Ecological Resilience and Sustainable Development: Dynamic Assessment and Evolution Mechanisms of Landscape Patterns and Ecotourism Suitability in the Yangtze River Delta Region

Abstract

Ecotourism, as a resilient and sustainable form of tourism, plays an increasingly vital role in regional economic growth and ecological conservation, particularly in the face of challenges such as climate change and rapid urbanization. This study employs spatial-temporal analysis tools including GIS, Fragstats, and GeoDa to examine the dynamic evolution of ecotourism suitability levels (ESL) and landscape patterns (LP) in the Yangtze River Delta (YRD) from 2002 to 2022. By incorporating spatial autocorrelation analysis, the relationship between ESL and LP is investigated to assess the adaptive capacity of the regional ecotourism system. The results reveal the following: (1) Overall Trends: ESL in the YRD has generally increased over the past two decades, with expansions observed in both high and very low suitability areas, while areas of low suitability have contracted. (2) Spatial Patterns: Core cities such as Shanghai, Hangzhou, Nanjing, and Hefei exhibit high ESL; however, these areas also face intensified landscape fragmentation and decreased ecological connectivity. (3) Landscape Patterns: The region has experienced increasing landscape fragmentation and diversity, particularly in economically advanced zones, posing significant challenges to ecological resilience. (4) Spatial Clustering: Notable spatial clustering of ESL and LP indices is identified in highly urbanized areas, underscoring the necessity for adaptive landscape planning and flexible policy frameworks. This study provides empirical evidence and strategic recommendations to enhance the resilience and sustainability of ecotourism in rapidly urbanizing regions, supporting adaptive responses to crises and informed long-term decision-making.

1. Introduction

Ecotourism, a concept introduced by the International Union for Conservation of Nature (IUCN) in 1983, has gained global recognition as it has evolved over time [1]. This form of tourism prioritizes ecological protection while promoting sustainable tourism development, encompassing three key components: conservation, tourism, and market dynamics [2]. According to recent reports, ecotourism is the fastest-growing segment of the tourism market in the 21st century [3]. On 17 May 2024, China hosted the National Tourism Development Conference, which emphasized the need to fully protect and responsibly utilize ecological resources. The conference advocated for the establishment of a green development model and lifestyle, highlighting the significant market potential and the substantial economic and social benefits associated with ecotourism [3].

The advancement of urban expansion and urbanization has caused significant damage to the ecological environment, which, in turn, has greatly impacted the ecotourism industry [4]. The Yangtze River Delta (YRD) region (including Shanghai, Jiangsu, Anhui, and Zhejiang), as the core area of China’s economic development and urbanization, is endowed with rich natural landscapes and tourism resources, providing a solid foundation for ecotourism development [4]. Currently, this region is systematically developing ecotourism by leveraging national natural resources and has successfully established several market-accepted ecotourism routes, such as forest tours, grassland tours, bird-watching tours, and ecological study tours, which have gradually become the preferred choice for daily travel. Previous studies indicate that the development of ecotourism not only promotes regional growth but also provides economic benefits. However, this development inevitably impacts the local ecological environment. Consequently, the coordination of ecological development and protection has become an important area of research [5,6,7].

Currently, the assessment and planning frameworks for ecotourism in the YRD region primarily focus on single-dimensional evaluations, lacking integration between ecological dynamics and tourism development patterns. Researchers have utilized Geographic Information Systems (GIS) to assess local ecological sensitivity [8], applied ArcGIS 10.8 to evaluate regional morphology [9], established ecotourism development indices through the Delphi method and Analytic Hierarchy Process (AHP) [10], and employed structural equation modeling to assess user satisfaction and revisit intentions in ecotourism contexts [11,12]. While these studies offer valuable insights into specific aspects of ecotourism, they often treat ecological attributes and user experiences in isolation, without considering their spatiotemporal interactions. Specifically, existing research has not systematically examined how the evolution of landscape patterns (LP) over time affects ecotourism suitability levels (ESL), particularly in rapidly urbanizing regions. This gap is critical, as urbanization fundamentally alters landscape structures, thereby influencing ecological integrity and tourism potential. Understanding the spatiotemporal correlation between ESL and LP is essential for developing adaptive management strategies that balance ecological conservation with tourism development. Without such an integrated perspective, planners cannot effectively anticipate how urban expansion may impact ecotourism resources, nor can they design interventions that support sustainable tourism growth while maintaining ecological resilience. Therefore, this study addresses these gaps by investigating the dynamic interactions between ESL and LP in the YRD from 2002 to 2022, offering important insights for optimizing ecological resource utilization strategies in urbanizing regions.

ESL is an assessment of how suitable a given area is for ecotourism activities [13]. It aims to evaluate the ecological potential and attractiveness of natural resources to ensure the delivery of sustainable tourism experiences [14]. ESL also examines ecosystem diversity and integrity, assesses the spatiotemporal impacts of human activities on the environment, and provides a scientific basis for ecotourism planning and development [15]. For instance, Ambecha et al. used geospatial techniques to assess ESL in Southwestern Ethiopia, finding high ecological potential in certain areas but inadequate infrastructure, underscoring the need for coordinated governance [16]. Kiper et al. demonstrated in their study of urban forests in Turkey that urbanization, while stressing ecosystems, can also foster new forms of ecotourism [17]. Similarly, Sofyan’s research on the Himba Kahui urban forest community in Indonesia highlighted the role of community engagement in shaping urban ecotourism functions, suggesting that improved urban forest management can enhance the ecotourism experience [18]. Liu Jie et al. employed remote sensing and GIS to assess ecological suitability for industrial land in Wuhan, aiding local land-use planning [19]. Arijit Das et al. proposed 18 indicators to analyze the link between urban green space and quality of life, revealing spatial inequality and a negative correlation between green space availability and life quality in urban centers [20]. Collectively, these studies provide valuable insights into the interaction between urbanization and ecotourism, offering a broader foundation for informed planning. This study focuses on the YRD to examine ESL’s spatiotemporal dynamics, contributing to regional planning, biodiversity conservation, ecological resource management, and policy development.

