Spatial Identification and Evolutionary Analysis of Production–Living–Ecological Space—Taking Lincang City as an Example

Abstract

Optimizing the “production–living–ecological” space (PLES) is critical for achieving the UN Sustainable Development Goals (SDGs), particularly in ecologically sensitive mountainous border regions. This study investigates the spatial patterns and dynamic evolution of PLES in Lincang City (2010–2020) to reveal the trade-offs between development and conservation. Methodologically, we proposed a coupling-coordination-based grid-level PLES identification framework. This framework integrates the coupling coordination degree model (CCDM) directly into the functional classification process at a 600 m grid scale—a resolution selected to balance the capture of spatial heterogeneity with the maintenance of functional integrity in complex terrains. Spatiotemporal dynamics were further quantified using transition matrices and a dimension-based landscape metric system. The results reveal that (a) ecological space and production–living–ecological space represent the predominant categories in the study area. During the study period, ecological space continued to decrease, while production–living space increased steadily, and other PLES categories showed only marginal variations. (b) Mutual transitions among PLES types primarily occurred among ecological space, production–ecological space, and production–living–ecological space. These transitions intensified markedly between 2015 and 2020 compared to the 2010–2015 period. (c) From 2010 to 2020, the landscape in Lincang evolved towards lower ecological risk yet higher fragmentation. High fragmentation values, often associated with grassland, cropland, and forested areas, were evenly distributed across northeastern and northwestern regions. Likewise, high landscape dominance and isolation appeared in these regions as well as in the southeast. Conversely, landscape disturbance remained relatively uniform throughout the city, with lower values detected in forested land.

1. Introduction

The production–living–ecological space (PLES) is a comprehensive land classification framework [1,2,3], emphasizing classifying land space pattern from the perspective of various land use functions, covering the space where people carry out material production, daily life and other activities. While the concept of PLES originated from China’s territorial spatial planning, it is theoretically grounded in the globally established framework of land-use multifunctionality (LUM) [4,5]. Similar to LUM, which emphasizes the provision of economic, social, and environmental functions by land systems, the PLES framework categorizes these functions into three actionable dimensions: production (economic output), living (social habitation), and ecological (environmental regulation). Specifically, production space refers to the land that can provide products and services for humans or carry out auxiliary production, living space refers to the land that can accommodate daily life, leisure, or other special needs, and the ecological space refers to the land that contains ecological elements or possesses ecological functions. Thus, PLES serves as an operational tool for implementing the multifunctionality paradigm in spatial planning, addressing the universal challenge of balancing human development with ecological preservation in data-scarce mountainous regions globally. Against the backdrop of the ongoing transformation of urban development [6] and the growing emphasis on high-quality, sustainable growth, the scientific coordination of PLES layout has become critical for regional sustainability. In this context [7,8,9], the identification and dynamic evolution of PLES have become crucial indicators for measuring the coordination of regional development, as well as a pivotal starting point for the rational allocation of land resources, the construction of an ecological civilization, and overall socioeconomic sustainability. Consequently, accurate identification and systematic evolutionary analysis of PLES have rapidly emerged as a research hotspot in fields such as geography, urban and rural planning, and ecology [10,11,12].

To date, a wide range of studies have addressed the conceptual framework, identification, optimization [13,14], pattern evolution [15,16,17], and coupling coordination analysis of PLES [18,19], forming an important analytical basis for territorial spatial structure adjustment and optimization [20]. From a methodological perspective, existing PLES identification and quantification approaches can be further synthesized into several major paradigms based on their core data sources and analytical logic [21]. The first paradigm is land-use-based functional merging or classification, which assigns production, living, and ecological functions to land-use types according to their dominant roles [22,23]. This approach is operationally straightforward and widely applicable at regional and national scales, but it may overlook functional heterogeneity within the same land-use category. The second paradigm is value- or intensity-based accounting, which constructs evaluation index systems grounded in multifunctional land-use theory [24,25,26]. By integrating biophysical measurements, ecosystem service values, or socio-economic coefficients, this approach aims to quantify functional intensity and often employs models such as the coupling coordination degree model to evaluate functional relationships [27,28,29]. However, differences in indicator selection and coefficient calibration have resulted in a lack of unified classification systems, limiting cross-study comparability. More recently, a third, data-driven paradigm has emerged, which integrates multi-source spatial big data—such as POI, nighttime light (NTL), and mobility data [30,31]—to infer functional mixtures and human activity intensity. While effective in urban cores, this approach is highly sensitive to data availability and quality, and its applicability in data-scarce mountainous or rural regions remains limited. Beyond differences in data sources, an important methodological issue concerns how functional interactions are represented. In many existing studies, coupling coordination analysis is applied only as a post-identification evaluation step, after PLES types have already been delineated. This separation constrains the ability of identification results to reflect composite functional states characterized by both functional intensity and inter-functional balance. Integrating land-use-based functional evaluation with the coupling coordination degree model at the identification stage therefore offers a promising pathway to simultaneously capture functional strength and coordination relationships, particularly in regions with complex terrain and strong spatial heterogeneity.

Furthermore, most existing studies employ administrative divisions as spatial units [32,33,34] and use regional averages to represent PLES characteristics within each division. This practice, while convenient, tends to obscure spatial heterogeneity and reduce identification precision [25,35]. In comparison, adopting grid-scale units to divide the space into smaller spatial identification units allows the spatial heterogeneity of land use to be explicitly captured, thereby enabling a more detailed and precise identification of PLES and facilitating the dynamic analysis of its evolution.

Exploring the spatiotemporal evolution of PLES is crucial for promoting the orderly, moderate, and sustainable development of national territory [36,37]. In mountainous regions, the complex topography and ecological fragility make PLES structures and their corresponding landscape patterns particularly sensitive to urbanization-induced change. However, most current PLES studies focus on plains, such as counties, cities, rural territories, and watersheds, with relatively few studies on mountainous areas [38,39,40]. Furthermore, given the strong correlation between landscape functionality and land-use decision-making [41], it is also necessary to incorporate an ecological perspective into PLES evolution analysis, for instance by assessing landscape ecological security and stability [42,43,44].

Accordingly, this study selected Lincang City as the study area, covering the time periods of 2010, 2015, 2018, and 2020. By adopting a PLES functional evaluation system and applying the coupling coordination degree model (CCDM), we quantitatively measure both the functional intensity and the degree of coordination among production, living, and ecological functions, achieving the fine-grid scale division of the PLES. Furthermore, by integrating land-use transfer matrices and landscape pattern indices, we not only analyze the spatial distribution, transition mechanisms, and landscape evolution of PLES but also elaborate on the intrinsic relationships between PLES dynamics and landscape pattern characteristics. The results provide empirical insights for optimizing PLES spatial configurations in Lincang City and offer policy-oriented references for promoting the coordinated development of production, living, and ecological functions in similar mountainous regions.

This study makes three major contributions:

• A refined identification framework for production–living–ecological spaces (PLES) is developed by integrating the functional scoring model with the coupling coordination degree model. Unlike previous studies that rely solely on land use classification or single-function assessment, the proposed approach simultaneously quantifies the intensity of production, living, and ecological functions and evaluates their interrelationships. This dual-dimensional assessment—combining function strength and coordination degree—enables more systematic and fine-grained scale recognition of PLES at a 600 m, thereby alleviating some limitations associated with conventional coarse-grained classifications.
• Using Lincang City as a case study, the study completes a four-stage (2010, 2015, 2018, 2020) evaluation of production, living, and ecological functions, the calculation of coupling coordination degrees, and the grid-scale identification of PLES. This dataset establishes a temporal–spatial sequence that provides an empirical foundation for analyzing the dynamic evolution of multifunctional land use in mountainous regions.
• By integrating landscape pattern indices, the study reveals the spatial configuration and ecological dynamics of PLES in Lincang City. The results demonstrate that ecological space has steadily decreased, production–living space has expanded in a dispersed pattern, and production–ecological space serves as a transitional “contested zone” between development and conservation. These findings not only enrich the understanding of PLES evolution in mountainous cities but also provide practical guidance for balancing economic growth, livelihood improvement, and ecological protection.

