Spatiotemporal Evolution of Land-Use Landscape Patterns Under Park City Construction: A GIS-Based Case Study of Shenyang’s Main Urban Area (2000–2020)

Abstract

Motivated by China’s new urbanization and ecological civilization construction initiatives, the Shenyang Municipal Committee has recently has proposed an ambitious goal of advancing the construction of a Park City with northern characteristics. The scientifically planned urban landscape is essential for balancing ecological protection with sustainable development,. This plan is crucial for driving the realization of the Park City initiative. This study employed ArcGIS 10.8 and Fragstats 4.2 to systematically examine land use transitions and landscape pattern dynamics in Shenyang’s main urban area (2000–2020). The results indicated that Shenyang’s urban core has experienced significant southward expansion across the Hun River over the last two decades. This expansion resulted in a substantial increase in constructed land of 490.84 km² (from 15.78% to 29.19% in total coverage). Conversely, cultivated land, forest land, and grassland exhibited negative dynamic rates of −0.99%, −0.54%, and −1.02%, respectively, with 76.89% of cultivated land converted to construction land. Landscape pattern indices revealed intensified fragmentation: the number of patches rose by 163, while the largest patch area, landscape aggregation index, and contagion index decreased by 16.74%, 0.40%, and 5.84%, respectively. However, the landscape division index increased by 0.12%, with Shannon’s diversity index and evenness index increasing by 0.19 and 0.11, respectively. These metrics demonstrated the positive correlation between urbanization intensity and landscape pattern alterations. The examination of the dynamic land use patterns in Shenyang integrated seven crucial indicators to assess the development of the emerging Park City. Results indicated challenges including urban land expansion, cultivated land loss, limited resources, and uneven green space distribution. The findings revealed the negative correlation between land use pattern evolution and Park City requirements. The research suggested strategies at the macro-, meso-, and micro-scales to address these issues and reconcile urbanization pressures with sustainable Park City development in Shenyang.

1. Introduction

The evolution of China’s urban development model has progressed through six distinct phases, reflecting the gradual refinement of urban planning concepts and ecological awareness. These stages include the Pastoral City, Landscape Garden City, Garden City, Forest City, Ecological City, and Park City, fully reflecting the gradual refinement of urban planning concepts and ecological awareness.

The Park City, as the most advanced form of the Landscape Garden City concept, adheres to a people-centered approach. It aims to advance the modernization of urban governance and high-quality development, offering a new pathway for China’s rapid urbanization. At its essence is the seamless fusion of urban and natural ecology, with the goal of creating a high-quality living environment that harmonizes ecological conservation with public well-being projects. It does not replace concepts such as Urban Park, Garden City, Ecological City, Landscape Garden City, Forest City, Sponge City, or Green Infrastructure; rather, it intricately interlinks with them while emphasizing distinct priorities. Embracing Ecological City principles as its conceptual framework, the Park City transcends mere theory to embody practical implementation. Drawing inspiration from the communal spaces of the Garden City, it accentuates the symbiotic relationship between urban landscapes and nature. It integrates the esthetic experience of the Landscape Garden City, the ecological foundation of the Forest City, the structural network of Green Infrastructure, and the water-resilience systems of the Sponge City. Ultimately, through systematic deliberation, the Park City synthesizes the strengths of these elements, striving toward the ideal urban archetype of “poetic dwelling”.

In 2018, General Secretary Xi Jinping initially introduced the notion of the “Park City” while evaluating Chengdu’s Tianfu New Area [1], and designated Chengdu as the inaugural pilot city in China to execute the “Park City” policy [2]. As a pioneering urban development model in the new era, the Park City prioritizes ecological civilization [3], which provides a new pathway for Chinese cities amid rapid global urbanization [4]. The Park City concept is rooted in Ebenezer Howard’s “garden city” theory, which dates back to 1898 and has become the foundational theory for the Park City approach [5]. In 2019, London, UK, adopted the “National Park City” framework to guide its urban development [6], which provided practical experience for the Park City’s development in China.

As the fusion of the “park” and “city” systems [7], the Park City construction emphasizes the harmonious integration of ecological, living, and production spaces, embodying people-centered development principles [8]. (1) In terms of conceptual theory, scholars have defined the Park City from multiple perspectives, including its strategic significance [9] and the four dimensions of “public”, “garden”, “city”, and “urban” [10]. They define it as a city with a park-shaped spatial structure as its framework [11], which promotes the green development of the city [12]. (2) Evaluation systems are mainly based on theories such as landscape ecology, human settlement environment [13], and social equity [14] to propose evaluation frameworks for the Park City, covering aspects like park typology [11], service levels [15], and accessibility [16]. (3) In terms of implementation pathways, Hubei Province issued the first local standard in China, the “Guidelines for Park City Construction”, in 2019 [17]. Scholars have explored the implementation path of Park City construction through the basic framework [18], typical characteristics [19], value dimensions [20,21,22], and integration with the urban development system of Park City construction [16].

The Park City model exemplifies an innovative approach to urban development in China [23]. Currently, projects such as the Chengdu International Garden City and the Shanghai Suburban Garden City embody the prototype of the Park City notion [24]. The construction of a Park City places a high priority on biodiversity and ecosystem integrity, which are intrinsically linked to the pattern of land use and the landscape. Land use modifications result from a combination of natural processes and anthropogenic factors [25,26,27]. The land use landscape pattern is a crucial indicator for evaluating regional land resource utilization [28,29]. With the application of GIS platforms and landscape structure analysis tools, such as Fragstats, researchers have developed a multidimensional landscape pattern analysis system. This system reveals the evolution rules of land surface cover through land use type transfer matrices [30,31,32,33,34,35,36], quantifies spatial heterogeneity characteristics using landscape pattern indices [37,38,39,40,41,42,43,44,45], evaluates habitat connectivity in combination with ecological network analysis [27], and establishes the ecological security index [46,47] and sensitivity evaluation system [48,49] for environmental carrying capacity assessment. For dynamic simulation, researchers have integrated spatially explicit models such as the CA–Markov model [50,51] and the FLUS model [52], coupled with the Geo-Detector for driving force analysis [53,54,55], to construct a research framework for landscape pattern evolution featuring “current situation assessment–mechanism analysis–scenario prediction.” These methodological advancements provide quantitative decision-making support for urban space optimization and the selection of sustainable development paths.

The construction of a Park City requires a well-designed land use plan to ensure sufficient green spaces and public areas, enhance forest land quality [3], increase green space ratios, and restore habitat quality [49]. This benefits the overall livability and environmental quality of the city. According to statistics from the Shenyang Statistical Yearbook, as of the conclusion of 2020, Shenyang’s built-up areas achieved a green coverage rate of 40.82%, with 14.00 m² of park green space per capita. Shenyang stands out as a leading example for revitalizing old industrial cities in northern China, ranking highest in the region for key green metrics. In November 2021, Shenyang was designated as one of the initial urban renewal pilot cities in China. Leveraging its robust ecological foundation and cultural resources, Shenyang is a typical representative of a Park City with northern characteristics. Through strategic initiatives such as the Implementation Plan for Recent Greening of Shenyang Park City Construction (2021), the Overall Planning of Shenyang Land Space (2021–2035), the Shenyang City North Characteristic Park City Construction Plan (2022), and the Shenyang City Action Program for Building Northeast Asia Internationalization Center City (2025–2035), Shenyang aims to establish the “northern characteristic Park City.” This initiative seeks to meet the aspirations of residents for an improved quality of life while reflecting the unique characteristics of the northeast region.

Nonetheless, challenges remain, such as low urbanization quality, uneven green space distribution, and the compression of green spaces due to rapid urban expansion. The evaluation index system for Park City construction typically includes green space area, ecological service functions, landscape quality, traffic connectivity, and community participation. These indicators assess the effectiveness of the park system and the optimal utilization of urban land. Changes in urban land use patterns influence the process of urbanization [56] and the direction of Park City development, offering a path to address the challenges faced by industrial cities and improve public well-being. Currently, many scholars’ studies focusing on Shenyang primarily concentrate on analyzing land use and landscape patterns using RS and GIS technologies [57], alongside methodologies such as land use transfer matrices [58], moving window methods [59], and the ecological contribution ratio of land use change [60]. However, a notable gap exists in Shenyang’s land use patterns within the framework of Park City construction. Addressing this gap, this study comprehensively analyzes the progression of land utilization and ecological environmental patterns in the primary urban region of Shenyang from 2000 to 2020, specifically concentrating on Park City construction. The integration of the seven preliminary evaluation indicators for the Park City with the land use evolution pattern reveals challenges in Shenyang’s Park City development, including limited land resources and uneven green space distribution. This study suggests that future strategies for northern Park City planning should align with Shenyang’s regional characteristics, focusing on three key dimensions: comprehensive ecological planning in urban renewal, public governance throughout the life cycle with community involvement, and multi-scale design tailored to all age groups based on public needs. The findings offer optimization pathways and management approaches to guide the establishment of ecological civilization in high-density urban environments, providing valuable insights for future development.

