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Article

Assessment and Spatial Optimization of Cultural Ecosystem Services in the Central Urban Area of Lhasa

by
Yuqi Li
1,2,†,
Shouhang Zhao
1,2,†,
Aibo Jin
1,2,
Ziqian Nie
1,2 and
Yunyuan Li
1,2,*
1
School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
2
National Engineering Research Center for Forestry and Grassland Landscape Architecture, No. 35 Qinghua East Road, Haidian District, Beijing 100084, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Land 2025, 14(9), 1722; https://doi.org/10.3390/land14091722 (registering DOI)
Submission received: 15 July 2025 / Revised: 19 August 2025 / Accepted: 21 August 2025 / Published: 25 August 2025
(This article belongs to the Special Issue Urban Landscapes for Well-Being: Designing Resilient Spaces for Healthier Communities)
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Abstract

Assessment of cultural ecosystem services (CESs) is a key component in advancing the sustainable development of urban ecosystems. Mapping the spatial distribution of CESs provides spatially explicit insights for urban landscape planning. However, most assessments lack regional adaptability, particularly in cities with pronounced environmental and cultural heterogeneity. To address this gap, this study focused on the central urban area of Lhasa, using communities as units to develop a tailored CES assessment framework. The framework integrated the MaxEnt model with multi-source indicators to analyze the spatial distribution of five CES categories and their relationships with environmental variables. Spatial statistics and classification at community level informed the CES spatial optimization strategies. Results indicated that high-value CES areas were predominantly concentrated in the old city cluster, typified by Barkhor and Jibenggang subdistricts, following an east–west spatial pattern along the Lhasa River. Distance to tourist spot contributed 78.3% to cultural heritage, 86.1% to spirit and religion, and 42.2% to ecotourism and aesthetic services, making it the most influential environmental variable. At the community level, CESs exhibited a distinct spatial gradient, with higher values in the central area and lower values in the eastern and western peripheries. For the ecotourism and aesthetic category, 61.47% of the community area was classified as low service, whereas only 1.48% and 7.33% were identified as excellent and high. Moreover, communities within subdistricts such as Barkhor and Zhaxi demonstrated excellent service across four CES categories, with notably lower performance in the health category. This study presents a quantitative and adaptable framework and planning guidance to support the sustainable development of CESs in cities with similar characteristics.