The rest of the paper is organized as follows. Section 2 introduces the study case and the methods for identifying and analyzing PLES. The assessment results and the primary findings are shown in Section 3. Section 4 outlines the main conclusions and some suggestions.

2. Materials and Methods

2.1. Study Area and Data Sources

Lincang City is located in the southwestern part of Yunnan Province with a total of 1 district and 7 counties, as shown in Figure 1, adjacent to the Lancang River and the Nujiang River, covering a total area of about 24,000 square km² It serves as a crucial hub, linking the Pacific and Indian Oceans from east to west and connecting the Chongqing–Xinjiang–Europe International Corridor, Yangtze River Economic Belt, and Pearl River Economic Circle from north to south. This unique border location not only confers strategic significance in cross-border cooperation and international trade but also provides a distinct advantage for regional development. The city is situated in the Hengduan Mountain System, with mountainous areas accounting for 98% of its territory. Characterized by a subtropical low-latitude plateau mountain monsoon climate and a forest coverage rate of 65.55%, Lincang possesses abundant ecological and natural resources. Meanwhile, ethnic minorities (such as Dai, Wa, and Lahu) account for 41.4% of the registered population, contributing to the richness of local cultural resources and forming a solid foundation for diverse modes of production and lifestyle. Due to the complex and vertically stratified mountainous–basin landform, different ethnic groups have historically developed relatively independent yet interwoven settlement patterns, characterized by vertical differentiation, spatial interspersion, and a delicate balance between coexistence and cultural autonomy. These socio-spatial arrangements have fostered complementary economic and cultural relationships while maintaining distinct cultural boundaries. However, such highly differentiated and spatially interwoven settlement patterns pose considerable challenges for land-use planning and spatial governance, as they require balancing development demands with ecological conservation and cultural preservation across heterogeneous landscapes [45].

However, Lincang’s mountain ecosystems are ecologically fragile, as defined by their high sensitivity to disturbance and limited capacity for recovery once degraded [46]. The region lies within the southwestern karst zone [47], where steep slopes, thin soils, and intense vertical relief lead to severe soil erosion and frequent geological hazards such as landslides, debris flows, and collapses. Once land productivity is lost, its restoration is extremely difficult. In addition, constrained by terrain, transportation, and fragile ecological conditions, resource exploitation and urban expansion often occur in a spontaneous or uncoordinated manner, aggravating ecological degradation and intensifying the tension between development and conservation [48].

Taken together, these geographical, ecological, and ethnic features correspond to the city’s production, living, and ecological spaces, endowing Lincang with both opportunities and challenges for sustainable development. Against this background, in May 2019, the State Council approved Lincang’s proposal to establish a National Innovation Demonstration Zone for the Sustainable Development Agenda, focusing on innovation-driven development in underdeveloped multi-ethnic border areas. Therefore, Lincang was selected as the study area in this research, not only because of its unique geographical, ecological, and cultural characteristics, but also due to its representative role as a frontier demonstration zone for balancing development and conservation in multi-ethnic border regions.

The land use data of Lincang City for the period of 2010–2020 were obtained were obtained from the Chinese Multi-Period Land Use and Land Cover Remote Sensing Monitoring Dataset (CNLUCC), provided by the Resource and Environment Science and Data Center of the Chinese Academy of Sciences (https://www.resdc.cn/, accessed on 5 October 2023 ). This authoritative dataset was generated through manual visual interpretation using Landsat as the main source, supplemented by auxiliary data, with a spatial resolution of 30 m. The CNLUCC dataset adopts a hierarchical classification system, including six primary categories (cropland, forests, grassland, water body, construction land, and unused land) and 23–25 secondary categories (e.g., paddy field, dry land, forests, lakes, rural settlements). The study area includes five primary categories as well as 16 secondary land types. After generating validation points by random sampling, Google Earth high-resolution images of the same period were used to assess the accuracy of the land use data of the four phases. The results, as shown in

Table 1. Land use data accuracy assessment.

Year	2010	2015	2018	2020
Overall classification accuracy	82.00%	85.00%	81.00%	84.00%
Kappa factor	77.87%	81.25%	79.72%	81.25%

, indicate that the data accuracy meets the research requirements. It is worth noting that while socioeconomic indicators (e.g., POI, nighttime light) are increasingly used in PLES identification, this study adopts a standardized LUCC-based functional identification framework. This choice is driven by the specific geographical characteristics of Lincang City. In this mountainous and predominantly rural border region, socioeconomic proxy data exhibit significant spatial bias (e.g., near-zero POI density in agricultural zones) and signal instability (e.g., weak NTL signals in complex terrain). To ensure spatial consistency across the study period and avoid interpolation errors, we rely on the functional attributes inherent in land-use types as the primary basis for classification [22].

2.2. Methods

Based on the data above, the overall research framework is shown in Figure 2. The 600 m grid is selected as the basic research unit, and a PLE function evaluation system is established with reference to existing studies. Considering the multifunctional and comprehensive nature of land [35], the analysis adopted a functional synergy perspective. The coupling coordination degree between production, living, and ecological functions is quantitatively measured based on the coupling coordination degree model, which serves as the evaluation standard for the quantitative identification of PLES patterns in different years. The results of the identification are analyzed using transition matrix to study the pattern evolution characteristics of PLES in Lincang City over the research period. Additionally, landscape pattern indices are selected and calculated to analyze the evolutionary trends of landscape features.

2.2.1. Land-Use-Based PLE Function Evaluation System

The PLES concept is grounded in land use functions [22]. In this study, “function” is defined as the potential functional orientation inherent to specific land-use types. It should be noted that this study employs a land-use-based functional mapping approach. The identified PLE functions are inferred proxies derived from the CNLUCC classification system, rather than variables obtained through independent socioeconomic measurements (e.g., industrial output or housing census). This approach is chosen to ensure consistency in long-term spatiotemporal analysis. While it captures dominant functional attributes, it does not measure the operational intensity of functions independently of land cover. Within the land use system, land use functions are categorized into production functions, living functions, and ecological functions, which are inextricably linked to each other as a whole [35]. The three functions are inseparable and interconnected as a whole. Firstly, based on the existing research [38,49], the primary categories of land use are divided into production land, living land, and ecological land according to the three functions. Combining the definitions and perceptions of different scholars on the PLES [2,50], in this paper, we define the production land as the land that provides biomass and non-biomass products and services for human beings; living land as the land that provides human beings with the functions of residence, consumption, recreation, medical care and education; and ecological land as the space that provides human beings with ecological products and services.

To further refine the classification, this framework introduces a secondary-level classification of PLES: strong production/living/ecological land, semi-production/living/ecological land, and weak production/living/ecological land. And the functional intensity for each category is evaluated using a semi-quantitative scoring method. Following the widely adopted framework in PLES research [22,51,52,53], we applied the “1–3–5” scoring system to characterize the relative dominance of production, living, and ecological functions for each land-use type.

Specifically, a score of 5 (Strong Function) is assigned when a land use type’s primary function is significantly dominant over its other functions. A score of 3 (Semi-Function) is assigned when a function is clearly present but not dominant, often co-occurring with other functions of similar strength. A score of 1 (Weak Function) is assigned when a function is present but is significantly subordinate to the primary function(s). This scoring scheme is designed as an ordinal, tiered classification to distinguish relative functional dominance rather than to quantify precise functional intensities. The choice of this standardized matrix over iterative expert weighting methods (such as the Delphi method) is deliberate: while Delphi relies on variable expert panels, which can limit reproducibility, the 1-3-5 paradigm provides a fixed, transparent, and literature-consistent rule set. This design facilitates methodological transparency and improves the potential reproducibility and comparability of the results across regional studies. Furthermore, the discrete levels (1, 3, 5) effectively balance interpretability and differentiation, allowing clear separation between dominant and subordinate functions without over-parameterization. It is important to note that these values are relative indicators. Accordingly, the identification of dominant PLES types depends on the functional rank order rather than the absolute magnitudes; thus, the classification results are relatively insensitive to linear rescaling of the scores.