2. Materials and Methods

2.1. Overview of the Study Area

Shenyang, the capital of Liaoning Province (Figure 1), is situated in the southern region of northeast China [61]. It is on the Liaohe Plain, where the mountainous hills in the northeast and southeast exist, and it serves as part of the extension of the Liaodong Hills [62]. This study focuses on the primary urban region of Shenyang City as the subject of investigation. The main urban area of Shenyang is the epicenter and is a typical representative of Shenyang’s urban development; it functions as the center of Shenyang’s politics, economy, and culture, while also encompassing the abundant history and culture.

2.2. Data Sources and Processing

This study obtained land use data for the main urban area of Shenyang City for the years 2000, 2005, 2010, 2015, and 2020. The data were acquired from remote sensing monitoring of land use types at a 30 m resolution, supplied by the Resources and Environmental Science Data Center (RESDC) of the China Academy of Sciences (available at https://www.resdc.cn). The overall accuracy of the land use data was reported at 88.95%, with Kappa calculation results ranging from −1 to 1, where coefficients exceeding 0.8 indicated high similarity.

Data integration was conducted with ArcGIS 10.8 software in accordance with the Classification Standard of Land Use Status (GB/T21010-2017) [63]. The data were categorized into six principal land categories, cultivated land, forest land, grassland, water area, construction land, and unutilized land, according to the characteristics of land resources and their utilization methods (Figure 2). Cultivated land is the most widely spread, and forest land and grassland are primarily located in the eastern section of the principal metropolitan region of Shenyang City.

2.3. Research Methods

2.3.1. Land Use Transfer Matrix Model

The land use type conversion matrix model provides a comprehensive representation of the transitions between distinct land cover categories [1]. This approach elucidates the magnitude and directionality of conversions between two land cover classes over the study period. It is a valuable tool for analyzing the evolutionary dynamics of diverse urban green space types [64] and assessing land use changes in the core urban area of Shenyang.

Using ArcGIS10.8 software to mask the data of five periods, reclassification, and raster calculation, we can calculate the change, like metadata of land use and cover types within the primary metropolitan region of Shenyang City. Subsequently, data processing is carried out to derive the land use/cover transfer matrices of the main urban area of Shenyang City in each period, which are classified and summarized to precisely quantify the area of different land use categories. The mathematical calculation formula is expressed in Equation (1):

$${\mathrm{A}}_{\mathrm{i}\mathrm{j}}=\left[\begin{array}{c}{\mathrm{A}}_{11}{\mathrm{A}}_{12}\cdots {\mathrm{A}}_{1\mathrm{n}}\\ {\mathrm{A}}_{21}{\mathrm{A}}_{22}\cdots {\mathrm{A}}_{2\mathrm{n}}\\ ⋮⋮⋮\\ {\mathrm{A}}_{\mathrm{n}1}{\mathrm{A}}_{\mathrm{n}2}\cdots {\mathrm{A}}_{\mathrm{n}\mathrm{n}}\end{array}\right],$$

(1)

where A denotes the land use area; n is the number of land use types; i and j denote the land use types at the beginning and at the end of the study, respectively; and A_ij denotes the area transitioned from type i to type j during the study period.

2.3.2. Single Land Use Dynamics

The single land use dynamic degree can indicate the pace of change among various land use categories during a specific interval, thereby intuitively reflecting the dynamic changes and the rate of change for each land use category [65]. Equation (2) is the formula for calculating the alteration in the attitude of a single land use motive:

$$\mathrm{K}=\left({\mathrm{U}}_{\mathrm{j}}-{\mathrm{U}}_{\mathrm{i}}\right)/{\mathrm{U}}_{\mathrm{i}}\times 1/\mathrm{T}\times 100\%,$$

(2)

where K represents the annual variation in the attitude of a land category within a specified time period; ${\mathrm{U}}_{\mathrm{i}}$ and ${\mathrm{U}}_{\mathrm{j}}$, respectively, denote the area of the land category at the beginning and the end of this period, in km²; and T indicates the duration of the study, expressed in years. Fluctuations in the change in the value of K reflect the number of land categories that have been transformed into alternative land categories.

2.3.3. Comprehensive Land Use Dynamics

The comprehensive land use degree denotes the total pace of land use alterations within a specific area. The relevance resides in illustrating the intensity of regional land use alteration; the higher value indicates the more profound shift in land use dynamics [66,67,68]. The formula for the change in the attitude of integrated land use dynamics is expressed in Equation (3):

$$\mathrm{M}=\left[{\displaystyle \frac{\sum _{i=1}^n∆{\mathrm{U}}_{\mathrm{i}-\mathrm{j}}}{2\sum _{i=1}^n{\mathrm{U}}_{\mathrm{i}}}}\right]\times {\displaystyle \frac{1}{\mathrm{T}}}\times 100\%,$$

(3)

where M signals the comprehensive land use dynamic degree; ${\mathrm{U}}_{\mathrm{i}}$ represents the area of the ith land use type at the beginning of the study period, km²; $∆{\mathrm{U}}_{\mathrm{i}-\mathrm{j}}$ is the absolute value of the area of land transitioned from the ith type to the jth type during the study period; and T indicates the duration of the study period, in years.

2.3.4. Center of Gravity Migration Model Construction

The evolution of landscape types may be described by the landscape center of gravity transfer model, which is an important means to study the regional spatial aggregation and development law and can effectively describe the transfer trend of various landscape types. The calculation formulas (Equations (4) and (5)) are as follows:

$$X_d={\sum }_{f=0}^n\left(C_{df}\times X_{df}\right)/{\sum }_{f=0}^n{C}_{df},$$

(4)

$$Y_d={\sum }_{f=0}^n\left(C_{df}\times Y_{df}\right)/{\sum }_{f=0}^n{C}_{df},$$

(5)

where $X_d$ and $Y_d$, respectively, represent the longitude and latitude coordinates of the distribution center of gravity of a certain landscape type in the $d$ year. $X_{df}$ and $Y_{df}$ represent the longitude and latitude coordinates of the center of gravity distributed by the $f$ patch in a landscape type in the $d$ year, respectively. $C_{df}$ denotes the area of the $f$ patch in the $d$ year of a certain landscape type.

2.3.5. Landscape Pattern Index Research Methodology

Landscape pattern indices are essential methods in landscape ecology research, serving as key metrics for quantifying both the structural and functional attributes of landscapes. These indices provide intuitive insights into changes in landscape patterns. In this study, 15 landscape indices from six categories at both the patch and landscape levels were selected based on prior research and research objectives [52] (

Table 1. Classification of landscape pattern index.

Index Classification	Landscape Pattern Index	English Abbreviation	Unit	Calculation Level
Area characteristic index	Patch class area	CA	ha	Patch class level
Area characteristic index	Total landscape area	TA	ha	Patch class level/Landscape level
Area characteristic index	Percent of landscape	PLAND	%	Patch class level
Area characteristic index	Largest patch index	LPI	%	Patch class level/Landscape level
Density size and difference index	Number of patches	NP	-	Patch class level/Landscape level
Density size and difference index	Patch density	PD	n/100 ha	Patch class level/Landscape level
Shape index	Landscape shape index	LSI	-	Patch class level/Landscape level
Aggregation/dispersion index	Aggregation index	AI	%	Patch class level/Landscape level
Aggregation/dispersion index	Interspersion and juxtaposition index	IJI	%	Patch class level
Aggregation/dispersion index	Contagion index	CONTAG	%	Landscape level
Aggregation/dispersion index	Diversion index	DIVISION	%	Landscape level
Connectivity index	Cohesion index	COHESION	-	Patch class level/Landscape level
Diversity index	Patch richness index	PR	-	Landscape level
Diversity index	Shannon diversity index	SHDI	-	Landscape level
Diversity index	Shannon evenness index	SHEI	-	Landscape level

and

Table A1. Calculation formulas and significance of landscape pattern indices.