1. Introduction

Ecosystem services constitute a basis for the sustainable development of human society, providing a wide range of benefits that support natural environments, social structures, and cultural systems [1]. As a key component, cultural ecosystem services (CESs) emphasize the non-material benefits that humans obtain from interactions with the natural environment [2]. CESs not only represent a key pathway for promoting ecosystem sustainability, but also offer a critical perspective for understanding the interactions between nature and society [2,3]. CESs have gained increasing recognition in global research and policy agendas [4]. They are now commonly integrated into urban management, landscape planning, and cultural heritage conservation, forming a crucial source of information to guide spatial governance [5,6]. However, with the accelerating pace of urbanization, the global urban population is projected to reach approximately 68% by 2050 [7], placing unprecedented pressure on urban ecosystems and their natural resources. Against this backdrop, ensuring the continued provision and effective management of CESs has become a significant challenge [8]. Notably, in densely built urban environments, once the natural and cultural carriers of CESs are degraded, their original functions are often difficult or impossible to restore through artificial means [9]. Accordingly, there is an urgent need to establish CES assessment frameworks that are responsive to the distinct geographical and cultural contexts of urban areas [10], in order to support effective ecosystem governance and the conservation of cultural resources [11,12].
CESs are characterized by their pronounced intangibility and subjectivity [13], as well as their dual attributes encompassing both ecological and socio-cultural dimensions [7,14]. These features introduce substantial complexities in the accurate quantification and practical application of CESs [10]. To address this complexity, spatial mapping approaches have been developed to provide urban decision-makers with effective and visualized information support [15,16]. Such methods not only facilitate the precise identification of ecological and cultural resources closely linked to human well-being in urban contexts [17,18], but also assist in delineating priority areas for resource management and functional restoration, thereby offering a scientific basis and policy-making for urban planning [19]. However, in urban environments characterized by high levels of geographical and cultural heterogeneity, existing studies often fall short in terms of methodological adaptability and operational efficiency [20]. This highlights the urgent need to establish regionally adaptive assessment frameworks to enable the effective integration and application of CES spatial data in urban planning and governance processes [11,12].
In the field of CES spatial mapping, researchers have explored a variety of models for spatial assessment. Villa et al. employed the ARIES model as the core method to map cultural services such as aesthetics and landscape perception in the CAZ area of Madagascar [21]. While the model supports the integration of multi-source data, it requires users to be proficient in semantic network modeling, Bayesian networks, and the SPAN algorithm, which makes the modeling process relatively complex [3]. Building on this work, Cybèle et al. applied the more user-friendly InVEST model in the Saint-Philippe region of Réunion Island, France, to conduct CES mapping [22]. The InVEST model offers better operability and efficient validation, but its modeling mechanism has limited capacity to reveal the underlying relationships between CESs and environmental variables [12]. To alleviate these limitations, Shi et al. introduced the Maximum Entropy (MaxEnt) model to simulate and predict CES spatial patterns in Mizhi County, China [23,24]. Based on species distribution probability theory, the MaxEnt model incorporates two main data components: sample selection and environmental variables [25]. It demonstrates high predictive accuracy and robustness, even under conditions of limited geographic data and complex environmental factors [26,27]. Moreover, MaxEnt enables the effective quantification of the contribution of various environmental variables to CES distribution [28], thus supporting the analysis of response relationships between CESs and ecological factors [29]. It is worth noting that the performance of the MaxEnt model largely depends on the availability and representativeness of input data in reflecting regional characteristics [30]. Therefore, when applying MaxEnt to CES assessment, it is essential to construct a comprehensive indicator system that incorporates both natural and socio-cultural dimensions. This includes integrating multi-source data such as remote sensing imagery, points of interest (POI), and official statistical records, in order to enhance the scientific rigor and practical relevance of CES mapping outcomes [7].
In MaxEnt-based CES assessments, existing frameworks are often grounded in the Millennium Ecosystem Assessment (MEA) and the Common International Classification of Ecosystem Services (CICES). The selected cultural value indicators primarily emphasize the direct benefits humans derive from ecosystems, such as ecotourism, recreation, and aesthetic appreciation [31]. However, many assessment frameworks continue to overlook other important cultural benefits that emerge from the interaction between nature and culture, particularly tangible and intangible cultural heritage associated with spiritual practices and religious beliefs [9,32]. Cultural heritage, as an important carrier of CESs, is widely recognized and safeguarded by society. It comprises both tangible and intangible resources of historical and cultural significance, closely connected to non-material dimensions such as spiritual and religious beliefs within society [33]. Therefore, when applying the MaxEnt model for CES evaluation, it is necessary to include cultural heritage and its spiritual dimensions within the indicator system [34]. Culturally relevant keywords and semantic labels should be carefully selected in alignment with the local geographical and cultural context, in order to improve the accuracy of CES representation and enhance spatial identification capabilities [20]. At the same time, the selection of environmental variables should also reflect cultural and regional adaptability [35]. Research has demonstrated that the influence of environmental factors on CES distribution is highly heterogeneous across different geographical contexts [36]. However, most current studies still rely on commonly used variables such as elevation, slope, hillshade, and land use type [37,38,39]. While these variables offer general applicability, they often fail to capture the complex relationships between local socio-cultural structures and natural environments [7]. Hence, constructing an indicator and variable system that is both regionally representative and culturally responsive is essential for improving the explanatory power and spatial suitability of the MaxEnt model in CES assessments [20]. This optimization approach not only facilitates a deeper understanding of the spatial characteristics of regional cultural services but also provides a theoretical foundation and practical support for CES optimization at both urban and regional scales [23].
Given the current lack of adaptive frameworks and methodologies for CES spatial mapping in geographically and culturally heterogeneous urban areas, this study aims to integrate multi-level indicator data with the MaxEnt model to establish a regionally adaptive framework for CES assessment and mapping. Focusing on the central urban area of Lhasa, the research takes communities as the basic analysis unit to identify key areas for cultural service management and propose spatial optimization strategies [40]. The objectives of this study are as follows: (1) to establish a CES assessment framework with spatial quantification capabilities, thereby enhancing its applicability to urban areas with complex geographical environments and unique cultural characteristics; (2) to identify key environmental variables that significantly influence local CES distribution, so as to clarify their driving effects on the spatial patterns of cultural services; (3) to reveal the spatial distribution of CESs at the community scale, with the aim of developing targeted strategies for cultural service optimization to alleviate spatial imbalances within the region. Through this research approach, the study establishes a quantitative CES framework and spatial optimization strategies for the central urban area of Lhasa, providing theoretical support and planning guidance for the sustainable development of CESs in cities with similar geographical and cultural characteristics.