When determining the land use types included in the tertiary category on the basis of the secondary category, careful consideration is given to the fact that the land has the characteristics of multifunctionality and main functions [34]. That is to say, the same land use type can possess multiple functions, but there is still a primary and secondary distinction between the functions. This results in the PLE land use classification and PLE function evaluation system shown in

Table 2. The PLES assessment of each land use class ¹.

Primary Category	Secondary Category and Function Scores	Tertiary Category
Production land	Strongly production land (5)	Industrial land
	Semi-production land (3)	Paddy land, dry land, town land, rural settlements
	Weak production land (1)	Forests, grasslands, rivers and canals, lakes, reservoirs and ponds, mudflats
Living land	Strong living land (5)	Town land, rural settlements
	Semi-living land (3)	Industrial land
	Weak living land (1)	Paddy land, dry land
Ecological land	Strong ecological land (5)	Forests, grasslands, rivers and canals, lakes, mudflats
	Semi-ecological land (3)	Paddy land, dry land
	Weak ecological land (1)	Reservoirs and ponds

¹ Note: “forests” and “grasslands” are primary categories encompassing all their respective sub-classes, which share identical function scores.

. For example, “forests” serve as an ecological land while also having a certain production function, but its ecological function is stronger than its production function, so it belongs to both weak production land and strong ecological land at the same time. It is also important to note that not every land-use type possesses all three PLES functions. When a land-use category does not exhibit a meaningful production, living, or ecological function, its score for that function is assigned as 0. Correspondingly, such land-use types do not appear under the tertiary classification of that specific function in Table 2.

In this study, a grid size of 600 m × 600 m was adopted as the basic spatial analysis unit. This choice was made for three reasons. First, 600 m is an integer multiple of the 30 m Landsat pixel size (20 × 20 pixels), which minimizes scale conversion errors and ensures consistency between land-use data and the analysis unit. Second, considering that Lincang is a mountainous area characterized by relatively large and contiguous land-use patches, we calculated the average patch size of the land-use map and, assuming approximate square patches, obtained an average side length close to 600 m. Using this scale as the evaluation unit, therefore, better matches the actual land-use pattern of the study area. Third, compared with conventional administrative units (e.g., counties or towns) or coarser grids (e.g., 1 km), the 600 m grid provides a finer resolution capable of capturing local spatial heterogeneity while still maintaining computational feasibility. Firstly, a geographic grid of 600 m × 600 m was created based on ArcGIS 10.2 software, and we calculated the area of each land use type within each geographic grid for different study periods in the study area. Then, the score value of production function, living function, and ecological function of each geographic grid was calculated by the comprehensive scoring model of the PLE functions. The comprehensive scoring model is shown in Equation (1) by weighting and summing the function scores of production land, living land, and ecological land in each evaluation cell.

$$W_j={\sum }_{i=1}^n{S}_i\times V_i$$

(1)

where W_j is the sum of the production/living/ecological function scores of the jth evaluation unit (For example, when calculating the production function score, W_j represents the sum of the production function scores of the jth evaluation unit, denoted as P_j), i is the land use type, n is the total number of each land use type in each evaluation unit, S_i is the area of each land use type in each evaluation unit in km², and Vi represents the production, living, or ecological functional intensity score assigned to the ith land-use type, where functional intensities are categorized into three levels (1, 3, and 5) to denote low, medium, and high functional contributions, respectively.

Finally, the grid-level functional scores were visualized using IDW interpolation solely to illustrate general spatial patterns. All subsequent analyses, classifications, and statistical summaries are based exclusively on the original 600 m grid-level results rather than on interpolated surfaces.

2.2.2. Coupling-Coordination-Based Grid-Level PLES Identification Framework

Considering the spatial scale differences, functional complexity, dynamic variability, and land use heterogeneity of PLES, this study adopts the multi-function space model, which classifies space into single-function areas (production, living, ecological) and multi-function areas (e.g., production–living, production–ecological, living–ecological) [54,55]. To quantitatively capture the complex interactions within these multi-functional spaces, the coupling coordination degree model is employed.

The concept of coupling originates from physics and refers to the degree of mutual influence among systems or system elements [56,57]. The coupling degree (C_cp) quantifies the intensity of interaction between subsystems—i.e., how strongly the production, living and ecological functions influence one another. By contrast, coordination emphasizes the quality of interaction: it describes whether subsystems operate in a harmonious, balanced, and mutually supportive way—that is, whether their interrelationship is synergistic and conducive to overall sustainable development. Because a high coupling degree does not necessarily imply a harmonious relationship, coupling alone cannot indicate whether subsystem interactions are synergistic or contradictory [58,59]. Therefore, on the basis of existing research [60,61], we employ the coupling coordination degree (D_cc), which integrates both coupling and coordination degree. The coupling coordination degree thus evaluates the coupling coordination degree of each analysis unit to reveal the coupling and coordination relationship between the PLE functions. The calculation formula is as follows:

$$C_{\_cp}=3{\left\{{\displaystyle \frac{P_i\times L_i\times E_i}{{(P_i+L_i+E_i)}^3}}\right\}}^{{\displaystyle \frac{1}{3}}}$$

(2)

$$T=\alpha P_i+\beta L_i+{\gamma E}_i$$

(3)

$${D\_}_{cc}={(C\_cp\times T)}^{{\displaystyle \frac{1}{2}}}$$

(4)

where C_cp is the coupling degree of production, living, and ecological functions of each analysis unit, C_cp ∈ [0, 1]; the larger its value, the stronger the interaction between the three subsystem elements, and vice versa, the weaker it is. P_i, L_i, and E_i denote the score value of production, living, and ecological functions of each analysis unit, respectively. T represents the coordination degree between the PLE functions, where α, β, and γ denote the contribution values of the production, living, and ecological functions, respectively, with α + β + γ = 1. In this study, the three weights are assumed to be equal (α = β = γ = 1/3). This assumption is adopted for two key reasons: (1) Evaluation Benchmark for Balanced Development: Theoretically, the weights define the normative standard of “ideal coordination” rather than the actual state of the land system. By setting equal weights, we establish a fixed benchmark aligned with the “Equilibrium Development” paradigm (essential to the UN SDGs and local planning goals). This allows the model to objectively measure how far the actual functional dominance (input scores) deviates from the desired state of structural balance. (2) Temporal Comparability: Methodologically, unlike data-driven weighting methods (e.g., entropy weighting), where coefficients fluctuate based on annual data distribution, a fixed equal-weighting scheme ensures that the definition of “coordination” remains consistent throughout the study period (2000–2020). This rigidity is crucial for enabling valid inter-annual comparisons of spatiotemporal evolution, ensuring that observed changes in coordination are due to real land-use changes rather than shifting weight parameters. It should be noted that this assumption represents a baseline planning scenario. Conceptually, alternative weighting schemes—such as ecology-prioritized or development-oriented scenarios—could be adopted to reflect biased policy preferences; however, the present study focuses on identifying general coordination patterns under this stable, equal-priority framework.

D_cc represents the degree of coupling coordination between the production, living, and ecological functions, D_cc ∈ [0, 1], and the higher the value is, the better the coupled and coordinated nature of the various functions, and the worse the opposite is.

Based on the values of D_cc, the study area is first divided into three zones using the natural breaks (Jenks) classification method: (1) the coordinated zone (high D_cc) represents a state in which production, living, and ecological functions are well-coupled and mutually reinforcing, indicating a relatively harmonious and stable multifunctional land-use structure. Thus, in this study the coordinated zone is defined as production–living–ecological space; (2) the break-in zone (medium D_cc), corresponds to a transitional or intermediate coordination state, in which two functions tend to dominate while the third remains constrained, reflecting an ongoing adjustment process toward higher functional integration; and (3) the incongruous zone (low D_cc), denotes a state of functional imbalance, where a single function strongly dominates and suppresses the development of the others, indicating disordered or uncoordinated PLE development.

