up zones is limited, overall landscape connectivity is suboptimal, and the degree of habitat degradation has increased over time.
4.2 Recommendations for Ecological Construction
From the perspective of spatial structure, ecological construction should be aligned with national and provincial regional development strategies. It is essential to base planning on natural geographical patterns, with a focus on the protection of large contiguous cropland areas in the plains and the strengthening of ecological conservation and restoration in the mountainous western regions. Urban construction land should be utilized in a compact and efficient manner to foster an open, networked, compact, and ecologically sustainable urban service structure.
The delineation of an urban greenbelt surrounding the city should be clarified in terms of scale, boundaries, and functional zones. This delineation should correspond with the city’s development scale, reserving land for future urban expansion, while enforcing stringent policy controls to minimize the impact of urbanization on greenbelt development. The establishment of this greenbelt should not be limited to the urban core; rather, it should extend toward the city periphery in the form of a metropolitan ecological corridor, linking urban and rural green spaces and promoting an integrated urban–rural ecological spatial system.
In conjunction with the updated master plan for Shijiazhuang, a “subtractive” approach should be adopted within the second ring road—reducing construction intensity while increasing greenery. To address the uneven distribution of current urban green spaces, a comprehensive improvement in the spatial layout of green infrastructure is necessary. Some green spaces with recreational functions also suffer from limited accessibility and underdeveloped infrastructure. Therefore, planning should take into account population density and activity hotspots to optimize urban green space construction in the metropolitan area.
- Conclusion
The Shijiazhuang metropolitan area is a highly urbanized region facing prominent environmental challenges, including the uneven spatial distribution of ecological land and rapid land use transformation. This study quantitatively assessed landscape patterns, landscape connectivity, and habitat degradation using remote sensing data. Based on the results, recommendations for ecological construction were proposed.
The findings provide useful insights for optimizing the design of public urban spaces and enhancing the overall vitality of Shijiazhuang. Furthermore, the study offers a valuable reference for urban ecological research in other cities of similar scale. A comprehensive functional division and structural planning of the study area were carried out, and a landscape pattern optimization plan was proposed. The proposal was made based on the quantitative analysis of the landscape pattern in the Shijiazhuang urban area and was in compliance with the regulations. Based on the multi-angle analysis of the landscape pattern in the previous text, it also conforms to the urban development trend. Targeted urban planning based on the model prediction results is conducive to the ecological development of the main urban area.
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