LP reflect environmental changes driven by natural forces and anthropogenic activities. By analyzing patch size, morphology, and fragmentation, LP analysis offers insights into the region’s environmental structure and functional changes, which are closely tied to ecotourism development [21]. While tourism supports economic growth, it often leads to increased landscape patchiness and fragmentation. LP analysis is therefore essential for quantifying landscape structure, spatial configuration, and regional ecological sustainability [22]. Commonly used metrics include mean shape index (MSI), patch density (PD), number of patches (NP), patch connectivity index (CONTAG), Shannon’s diversity index (SHDI), edge density (ED), and largest patch index (LPI) [21,22,23]. For example, Chunbo Huang et al. found that LP changes in the Three Gorges Reservoir significantly affected ecosystem service values—forested areas expanded during ecological development and declined during economic growth phases [24]. Hanni Jin et al. identified LP as a key determinant of biodiversity, with their results showing an inverse relationship between biodiversity and patch size [25]. Miao Yang et al. analyzed land use changes in China’s lakes and rivers from 1980 to 2017 using LP analysis and PCA, finding a general increase in water bodies [26]. Yang Han et al. explored the relationship between LP and crop growth in the North China Plain, revealing scale-dependent effects and a negative correlation between patch complexity and plant productivity [27].

Despite the valuable contributions of the aforementioned studies, a critical research gap remains in understanding how the evolution of LP spatially correlates with ESL, particularly in rapidly urbanizing regions such as the YRD. This gap is significant for several reasons. First, although previous studies have examined LP and ESL independently, few have explored their spatial interactions, limiting planners’ ability to understand how landscape transformations influence tourism potential across different locations. Second, urbanization creates complex spatial mosaics that affect ecological integrity and tourism appeal in heterogeneous ways. Without spatial correlation analysis, these spatial variations cannot be fully understood. Third, the YRD represents a globally significant case where rapid urbanization and ecological conservation efforts coexist, making it essential to explore the spatial linkages between landscape transformation and ecotourism potential. Lastly, without investigating the spatial correlation between ESL and LP, it remains unclear whether areas undergoing landscape fragmentation consistently exhibit reduced ecotourism suitability, or whether certain landscape configurations might actually enhance ESL under urbanization pressure. Addressing this gap through spatial correlation analysis can yield critical insights for targeted planning interventions that sustain ecological resilience while promoting sustainable tourism development.

This study employs LP analysis to investigate changes in spatial structure, landscape diversity, connectivity, and land use transformation. Based on ESL evaluation results, it considers the spatio-temporal dynamics of regional LP to explore how LP evolution influences ESL under the urbanization process. The study further aims to formulate optimization strategies, clarify future development pathways, and propose refined planning frameworks from both ecological conservation and tourism development perspectives, thereby enhancing the overall ecotourism planning system.

In this study, 14 ecotourism evaluation indices were proposed based on dimensions such as geomorphology, tourism resource conditions, human activities, and climatic factors specific to the YRD. A factor weighting analysis was conducted to assess the significance of these indices. The changes in ESL in the YRD and its provinces from 2002 to 2022 were examined. Additionally, six indicators were selected from three perspectives—landscape fragmentation, landscape aggregation, and landscape diversity—to analyze the spatial and temporal trends of LP in the YRD. Finally utilizing the assessment of ESL and the findings related to LP, the study explored the relationship and correlation characteristics between changes in ESL and LP, as well as the aggregation characteristics of these changes through bivariate spatial autocorrelation analysis.

To clarify the central focus of this research, the study addresses the following research questions:

(1)

How have ecotourism suitability levels (ESL) in the core YRD region changed spatially and temporally from 2002 to 2022?

(2)

What are the main trends and characteristics of landscape pattern (LP) evolution in the core YRD region during the same period?

(3)

What is the spatial correlation between ESL and LP, and how do changes in landscape structure influence ecotourism suitability in the context of rapid urbanization?

The main contributions of this study are summarized as follows:

(1)

This study constructs an ESL evaluation system for the YRD, systematically analyzing the spatial and temporal changes of ESL from 2002 to 2022. The goal is to provide a scientific basis for the construction and protection of ecotourism in the region.

(2)

Utilizing land use data at a 10 km × 10 km grid scale for five periods—2002, 2007, 2012, 2017, and 2022—this study comprehensively examines the spatio-temporal change characteristics of regional LP in the YRD. The findings will offer important support for ecotourism planning and decision-making.

(3)

By analyzing the spatial correlation between ESL evaluation and LP indices (NP, PD, LPI, CONTAG, PR, and SHDI) in the YRD, this study explores the relationship between LP and regional ecotourism. This analysis aims to enhance understanding and assessment of the landscape structure and ecosystem health of the region, providing a foundation for ecological diversity conservation and effective ecotourism management.

2. Materials and Methods

2.1. Research Area

YRD, located in the middle and lower reaches of the Yangtze River in China, covers an area of approximately 33.5 × 10⁴ km² (Figure 1). This region is one of the most economically active areas in China, characterized by rapid urbanization, a high degree of openness, and strong innovation capacity, significantly contributing to the country’s modernization [28]. Since the integration of the YRD was upgraded to a national strategy, the region’s role as a dynamic and robust growth pole has continued to be strengthened. Although the YRD accounts for only about 4% of China’s land area, it is home to about 17% of the country’s population and generates nearly a quarter of the country’s economic output. According to statistics, the region’s combined GDP surpassed 30 trillion yuan in 2023, further enhancing the region’s contribution to China’s high-quality development [29]. The YRD features a subtropical eastern climate with four distinct seasons and a mild climate. It encompasses a variety of terrains, including plains, hills, and mountains, providing rich natural conditions and diverse ecological resources. The region supports a wide range of ecosystems, such as wetlands, rivers, lakes, forests, and urban green spaces [30]. In recent years, driven by the promotion of ecological civilization, the YRD has been actively developing its ecotourism industry, presenting new opportunities for local economic development [31]. However, the rapid pace of economic growth and urbanization has led to significant changes in the region’s spatial structure, posing challenges to the ecological environment and threatening ecological diversity [32,33]. Consequently, this study focuses on the YRD to analyze the impact of LP evolution on ESL over the past 20 years.

2.2. Data Sources

This paper utilizes multi-source geospatial data, encompassing remote sensing, meteorology, and basic geographic information. Specifically, it includes vector boundary information, elevation data (DEM), ecotourism point location data, ecological core protection areas, watershed buffers, the normalized difference vegetation index (NDVI), attraction ratings, the number of attraction reviews, LUCC for the YRD, population density (POP), road network data, precipitation (PR), and surface temperature (Temp). The sources of data acquisition are detailed in

Table 1. Data sources.