Secondly, the dominant functions of the break-in and incongruous zones are identified according to the scores of P, L, and E: for the break-in zone, the spaces where the scores of production and living were greater than or equal to the ecological scores (P ≥ E AND L ≥ E) were classified as production–living spaces, and the spaces where the scores of production and ecology were greater than the scores of living (P > L AND E > L) were classified as production–ecological spaces; for the incongruous zones, since most of the areas have greater ecological functions than the production and living scores, and the production–living space covers too little area, the areas in the incongruous zone with production scores or living scores greater than the ecological scores (P > E OR L > E) are included in the production–living space, and the rest of the geographic grid is classified as the ecological space.

Table 3. Classification criteria for single-function and multi-function PLES.

Coordination Zone (Based on D_cc Value)	Dominant Function Score	PLES Type	Classification
Coordinated Zone (High D_cc)	-	Production–Living–Ecological Space (PLE)	Multi-function
Break-in Zone (Medium D_cc)	P ≥ E AND L ≥ E	Production–Living Space (PL)	Multi-function
	P > L AND E > L	Production–Ecological Space (PE)	Multi-function
Incongruous Zone (Low D_cc)	P > E OR L > E	Production–Living Space (PL)	Multi-function
	All other cases (i.e., E is dominant)	Ecological Space (ES)	Single-function

presents the classification criteria for distinguishing between single-function and multi-function spaces.

2.2.3. Analysis of the Spatial Evolution of PLES by Incorporating the Transition Matrix and Landscape Pattern Indexes

The transition matrix method, originating from systems analysis, can not only represent the type structure of PLES at the beginning and end of the study period but also reflect the direction and quantity of type transitions within PLES during that period. The corresponding transition matrix can be calculated in ArcGIS by using methods like map algebra, which can visually and quantitatively reflect the evolutionary characteristics of the mutual transfer of the PLES in the study area over different time periods. The formula is as follows:

$$S_{ij}=\left[\begin{array}{ccccc}{S}_{11}& S_{12}& S_{13}& \dots & S_{1n}\\ S_{21}& S_{22}& S_{23}& \dots & S_{2n}\\ S_{31}& S_{32}& S_{33}& \dots & S_{3n}\\ ⋮& ⋮& ⋮& ⋮& ⋮\\ S_{n1}& S_{n2}& S_{n3}& \dots & S_{nn}\end{array}\right]$$

(5)

where S denotes the area, i and j denote the types of biospace at the beginning and end of the study period, and n denotes the number of all types of biospace in the study area. The row elements of the matrix represent the direction of flow from type i to type j, and the column elements represent the source of type j.

In addition, the landscape pattern index is an index that contains certain information about the landscape pattern, which quantitatively reflects the structural composition and spatial distribution characteristics of the landscape [62]. The landscape pattern index and its changes are often used to analyze the characteristics and evolution of the landscape pattern in related studies. Following established principles in landscape ecology, the selection of landscape metrics in this study was guided by a dimension-oriented and theory-driven framework, rather than by purely statistical criteria. Specifically, landscape structure is commonly conceptualized as comprising a limited number of fundamental and complementary dimensions, each representing distinct ecological and spatial processes [63,64,65].

In the context of PLES, landscape structure can be universally characterized along three fundamental and conceptually independent dimensions—fragmentation and subdivision, spatial configuration and aggregation, and compositional heterogeneity. These dimensions correspond to distinct ecological and spatial processes and are commonly adopted in landscape ecological analysis to ensure conceptual completeness while avoiding indicator redundancy. Accordingly, the landscape metrics used in this study were selected a priori to represent these theoretically recognized dimensions, rather than to maximize statistical orthogonality among indices. This dimension-based selection strategy is widely recommended in landscape ecology, as statistical correlation among metrics is common and does not necessarily imply conceptual redundancy or reduced ecological interpretability. To ensure that the analysis captures these essential structural properties in a theoretically grounded and non-redundant way, eight representative metrics were selected to describe these dimensions. This indicator set provides a comprehensive basis for interpreting changes in landscape integrity, configuration patterns, and overall heterogeneity across different PLES categories. Each selected metric targets a specific structural attribute and ecological meaning, and together they form a complementary rather than substitutive indicator system. To analyze the landscape pattern dynamics of PLES in Lincang City, the following landscape metrics were selected: (1) landscape disturbance index (E), a composite indicator integrating fragmentation, division, and dominance to provide a holistic assessment of ecological disturbance and landscape risk, with higher values indicating stronger external disturbances and greater ecological risk; (2) landscape fragmentation index, which quantifies the degree of landscape subdivision and spatial complexity, often resulting from natural or anthropogenic disturbances and closely linked to biodiversity loss; (3) landscape division index, which measures the degree of spatial separation of patches within a landscape type, with higher values indicating greater complexity and lower ecological stability; (4) land dominance index, which reflects the relative importance of dominant patches in shaping landscape patterns and ecological risk; (5) aggregation index (AI), a class-level metric measuring the internal connectivity of a specific PLES type and designed to be independent of landscape composition. It evaluates the connectivity among patches of the same type, with lower values indicating more dispersed landscapes; (6) contagion index (CONTAG), a landscape-level metric reflecting the overall clumping and interspersion of multiple patch types and sensitive to both composition and configuration. It describes the degree of clumping or interspersion of different patch types, where higher values suggest stronger connectivity of dominant patches and lower values indicate higher fragmentation; (7) Shannon’s diversity index (SHDI), which measures the richness and proportional distribution of landscape elements, emphasizing heterogeneity and sensitivity to rare patch types; and (8) Shannon’s evenness index (SHEI), a normalized diversity metric emphasizing the balance of area distribution among patch types and distinguishing dominance from diversity magnitude. It evaluates the evenness of patch type distribution, where values approaching 1 indicate uniformity and the absence of dominant patch types. By jointly applying these metrics, the analysis is able to capture multiple, theoretically distinct aspects of PLES landscape structure, enabling a nuanced and interpretable characterization of landscape pattern evolution. The calculation of each landscape pattern index is demonstrated in

Table 4. Selection and calculation of landscape pattern indexes.

Landscape Pattern Index	Formulas
Landscape disturbance index Ei	$E_i=a\times C_i+b\times F_i+c\times D_i$ a = 0.5, b = 0.3, c = 0.2
Landscape fragmentation index Ci	$C_i={\displaystyle \frac{N_i}{A_i}}$ Ni is the number of land use type patches, and Ai is the area of patches of land use type i
Landscape division index Fi	$F_i={\displaystyle \frac{\sqrt{S_i}}{2\times P_i}}$ Si is the landscape type distance index, Pi is the landscape type relative cover
Landscape dominance index Di	$D_i=d\times L_i+e\times P_i$ Li is the relative density of landscape types, Pi is the relative cover of landscape types, d = 0.6,e = 0.4
Aggregation Index AI	$\mathrm{A}\mathrm{I}=\left[{\displaystyle \frac{{\mathrm{g}}_{\mathrm{i}\mathrm{i}}}{\mathrm{m}\mathrm{a}\mathrm{x}\to {\mathrm{g}}_{\mathrm{i}\mathrm{i}}}}\right](100)$ gii indicates the number of similar neighboring patches for the corresponding landscape type
Contagion index CONTAG	$\mathrm{C}\mathrm{O}\mathrm{N}\mathrm{T}\mathrm{A}\mathrm{G}=\left[1+\left[{\displaystyle \sum _{\mathrm{i}=1}^{\mathrm{m}}}{\displaystyle \sum _{\mathrm{k}=1}^{\mathrm{m}}}\left[\left({\mathrm{P}}_{\mathrm{i}}\right)/\left({\displaystyle \raisebox{1ex}{${\mathrm{g}}_{\mathrm{i}\mathrm{k}}$}\!\left/ \!\raisebox{-1ex}{$\sum _{\mathrm{k}=1}^{\mathrm{m}}{\mathrm{g}}_{\mathrm{i}\mathrm{k}}$}\right.}\right)\right][\mathrm{l}\mathrm{n}({\mathrm{P}}_{\mathrm{i}})/\left({\displaystyle \raisebox{1ex}{${\mathrm{g}}_{\mathrm{i}\mathrm{k}}$}\!\left/ \!\raisebox{-1ex}{$\sum _{\mathrm{k}=1}^{\mathrm{m}}{\mathrm{g}}_{\mathrm{i}\mathrm{k}}$}\right.}\right)\right]\right]/2\mathrm{l}\mathrm{n}(\mathrm{m})](100)$ Pi indicates the percentage of area occupied by type i patches, gik indicates the number of type i and k patches adjacent to each other, and m is the total number of patch types
Shannon’s diversity index SHDI	$\mathrm{S}\mathrm{H}\mathrm{D}\mathrm{I}=-{\displaystyle \sum _{\mathrm{j}=1}^{\mathrm{m}}}{\mathrm{P}}_{\mathrm{j}}\times \mathrm{l}\mathrm{n}{\mathrm{P}}_{\mathrm{j}}$ Pj denotes the percentage of area occupied by type j patches, and m is the total number of patch types
Shannon’s evenness index SHEI	$\mathrm{S}\mathrm{H}\mathrm{E}\mathrm{I}=-{\displaystyle \frac{-{\sum }_{\mathrm{j}=1}^{\mathrm{m}}({\mathrm{P}}_{\mathrm{j}}\times \mathrm{l}\mathrm{n}{\mathrm{P}}_{\mathrm{j}})}{\mathrm{l}\mathrm{n}\mathrm{m}}}$ Pj denotes the percentage of area occupied by type j patches, and m is the total number of patch types