Calculation Level	Landscape Pattern Index	English Abbreviation	Meaning	Calculation Formula
Patch class level	Patch class area	CA	The patch class area (CA) quantifies the compositional structure of the landscape, that is, reflecting how much of the landscape area is composed of this patch type. As CA approaches 0, it indicates that the patch type is increasingly rare in the landscape; when CA equals the total landscape area (TA), the landscape is entirely dominated by a single patch type.	$I_{CA}=\sum _{j=1}^n{a}_{ij}\times {\displaystyle \frac{1}{10000}}$, where $a_{ij}$ is the area of the patch $ij$.
Patch class level	Number of patches	NP	The number of patches (NP) serves as a straightforward metric for landscape heterogeneity and fragmentation. A higher NP indicates a greater degree of landscape fragmentation.	$I_{NP}=n_i$, where $n_i$ is the number of patches included in patch type $i$ in the landscape.
Patch class level	Patch density	PD	Patch density (PD) is an index that reflects the number of patches per unit area.	$I_{PD}={\displaystyle \frac{n_i}{A}}\times 10000\times 100$, where $n_i$ is the number of patches contained in patch type $i$ in the landscape; $A$ is the area of the entire landscape, including the background that exists within the landscape.
Patch class level	Percent of landscape	PLAND	The percentage of landscape (PLAND) is an index used to measure the relative abundance ratio of a patch type within a landscape.	$I_{PLAND}=p_i={\displaystyle \frac{\sum _{j=1}^n{a}_{ij}}{A}}\times 100$, where $p_i$ represents the area of individual patches of that type $i$; $a_{ij}$ is the area of patches $ij$; $A$ is the area of the entire landscape, including the background that exists within the landscape.
Patch class level	Largest patch index	LPI	The largest patch index (LPI) quantifies landscape dominance, with higher values indicating greater dominance.	$I_{LPI}={\displaystyle \frac{a_{max}}{A}}\times 100$, where $a_{max}$ is the area of the largest patch within a landscape type or the entire landscape, and $A$ is the total area of that landscape type or the entire landscape.
Patch class level	Landscape shape index	LSI	The landscape shape index (LSI) quantifies the complexity of a landscape’s shape. A higher LSI value indicates a more intricate landscape configuration.	$I_{LSI}={\displaystyle \frac{0.25\mathrm{P}}{\sqrt{A}}}$, where $A$ is the total area of a certain landscape type or the total area of the landscape. $P$ is the total length of a landscape type boundary or the overall landscape boundary.
Patch class level	Interspersion and juxtaposition index	IJI	The interspersion and juxtaposition index (IJI) quantifies the ratio of actual to maximum spread for a set of patch types.	$I_{IJI}={\displaystyle \frac{-{\sum }_{k=1}^m\left[\left(\frac{e_{ik}}{\sum _{k=1}^m{e}_{ik}}\right)ln\left(\frac{e_{ik}}{\sum _{k=1}^m{e}_{ik}}\right)\right]}{ln(\mathrm{m}-1)}}\times 100$, where $e_{ik}$ is the total edge length between patch $i$ and patch $k$; $m$ is the number of patch types in the landscape.
Patch class level	Cohesion index	COHESION	The patch cohesion index (COHESION) assesses the natural connectivity among these patch types. Patch cohesion is particularly sensitive to aggregation levels below the penetration threshold. As patch distribution becomes more aggregated, the patch cohesion index rises, reflecting increased natural connectivity.	$I_{COHESION}=\left[1-{\displaystyle \frac{\sum _{j=1}^n{p}_{ij}^{*}}{\sum _{j=1}^n{p}_{ij}^{*}\sqrt{a_{ij}^{*}}}}\right]{\left[1-{\displaystyle \frac{1}{\sqrt{Z}}}\right]}^{-1}\times 100$, where $p_{ij}^{*}$ is the perimeter of the patch $\mathrm{i}\mathrm{j}$; $a_{ij}^{*}$ is its area; and $Z$ is the total number of grids in the landscape. Total landscape area ($Z$) excludes interior background.
Patch class level	Aggregation index	AI	The aggregation index (AI) quantifies the likelihood of adjacent placement of various patch types, encompassing similar nodes within the same categories, on a landscape map.	$I_{AI}=\left[{\displaystyle \frac{g_{ij}}{max\to g_{ii}}}\right]\times 100$, where $g_{ij}$ is the number of nodes between the patch type $i$ cells based on the haploidy method; max $g_{ii}$ is the maximum number of nodes between the patch type $i$ cells based on the haploidy method; and $I_{AI}$ is the actual value of $g_{ij}$ divided by the maximum value of $g_{ii}$ when the type is clustered together to the maximum extent.
Landscape level	Total landscape area	TA	The total landscape area (TA) denotes the overall area of the landscape, serving as a fundamental parameter for deriving numerous other metrics.	$I_{TA}=A\times {\displaystyle \frac{1}{10000c}}$, where $A$ is the total landscape area.
Landscape level	Number of patches	NP	The number of patches (NP) represents the total count of patches within the landscape, excluding those in the background and on the boundary.	$I_{NP}=N$, where $N$ is the total number of patches in the landscape.
Landscape level	Patch density	PD	The patch density (PD), defined as the number of patches per unit area, serves as an indicator of landscape fragmentation.	$I_{PD}={\displaystyle \frac{N}{A}}\times 10000\times 100$, where $N$ is the total number of patches in the landscape; $A$ is the total area of the landscape. The unit is units/KM2.
Landscape level	Largest patch index	LPI	The largest patch index (LPI) represents the proportion of the largest patch area relative to the entire landscape area, serving as a metric for assessing the dominance of the largest patch within the landscape.	$I_{LPI}={\displaystyle \frac{max\left(a_{ij}\right)}{A}}\times 100$, where $a_{ij}$ is the area of the patch $ij$; $A$ is the total area including the landscape interior background.
Landscape level	Landscape shape index	LSI	The landscape shape index (LSI) offers a standardized measure of total edge or edge density, which can be adjusted based on landscape size.	$I_{LSI}={\displaystyle \frac{0.25E}{\sqrt{A}}}$, where $E$ is the patch perimeter; $A$ is the patch area.
Landscape level	Contagion index	CONTAG	The contagion index (CONTAG) indicates the degree of aggregation or the spatial trend of various patch types within the landscape, thereby describing the extent of landscape heterogeneity. A lower CONTAG value signifies a more even distribution of landscape elements and reduced landscape heterogeneity.	$I_{CONTAG}=\left[1+{\displaystyle \frac{{\sum }_{i=1}^m{\sum }_{i=1}^m\left|(p_i)\left\{\frac{g_{ik}}{{\sum }_{k=1}^m{g}_{ik}}\right\}\right|\left|ln(p_i)\left\{\frac{g_{ik}}{{\sum }_{k=1}^m{g}_{ik}}\right\}\right|}{2ln\left(m\right)}}\right]\times 100$, where $p_i$ is the area proportion of patch type $i$ in the landscape, $g_{ik}$ is the number of nodes between patch type $i$ and patch type $k$ landscape based on the doubling method, and $m$ is the number of landscape types.
Landscape level	Interspersion and juxtaposition index	IJI	The interspersion and juxtaposition index (IJI) quantifies the dispersion or mixing attributes of patch types within the landscape.	$I_{IJI}={\displaystyle \frac{-{\sum }_{i=1}^m{\sum }_{k=i+1}^m\left[\left(\frac{e_{ik}}{E}\right)ln\left(\frac{e_{ik}}{E}\right)\right]}{ln\left\{0.5\left[m(m-1)\right]\right\}}}\times 100$, where $e_{ik}$ is the total length of edges in the landscape between patches $i$ and $k$; $E$ is the total length of edges in the entire landscape excluding background; $m$ is the number of patch types in the landscape.
Landscape level	Cohesion index	COHESION	The patch cohesion index (COHESION) assesses the inherent connectivity of the relevant patch types. It is responsive to the level of aggregation below the penetration threshold. As the patch type becomes more aggregated in distribution, the patch cohesion index increases as the natural connectivity increases.	$I_{COHESION}=\left[1-{\displaystyle \frac{\sum _{j=1}^n{p}_{ij}}{\sum _{j=1}^n{p}_{ij}\sqrt{a_{ij}}}}\right]{\left[1-{\displaystyle \frac{1}{\sqrt{Z}}}\right]}^{-1}\times 100$, where $p_{ij}$ is the perimeter of the patch $ij$; $a_{ij}$ is its area; $Z$ is the total number of grids in the landscape. Total landscape area ($Z$) excluding interior background.
Landscape level	Patch richness index	PR	The patch richness index (PR) offers a basic evaluation of landscape composition but lacks the ability to indicate the comparative richness among various patch types.	$I_{PR}=m$, where $m$ is the number of patch types in the landscape, excluding patch types in landscape boundaries.
Landscape level	Shannon diversity index	SHDI	Shannon’s diversity index (SHDI) quantifies landscape heterogeneity by considering the variety of landscape elements and their proportional changes. A higher SHDI value indicates a more evenly distributed landscape and lower heterogeneity.	$I_{SHDI}=-{\sum }_{i=1}^m(p_i\times {lnp}_i)$, where $p_i$ is the percentage of area occupied by landscape type $i$ and $m$ is the number of landscape types.
Landscape level	Shannon evenness index	SHEI	Shannon’s evenness index (SHEI) assesses landscape heterogeneity based on the proportional distribution of different landscape types within the total area. A higher SHEI value signifies a more balanced landscape distribution and reduced heterogeneity.	$I_{SHEI}={\displaystyle \frac{-{\sum }_{\mathrm{i}=1}^{\mathrm{m}}(p_i\times {lnp}_i)}{\mathrm{l}\mathrm{n}m}}$, where $p_i$ is the percentage of area occupied by landscape type $i$; $m$ is the number of landscape types.
Landscape level	Aggregation index	AI	The aggregation index (AI) quantifies the likelihood of neighboring patch types, including similar nodes within the same type, on a landscape map.	$I_{AI}=\left[{\sum }_{i=1}^m{\displaystyle \frac{g_{ij}}{\mathrm{m}\mathrm{a}\mathrm{x}\to g_{ii}}}{p}_i\right]\times 100$, where $g_{ij}$ is the number of nodes between cells of patch type $i$ based on the haploidy method; max $g_{ii}$ is the maximum number of nodes between cells of patch type $i$ based on the haploidy method; $p_i$ is the percentage of area occupied by landscape type $i$; $I_{AI}$ is the actual value of $g_{ij}$ divided by the maximum value of $g_{ii}$ when the type is clustered together to the maximum extent.
Landscape level	Diversion index	DIVISION	The landscape division index (DIVISION) assesses the extent of isolation between individual patches within landscape types.	$I_{DIVISION}=1-\sum _{i=1}^m\sum _{j=1}^n{\left({\displaystyle \frac{a_{ij}}{A}}\right)}^2$, where $a_{ij}$ is the area of the patch $ij$; $A$ is the area of the entire landscape.