Data Type	Data Sources	Data Description
Vector boundary information	Resource and Environmental Sciences Data Center (RESDC)	Administrative boundaries of the study area by vector map masking each indicator raster
DEM	Geospatial data cloud	Reflects regional elevation information (30 m spatial resolution)
Ecotourism site data Ecological Core Protection Zone Watershed Buffer Zone	Baidu Map Database	POI point data for ecotourism sites; Ecological reserves as well as areas of water bodies
NDVI	NASA MOD13A2	Vegetation cover index
Attraction Ratings Attraction Review Count	Attraction ratings and expert scores	Attraction ratings and number of attractions
LUCC	ZENODO database	Reflective of ground cover (30 m spatial resolution)
POP	RESDC	POP in the study area
Road network data	OSM	Reflects transportation in the study area
PR TEMP	Terraclimate database	Average precipitation data, air temperature

[34,35,36,37,38,39,40,41,42,43].

The study incorporates relevant data from five specific years: 2002, 2007, 2012, 2017, and 2022 [44,45]. To ensure uniformity in data processing, all datasets underwent scale transformation and standardization of indicators, and were matched to the Krasovsky 1940 Albers projected coordinate system.

2.3. Methods

2.3.1. Ecotourism Suitability Evaluation System

The ESL evaluation indicators for the YRD were systematically selected based on four critical dimensions that directly influence ecotourism development potential [32,33]. Each indicator was chosen for its specific relevance to both ecological integrity and tourism viability in the YRD context, following a comprehensive literature review and regional analysis. The selection process prioritized indicators that: (1) have established relationships with ecotourism suitability in previous studies; (2) address the unique geographical and socioeconomic characteristics of the YRD; and (3) provide comprehensive coverage of both ecological and tourism dimensions. To construct the ESL evaluation system, we identified 14 specific evaluation indicators across four dimensions (

Table 2. Indicators for Evaluating the Suitability of Ecotourism in the YRD.

Factors	Feature Selection	Characterization	Calculation Method
Geological feature	DEM	Influencing human activities and climate	DEM value
	Aspect	Impact on natural ecology and LP	ArcGis Aspect Analysis
	Slope	Affects plant growth, the smaller the slope the better for plant growth	ArcGis Slope Analysis
Conditions of tourism resources	EAD	The higher the density of ecological attractions, the better the ecological resources	ArcGis Density Analysis
	ECPD	The closer the distance to the ecological core conservation area, the less favorable the ecotourism attraction will be	ArcGis Distance Analysis
	WBD	The closer you are to a body of water, the better the natural resources will grow	ArcGis Distance Analysis
	NDVI	Reflects the degree of vegetation cover of the area, the higher the value the better the natural resources	MOD13A2 Acquisition
	AQ	Reflects the degree of user recognition of the attraction, the higher the value the better the natural resources	ArcGis Density Analysis
	AP	Reflects the popularity of the eco-tourism attraction, the higher the value the more popular it is	ArcGis Density Analysis
Human activity	LUCC	Different types of land use matched with different environmental carrying capacities	ZENODO database
	POP	Reflects the POP of the region	RESDC Acquisition
	AA	Reflects the level of regional accessibility, the higher the value the more convenient it is	ArcGis Distance Analysis
Climatic factor	PR	Multi-year average precipitation, affecting regional climate	Terra climate database
	TEMP	Multi-year average temperatures, affecting regional comfort

):

(1)

Geomorphology indicators (DEM, Aspect, Slope): These were selected because topographical variation directly affects habitat diversity, visitor accessibility, and landscape aesthetic value [46]. In the YRD, which features diverse terrain from coastal plains to mountains, these indicators are crucial for understanding the physical foundations of ecotourism potential.

(2)

Tourism resource conditions indicators (EAD, ECPD, WBD, NDVI, AQ, AP): These indicators were chosen to capture both the ecological resources that attract tourists and the quality of existing tourism infrastructure. NDVI was selected over other vegetation indices because of its established relationship with biodiversity [47], while EAD, AQ, and AP were included to measure the existing tourism appeal of locations, which is essential for predicting future ecotourism potential.

(3)

Human activity indicators (LUCC, POP, AA): These indicators were selected to assess the human influence on landscapes and the practical feasibility of ecotourism development. LUCC captures land use changes that directly impact habitat quality, while POP and AA measure both pressure on ecosystems and accessibility for tourists—factors particularly relevant in the densely populated and rapidly urbanizing YRD [32].

(4)

Climatic factors (PR, TEMP): These were included because climate directly affects both visitor comfort and ecosystem functioning. These indicators are especially relevant in the YRD, where seasonal variations significantly impact tourism patterns and ecological processes [33].

Alternative indicators such as soil quality, biodiversity indices, and economic development metrics were considered but not included either due to data availability limitations across the 20-year study period or because their effects were adequately captured by the selected indicators [35,36,37,38,39,40,41,42,43]. The final selection of indicators thus represents a balanced approach that comprehensively addresses both ecological conditions and tourism potential in the YRD context.

Second, this study employs the Analytic Hierarchy Process (AHP) to calculate the weight coefficients of the ESL evaluation indicators. AHP is a widely used research method that determines weight coefficients by comparing each indicator pairwise [47,48,49]. In this study, we constructed the AHP judgment matrix, following these main steps: preparing the AHP questionnaire; inviting 18 experts and scholars engaged in ecological landscape or ecotourism to complete the questionnaire; collecting the responses for calculation; and determining the specific weight coefficients of the indicators using the geometric mean method. The detailed calculation steps are as follows [48,49].

(1)

A judgement matrix A was constructed, and pairwise comparisons of indicators within each level were made, choosing the Satty scale (1–9 scale) to compare elements.

$$A = \left[\begin{array}{cccc}{a}_{11}& a_{12}& \dots & a_{1m}\\ a_{21}& a_{22}& \dots & a_{2m}\\ ⋮& ⋮& \ddots & ⋮\\ a_{m1}& a_{m2}& \dots & a_{mm}\end{array}\right]$$

(1)

(2)

Calculate the maximum number of features of the judgement matrix (${\lambda }_{\max }$):

$${\lambda }_{\max } = {\displaystyle \frac{1}{n}}{\displaystyle \sum _{i = 1}^n\frac{{(Aw)}_i}{{\mathrm{w}}_i}}$$

(2)

where n is the order of the judgement matrix; w_i is the weight of each factor (i = 1,2, .…, n).