.

3. Results

3.1. Evaluation and Analysis of the Functions of the PLES

The production, living, and ecological functions of Lincang City were evaluated for 2010, 2015, 2018, and 2020. The spatial distribution patterns of the three functions were generally consistent across the four periods, with only minor variations:

(1) Areas with high production function scores were concentrated in the southern Gengma Dai and Wa Autonomous County, and in the northeastern Fengqing, Yun, and Linxiang Districts. The areas are scattered in bands and clusters, which is more in line with the distribution of paddy fields and drylands. Areas with low production function scores corresponded largely to forests and grasslands, and so on. Overall, the production function scores exhibited a pattern of higher values in the east and lower values in the west, as well as higher values in the north and south, lower values in the central region, and localized aggregation. (2) The spatial distributions of living function values and production function values show an obvious consistency: high living function areas are centrally distributed in cultivated land areas such as Gengma Dai and Wa Autonomous County in the south and dispersed in settlements and construction land in the east and north, such as Linxiang District and Fengqing County. (3) The spatial pattern of the ecological function value clearly complements that of the production and living function values: high ecological function value areas are distributed in different areas of each province in Lincang City, mainly in forested land, grass-covered areas and waters; areas with lower ecological functions correspond to those with stronger production and living functions, such as arable land, residential settlements and other construction land; in general, the ecological function scores display higher values in the west and lower values in the east, with lower values in the north and south and higher values in the center, and a broadly scattered distribution. The spatial pattern of the PLE functions shows that the production and living functions show spatial consistency, and the ecological functions and the production and living functions are complementary, with higher scores and even distribution in the study area, which matches the characteristics of Lincang City’s outstanding ecological resource advantages.

Notably, the limited interannual variation in functional scores—derived solely from land-use data—suggests that land-use-based evaluation alone may not fully capture the fine-scale spatial and functional dynamics and interrelationships of the PLES.

This observation highlights the need to move beyond static land-use classification toward a more relational understanding of how production, living, and ecological functions interact. Therefore, in the subsequent analysis, we employed the Coupling Coordination Degree Model (CCDM) to quantify the inter-functional relationships and identify PLES types in a more refined and dynamic manner.

Since the functional maps serve primarily as intermediate results for PLES identification and the spatial patterns exhibit strong similarities, we present the 2010 and 2015 functional maps in the main text (Figure 3) as a representative example. The full set of functional maps for all years is provided in Appendix A (Figure A1, Figure A2, Figure A3 and Figure A4) to ensure completeness and reproducibility of the analysis.

3.2. Grid-Scale Delineation of the PLES Based on the Analysis of the Coupled Coordination of the PLE Functions

On the basis of the results of the PLE function score, the coupling coordination degree between the production, living, and ecological functions of each analysis unit was calculated using the coupling coordination degree model. Since IDW is a widely used and computationally simple interpolation technique. It assumes that the influence of a sample point on an unknown location decreases with distance, and therefore, closer points are assigned higher weights than distant ones. The spatial distribution map of the coupled coordination degree of the PLE functions in the study area was obtained by interpolating the discrete values into continuous surfaces through the Inverse Distance Weighting (IDW) method, as shown in Figure 4. Here, the IDW interpolation is employed only for visualization purposes to illustrate broad spatial patterns, while all quantitative analyses and classifications are strictly based on the original grid-level results. Therefore, the interpolated maps should be interpreted as schematic representations rather than as spatially continuous measurements. It should be noted that the overall spatial pattern of coupling coordination exhibits relatively limited visual variation across the four study years, indicating a high degree of structural stability at the regional scale. This visual similarity reflects the slow and path-dependent evolution of PLE functional coordination in mountainous regions, where terrain constraints and long-term land-use policies restrict abrupt spatial restructuring.

In order to maximize the differences in the development levels of the coupling coordination among the PLE functions in the study area, this study used the classification and grading method from the references and employed the natural discontinuity method (Jenks) to categorize the spatial distribution of the degree of coupling coordination in the study area into three zones by grade—the coordinated zone of PLE functions, the break-in zone of PLE functions, and the incongruous zone of PLE functions [66]. The coupling and coordination degree between the PLE functions in the incongruous zone is low, which is manifested in the obvious dominance of one function in production, living, or ecology, squeezing the space of other functions; the coupling and coordination degree between the PLE functions in the break-in zone is improved relatively, and the PLE functions is developing towards being coordinated and orderly; the coupling and coordination level of the PLE functions in the coordination zone is higher, and there is mutual promotion between the PLE functions while the land system is developing orderly, which can meet the needs of human beings in production, living, and ecology. From 2010 to 2020, the pattern of the coupling and coordination of the PLE functions in the study area was consistent: the incongruous zone was mainly distributed in Zhenkang County, Yongde County, and Gengma Dai Wa Autonomous County, and overlapped with the forested land coverage area; the break-in zone was mainly distributed between the incongruous zone and the coordination area point-like pattern, and was distributed throughout Lincang City; and the coordinated area was mainly located in Fengqing County, Yun County, Linxiang District, and the southern part of Lincang City. Although the overall coordination pattern remains stable over time, localized changes can be observed in the enlarged panels of Figure 4. These areas exhibit gradual shifts in coordination levels, indicating fine-scale adjustment processes that are not captured by regional-scale pattern comparison alone. To further substantiate these localized changes, the areal proportions of different coupling coordination zones for each study year were statistically summarized, and the results are provided in

Table A1. Areal proportions of coupling coordination zones in Lincang City (2010–2020).

	2010	2015	2018	2020
Incongruous zone	42.56%	42.40%	41.99%	42.03%
Break-in zone	20.25%	20.34%	20.59%	20.65%
Coordinated zone	37.19%	37.26%	37.42%	37.32%

in Appendix B. Overall, the PLE functions demonstrate the characteristics of being coordinated in the northern and southern part, and incongruous in the central part.

In summary, it can be seen that the types of PLES in the study area include: single-function PLES (ecological space), multi-function PLES (production–living space, production–ecological space, and production–living–ecological space), and cover four of all PLES combination types, as shown in Figure 5. To ensure terminological clarity for the spatial categories identified through our subsequent analysis, we establish the following naming conventions. The overarching concept is referred to as production–living–ecological spaces (PLES). The specific types of functional space resulting from our grid-scale identification are termed: ecological space (ES), production–living space (PL space), production–ecological space (PE space), and production–living–ecological space (PLE space), denoting a highly coordinated, multi-functional composite.