). Through a comprehensive multi-index analysis, the ecological compatibility of land use in the central metropolitan region of Shenyang was systematically evaluated. These indices were computed using Fragstats 4.2 software and relevant mathematical formulas.

3. Results

3.1. Transformational Land Use Change

3.1.1. Land Use Changes

Based on the GIS platform, a spatial overlap analysis was carried out for five periods of land use data in the primary urban region of Shenyang City from 2000 to 2020. The results showed the alterations in area for different land use types and the structural characteristics of land use changes in the primary urban region of Shenyang City over these years (Figure 3).

From an overall perspective, the urban expansion of Shenyang has primarily involved the conversion of cultivated and construction land, with minimal allocation to ecological spaces (Figure 4 and Figure 5). This pattern highlights how the rapid urbanization in Shenyang City has prioritized development at the expense of conservation, leading to the restricted availability of ecological spaces. By 2020, cultivated land and construction land constituted the dominant land use types, comprising 57.35% and 29.19% of the overall area, respectively. By comparison, the aggregate area of forest land, grassland, and water area constituted merely 12.88% (

Table 2. Areas of land use/cover types and their proportions in the main urban area of Shenyang.

Land Use Type	2000		2005		2010		2015		2020
	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)
Cultivated Land	2616.97	71.53	2561.14	70.00	2348.54	64.19	2317.23	63.34	2098.31	57.35
Forest Land	358.60	9.80	354.15	9.68	327.51	8.95	327.68	8.96	319.72	8.74
Grassland	50.09	1.37	49.47	1.35	29.76	0.81	29.73	0.81	39.86	1.09
Water Area	46.93	1.28	46.51	1.27	99.02	2.71	99.06	2.71	111.48	3.05
Construction Land	577.22	15.78	639.02	17.47	842.28	23.02	873.43	23.87	1068.06	29.19
Unutilized Land	8.73	0.24	8.26	0.23	11.43	0.31	11.41	0.31	21.11	0.58
Total	3658.54	100	3658.54	100	3658.54	100	3658.54	100	3658.54	100

). This distribution diverges significantly from the ideal “three-life integration” model, which emphasizes the harmonious balance between productive, residential, and ecological areas in the Park City setting.

During the 20-year period, with the increase of urban development and construction, the construction land expanded by 490.84 km², rising from 15.78% to 29.19%. This expansion primarily occurred as the city expanded outward from its center. Concurrently, the water demand of the Hun River, which flows through the city center, has risen. Efforts to establish waterfront parks and slow roads along the river have led to the 64.55 km² (1.77%) increase in the water area. In contrast, the original areas of cultivated land, forest land, and grassland have been encroached upon, resulting in a decrease. The cultivated land area has shrunk significantly, decreasing by 518.66 km² (14.18%). The forest land and grassland areas have also decreased by 38.88 km² (1.06%) and 10.23 km² (0.28%), respectively. This indicates that urbanization has come at the expense of natural ecological spaces. The “ecological priority” principle of the Park City concept necessitates ecological restoration to compensate for this loss. The increase in water area through the construction of waterfront parks exemplifies local efforts towards this ecological restoration.

3.1.2. Land Use Transfer Matrices

In the last two decades, substantial alterations have transpired in the different types of land use within the central urban region of Shenyang. There has been a reduction in cultivated land and a gradual expansion of constructed land, accompanied by an incremental enhancement in the efficiency of utilizing unutilized land resources. Meanwhile, variations in the utilization degree of natural assets, including forest land, grassland, and water area, show various levels of fluctuation. The transformation dynamic trend between different land use types can be analyzed in detail through establishing land use transfer matrices (Figure 6 and Appendix A 

Table A2. Land use/cover transfer matrices in the main urban area of Shenyang.

Land Use Type	Year Period	Cultivated Land (km2)	Forest Land (km2)	Grassland (km2)	Water Area (km2)	Construction Land (km2)	Unutilized Land (km2)
Cultivated Land	2000–2005	2550.2616	2.1726 (3.26%)	0.3897 (0.58%)	0.0531 (0.08%)	64.0611 (96.03%)	0.0351 (0.05%)
	2005–2010	2178.9054	52.2981 (13.68%)	17.7372 (4.64%)	49.5954 (12.98%)	257.7096 (67.42%)	4.8951 (1.28%)
	2010–2015	2310.5115	2.0322 (5.34%)	0.2097 (0.55%)	0.3987 (1.05%)	35.3304 (92.90%)	0.0612 (0.16%)
	2015–2020	2033.2674	11.6226 (4.09%)	10.6110 (3.74%)	23.1642 (8.16%)	229.9248 (80.97%)	8.6355 (3.04%)
	2000–2020	1951.5285	47.4246 (7.13%)	25.8624 (3.89%)	66.2823 (9.96%)	511.659 (76.89%)	14.2164 (2.14%)
Forest Land	2000–2005	3.8511 (55.06%)	351.6075	0.2295 (3.28%)	0.0189 (0.27%)	2.8143 (40.24%)	0.0801 (1.15%)
	2005–2010	58.0293 (58.89%)	255.6207	5.7087 (5.79%)	4.9626 (5.04%)	27.5400 (27.95%)	2.2905 (2.32%)
	2010–2015	1.7784 (77.46%)	325.2177	0.0972 (4.23%)	0.1215 (5.29%)	0.2862 (12.47%)	0.0126 (0.55%)
	2015–2020	14.8869 (64.71%)	304.6734	0.8046 (3.50%)	0.4905 (2.13%)	6.7203 (29.21%)	0.1017 (0.44%)
	2000–2020	56.6667 (53.98%)	253.6146	5.3802 (5.12%)	4.0806 (3.89%)	38.2347 (36.42%)	0.6246 (0.59%)
Grassland	2000–2005	0.9747 (76.75%)	0.2358 (18.57%)	48.8214	0.0018 (0.14%)	0.0576 (4.54%)	0.0000 (0.00%)
	2005–2010	20.3310 (45.83%)	15.6807 (35.35%)	5.1048	0.4050 (0.91%)	7.6527 (17.25%)	0.2943 (0.66%)
	2010–2015	0.1953 (50.70%)	0.1125 (29.21%)	29.3715	0.0063 (1.64%)	0.0675 (17.52%)	0.0036 (0.93%)
	2015–2020	1.4094 (28.31%)	0.6192 (12.44%)	24.7536	0.0720 (1.45%)	2.8782 (57.81%)	0.0000 (0.00%)
	2000–2020	19.3635 (43.08%)	15.0903 (33.57%)	5.1444	0.1611 (0.36%)	10.0017 (22.25%)	0.3303 (0.73%)
Water Area	2000–2005	0.1134 (22.74%)	0.0261 (5.23%)	0.0198 (3.97%)	46.4283	0.3393 (68.05%)	0.0000 (0.00%)
	2005–2010	4.5072 (66.12%)	1.0035 (14.72%)	0.1179 (1.73%)	39.6891	0.9810 (14.39%)	0.2070 (3.04%)
	2010–2015	0.4059 (69.07%)	0.0774 (13.17%)	0.0081 (1.38%)	98.4312	0.0963 (16.39%)	0.0000 (0.00%)
	2015–2020	3.7287 (23.30%)	0.8415 (5.26%)	0.7227 (4.52%)	83.0610	2.2986 (14.37%)	8.4096 (52.56%)
	2000–2020	3.0168 (28.24%)	0.9909 (9.28%)	0.6867 (6.43%)	36.2439	1.2924 (12.10%)	4.6962 (43.96%)
Construction Land	2000–2005	5.3919 (97.78%)	0.1062 (1.93%)	0.0081 (0.15%)	0.0036 (0.07%)	571.7097	0.0045 (0.08%)
	2005–2010	85.0653 (92.81%)	2.8638 (3.12%)	0.9783 (1.07%)	1.7757 (1.94%)	547.3620	0.9738 (1.06%)
	2010–2015	4.2579 (91.56%)	0.2124 (4.57%)	0.0459 (0.99%)	0.1017 (2.19%)	837.6336	0.0324 (0.70%)
	2015–2020	41.9085 (85.07%)	1.8450 (3.74%)	2.9529 (5.99%)	2.2680 (4.60%)	824.1651	0.2916 (0.59%)
	2000–2020	65.2050 (88.08%)	2.5839 (3.49%)	2.6721 (3.61%)	2.8395 (3.83%)	503.1954	0.7281 (0.98%)
Unutilized Land	2000–2005	0.5481 (93.12%)	0.0036 (0.61%)	0.0000 (0.00%)	0.0000 (0.00%)	0.0369 (6.27%)	8.1378
	2005–2010	1.7055 (31.06%)	0.0468 (0.85%)	0.1098 (2.00%)	2.5911 (47.18%)	1.0386 (18.91%)	2.7657
	2010–2015	0.0765 (62.96%)	0.0252 (20.74%)	0.0000 (0.00%)	0.0027 (2.22%)	0.0171 (14.07%)	11.3049
	2015–2020	3.1104 (40.15%)	0.1179 (1.52%)	0.0171 (0.22%)	2.4282 (31.34%)	2.0736 (26.77%)	3.6675
	2000–2020	2.5308 (30.80%)	0.0153 (0.19%)	0.1161 (1.41%)	1.8765 (22.84%)	3.6774 (44.76%)	0.5103