(3)

Consistency test on the judgement matrix:

$$CI = {\displaystyle \frac{{\lambda }_{max} - n}{n - 1}}$$

(3)

$$CR = {\displaystyle \frac{CI}{RI}}$$

(4)

where RI is the consistency index, CI is the consistency ratio, and n represents the corresponding value of the judgment matrix evaluation scale. To ensure the scientific validity and accuracy of the results, a consistency test is performed on the final outcomes. If the CI is less than or equal to 0.1, the test is considered successful. In this study, the consistency test value is 0.0958, which is below the threshold of 0.1, indicating that the calculation is scientifically sound. The derived weight coefficients for the ESL indicators will be utilized in subsequent evaluation processes. The results are presented in

Table 3. Weights of indicators for evaluating the suitability of ecotourism in the Triangle region.

Target Layer A	Guideline Layer B	Indicator Layer C	Weighting Factor
Suitability of Ecological Ecotourism in the YRD (A)	Geological feature (B1)	DEM (C1)	0.0178
		Aspect (C2)	0.0113
		Slope (C3)	0.0147
	Conditions of tourism Resources (B2)	EAD (C4)	0.0808
		ECPD (C5)	0.0458
		WBD (C6)	0.0580
		NDVI (C7)	0.0238
		AQ (C8)	0.1722
		AP (C9)	0.1413
	Human Activity (B3)	LUCC (C10)	0.1022
		POP (C11)	0.0828
		AA (C12)	0.1302
	Climatic Factor (B4)	PR (C13)	0.0855
		TEMP (C14)	0.0336

.

Finally, the grading of the evaluation indicators is conducted. To scientifically assess the ESL in the YRD, each indicator’s specific definition and grading criteria are clarified based on actual surveys and a review of relevant literature. In this study, the natural breakpoint method and expert assessment were employed to assign scores ranging from 1 to 5 for each evaluation index, as detailed in

Table 4. Grading Indicators for Evaluating ESL in the YRD.

Normative Layer	Indicator Layer	Appropriateness Indicator Assignment and Grading					Standard
		Very High Suitability	High Suitability	Medium Suitability	Low Suitability	Very Low Suitability
Geologic Feature	DEM(m)	0–115	115–315	315–560	560–880	>880	Natural Breakpoint
	Aspect	South, Flat ground	Southwest, Southeast	West, East	Northwest, Northeast	North	[50,51]
	Slope(°)	0–2	2–6	6–15	15–20	>20	[52]
Human Activity	LUCC	Wetland, Forest	Water body	Shrub, Grassland	Cropland	Others	[53]
	POP	0–2	2–6	6–15	15–33	>33	Natural Breakpoint
	AA	0–0.22	0.22–0.41	0.41–0.69	0.69–1.09	>1.09	Natural Breakpoint
Conditions of Tourism Resources	EAD	>0.025	0.015–0.025	0.008–0.015	0.004–0.008	0–0.004	Natural Breakpoint
	ECPD(m)	>5000	2000–5000	1000–2000	500–1000	0–500	Expert Evaluation
	WBD	0–200	200–500	500–800	800–1000	>1000	Expert Evaluation
	NDVI	>0.58	0.46–0.58	0.34–0.46	0.12–0.34	0–0.12	Natural Breakpoint
	AQ	>0.253	0.156–0.253	0.070–0.156	0.018–0.070	0–0.18	Natural Breakpoint
	AP	>992	592–992	272–592	74–272	0–74	Natural Breakpoint
Climatic Factor	PR	>130	110–130	95–110	80–95	0–80	Natural Breakpoint
	TEMP	>21	21	20	19	18	Natural Breakpoint
value		5	4	3	2	1

[50,51,52,53]. To derive the comprehensive ESL indicators, the indicators were standardized, ensuring consistency in units, attributes, and value ranges across all indicators.

2.3.2. LP Index Analysis

The LP index reflects the structural characteristics of the region, providing insights into ecosystem evolution, environmental pressures, biodiversity, and other relevant information. By comprehensively analyzing the size, shape, distribution, and connectivity of landscape patches, it is possible to assess the integrity and connectivity of ecosystems, as well as to reevaluate landscape structure and types [54,55]. To scientifically investigate the spatial and temporal evolution mechanisms of LP in the YRD, this study reviewed relevant literature and proposed similar indices. Six indices were selected from three perspectives: landscape fragmentation, landscape aggregation, and landscape diversity. These indices include the number of patches (NP), patch density (PD), connectivity index (CONTAG), Largest Patch Index (LPI), Shannon’s diversity index (SHDI), and patch richness (PR). This approach enables a multidimensional analysis of the ecological landscape characteristics in the YRD, considering factors such as urbanization processes, levels of anthropogenic interference, aggregation and disaggregation, and changes in patch dynamics. Detailed information on the LP indices is provided in

Table 5. LP index information.

Form	Norm	Formulas	Interpretation
Landscape fragmentation	Number of patches (NP)	$\mathrm{N}\mathrm{P}=\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{n}\mathrm{u}\mathrm{m}\mathrm{b}\mathrm{e}\mathrm{r} \mathrm{o}\mathrm{f} \mathrm{p}\mathrm{a}\mathrm{t}\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{s}$	Patches are relatively homogeneous geomorphic units in the landscape, and NP is used to describe the number of patches in the landscape.
	Patch Density (PD)	$\mathrm{P}\mathrm{D} =$${\displaystyle \frac{N}{A}}$ N: total number of patches in the region; A: indicates the total area of the region	PD is used to measure the distribution of patches in a given area and can reflect the degree of ecosystem fragmentation and the density of patch distribution
Landscape aggregation	Contagion Index (CONTAG)	$\mathrm{C}\mathrm{O}\mathrm{N}\mathrm{T}\mathrm{A}\mathrm{G} =$${\displaystyle \frac{{\displaystyle {\sum }_{i = 1}^{\mathrm{n}}{\displaystyle {\sum }_{j = 1}^n{P}_{ij}\text{·} (\frac{A_i\text{·}{A}_j}{A})}}}{{\displaystyle {\sum }_i^n(\frac{A_i^2}{A})}}}$ (n: total number of patch types; Pij: spatial contact between patch type i and patch type j; Ai and Aj are the total area of patch type i and patch type j, respectively; A: total area of the study area)	Used to measure the connectivity and spatial aggregation of patch distribution, with higher values indicating a more connected and aggregated spatial distribution.
	Largest Patch Index (LPI)	$\mathrm{L}\mathrm{P}\mathrm{I} = {\displaystyle \frac{{\mathrm{A}}_{\mathrm{m}\mathrm{a}\mathrm{x}}}{\mathrm{A}}}$ (Amax: Area of the largest patch in the region; A: Area of the study area)	Used to measure the relative size of the largest patches in the region and to assess the equilibrium of patch distribution
Landscape diversity	Shannon’s Diversity Index (SHDI)	$\mathrm{H} =$$-{\displaystyle {\sum }_{i = 1}^S(P_i\cdot \ln (p_{\mathrm{i}}))}$ (S: total number of species in the sample; Pi: relative abundance of species i in the sample; ln is the natural logarithm)	Indicators used to measure landscape diversity in ecosystems
	Patch Richness (PR)	$\mathrm{P}\mathrm{R} = \mathrm{O}$ (O: total number of patch types in the study area)	For assessing the diversity and complexity of different types of patches in ecosystems

[56,57,58].