In terms of spatial distribution, during the period of 2010–2020, the spatial distribution pattern of the PLES in Lincang City present an obvious consistency as shown in

Table 5. Area and percentage of production–living–ecological space (Area: km²).

	2010		2015		2018		2020
	Area	Ratios	Area	Ratios	Area	Ratios	Area	Ratios
ES	12,711.1	45.01%	12,662.64	44.84%	12,545.01	44.43%	12,558.1	44.48%
PL	13.68	0.05%	22.18	0.08%	56.6	0.20%	61	0.22%
PE	3119.26	11.05%	3113.73	11.03%	4804.88	17.02%	3195.06	11.32%
PLE	12,394.22	43.89%	12,439.71	44.05%	10,828.53	38.35%	12,420.86	43.99%

: the production–living–ecological space and ecological space were spread throughout the entire study area; the ecological space was continuously distributed in Zhenkang County, Yongde County, and Gengma Dai Wa Autonomous County, etc.; the production–living–ecological space was concentrated in the southern part of Lincang City, and distributed in a band-like pattern across the northeastern part of Lincang City; the production–ecological space was distributed in a point-like manner between the production–living–ecological space and ecological space; and the production–living space was distributed in a small aggregated area. Quantitatively, ES and PLE spaces consistently accounted for the largest shares, while PL remained the smallest. ES declined slightly between 2010 and 2020 (from 45.01% to 44.48%), reflecting the encroachment of cropland expansion and settlement growth into ecological land. PL showed a gradual but continuous increase (from 0.05% to 0.22%), largely driven by population growth, infrastructure construction, and the “scattered and infiltrative” expansion of settlements into farmland and forest. PE fluctuated substantially, peaking at 17.02% in 2018 before decreasing to 11.32% in 2020. This fluctuation illustrates the transitional nature of PE: in periods of intensified land development, ecological land is converted into production-oriented use, while in periods of ecological restoration or land-use regulation, PE tends to shrink. Correspondingly, the decline of PLE space in 2018 (38.35%) and its rebound in 2020 (43.99%) reveal the dynamic process of balancing multiple functions.

3.3. Results and Analyses of the Spatial Evolution of the PLES

3.3.1. Analysis of the Evolutionary Pattern of PLES Types Based on Transition Matrix

In order to identify the evolutionary pattern of PLES types in Lincang City, as shown in

Table 6. The transition matrix of production–living–ecological space in 2010–2015.

2015
2010	ES	PE	PL	PLE	Total transfers out
ES	12,566.77	86.64	3.92	53.77	144.33
PE	95.51	2920.1	0.62	103.02	199.16
PL	0	0.36	12.96	0.36	0.72
PLE	0.36	106.63	4.68	12,282.56	111.67
Total transfers in	95.87	193.63	9.22	157.15	455.87

,

Table 7. The transition matrix of production–living–ecological space in 2015–2018.

2018
2015	ES	PE	PL	PLE	Total transfers out
ES	12,419.91	218.79	0.08	21.83	240.7
PE	122.41	2969.34	4.33	17.56	144.3
PL	0.3	0	17.91	0.36	0.66
PLE	1.44	1615.47	34.9	10,787.81	1651.81
Total transfers in	124.15	1834.26	39.3	39.76	2037.47

and

Table 8. The transition matrix of production–living–ecological space in 2018–2020.

2020
2018	ES	PE	PL	PLE	Total transfers out
ES	12,393.08	143.94	0.07	7.92	151.93
PE	162.16	3048.96	2.16	1591.6	1755.92
PL	0	1.08	53.36	2.16	3.24
PLE	2.87	1.08	5.4	10,819.18	9.35
Total transfers in	165.03	146.1	7.63	1601.68	1920.43

, the results of PLES identification in 2010, 2015, 2018, and 2020 on the basis of overlay analysis, and the transition matrix was used to calculate the direction and quantity of entries and exits of each PLES in the phases of 2010–2015, 2015–2018, and 2018–2020. Besides, the corresponding Sanky diagram is shown in Figure 6.

From Table 6, it can be seen that from 2010 to 2015, the PLES transition in Lincang City mainly reflected the mutual transformation between production–living space, ecological space and production–living–ecological space: the total area of transition was 455.87 km², the transition inflow and outflow areas for the production–ecological space were both the largest, with the areas of 193.63 km² and 199.16 km², respectively; production–ecological space was mainly transitioned from production–living–ecological space, while production–ecological space was mainly transitioned out into ecological space; and production–living space and production–living–ecological space showed net transition inflow, while other types of space show net transition outflow.

The spatial transition of the PLES from 2015 to 2018 is primarily evident in the mutual transformation between production–ecological space and production–living–ecological space: the total area of transition is 2037.47 km², with the production–ecological space having the largest transition inflow area (1834.26 km²) which was mainly transitioned from production–living–ecological space. And the largest transition outflow area (1651.81 km²) occurs from production–living–ecological space to production–ecological space. Also, production–ecological space and production–living space showed net transition inflow, and other types showed a net transition outflow.

In 2018–2020, the transfer of the PLES is mainly characterized by the mutual transformation between production–ecological space and production–living–ecological space: the total area of transition is 1920.43 km², and the production–living–ecological space has the largest area transition inflow (1061.68 km²) which was mainly transitioned from production–ecological space. Meanwhile the largest transition outflow area (1755.92 km²) occurs when transitioning from production–ecological space to production–living–ecological space. And ecological space, production–living space and production–living–ecological space showed net transition inflow, while other types showed net transition outflow. During the study period, production–living space has consistently revealed a net transition inflow, whereas ecological space has undergone a relatively substantial net transition outflow, which may be associated with the accelerating process of urbanization in Lincang City, rather than attributable to a single driving factor.

Furthermore, as shown in Figure 5, the anomalous fluctuation of PE space in the northern study area, characterized by its abrupt emergence in 2018 and subsequent disappearance in 2020, can be interpreted in the context of the preparation and implementation cycle of the National Innovation Demonstration Zone policy. Although Lincang was officially designated as a National Innovation Demonstration Zone for Sustainable Development in May 2019, the national application process began following the guidelines issued by the Ministry of Science and Technology in April 2017. To meet the rigorous criteria for designation, the local government was required to identify sustainability bottlenecks and initiate specific action plans during the application phase. Consequently, the massive conversion from PLES to PES (1615.47 km²) observed between 2015 and 2018 (Table 7) coincides with this intense “pre-designation” period (2017–2018). During this phase, the simultaneous pressures of agricultural modernization for poverty alleviation and the structural adjustments required for the sustainability application likely triggered transitional instability in land-use functions, resulting in the expansion of the highly sensitive PES. Subsequently, the substantial reversal from PES back to the more coordinated PLES (1591.6 km²) observed during 2018–2020 (Table 8) temporally aligns with the formal establishment of the Demonstration Zone. This suggests that the spatial patterns in 2020 reflect the cumulative effect of the corrective actions taken during the preparation phase (2017–2019) and the stabilization of development strategies following official approval, rather than a sudden shift occurring solely in 2019. Thus, the observed fluctuation of PES reflects its role as a “contested zone” shaped by the interaction of policy reorientation, development demand, and the transition from pre-application adjustments to post-designation implementation.

3.3.2. Analysis of Changes in Landscape Patterns in PLES Based on the Landscape Pattern Index

Landscape disturbance (E), which reflects the degree of ecological risk, is derived from a weighted integration of landscape fragmentation (C), separation (F), and dominance (D).

Table 9. Landscape pattern index of production–living–ecological space from 2010 to 2020 ².