).

During the span of 2000–2020, the most significant land use change in the main urban area of Shenyang City was the transformation of cultivated land into construction land, totaling 511.66 km², which constituted 76.89% of the overall cultivated land transfer. This trend aligns with urban expansion patterns. Conversely, 65.21 km² of construction land reverted to cultivated land, representing 88.08% of the construction land conversion rate, suggesting land idling or inefficient development, such as abandoned areas following unchecked development zone expansions. For Park City development, it is crucial to minimize cultivated land encroachment through intensive land use and brownfield restoration, such as transforming abandoned industrial sites into parks. Furthermore, 56.67 km² of forest land was turned into cultivated land, contributing to 53.98% of the forest land transfer rate. Other notable conversions included 47.42 km² of cultivated land to forest land, 38.23 km² of forest land to construction land, and 25.86 km² of cultivated land to low-coverage grassland, indicating ecological fragmentation by agricultural and development activities. The core issue for the Park City is maintaining “green space connectivity,” yet the data indicate a trend of fragmented ecological land reduction.

The tendency of reciprocal conversion between cultivated land and construction land is pronounced. In addition to construction land, forest land and grassland are frequently converted to cultivated land. The exchange between cultivated land and forest land has essentially reached equilibrium. From 2000 to 2020, the area of cultivated land transitioned to construction land exhibited fluctuating changes, with significant conversions observed during 2005–2010 and 2015–2020, totaling 257.71 km² and 229.92 km², respectively. Concurrently, the area of construction land transferred to cultivated land also showed fluctuations, peaking at 85.07 km² during 2005–2010, which is nearly 20 times greater than that during 2010–2015.

3.1.3. Changes in Land Use Dynamics

In accordance with Equation (2), the land use dynamics in the primary urban region of Shenyang City were calculated (Figure 7 and

Table 3. Single dynamic attitudes to land use change in the main urban area of Shenyang.

Land Use Type	2000–2005		2005–2010		2010–2015		2015–2020		2000–2020
	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)	Area (km2)	Ratio (%)
Cultivated Land	−55.83 24	−0.42 67	−212.5 971	−1.66 02	−31.31 82	−0.26 67	−218.9 142	−1.88 95	−518.6 619	−0.99 10
Forest Land	−4.44 96	−0.24 82	−26.63 82	−1.50 43	0.16 38	0.01 00	−7.95 78	−0.48 57	−38.8 818	−0.54 21
Grassland	−0.62 28	−0.24 87	−19.7 118	−7.96 94	−0.02 43	−0.01 63	10.1 295	6.81 38	−10.2 294	−1.02 11
Water Area	−0.42 12	−0.17 95	52.51 32	22.58 36	0.04 32	0.00 87	12.42 18	2.50 79	64.55 70	6.87 85
Construction Land	61.79 49	2.14 11	203.2 650	6.36 18	31.14 72	0.73 96	194.6 295	4.45 67	490.8 366	4.25 17
Unutilized Land	−0.46 89	−1.07 47	3.16 89	7.67 52	−0.01 17	−0.02 05	9.69 12	16.98 02	12.37 95	7.09 31

). The results demonstrate that from 2000 to 2020, significant differences exist in the single land use cover change (LUCC) dynamics across various land categories. Except for construction land, which consistently exhibits a positive trend, and cultivated land, which consistently shows a negative trend, the other four land categories demonstrate varying degrees of increase or decrease due to uncertainties. The single LUCC dynamics in the principal urban region of Shenyang City, ranked in decreasing order, are as follows: unutilized land, water area, construction land, grassland, cultivated land, and forest land.

Throughout the 20-year duration, the single dynamic degree of cultivated land, forest land, and grassland predominantly exhibited a negative trend. This trend is primarily attributable to the extensive transformation of cultivated land to construction land, the transfer of forest land to both cultivated land and construction land, and the conversion of grassland to cultivated land, forest land, and construction land. During the period of 2000–2005, the main urban area of Shenyang was in the initial stage of urban development, and the fluctuations in land use conversions were relatively minor. However, from 2005 to 2010 and 2015 to 2020, the area of cultivated land decreased rapidly, while the decline slowed during 2010–2015, with the smallest single dynamic degree (−0.27%) observed in this period. Forest land area experienced a marginal increase during 2010–2015 but fluctuated and decreased during other periods, with the most significant decline (−1.5%) occurring from 2005 to 2010. Grassland area initially decreased and then increased, reaching its highest single dynamic degree (−7.97%) during 2005–2010 due to successive exploitation, followed by a positive increase during 2010–2020, peaking at 6.81% from 2015 to 2020. Notably, the area of the water area increased sharply by 52.52 km² with a single dynamic degree of 22.58% during 2005–2010, which is preeminent among all land use categories. This indicates that the construction of the Hunhe Riverside Park has achieved remarkable results. Construction land consistently showed a positive transformation, with the highest single increase of 6.36% occurring from 2005 to 2010, which reflects the accelerated urban expansion. Unutilized land significantly increased due to the large-scale conversion of cultivated and construction land, with a dynamic degree of 16.98% during 2015–2020, likely due to land consolidation or abandonment. This is also an opportunity for the construction of a Park City by converting unutilized land into community parks or ecological corridors to enhance land value.

During the period of 2000–2020, the population of the main urban area of Shenyang City increased from 5,763,100 to 7,490,100. This rapid social development and population growth significantly accelerated the process of land urbanization. Based on Equation (3), the comprehensive land use dynamic degree in the main urban area of Shenyang City was calculated, with the following order from highest to lowest: 2005–2010, 2015–2020, 2000–2005, and 2010–2015 (Figure 8). In general, it is fluctuating up and down, with two peaks in 2005–2010 and 2015–2020, where the comprehensive land use dynamic degrees reached 1.72% and 1.05%, respectively. In the subsequent construction of the Park City, the main urban area of Shenyang City needs to have enhanced ecological service functions in its limited space through vertical greening and park networks.

3.1.4. Landscape Center of Gravity Shift

The Centre of Mean tool in ArcGIS 10.8 was utilized to determine the migration distance and direction of the center of gravity for each landscape type in the study area across the five periods of 2000, 2005, 2010, 2015, and 2020 (Figure 9 and

Table 4. Changes in the center of gravity of each landscape type in the main urban area of Shenyang, 2000–2020.