2.3.3. Spatial Autocorrelation Analysis

Spatial autocorrelation analysis is a statistical method used to assess the degree of correlation and variation within regional spatial data. In this study, GeoDa software was employed, utilizing a 10 km × 10 km grid as the basic unit of analysis. The adjacency between spatial units was represented by constructing a spatial weight matrix [59]. In selecting the weight matrix, the adjacency approach was adopted, considering two spatial units to be adjacent if they share a common boundary or node, which aligns with the spatial characteristics of the study area [60]. The bivariate Moran’s I index was primarily used to analyze the spatial correlation between LP and ESL in the YRD [60]. The specific calculation formula is as follows:

$$I = {\displaystyle \frac{n{\sum }_{i = 1}^n{\sum }_{j - 1}^n{w}_{ij}(x_i - \overline{x})(y_i - \overline{y})}{\left({\sum }_{i = 1}^n{\sum }_{j = 1}^n{w}_{ij}\right)\sqrt{\left({\sum }_{i = 1}^n{(x_i - \overline{x})}^2\right)\left({\sum }_{j=1}^n{(y_i - \overline{y})}^2\right)}}}$$

(5)

where x_i and y_i denote the ESL value and LP index value of region i, respectively, $\overline{x}$ and $\overline{y}$ are the mean values of the corresponding variables, $w_{ij}$ is the spatial weight matrix element, and n is the number of spatial units. The value of Moran’s I ranges from −1 to 1, where a positive value indicates a spatial positive correlation between the variables, a negative value indicates a spatial negative correlation, and a value of 0 signifies no correlation [61]. To test the significance of spatial autocorrelation, this study employed the Monte Carlo random permutation method for significance testing. Based on the local spatial autocorrelation analysis (LISA), the study area can be classified into four types of spatial correlation: (1) High-High Aggregation (H-H): regions and their neighbors exhibit high ESL and LP indices; (2) Low-Low Aggregation (L-L): regions and their neighbors show low ESL and LP indices; (3) High-Low Aggregation (H-L): a region has higher values while its neighboring regions have lower values; (4) Low-High Aggregation (L-H): a region has lower values while its neighboring regions have higher values [62,63,64].

Through the visual analysis of LISA agglomerative maps, the spatial association patterns of ESL and LP indices in the study area and their significance levels can be visually identified, thus revealing the spatial heterogeneity characteristics of regional development. This analysis method can effectively identify the synergistic evolution characteristics of ESL and LP in the spatial dimension in the YRD, and provide spatial decision support for regional ecotourism development.

The research path of this paper is illustrated in Figure 2. In this study, ArcMap 10.8, Fragstats 4.2, and GeoDa1.2 were used to complete the model analysis and drawing. and drawings [34,65,66]. Figure 2a depicts the process of ESL assessment, where we first screened 14 indicators. We then generated ESL maps for the YRD from 2002 to 2022 through a series of steps, including cropping, standardization, coordinate system transformation, and reclassification, combined with hierarchical analysis and factor weighting methods. The trend analysis of ESL was subsequently plotted based on these maps. Figure 2b outlines the LP analysis process. We obtained LUCC data from 2002 to 2022, and after processing this data using GIS and Fragstats software, we produced the LP index information for the YRD. Trend analysis mapping of the LP was also conducted. Figure 2c employs the bivariate Moran’s I index to analyze the spatial correlation between LP and ESL in the YRD.

3. Results and Analysis

3.1. Evaluation of ESL

Figure 3 illustrates the ESL of the YRD from 2002 to 2022, categorized into five levels based on factor weighting and reclassification. Overall, a significant portion of the area falls into very low, low, and medium suitability categories, with medium suitability covering the largest area. Spatially, the ESL tends to be higher in the eastern and southern parts of the region, while the western and northern areas exhibit lower suitability levels. Notably, regions with higher urbanization and economic development, such as Shanghai, Nanjing, Hefei, and Hangzhou, show higher ESL ratings. This pattern suggests that urbanization can have both positive and negative influences on the ecological attractiveness of certain areas. On one hand, urbanization may contribute positively by improving infrastructure, accessibility, and public awareness of environmental protection, which can enhance the appeal and management of ecotourism sites. On the other hand, intensified urban development often leads to increased landscape fragmentation and reduced ecological connectivity, which can undermine ecosystem integrity and long-term sustainability. Therefore, while urbanization may create opportunities for ecotourism development in some regions, it simultaneously poses significant challenges to ecological resilience. A more nuanced approach is needed to balance these competing effects, ensuring that the benefits of urbanization are realized without compromising the ecological foundations essential for sustainable ecotourism.

The comparison of ESL changes over the past 20 years reveals notable trends in the YRD (Table 4). Very Low Suitability Area: This category accounted for 28.10% (9.24 × 10⁴ km²) in 2002, decreased to 25.01% (8.23 × 10⁴ km²) in 2012, but then increased again to 28.69% (9.43 × 10⁴ km²) in 2022. Low Suitability Area: Initially, this area made up 26.41% (8.69 × 10⁴ km²) in 2002, fluctuated over the years, and decreased to 25.06% (8.24 × 10⁴ km²) by 2022. Medium Suitability Area: This category started at 27.28% (8.97 × 10⁴ km²) in 2002 and, despite fluctuations, returned to 27.39% (9.01 × 10⁴ km²) in 2022. High Suitability Area: The area increased from 13.55% (4.45 × 10⁴ km²) in 2002 to 14.34% (4.71 × 10⁴ km²) in 2022, indicating a positive trend. Very High Suitability Area: This area represented 4.65% (1.53 × 10⁴ km²) in 2002, fluctuating slightly to 4.53% (1.49 × 10⁴ km²) in 2022.