Year	PLE Type	C	F	D	E
2010	ES	2.84	2.08	0.41	2.13
	PE	2.84	4.23	0.2	2.73
	PL	2.85	1987.67	0	597.73
	PLE	2.78	2.11	0.39	2.1
2015	ES	2.84	1.88	0.45	2.07
	PE	2.84	7.64	0.11	3.74
	PL	4.46	1602.13	0	482.87
	PLE	2.78	1.89	0.44	2.05
2018	ES	2.84	1.9	0.45	2.08
	PE	2.82	4.94	0.17	2.93
	PL	3.23	448.53	0	136.18
	PLE	2.78	2.17	0.38	2.12
2020	ES	2.84	1.89	0.45	2.08
	PE	2.84	7.45	0.11	3.68
	PL	3.21	414.94	0	126.09
	PLE	2.78	1.9	0.44	2.05

² Note: C = Landscape fragmentation index, F = Landscape division index, D = Landscape dominance index, E = Landscape disturbance index.

summarizes the C, F, D, and E values of various PLES types from 2010 to 2020. To further illustrate the interannual variations in these landscape indices, corresponding line charts are provided in Appendix D for visual comparison.

The statistical results show clear differences among PLES types.

(1) Ecological space (ES), as the dominant land category, exhibited remarkable stability: its C and F remained consistently low (C ≈ 2.84, F fluctuating between 1.88–2.08), and E declined slightly (from 2.13 to 2.08), reflecting the buffering effect of contiguous forest patches. (2) Production–ecological space (PE), by contrast, showed pronounced fluctuations. Its disturbance index peaked in 2015 and 2020 (E = 3.74, 3.68), corresponding to major mutual conversions between PE and PLE revealed in the transfer matrix (Table 6 and Table 7), highlighting its role as a contested zone between development and conservation. Specifically, between 2015 and 2018, a large-scale conversion of PLES into PE (1615.47 km²) shifted the balanced multifunctional space toward a production–ecological orientation, leading to an increase in both F and E. Conversely, from 2018 to 2020, PE reverted back into PLES (1591.6 km²). This back-and-forth transformation intensified the fragmentation and disturbance levels of PE in 2020. These dynamics illustrate PE as a “contested zone” where development and conservation constantly interact and adjust, reflecting Lincang’s ongoing struggle to reconcile ecological protection with economic growth. (3) Production–living space (PL), although covering the smallest proportion, displayed the strongest disturbance effect (E = 126.09 in 2020). Its F value decreased sharply (from 1987.67 to 414.94) despite area expansion (from to 0.05% to 0.22%). This indicates that PL did not expand through large, continuous blocks, but rather through a “scattered and infiltrative” growth pattern. Small and fragmented urban and settlement patches progressively penetrated into and along the edges of grassland, farmland, and forests, directly causing the high fragmentation observed in these landscapes. (4) Multi-functional PLE space showed moderate and relatively stable values, serving as an intermediate type. Its role lies in buffering transitions among the three functions, maintaining partial stability while also reflecting adjustments in the balance between human use and ecological protection.

The spatial distribution of C, F, D, and E was visualized using the moving window method in FRAGSTATS. In the context of our study area’s complex terrain and 600-m grid scale, a radius of 1000 m was selected as it provided an optimal balance, effectively highlighting regional spatial gradients while minimizing local noise in the pattern maps [67,68]. Since the spatial distributions of indices across the four study years exhibit consistent patterns, the results for 2018 are presented here as a representative example for illustration (Figure 7), and the full set of maps for all years is provided in Appendix C (Figure A5, Figure A6, Figure A7 and Figure A8). The spatial distribution patterns further support these findings: high C values were concentrated in the northeast and northwest, coinciding with grassland, farmland, and fragmented forest areas. High F values appeared in the northeast, northwest, and southeast, while D showed spotty high-value clusters in the northeast and northwest. E displayed a relatively uniform pattern, with low values in forest-covered areas. These spatial patterns not only explain the numerical trends but also reveal how human activities (e.g., agricultural expansion and scattered urban growth) drive fragmentation and disturbance, thereby linking landscape dynamics directly to the evolution of PLES.

As shown in Figure 8 and Figure 9, Shannon’s diversity index (SHDI) and Shannon’s evenness index (SHEI), which demonstrate the aggregation and dispersion characteristics of landscape pattern, exhibited a certain upward trend in general during the period from 2010 to 2020. And this indicates that the distribution of patches within the landscape system of Lincang City has become relatively uniform. Concurrently, the landscape fragmentation has increased, and landscape diversity has grown under the dominance of ecological space. Overall, CONTAG showed a decreasing trend, revealing that the density of patches has increased, with the same spatial type tending to distribute sparsely and discontinuously, resulting in reduced connectivity between landscapes. And the aggregation index (AI) shows a tendency to decrease in general, indicating that the dispersion of patches in the PLES has increased, and the degree of patch fragmentation is expanding.

4. Discussion

The findings reveal significant spatial heterogeneity and dynamic imbalances among production, living, and ecological functions in Lincang City. The coupling coordination analysis revealed a clear north–south coordination and central incongruity pattern, while the landscape pattern indices demonstrated increasing fragmentation and functional transitions. These results reflect the complex interplay between socioeconomic development and ecological conservation in mountainous regions, where terrain constraints, policy implementation, and human activities collectively shape the spatial organization of multifunctional land use.

4.1. Comparison with Existing Studies and Contribution of This Research

Positioning this study within the broader PLES literature clarifies its specific contributions in terms of research scope, methodology, and resultant insights. Regarding research scope, existing PLES studies have predominantly focused on flat or peri-urban areas and commonly relied on administrative units or coarse spatial grids. Such approaches implicitly assume relatively continuous land-use expansion and gradual functional transitions, assumptions that are difficult to sustain in highly fragmented mountainous landscapes. In contrast, this study explicitly targets a mountainous border city characterized by steep terrain, ecological sensitivity, and dispersed human activities. The adoption of a 600 m neutral grid is therefore not merely a technical choice but a critical methodological adaptation to the spatial realities of mountainous environments. This scale enables the identification of intra-regional functional mosaics and scattered production–living incursions that are typically smoothed out or concealed when analyses are conducted using administrative units or coarser grids. As a result, spatial heterogeneity, functional conflicts, and localized coordination imbalances become more explicitly observable. Methodologically, while prior research frequently identifies PLES through land-use overlay or single-function scoring, our framework integrates grid-based functional identification with the coupling coordination degree model (CCDM). Although this study relies primarily on land-use data—acknowledging the limitation in capturing the full varying intensity of human socioeconomic activities—this approach ensures high spatial continuity and comparability in data-scarce mountainous regions. It should be emphasized that the land-use-based functional identification serves as a foundational layer of the PLES framework. The proposed analytical approach prioritizes diagnosing the spatial coordination and conflict patterns rather than absolute economic intensity. This framework is inherently extensible and can readily incorporate socioeconomic or behavioral indicators in future works where high-precision grid-level data becomes available.

Consequently, the results reveal coordination structures and transformation mechanisms that have rarely been explicitly discussed in previous PLES studies. Notably, the identified “north–south coordination and central incongruity” pattern represents an internal spatial structure that is difficult to detect without grid-scale spatial units and coordination-based metrics. Moreover, the expansion of production–living space in this mountainous context exhibits a distinctly “scattered and infiltrative” pattern, contrasting sharply with the contiguous urban expansion commonly reported in plain regions. The use of landscape indices elucidates how such dispersed growth erodes landscape connectivity and elevates ecological disturbance. This finding departs from the dominant PLES transformation narrative derived from flat regions and suggests that, in mountainous areas, multifunctional land-use change proceeds primarily through dispersed infiltration rather than large-scale contiguous conversion.

Thus, this study not only extends PLES research into an ecologically fragile, fragmented mountainous context but also provides a methodological framework and mechanistic insights for understanding PLES dynamics in similar regions.

4.2. Mechanistic Interpretation of the Spatial Patterns and Policy Implications

From a mechanistic perspective, three major drivers, uniquely expressed through the mountainous context, underlie these spatial patterns.

First, accelerated urbanization and agricultural expansion have intensified the interaction between production and living functions, resulting in the scattered and infiltrative expansion of PL space into grassland and forested areas. Unlike the contiguous outward expansion typical of plains, this dispersed growth mode leads to the subdivision of ecological patches and is directly reflected in elevated fragmentation and division indices. This grid-scale, patchy expansion pattern is a hallmark of urban growth in rugged terrain, contrasting with the contiguous sprawl observed in flat plains.