2. Materials and Methods

2.1. Study Area

Lhasa is situated in the river valley region of the Lhasa River Basin on the Qinghai–Tibet Plateau, with an average elevation of 3650 m. It is flanked by the Nyenchen Tanglha Mountains to the north and the Guokaraji Mountains to the south. The central and southern areas comprise the alluvial plain along the middle reaches of the Lhasa River, together with natural water systems such as the Lhasa River and the Duilongqu River, forming a highly distinctive landscape pattern of mountains and water. As a nationally significant historical and cultural city and an internationally renowned religious tourism destination, the central urban area of Lhasa hosts a number of culturally and historically valuable landmarks, including the Potala Palace, Jokhang Temple, and Norbulingka. It also encompasses key ecological resources such as the Lalu Wetland, the Lhasa River Scenic Area, and the surrounding northern and southern mountain ranges. The area features typical plateau geomorphological characteristics, numerous world-class cultural heritage sites, and a profound religious and cultural background, endowing it with strong geographical representativeness and unique cultural significance. Spatial development within the central urban area of Lhasa is organized around communities as the basic administrative units, which not only support urban governance but also serve as the core scale for residents’ daily activities and access to cultural services. Based on the Lhasa Territorial Spatial Master Plan (2021–2035), this study defines the central urban area of Lhasa (90°50′ E–91°23′ E, 29°32′ N–29°43′ N) as the study area (Figure 1). It includes Chengguan District as well as parts of Doilungdêqên District and Dagzê County, covering a total area of 360.08 km2 and comprising 19 sub-districts and 75 communities (Table 1) [20].

2.2. Data Sources

This study integrated multiple data sources, including administrative boundary vector data, land use and land cover (LULC) data, normalized difference vegetation index (NDVI), and digital elevation model (DEM) (Table 2). To ensure data accuracy and completeness, cultural heritage POIs within the central urban area of Lhasa were identified based on the Lhasa Territorial Spatial Master Plan (2021–2035) and the List of Cultural Relic Protection Units at All Levels in Lhasa. These data were further refined through high-resolution imagery from Google Earth and field investigations. A total of 131 cultural heritage POIs were identified within the study area. The vector data for urban parks and green spaces were obtained from the Lhasa Green Space System Plan (2021–2035) and verified through field surveys, resulting in the identification of 114 park locations. In addition, POIs representing preferences for various categories of CESs were extracted using Python 3.13 from Amap (https://www.amap.com/) (accessed on 20 May 2025).

2.3. Methods

This study developed a comprehensive framework for CES assessment and spatial mapping (Figure 2). First, based on the geographical characteristics and cultural context of central Lhasa, five representative CES categories were identified. A total of ten environmental variables were selected as driving factors from both natural and social dimensions. Subsequently, the CES preference data and environmental variables were input into the MaxEnt model for simulation. The model was used to evaluate the relative contribution of each environmental factor to the spatial distribution of different CES categories. Finally, the output results of the MaxEnt model were processed using ArcGIS 10.8 for zonal statistics, allowing CES spatial mapping at the community level. Based on the resulting spatial patterns and the influence of key environmental variables, targeted spatial optimization strategies for CESs were proposed to support the enhancement of cultural service functions and the optimization of ecological spatial layout in urban planning.

2.3.1. Indicators Classification of CESs

Defining locally appropriate CES categories is essential for the effective application of the MaxEnt model [41]. This study referenced the classification frameworks of the Millennium Ecosystem Assessment (MEA) and The Economics of Ecosystems and Biodiversity (TEEB) [31,42], and adapted CES-related tags and keywords based on the specific geographical and cultural characteristics of central urban area in Lhasa. Five primary CES categories were ultimately selected (Table 3): ecotourism and aesthetic, cultural heritage, spirit and religion, health, and education and knowledge [43,44,45]. Each category is explicitly defined in Table 3, with corresponding keywords and semantic tags that help articulate both the conceptual meaning and practical expressions of the services. This classification system provides a structured foundation for CES spatial mapping in central urban area in Lhasa, and supports model development and interpretation through a semantically consistent and regionally relevant framework.
To extract spatial sample points representative of CES categories, this study obtained point of interest (POI) data using Python 3.13 from Amap (https://www.amap.com/) (accessed on 20 May 2025). Based on policy documents such as the Lhasa Territorial Spatial Master Plan (2021–2035), the raw POI data were cross-checked and verified. Coordinate corrections and deduplication were performed using ArcGIS, and selected POIs were further adjusted through field validation. As a result, a total of 707 CES-related POI samples were identified within the central urban area of Lhasa (Figure 3). Among them, 192 were associated with ecotourism and aesthetic, 131 with cultural heritage, 146 with spirit and religion, 114 with health, and 124 with education and knowledge. The geographic coordinates (longitude and latitude) of all POIs were extracted and saved in .csv format to serve as input sample data for the MaxEnt model.