In summary, while the areas classified as very high and medium suitability have remained stable, the very low and high suitability areas have shown slight increases, and low suitability areas have decreased. This suggests a complex dynamic in ESL, reflecting both ecological changes and human activities impacting ecotourism potential in the region over the last two decades.

The development trend of ESL in the YRD from 2002 to 2022 indicates an overall upward trajectory (Figure 4a). Here are the key findings:

Significant Increases: About 6% of the area (approximately 1.97 × 10⁴ km²) experienced a marked increase in ESL. Significant Decreases: Approximately 1.5% of the area (around 0.49 × 10⁴ km²) saw a significant decline in ESL. Stable Changes: A substantial 92.5% of the region (about 30.42 × 10⁴ km²) demonstrated stability in ESL, showing no significant changes.

When examining the ESL trends in individual cities (Figure 4b), most cities within the YRD reflect an increasing ESL over the two decades. Notable cities with significant increases include Xuancheng, Wuhu, Huaibei, Quzhou, and Taizhou. These cities have benefitted from industrial restructuring, reducing reliance on high-emission industries and implementing stricter environmental protection measures, positively impacting their ecological environment and biodiversity. Conversely, some cities, such as Hefei, Maanshan, and Fuyang, have shown a decreasing trend in ESL. This decline is attributed to the pressures of urbanization and increasing human activities, which have adversely affected local ecological conditions. Overall, the ESL development trend illustrates a complex interplay between urbanization, environmental management, and ecological preservation in the YRD.

3.2. Analysis of Spatial and Temporal Changes in LP

Figure 5 and Figure 6 illustrate the spatial and temporal evolution of LP in the YRD from 2002 to 2022. The LP exhibit significant spatial heterogeneity, with the distribution and combination of spatial elements demonstrating notable diversity and complexity.

(1)

Landscape Fragmentation Analysis: NP and PD provide a comprehensive assessment of landscape fragmentation. Spatially, NP values are lower in the south and higher in the north, while PD is notably higher in the eastern region, with other areas exhibiting lower levels. Temporally, NP and PD showed an increasing trend from 2002 to 2017, followed by a decrease from 2017 to 2022. Overall, from 2002 to 2022, NP and PD exhibited a significant upward trend, particularly in eastern cities such as Shanghai and Nanjing. This indicates a high degree of landscape fragmentation in the region.

(2)

Landscape Aggregation Analysis: CONTAG is employed to evaluate the aggregation within the region, while LPI assesses the balance of patch distribution. Spatially, the southern and western areas of the YRD, such as Quzhou, Lishui, Huangshan, and Anqing, exhibit higher CONTAG values, whereas central cities like Nanjing, Suzhou, and Shanghai show lower values. Overall, LPI is high, with Quzhou, Lishui, Huangshan, and Suzhou recording elevated values, while Shanghai, Hangzhou, and similar areas have lower values. Temporally, both CONTAG and LPI demonstrate a downward trend, with regions like Shanghai, Hangzhou, and Nanjing experiencing a more pronounced decline.

(3)

Landscape Diversity: SHDI is utilized to measure species diversity within the region, while PR assesses patch diversity and complexity. Spatially, SHDI values are higher in central areas such as Nanjing, Hangzhou, and Suzhou. In contrast, southern regions like Ningbo, Lishui, and Huangshan, as well as northern areas such as Fuyang, Suzhou, Suqian, and Huai’an, exhibit lower SHDI values. Overall, PR is higher in the southern region and lower in the northern region. Temporally, both SHDI and PR demonstrate an overall increasing trend, with Suzhou, Yancheng, and Fuyang showing rising SHDI values, while Taizhou, Maanshan, and Jiaxing indicate an increase in PR.

In summary, the LP of the YRD exhibits significant spatial heterogeneity. Analysis of NP and CONTAG reveals that the degree of landscape fragmentation in the eastern region, characterized by high urbanization and construction indices, has increased over the past 20 years. Originally continuous patches have been fragmented into smaller units, leading to reduced spatial connectivity and aggregation. This finding aligns with results from CONTAG and LPI, indicating that urbanization and construction have supplanted natural landscapes with man-made ones, thereby compromising the integrity and continuity of the original landscape. Conversely, SHDI and PR indicate an overall increasing trend in landscape and species diversity within the YRD.

3.3. Correlation Analysis Between ESL and LP

Table 6. Bivariate Moran’s Index of ESL and LP Indices in the YRD, 2002–2022.

Years	NP	PD	CONTAG	LPI	SHDI	PR
2002	0.066	−0.058	−0.062	−0.118	0.177	0.122
2007	0.157	−0.011	−0.121	−0.146	0.196	0.121
2012	0.063	−0.038	−0.154	−0.199	0.218	0.175
2017	0.122	−0.022	−0.113	−0.185	0.201	0.055
2022	0.125	−0.056	−0.134	−0.230	0.257	0.132

presents the bivariate global Moran’s I values between ESL and LP indices in the YRD from 2002 to 2022, allowing for the assessment of correlations and spatial characteristics. The Moran’s I for ESL and NP consistently shows a positive trend, increasing from 0.066 to 0.125 after fluctuations, indicating an enhancement of approximately 89% in the spatial positive correlation between ESL and NP. In contrast, the Moran’s I for ESL and PD consistently remains negative, declining from −0.058 to −0.056, suggesting a spatial negative correlation between these two indices. Both CONTAG and LPI exhibit negative Moran’s I values, decreasing from −0.062 to −0.134 and from 0.118 to −0.230, respectively. This indicates a spatial negative correlation between CONTAG, LPI, and ESL. Conversely, the Moran’s I for SHDI and PR consistently demonstrates a positive trend, increasing from 0.177 to 0.257 and from 0.122 to 0.132, respectively. This finding highlights a spatial positive correlation between SHDI, PR, and ESL.

From a temporal perspective (Figure 7), the spatial aggregation pattern between ESL and LP indices in the YRD from 2002 to 2022 appears relatively stable.

Analyzing landscape fragmentation, the spatial aggregation types of ESL, NP, and PD in the eastern areas (e.g., Shanghai, Hangzhou), central areas (e.g., Nanjing, Hefei), and northern areas (e.g., Suqian) exhibit a “high-high” aggregation. This indicates that these regions possess higher ESL alongside a greater number and density of patches, resulting in increased landscape fragmentation.