Year Period	Cultivated Land		Forest Land		Grassland		Water Area		Construction Land		Unutilized Land
	Migratory Direction *	Migration Distance (km)	Migratory Direction	Migration Distance (km)	Migratory Direction	Migration Distance (km)	Migratory Direction	Migration Distance (km)	Migratory Direction	Migration Distance (km)	Migratory Direction	Migration Distance (km)
2000–2005	Northeast 64.2°	0.01 83	East 101.2°	0.29 04	Northeast 54.4°	0.52 85	West 267.8°	0.12 10	Southwest 241.7°	0.11 50	Northeast 50.4°	0.64 51
2005–2010	Northeast 8.4°	0.19 97	East 90.3°	4.45 68	Southwest 232.5°	3.49 60	Southwest 220.6°	10.08 21	Southeast 126.6°	0.18 13	Northeast 44.1°	4.46 29
2010–2015	Southwest 234.6°	0.10 94	Southeast 127.1°	0.01 04	South 185.3°	0.01 81	South 175.2°	0.03 13	Northeast 35.1°	0.31 52	West 270.5°	0.07 14
2015–2020	Southeast 150.1°	0.18 98	East 107.8°	0.26 21	Northeast 31.0°	5.33 33	Northwest 336.0°	1.58 96	Southwest 213.0°	0.52 87	Northeast 31.2°	32.60 12
2000–2020	Southeast 133.6°	0.51 72	East 91.9°	5.01 97	Northwest 331.6°	9.37 59	Southwest 239.0°	11.82 40	South 179.7°	1.14 02	Northeast 45.6°	37.78 06

* Eight directions are determined based on the angle magnitudes: 337.5°–22.5° represents north; 22.5°–67.5° represents northeast; 67.5°–112.5° represents east; 112.5°–157.5° represents southeast; 157.5°–202.5° represents south; 202.5°–247.5° represents southwest; 247.5°–292.5° represents west; 292.5°–337.5° represents northwest.

).

Between 2000 and 2020, the centroid shifts in various landscape types in Shenyang City’s main urban area revealed notable spatial differentiation. Urban expansion encroaching on suburban farmland and promoting outer suburban development caused the cultivated land centroid to move slightly southeast by 0.51 km. The greening of the eastern mountains and the protection of Qipanshan Scenic Area resulted in a 5.01 km eastward migration of the forest land centroid. The ecological restoration of the Liaohe Plain and urban expansion jointly prompted the centroid of grassland to migrate 9.37 km to the northwest. The water system governance in the southwest and the construction of Hunhe Central Park led to the significant southwestward migration of the centroid of water area by 11.82 km. Under the “Southward Expansion” strategy, the centroid of construction land first fluctuated northeastward and then southwestward due to new town construction and urban renewal. The encroachment due to urban expansion and ecological restoration led to a substantial 37.78 km migration of the centroid of unutilized land to the far northeast suburbs.

The spatial development strategy of “expanding the urban footprint southward, cultivating forested areas eastward, harnessing water resources westward, and restoring grasslands northward” closely aligns with the Park City paradigm, exemplifying the integration of industrial, urban, and ecological elements. However, it is necessary to be vigilant against the risk of ecological overloading in the outer suburbs and strengthen sustainable management across the entire region.

3.2. Landscape Pattern Index

3.2.1. Landscape Index Analysis at the Patch Type Level

The landscape pattern metrics of diverse land types in the main urban region of Shenyang City exhibited distinct variations across the five periods of 2000, 2005, 2010, 2015, and 2020 (Figure 10 and

Table 5. Dynamics of landscape pattern indices at the level of the patch type in the main urban area of Shenyang, 2000–2020.

Year	Land Use Type	CA (ha)	PLAND (%)	NP	PD (n/100 ha)	LPI (%)	LSI	IJI	COHESION	AI
2000	Cultivated Land	261,692.37	71.5301	73	0.02	42.6781	28.8027	68.3011	99.9644	98.3683
	Forest Land	4692.69	1.2827	47	0.0128	0.5973	15.6608	53.7139	98.9987	93.5468
	Grassland	57,722.40	15.7776	861	0.2353	5.8928	31.8015	18.7907	98.1170	96.1484
	Water Area	5009.13	1.3692	181	0.0495	0.1111	23.0254	52.175	95.8014	90.6209
	Construction Land	35,860.14	9.8019	273	0.0746	1.9917	32.4505	40.1305	99.2058	95.0075
	Unutilized Land	872.64	0.2385	10	0.0027	0.0661	7.0254	45.7849	97.2828	93.8161
2005	Cultivated Land	256,113.72	70.0044	126	0.0344	41.4174	31.1313	66.4349	99.9614	98.2127
	Forest Land	4650.57	1.2712	52	0.0142	0.5958	15.7604	55.4782	98.9947	93.4727
	Grassland	63,901.89	17.4665	859	0.2348	6.5951	34.032	18.9879	98.7869	96.0735
	Water Area	4946.85	1.3521	180	0.0492	0.0955	23.6951	52.3578	95.7717	90.276
	Construction Land	35,415.18	9.6801	286	0.0782	2.0027	33.7347	41.0491	99.2025	94.7716
	Unutilized Land	825.75	0.2257	11	0.003	0.0659	6.8333	50.6951	97.2210	93.8319
2010	Cultivated Land	9901.89	2.7065	74	0.0202	1.5976	16.9187	56.9775	99.3443	95.1818
	Forest Land	234,854.37	64.1934	103	0.0282	29.1269	34.0062	62.4538	99.9417	97.9554
	Grassland	84,228.39	23.0224	1104	0.3018	9.5928	36.0537	26.4263	98.9631	96.3724
	Water Area	32,751.36	8.952	257	0.0702	2.2001	32.2154	44.5941	99.3213	94.8146
	Construction Land	1142.64	0.3123	31	0.0085	0.0752	9.3496	61.5642	96.7705	92.5018
	Unutilized Land	2975.67	0.8133	100	0.0273	0.0564	16.9203	67.2483	95.5505	91.1879
2015	Cultivated Land	9906.21	2.7077	75	0.0205	1.5569	16.8599	57.5176	99.3189	95.2017
	Forest Land	231,722.46	63.3374	129	0.0353	28.4393	34.1592	61.9966	99.9402	97.9316
	Grassland	87,343.11	23.8737	1096	0.2996	9.7198	35.8432	26.8628	99.0541	96.4582
	Water Area	32,767.74	8.9565	261	0.0713	2.2043	31.7266	45.4837	99.3213	94.8984
	Construction Land	1141.47	0.312	31	0.0085	0.0751	9.3097	61.7287	96.7750	92.5298
	Unutilized Land	2973.24	0.8127	104	0.0284	0.0565	16.7967	67.4296	95.5409	91.2492
2020	Cultivated Land	11,148.39	3.0472	90	0.0246	1.5825	17.5412	71.6622	99.2847	95.2862
	Forest Land	2110.59	0.5769	30	0.0082	0.2467	6.355	81.3569	98.2242	96.4717
	Grassland	209,831.13	57.3537	103	0.0282	25.9337	35.9551	60.2458	99.9342	97.7091
	Water Area	106,806.06	29.1936	1050	0.287	13.4458	33.6159	28.4687	99.3479	97.0029
	Construction Land	31,971.96	8.739	239	0.0653	2.1899	31.8701	45.6447	99.3351	94.8078
	Unutilized Land	3986.19	1.0896	96	0.0262	0.2657	16.6983	74.9581	97.2145	92.5035

):

From 2000 to 2020, the overall expanse of cultivated land, forest land, and grassland in the main urban area of Shenyang City exhibited a decreasing trend, with cultivated land showing the most pronounced decline. The cultivated land area within the Shenyang metropolitan region has undergone a marked decline, accompanied by increases in the number of patches (NP) and patch density (PD), the continuous decrease in the aggregation index (AI), and the increase in the landscape shape index (LSI). These landscape metrics collectively indicate that the cultivated land has experienced persistent shrinkage and fragmentation due to urban expansion. This trend poses significant risks to food security, necessitating proactive measures to protect the remaining cultivated land and delineate permanent basic farmland. For the fragmented patches that remain, future transformation into agricultural parks or community farms could integrate these areas into a broader green infrastructure network. Although the greening patches in forest land slightly increased due to compensation, the overall expanse has diminished, and the aggregation index (AI) has weakened, indicating fragmentation. To restore connectivity, the construction of ecological corridors is essential. Over the past two decades, the grassland area has decreased, the landscape shape index (LSI) has declined, and the aggregation index (AI) has increased, indicating that the grassland shape has become more regular and the artificial management has been strengthened.

These reveal the differential responses of ecological elements under urban expansion. Moving forward, strategies such as the establishment of agricultural parks and ecological corridors could be adopted to bolster the recreational and ecological functions of core areas, thereby providing a transformation path for coordinating ecological protection and urban development.

From 2000 to 2020, the aggregate expanse of water area in the main urban area of Shenyang City showed an increasing trend, with a particularly significant rise after 2010. The number of patches (NP) and patch density (PD) increased, with the number of patches (NP) growing from 47 to 90. The interspersion and juxtaposition index (IJI) and aggregation index (AI) also showed an increasing tendency, reflecting that the distribution of water area became more uniform and dispersed. This change enhances the landscape’s hydrophilicity and improves citizens’ access to urban leisure spaces, thereby significantly advancing the development of a “blue-green symbiosis” system.