Second, the ecological restoration programs and topographic limitations restrict the spread of production and living functions in high-altitude regions, stabilizing ecological space but simultaneously increasing its isolation. Landscape pattern indices indicate that, despite the overall dominance of ecological space, internal connectivity has declined, giving rise to a highly fragmented but functionally persistent ecological matrix. This spatial configuration is not merely a statistical result but a reflection of the unique human–environment interactions in Lincang’s multi-ethnic, border mountainous context: (1) the observed fragmentation is intrinsically linked to the terrain-adaptive livelihoods of ethnic minorities. In Lincang, major ethnic groups such as the Wa and Dai peoples have historically adopted dispersed settlement patterns and small-scale agriculture adapted to vertical climate zones. This traditional livelihood practice creates a mosaic of small, scattered production and living patches (e.g., terrace farming, tea plantations, and hillside villages) embedded within the rugged Ecological matrix, which quantitatively manifests as high patch density (PD) and lower contagion (CONTAG) in our results. (2) The stability of large ecological patches, particularly in the peripheral zones, correlates with the region’s function as a border ecological barrier. As a border area, land-use intensity in certain zones is regulated not only by topography but also by border security and ecological protection policies, preserving large, continuous tracts of forest that serve as critical ecological screens. Therefore, although ethnic and geopolitical attributes were not explicitly modeled as input variables, their influence is structurally embedded in the PLES configuration: the “fragmentation” reflects the diverse, smallholder-based ethnic economy, while the “dominance” of ecological space underscores the region’s role in regional ecological security.

Third, policy-driven land-use reallocation—such as the establishment of ecological redlines and basic farmland—has improved overall ecological stability but also accentuated the spatial mismatch between economic intensity and ecological carrying capacity. In mountainous areas, this mismatch is further magnified by the geometric fragmentation of suitable land, causing localized competition between production, living, and ecological functions. These factors collectively explain the uneven evolution of coupling coordination across regions.

Building upon the mechanistic understanding above, our findings offer several targeted implications for spatial planning and governance in Lincang and similar mountainous cities:

• Production–ecological (PE) space has been identified as a dynamic “contested zone,” requiring adaptive and differentiated governance. Management strategies should be delineated according to the coupling coordination degree (D_cc). Areas with lower D_cc values should be prioritized for ecological restoration and strict protection to prevent further degradation, whereas zones with higher D_cc values may be designated for sustainable agroforestry or eco-tourism development, serving as transitional buffers between intensive production areas and core ecological reserves.
• The infiltrative expansion of production–living (PL) space has significantly intensified ecological disturbances, indicating an urgent need to enhance landscape connectivity conservation. In the northeastern and northwestern regions—where high fragmentation and division indices coincide with the expansion of PL space—urban growth boundaries should be clearly defined to limit further encroachment. Meanwhile, the restoration of ecological corridors linking large forest patches in the central-western regions is essential to mitigate the ecological impacts of landscape subdivision and fragmentation.
• The spatial divergence in coupling coordination levels underscores the necessity for region-specific optimization strategies. The well-coordinated northern and southern areas, where multifunctional integration is relatively strong, are suitable for promoting high-value green industries and compact settlement development. In contrast, the central regions with low coordination levels, characterized by ecologically fragile but functionally critical ES, should be subject to stringent protection measures aligned with the national Ecological Redline policy.

Beyond the specific context of China, the analytical framework proposed in this study holds broader implications for global sustainable land management, particularly in alignment with the United Nations Sustainable Development Goals (SDGs). For instance, the identification and optimization of “living space” directly support SDG 11 (Sustainable Cities and Communities) by highlighting areas for human settlement improvement, while the preservation of “ecological space” contributes to SDG 15 (Life on Land). Our grid-based coordination analysis offers a transferable methodological prototype for other developing countries facing similar trade-offs between agricultural expansion and ecosystem conservation.

4.3. Limitations and Future Research Directions

While this study provides valuable insights into the spatiotemporal evolution of PLES in mountainous border regions, several limitations should be acknowledged to guide future research.

First, regarding the evaluation framework and data sources: The functional identification in this study relies primarily on land-use structural proxies. While this approach ensures high spatial continuity and temporal comparability (2000–2020) for regional analysis, it assumes functional homogeneity within each land-use class. For instance, the current 1-3-5 scoring system assigns a uniform high ecological score to all forest lands, potentially overestimating the functional intensity of degraded secondary forests compared to pristine ones. This structural approach prioritizes the identification of dominant functional types but may not fully capture intra-class variations in ecosystem health or socio-economic output. Future research could address this by integrating multi-source data—such as biophysical indicators (e.g., NDVI, NPP) or high-precision socio-economic grids (e.g., POI, corrected NTL)—to refine the functional scoring system [69,70], provided that the challenges of data availability and scale mismatch in mountainous terrain can be effectively managed.

Second, regarding model assumptions and scenarios: The coupling coordination degree model (CCDM) adopted an equal-weight assumption. This reflects a normative “Equal-Priority Planning Scenario” aimed at assessing balanced development, consistent with the region’s sustainable development goals. However, we acknowledge that this baseline scenario does not capture alternative policy orientations where specific functions are prioritized (e.g., strict conservation zones). Future studies could employ scenario-based sensitivity analyses by varying these weights or testing alternative scoring schemes to evaluate how coordination outcomes shift under different policy priorities (e.g., Development-First vs. Conservation-First scenarios).

Third, regarding spatial scale and local context, the spatial analysis is subject to scale effects inherent to the 600 m grid resolution. While this scale effectively captures regional functional coupling, it may induce a smoothing effect in highly fragmented areas, such as narrow river valleys, where linear settlement expansion may be diluted by the surrounding steep ecological slopes. Additionally, the unique socio-cultural drivers of Lincang—specifically its ethnic diversity and border geopolitics—are indirectly reflected in the landscape metrics but not explicitly quantified due to data constraints. Future research utilizing multi-resolution analysis or micro-scale participatory surveys could offer deeper insights into these localized mechanisms and verify the robustness of PLES boundaries across scales.

5. Conclusions

Lincang City holds significant strategic importance in regional development and opening up patterns. The PLES pattern and its evolution have major implications for the economy, society, and ecological environment. This study comprehensively evaluated the functional level of the PLES in Lincang City based on the multifunctionality of land use, identified the PLES at the grid scale from the perspective of functional synergy, and finally analyzed the characteristics of the evolution of the PLES pattern by using the land use transition matrix and the landscape pattern indices.

The main outcomes show that the distribution of production, living, and ecological function values in Lincang during the study period showed a consistent spatial pattern, while the ecological function showed significant spatial complementarity with the production function and living function. And, viewed from the spatial distribution pattern, the degree of coupling and coordination of the PLE functions in Lincang within the study time period showed consistency: incongruous and coordinated zones were continuously distributed in patches throughout the study area, and break-in areas were distributed between these two zones in a point-like manner. And from 2010 to 2020, Lincang contained four types of PLES combinations: ES, PL space, PE space, and PLE space. Ecological space is the main type of PLES in Lincang, followed by PLE space and PE space, with PL space occupying the smallest amount of land. During the study period, the number of ES has been decreasing, while the number of PL spaces has been increasing, and the other types of PLES have presented slight fluctuations. Changes in the spatial structure of the PLES in Lincang during the study period are manifested in the occurrence of mutual transition between different types of PLES: a larger amount of transition outflow in 2015–2018 and 2018–2020, mainly containing transition between ES, PE space, and PLE space. In terms of changes in the landscape pattern of the PLES, between 2010 and 2020, the PLE space generally experienced a decline in the degree of landscape disturbance, a decreasing trend in the ecological risk, an increase in the landscape fragmentation degree, a shift from simplicity to complexity in the landscape, and an enhancement in the spatial heterogeneity.