In terms of landscape aggregation, the spatial aggregation types of ESL, CONTAG, and LPI in the southern part of the YRD demonstrate a “high-low” aggregation. This suggests that while these areas have higher ESL, they experience lower landscape connectivity and aggregation, indicating more pronounced landscape fragmentation. This finding aligns with previous studies.

Regarding landscape diversity, the ESL, SHDI, and PR in the eastern coastal region (e.g., Ningbo, Taizhou) and the central region (e.g., Nanjing, Hangzhou) exhibit a “high-high” aggregation. This indicates that both ESL and landscape diversity are elevated in these areas. Conversely, Yancheng and Nantong display a “low-low” clustering pattern, suggesting that these regions have lower ESL and also exhibit weaker ecological diversity.

4. Discussion

Ecotourism, as a sustainable form of tourism, positively contributes to environmental, economic, and social development [67]. However, with the advancement of urbanization, urban construction significantly impacts both the ecological environment and the tourism industry [68,69,70,71,72,73,74,75,76]. This study employs GIS, Fragstats, and GeoDa to investigate the effects of LP evolution on ESL in the YRD from 2002 to 2022. The aim is to systematically assess the development and changing trends of ESL, as well as its relationship with LP evolution, thereby providing a scientific basis for the construction and development of ecotourism.

4.1. Theoretical Implications of Spatiotemporal ESL Dynamics

The spatiotemporal evolution of ESL in the YRD from 2002 to 2022 reveals complex patterns that challenge conventional understanding of urbanization impacts on ecotourism potential. Our findings demonstrate that ESL exhibits an overall upward trend, with high suitability areas increasing from 13.55% (4.45 × 10⁴ km²) to 14.34% (4.71 × 10⁴ km²), while very low suitability areas expanded from 28.10% (9.24 × 10⁴ km²) to 28.69% (9.43 × 10⁴ km²). This polarization effect suggests that urbanization creates a heterogeneous landscape of ecotourism opportunities rather than uniform degradation [76].

The concentration of high ESL in economically developed cities (Shanghai, Nanjing, Hefei, and Hangzhou) contradicts the traditional assumption that urbanization invariably reduces ecotourism potential. This phenomenon can be explained through the lens of coupled human-natural systems theory, where urban development enhances certain aspects of ecotourism (infrastructure, accessibility, environmental awareness) while degrading others (ecological integrity, natural landscapes) [77,78]. The stability observed in 92.5% (30.42 × 10⁴ km²) of the region indicates that these competing forces have reached a dynamic equilibrium in most areas, while the 6% (1.97 × 10⁴ km²) showing significant increases (particularly in Xuancheng, Wuhu, Huaibei, Quzhou, and Taizhou) suggests successful implementation of ecological restoration and sustainable development policies [79].

The temporal fluctuations observed, particularly the decrease in low suitability areas from 26.41% (8.69 × 10⁴ km²) in 2002 to 25.06% (8.24 × 10⁴ km²) in 2022, indicate that the region is experiencing a transition where marginal areas are either improving toward medium suitability or degrading to very low suitability. This bifurcation process reflects the differential impacts of development policies and local environmental conditions across the region [80,81].

4.2. Evolution Characteristics and Influences of LP

The analysis of the LP index reveals that between 2002 and 2022, the overall landscape fragmentation in the YRD increased, with particularly significant changes in the eastern areas where urban construction is concentrated. Indicators such as NP, PD, CONTAG, and LPI show that previously continuous ecological patches have gradually been divided into smaller units, with a reduction in spatial aggregation and a disruption of ecological connectivity. This process is influenced by both human activities and natural evolution, with urbanization being the primary driving factor [82]. Landscape fragmentation not only affects the integrity and reproductive capacity of habitats for flora and fauna but also weakens the stability of ecosystems [83].

Regarding diversity, indices such as SHDI and PR indicate that the landscape structure in the YRD has become increasingly complex, with an upward trend in landscape diversity. This trend partly reflects the diversification of urban land use types, but it may also lead to an increase in the heterogeneity of ecosystem functions.

4.3. Spatial Clustering Patterns of ESL and LP

The analysis of the spatial clustering patterns of the ESL and LP indices from 2002 to 2022 shows that the development trend has been relatively stable over the past two decades. In areas with high UI, such as Nanjing, Hefei, Shanghai, and Hangzhou, landscape fragmentation exhibits a “H-H” clustering trend, while these areas show an “H-L” clustering pattern in terms of landscape connectivity, and an “H-H” trend in landscape diversity. This suggests that areas with high UI generally have high ESL and landscape diversity indices; however, they also exhibit significant landscape fragmentation, low connectivity, and aggregation, which is consistent with previous studies.

In the future, relevant authorities should prioritize ecological connectivity in urban planning to prevent disorderly expansion that could lead to further fragmentation of ecological spaces. Building ecological corridors to link fragmented environments can enhance spatial connectivity and facilitate species migration [84,85]. Additionally, ecosystem restoration measures should be implemented in degraded areas to enhance the integrity and functionality of ecosystems such as wetlands and forests [86,87].

4.4. Implications for Urban-Ecological Theory and Practice

Our findings contribute to emerging theories of novel ecosystems and urban ecology by demonstrating that ecotourism in highly modified landscapes operates under different principles than in pristine environments. The positive correlation between ESL and landscape diversity indices (SHDI: 0.177 to 0.257; PR: 0.122 to 0.132) suggests that urban ecosystems can support ecotourism through mechanisms distinct from traditional nature-based tourism.

The urbanization paradox identified in our study—where cities maintain high ESL despite severe fragmentation—has important implications for sustainable development theory. It suggests that sustainability in urban contexts may require reconceptualizing the relationship between ecological integrity and human use. Rather than viewing urbanization as antithetical to ecotourism, our results indicate that certain forms of urban development can create new opportunities for human-nature interactions.

The differential impacts observed across cities (with 1.5% of areas showing significant ESL decreases, primarily in Hefei, Ma’anshan, and Fuyang) highlight the importance of local governance and planning approaches. Cities experiencing ESL decline despite regional improvements may be pursuing development strategies that fail to balance economic growth with ecological preservation. This underscores the need for adaptive management approaches that can respond to local conditions while maintaining regional coherence.

4.5. Future Pathways for Sustainable Ecotourism Development

The complex relationships revealed between ESL and LP suggest that future ecotourism development in the YRD must adopt innovative approaches that work with, rather than against, urbanization processes. The increasing landscape diversity alongside declining connectivity presents both challenges and opportunities. While traditional conservation strategies focus on maintaining large, connected natural areas, urban ecotourism may benefit from creating diverse, accessible green spaces that serve multiple functions.