From 2000 to 2020, the overall area of construction land in the main urban area of Shenyang City showed an obvious expansion trend, increasing from 57,722.4 hectares in 2000 to 106,806.06 hectares in 2020, a growth of approximately 85%. Specifically, the number of patches (NP), patch density (PD), and landscape shape index (LSI) all increased substantially, while the aggregation index (AI) declined. This pattern reflected the scattered development of industrial areas, residential zones, and transportation infrastructure during the urbanization process, which has weakened ecological connectivity. In the future development of Park Cities, it is crucial to transform the pressure for expansion into an opportunity to enhance ecological function. This can be achieved by constructing ecological corridors and integrating green spaces such as greenways and pocket parks, thereby mitigating the detrimental effects of landscape fragmentation.

From 2000 to 2020, the gross area of unutilized land in the main urban area of Shenyang City revealed a slow growth tendency. The number of patches (NP), the landscape shape index (LSI), and the patch cohesion (COHESION) significantly improved. This was the result of the combined effects of the fragmentation pressure of urban expansion and the systematic integration of ecological restoration. Looking ahead, the remediation of brownfield sites and the micro-renewal of street-level green spaces will facilitate the transformation of low-efficiency land into functional areas, thereby highlighting the ecological and social benefits in the development of the Park City.

3.2.2. Landscape Index Analysis at the Landscape Level

Using the grid analysis method, this study established a 2500 m × 2500 m grid across the study area and generated distribution maps of landscape indices to analyze changes at the landscape level, which intuitively presented the “sprawling” characteristics of urban expansion (Figure 11 and Figure 12). The results reveal significant changes in the landscape pattern indices in the main urban area of Shenyang City from 2000 to 2020 (

Table 6. Dynamics of landscape pattern indices at the level of the landscape in the main urban area of Shenyang, 2000–2020.

Year	TA (ha)	NP (ha)	PD (n/100 ha)	LPI (%)	LSI	CONTAG (%)	IJI (%)	COHESION (%)	PR	SHDI	SHEI	AI (%)	DIVISION (%)
2000	365,849.37	1445	0.3950	42.6781	27.1977	71.5793	52.1899	99.8803	6	0.8877	0.4954	97.5098	0.7324
2005	365,853.96	1514	0.4138	41.4174	29.0207	70.7957	51.1834	99.8764	6	0.9079	0.5067	97.3285	0.7454
2010	365,854.32	1669	0.4562	29.1269	30.7157	68.1557	50.9048	99.8433	6	0.9936	0.5545	97.1626	0.8316
2015	365,854.23	1696	0.4636	28.4393	30.7131	67.9116	50.8244	99.8415	6	1.0022	0.5593	97.1631	0.8363
2020	365,854.32	1608	0.4395	25.9337	31.2586	65.7364	51.6639	99.8349	6	1.0766	0.6009	97.1117	0.8544
2000– 2020	4.95	163	0.0445	−16.7444	4.0609	−5.8429	−0.5260	−0.0454	0	0.1889	0.1055	−0.3981	0.1220

).

The number of patches (NP) increased from 1445 to 1608, accompanied by a rise in patch density (PD) from 0.40 to 0.44, indicating heightened landscape fragmentation. Concurrently, the Shannon diversity index (SHDI) and Shannon evenness index (SHEI) increased from 0.89 and 0.49 to 1.08 and 0.60, respectively, reflecting enhanced landscape diversity and more balanced type distribution. The largest patch index (LPI) declined from 42.68% to 25.93%, while the contagion index (CONTAG) decreased from 71.58% to 65.74%, signaling the reduced dominance of large contiguous patches and a shift toward fragmented, heterogeneous landscapes. The landscape aggregation index (AI) gradually decreased, whereas the landscape division index (DIVISION) rose from 0.73% to 0.85%, further confirming the intensification of spatial disaggregation. Additionally, the patch cohesion index (COHESION) slightly declined from 99.88 to 99.83, suggesting diminished natural connectivity.

These trends collectively demonstrate that the rapid urbanization of Shenyang’s main urban area is characterized by the gradual expansion of construction land, primarily in the form of encroachment on land type landscapes such as cultivated land and grassland, which directly lead to landscape fragmentation, dispersed patch distribution, and reduced ecological connectivity, ultimately forming a more complex and heterogeneous urban landscape pattern. This expansion leads to landscape fragmentation, dispersed patch distribution, and diminished ecological connectivity, resulting in a more complex and heterogeneous urban landscape. The urban spatial structure has evolved into a “small-scale concentration, large-scale dispersion” model. Moving forward, the development of the Park City should prioritize structural optimization over quantitative expansion. This involves the strict protection of the remaining large-scale natural patches and the construction of ecological corridors connecting parks and natural areas, promoting embedded green spaces in densely built areas and enhancing ecological efficiency through multi-functional design.

3.3. The Current State of Park City Development in Shenyang

At the macro-planning level, while the “Park City” concept is integrated into Shenyang’s top-level design with a focus on ecological priorities, there is a significant disconnect between planning goals, actual development, and financial support. This gap hinders the effective implementation of ecological objectives and inadequately addresses residents’ needs.

At the meso-management level, there are unclear responsibilities of governing agencies, limited public engagement, and inadequate GIS-based monitoring throughout project cycles. These deficiencies result in a lack of risk early-warning systems and slow project progress.

At the micro-design level, the lack of long-term maintenance planning and failure to address the diverse needs of the population compromise the sustainability of Park City development.

4. Discussion

This study adopts landscape ecology theories and quantitative research methods to perform a comprehensive analysis of the distribution and modifications in the urban landscape pattern of the Shenyang City’s main urban region from 2000 to 2020. It explores the spatial distribution and evolutionary trends of land use types and summarizes the patterns and features of landscape evolution. The findings reveal the following.

4.1. Urbanization Rates Are Positively Correlated with Changes in Land Use Landscape Patterns

Between 2000 and 2020, Shenyang City underwent rapid urbanization, with its urbanization rate reaching 84.52% in 2020, an increase of 7.45 percentage points compared to 2010 and 1.3 times higher than that of 2000. This represents a median yearly growth rate of 0.965%, significantly surpassing the national average [69]. This rapid urbanization process drove significant changes in land use patterns [70]. During this 20-year period, cultivated land and construction land predominated the main urban area of Shenyang City, comprising more than 65% of the whole area. Cultivated land decreased by a total of 518.66 km², with 76.89% of this area converted to construction land. Forest land and grassland experienced slight declines, while construction land increased by 490.84 km², and water area increased by 64.55 km², likely due to climate change and water conservancy projects [71].

The land use dynamic degree in Shenyang City indicates that construction land consistently showed growth, while cultivated land exhibited a continuous decline. This trend of change coincides with the rapid growth of land utilization throughout urbanization. Meanwhile, landscape pattern metrics at the landscape level reveal significant changes: the number of patches increased by 163, patch density rose by 0.0445, and indices of landscape separation, Shannon diversity, and evenness increased by 0.12%, 0.19, and 0.11, respectively. In contrast, the largest patch index, landscape cohesion, and sprawl index declined by 16.74%, 0.40%, and 5.84%, respectively. These changes suggest that during the 20 years of the urbanization process, population growth and intensified human activities caused ecological damage [72], vegetation degradation, and soil erosion, resulting in a more significant change in the urban landscape pattern [73].

The primary manifestation of urban landscape fragmentation is the increasing dispersion of various patch types, leading to reduced natural connectivity. This fragmentation affects wildlife habitats and migratory pathways, potentially damaging biodiversity, weakening ecosystem stability, diminishing resistance to external disturbances, and gradually forming a more complex landscape. These trends indicate a positive correlation between the urbanization rate in Shenyang’s main urban area and changes in land use landscape patterns. The land use change in Shenyang is a typical microcosm of China’s rapid urbanization. Accelerated urbanization drives the alteration of land use types, alters landscape patterns, and improves land use efficiency. The Park City construction is the key path to address the “ecological deficit” in Shenyang. In the short-term development, ecological capacity can be rapidly enhanced through the restoration of waterfront spaces and the reuse of brownfields. Long-term planning involves restricting the transformation of cultivated land and activating unused land; the spatial structure can be optimized to achieve the transformation from “building parks in the city” to “building the city in the park”.