The stability observed in most of the region (92.5%) suggests that current development patterns have reached a quasi-equilibrium state. However, the ongoing fragmentation and connectivity loss indicate that this equilibrium may be unstable in the long term. Without intervention, continued urbanization could push the system past critical thresholds, leading to irreversible losses in ecotourism potential. Therefore, proactive planning that anticipates future urbanization patterns while preserving essential ecological functions is crucial for maintaining the region’s ecotourism viability.

5. Conclusions and Recommendations

5.1. Conclusions

This study provides a comprehensive assessment of the dynamic evolution of ESL and LP in the YRD from 2002 to 2022. By integrating multi-source geospatial data and advanced spatial analysis methods, we reveal several key findings:

(1)

Overall Trends: The ESL in the YRD has shown a general upward trend over the past two decades, with both high and very low suitability areas expanding, while low suitability areas have decreased. This reflects the complex interplay between ecological restoration efforts and the pressures of rapid urbanization.

(2)

Spatial Patterns: High and very high suitability areas are mainly concentrated in economically developed cities such as Shanghai, Nanjing, Hefei, and Hangzhou. However, these areas also experience intensified landscape fragmentation and reduced ecological connectivity, which may threaten long-term ecological resilience.

(3)

Landscape Pattern Evolution: The region has witnessed increased landscape fragmentation (as indicated by rising NP and PD values) and declining connectivity (as shown by decreasing CONTAG and LPI), especially in urbanized areas. At the same time, landscape diversity (SHDI and PR) has increased, suggesting a more heterogeneous but also more fragmented ecological environment.

(4)

Spatial Correlation: Bivariate spatial autocorrelation analysis indicates that areas with high ESL often coincide with high landscape diversity but also with high fragmentation and low connectivity. This spatial heterogeneity highlights the need for differentiated and adaptive management strategies.

Overall, the findings underscore the dual impact of urbanization: while it can enhance infrastructure and accessibility for ecotourism, it also poses significant challenges to ecological integrity and sustainability. The results provide empirical support for the development of integrated planning and management frameworks that balance economic growth with ecological protection.

5.2. Policy Recommendations

Based on the findings of this study, the following policy recommendations are proposed to achieve a balance between sustainable urbanization and ecotourism development in the YRD:

5.2.1. Establishing a Regional Coordination Mechanism to Promote Integrated Ecotourism Development

As one of the regions with the highest level of urbanization and the most diverse ecological resources in China, the YRD faces complex administrative divisions, uneven resource distribution, and significant development disparities. Therefore, it is essential to establish a cross-administrative regional coordination mechanism to promote the integrated development of ecotourism. It is recommended to set up a unified coordination body for ecotourism in the Yangtze River Delta to formulate regional ecotourism development strategies, clearly define the functional zones of different cities within the ecotourism system, and optimize resource allocation and industrial coordination. At the same time, an ecological compensation mechanism should be improved to guide economically developed regions to compensate and support regions that export ecological resources, thereby promoting regional ecological equity and coordinated development. Furthermore, in conjunction with the construction of the regional high-speed rail network and ecological transport corridors, a spatial development pattern that integrates “ecology + transport + tourism” should be created to enhance the accessibility and overall attractiveness of ecotourism resources, achieving interconnection and resource sharing among regions.

5.2.2. Optimizing Urban Spatial Structure to Enhance Ecosystem Service Functions

Ecological fragmentation caused by urban expansion is a key factor limiting the improvement of ecotourism suitability. Therefore, optimizing urban spatial structure and ecological layout is particularly important. It is recommended to implement a “multi-center, cluster-style” development model in urban planning to prevent disorderly urban sprawl, and reserve ecological corridors, green belts, and other ecological spaces between urban clusters to enhance the connectivity and integrity of urban ecosystems. At the same time, ecological protection red lines and construction control zones should be delineated to include key ecological patches, water conservation areas, wetland systems, etc., within protected areas to prevent their degradation due to construction and development. Within cities, wetland parks, waterfront green belts, urban forests, and other “blue-green interwoven” spaces can be developed to create an ecological security pattern and improve urban ecosystem service capacity. Additionally, incorporating green development concepts such as “sponge cities” and “park cities” can enhance urban ecological infrastructure and provide better environmental conditions and sustainable spatial carriers for ecotourism.

5.2.3. Implementing Differentiated Strategies and Ecological Restoration to Promote Win-Win Development of Tourism and Ecology

Given the differences in ecological resources and urbanization levels in the YRD, differentiated ecotourism development strategies should be implemented. In areas with high levels of urbanization, such as Shanghai and Nanjing, urban ecotourism should be prioritized, including wetland parks, suburban green spaces, and ecological education bases, to meet the needs of citizens for short-distance leisure and environmental experiences. In regions with rich ecological resources but lower development levels, such as the southern Anhui Mountains and northern Jiangsu Plains, the principle of “protection first, moderate development” should be followed, with the development of low-impact tourism forms such as ecological health tourism and rural tourism, to avoid excessive ecological exploitation. In ecologically sensitive areas, such as lakes, wetlands, and nature reserves, development intensity should be strictly controlled, and measures such as limiting visitor numbers and zoning management should be implemented to maintain ecosystem stability. Meanwhile, ecological restoration projects should be strengthened to restore the connectivity of ecological corridors and key patches and enhance ecosystem service functions. By guiding community participation and strengthening public ecological awareness, the positive interaction between ecotourism and ecological protection can be further promoted, achieving the goal of win-win development.

6. Limitations

This study examines how LP evolution affects ESL in the YRD amid urbanization, analyzing both temporal and spatial dimensions. However, several limitations exist. First, the use of a 1000 m spatial resolution may miss finer spatial variations in LP; future work could adopt higher resolutions (e.g., 500 m or 100 m) and field surveys. Second, although 14 indicators are employed, additional social (e.g., tourist satisfaction), infrastructure (e.g., information facilities), and economic (e.g., income, employment) metrics would yield a more comprehensive ESL evaluation. Third, due to synchronization requirements across all data layers, the multi-source dataset can only be validated up to 2022; forcing a subset of indicators to 2024 would create structural breaks and undermine comparability. We will extend the time series as soon as complete, same-definition data for 2023–2024 become available. Lastly, this research is confined to the YRD; expanding the study area would help reveal how diverse urban-development strategies affect ESL in other contexts. Addressing these limitations will enhance our understanding of LP evolution and support sustainable regional development.