4.2. Negative Coupling Between the Evolution Pattern of Land Use and the Demand for Park City Construction in Shenyang City

Based on the seven evaluation indicators for an initial-stage Park City outlined in the 2021 Group Standard Evaluation Criteria for Park City compiled by the Chinese Society of Landscape Architecture, this study found that the construction requirements for a Park City emphasize the importance of ecological environmental preservation, increasing green spaces, developing park systems, and improving urban ecological service functions to promote the coordination between urban development and ecological preservation. We integrated the seven indicators of the emerging Park City evaluation framework into the land use evolution analysis system. Over the past two decades, the evolution of the land use pattern in the main urban region of Shenyang City has primarily been characterized by an average annual expansion of construction land at a rate of 0.67%, while cultivated land has shrunk at a rate of 0.71% per year. The natural landscape, comprising forest land and grassland, exhibited corresponding reductions, with grassland, cultivated land, and forest land showing negative annual dynamic degrees of −1.02%, −0.99%, and −0.54%, respectively, directly eroding the blue-green spatial foundation. Conversely, unutilized land, water areas, and construction land displayed positive growth trends at 7.09%, 6.88%, and 4.25%, respectively. This pattern aligned with typical land use expansion during the urbanization process.

However, it simultaneously indicates that rapid urban sprawl, characterized by construction land encroachment on cultivated land and grassland, has degraded the urban ecological foundation and compromised the urban ecological environmental quality. These findings reveal the structural conflict between urban expansion and ecological conservation, directly threatening the achievement of the “the proportion of blue-green space coverage ≥50%” as outlined in the initial-stage ecological environment indicators of the “Evaluation Criteria for Park Cities” set by the Chinese Society of Landscape Planning and Design in 2021.

Furthermore, the landscape metric analysis demonstrated significant ecological fragmentation: the number of patches increased by 163, the density increased by 0.0445, the area of the largest patch decreased by 16.74%, and the separation degree increased by 0.12%. These alterations in landscape patterns underscore the detrimental impact of urban development on habitat fragmentation, compromising ecological connectivity. This disruption of ecological connectivity directly contravenes the core ecological environment indicator of “ecological network connectivity” within the Park City construction evaluation framework. Furthermore, the decline in ecosystem service functions driven by landscape fragmentation undermines the goals of “enhancing urban ecological service functions” and “promoting the coordination between urban development and ecological protection” emphasized in the construction requirements. Such landscape-scale changes present substantial obstacles to realizing the sustainable “Park City” development goals.

In the process of urbanization, there is a competitive relationship between the shortage of land resources and the demand for urban expansion and the demand for green space and ecological space in Park City construction. The systematic conversion of substantial cultivated land, forest land, and grassland areas to urban construction land has diminished ecological green spaces and disrupted habitats. This is particularly evident in high-density residential zones lacking adequate public green infrastructure and exhibiting uneven spatial distribution of vegetated areas. This results in an uneven distribution of green spaces and alters land use patterns, thereby impacting ecosystem stability and ecological services. These changes in land use patterns can subsequently impact the stability and ecological service functions of the urban ecosystem.

Therefore, the observed degradation of ecological patterns, characterized by diminished connectivity and heightened fragmentation, hinders key objectives of park city development, including ecological network connectivity, blue-green space proportion, and enhancement of ecological service functions. This underscores a negative coupling between the land use pattern evolution in Shenyang’s main urban area and the demands of Park City construction.

4.3. The Evolution Law of Land Use Patterns and Park City Construction Strategies in the Main Urban Area of Shenyang City

4.3.1. Macro-Level: Overall Ecological Planning and Control Strategy Based on Urban Renewal

In the context of urban renewal and expansion, the notion of a Park City will be integrated into the comprehensive urban planning process. This integration will be closely aligned with the strategic goal of optimizing the urban spatial structure outlined in the 14th Five-Year Plan for Urban and Rural Construction and Development of Shenyang City. The overall urban development blueprint for Shenyang City will be carefully designed as part of this process. By implementing refined land planning strategies, the plan aims to improve public green spaces [74] and enhance the environment of older urban and industrial areas. The ultimate goal is to establish Shenyang City as a modern urban center that is livable, harmonious, ecological, and sustainable, thereby fostering the comprehensive urban and rural development plan.

Furthermore, the development of a Park City in the primary urban zone of Shenyang City necessitates the adoption of a comprehensive ecological planning and control strategy. Adhering to the principle of “ecological priority, green development,” the urban planning process should entail the establishment of an ecological protection red line to safeguard the integrity of the natural landscape and ecosystem. Through scientific ecological restoration, the design of green space connections, and the creation of ecological corridors, elements such as parks, green areas, and wetlands should be seamlessly integrated into the urban planning framework [75]. This approach facilitates the organic fusion of ecological considerations with production and living spaces, promoting a sustainable urban environment.

4.3.2. Meso-Level: Public Governance Strategy Based on Public Participation Throughout the Life Cycle

In the urban management of Shenyang, a dedicated Park City management institution can be established to construct a public-centered Park City governance model. By setting up an open and transparent information exchange platform, citizens, community groups, and various social organizations can be invited to participate in the planning and decision-making processes of Park City construction, strengthening the collaboration between the city government and communities, and encouraging citizens to participate in the daily maintenance of Park City construction to enhance their sense of belonging and responsibility. By integrating remote sensing data through GIS and combining virtual technology to construct the dynamic model of the urban ecosystem, the digitalization level of the park system can be comprehensively enhanced [76]. Regular dynamic monitoring of land use changes and green space systems, integrating landscape health early warning and public participation modules, and promptly revising the urban park construction strategy and landscape planning scheme can ensure the full life cycle management of Park City construction projects.

4.3.3. Micro-Level: All-Ages, Multi-Scale, Detailed Design Strategy Based on Public Needs

Adopting the age-friendly and multi-scalar planning approach, the micro-design should focus on the functional needs of diverse age groups, from infants to the elderly. The specific design can involve creating diverse facilities, such as vibrant sports areas, comfortable leisure zones, and children’s playgrounds, to foster user-friendly, flexible, and varied urban open spaces that promote social interaction [77,78]. Concurrently, the refined and multi-scalar design should be applied to the overall city and local contexts. By integrating the natural environment, the design should establish an ecological esthetic [74], utilizing native plants and eco-engineering technologies to enhance the ecological value and esthetic appeal of the park, thereby better adapting to the seasonal climatic conditions. Safety considerations should be prioritized, with the provision of adequate seating, walkways, signage, surveillance, and lighting to ensure the convenience and security of park users. Additionally, the park design should incorporate a rainwater harvesting system and employ permeable materials and ecological ramps to uphold the principles of sustainable design, minimizing the ecological impact of construction activities.

This study primarily analyzes land use evolution and its impacts in the main urban area of Shenyang against the background of the Park City based on landscape pattern indices. However, a deeper quantitative analysis of the driving mechanisms, particularly the complex interplay between socioeconomic factors and policies, remains lacking. Future research could enhance the analysis of driving mechanisms by integrating multi-source socioeconomic data and spatial econometric models. This would enable accurate quantification of the intensity of various driving factors, such as industrial policies, population dynamics, and land markets, as well as their interactive effects. A dynamic model coupling “land use change–landscape pattern–ecosystem services–Park City efficiency” can be constructed to simulate ecological responses and socioeconomic benefits under different planning scenarios, providing more accurate scientific support for optimized decision-making.

5. Conclusions

This study analyzes the evolutionary characteristics of the land use landscape pattern in the main urban area of Shenyang City from 2000 to 2020 and its impact on the construction of a Park City. During the rapid urbanization process, particularly driven by industrialization and the real estate sector, cultivated land has been extensively transformed into construction land. This shift has markedly degraded the landscape, marked by increased fragmentation, more complex morphology, and reduced natural connectivity, adversely affecting ecological quality. This transformation starkly contrasts with the core demands for Park City construction, which emphasize the scale, connectivity, and quality of ecological spaces.

Therefore, this study proposes strategies for the evolution of land use patterns and the construction of the Park City in Shenyang’s main urban area from three perspectives:

(1) At the macro-level, focusing on the holistic nature of ecological planning and urban renewal, integrating scientific ecological restoration, ecological corridors, and green space networks into urban planning can enhance the blue-green space system’s ecological connectivity and resilience.

(2) At the meso-level, emphasizing public participation and whole-life-cycle governance, establishing the Park City governance framework focused on public involvement, and leveraging GIS and digital technologies can create an open platform and dynamic monitoring system for continuous and intelligent oversight of land use changes and the green space system.

(3) At the micro-level, addressing the needs of diverse groups through detailed design and incorporating age-friendly and multi-scale design principles can effectively address the diverse needs of various populations, fostering public spaces that support dynamic sports and comfortable leisure.

The proposed strategies seek to systematically mitigate the adverse effects of urbanization on landscape patterns by optimizing spatial layouts, innovating governance models, and enhancing spatial quality. These strategies offer practical solutions for Shenyang and similar cities to realize the vision of Park City construction and sustainable development despite limited land resources.