2.3.2. Selection and Validation of Environmental Variables

When selecting environmental variables, it is essential to ensure that the chosen indicators reflect both the key characteristics of CESs and the natural and social attributes of the study area [46]. Lhasa is rich in ecological and cultural tourism resources, and local residents have long upheld cultural values centered on the reverence for natural landscapes. In addition, land use and land cover (LULC) are closely related to ecosystem structure and functions [47], water distribution is known to influence residents’ sense of well-being [12,48], and spatial distance is an important factor in determining resource accessibility and distribution [49]. Based on existing studies [32,45], this study identified a group of environmental variables affecting the spatial distribution of CESs in Lhasa (Table 4). Among these, variables such as “distance to cultural heritage” and “distance to religious facilities” were initially considered. However, their reference locations significantly overlapped with the actual locations of cultural heritage and spiritual-religious sites already represented in the CES indicators. Methodological pre-experiments revealed signs of overfitting when these variables were included. Therefore, due to their high correlation, they were ultimately excluded from the final set of selected variables.
On this basis, this study constructed an environmental variable dataset incorporating both natural and social dimensions (Table 5), from which ten variables were selected for CES modeling. The six natural variables included normalized difference vegetation index (NDVI), elevation (ELEV), slope (SLOPE), distance to water (DTW), distance to forest (DTF), and distance to natural features (DTN). The four social variables included land use and land cover (LULC), distance to tourist spot (DTT), distance to road (DTR), and distance to settlement (DTS) [26,44,45,50,51]. All spatial distance variables, including DTW, DTF, and DTN, were calculated using the Euclidean Distance tool in ArcGIS. LULC data were reclassified into six categories according to the National Standard of Land-Use Classification of China (GB/T 21010-2017) [52]: forest, grassland, water bodies, cultivated land, built-up land, and unused land. All processed environmental variables were finally exported in ASCII file format for input into the MaxEnt model.
In addition, to ensure that the final set of selected environmental variables could robustly support the MaxEnt modeling process, a multicollinearity analysis was conducted using OriginPro 2023. The results indicated that all correlation coefficients were below 0.8, suggesting that the selected variables passed the collinearity test (Figure 4) and were suitable for use in the MaxEnt modeling process [53].

2.3.3. Spatial Mapping and Classification of CESs

This study conducted CES spatial mapping for the central urban area in Lhasa using MaxEnt version 3.4.4. The model outputs were then aggregated and classified at the community level to produce the final CES distribution results.
1.
MaxEnt Model Construction
The MaxEnt model consists of two main input modules: a sample data module and an environmental variable module. Latitude and longitude coordinates for the five CES categories were input in .csv format as sample data, while ten environmental variables were input in ASCII format as environmental layers. To enhance model stability, 75% of the samples were used for training and 25% for validation [54]. The regularization multiplier was set to 1, and 10,000 background points were used [53]. The default prevalence was set to 0.5, representing the probability of CES presence at typical occurrence points, and other parameters, such as the convergence threshold and threshold rules, were kept at system defaults [55]. For the run configuration, the options “Write plot data” and “Write background predictions” were enabled. The Bootstrap method was used to repeat simulations 10 times [41]. To assess the contribution of individual environmental variables, the “Jackknife” test was activated. Model accuracy and predictive performance were evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), a widely accepted metric for assessing the reliability of probability outputs in identifying potential CES spaces [56,57]. An AUC value below 0.5 indicates random prediction, values between 0.7 and 0.9 suggest good predictive accuracy, and values above 0.9 indicate a highly accurate model. The closer the AUC is to 1.0, the more reliable the prediction results [58]. This modeling process was conducted separately for each of the five CES categories, and all output results were generated in logistic format.
To ensure data consistency and comparability, model outputs were imported into ArcGIS and converted into raster format. The CES raster layers were subsequently normalized using a normalization index. Referring to relevant literature [23], this study applied the geometric interval classification to divide the output raster into ten levels. This classification approach is particularly effective in capturing relative spatial variations, especially when CES distributions exhibit significant heterogeneity [19]. It helps mitigate the influence of extreme values, preserves key spatial information, and improves visual interpretability while maintaining simplicity [23,45].
2.
Classification at the Community Level
Building upon the raster results, CES data were further aggregated at the community level to support spatial optimization strategies. The 75 communities in the central urban area of Lhasa were used as the statistical units (Table 1), and zonal statistics were performed using ArcGIS. Following the methods of Shi et al. and He et al. [23,45], the spatial probability values for each CES category were classified using the geometric interval method into four service levels: excellent service, high service, medium service, and low service. Communities categorized as excellent service areas contain abundant CES resources readily accessible to local residents. High service communities demonstrate relatively balanced spatial configurations of CESs. Medium service communities exhibit partial functional deficiencies, while low service communities are characterized by a significant lack of CES resources, indicating strong potential for future spatial improvement.

3. Results

The geographic coordinates of each CES category, along with the corresponding environmental variables, were input into the MaxEnt model. The area under the receiver operating characteristic curve (AUC) was used as the primary metric to evaluate model performance. After 10 bootstrap replications, all five CES models achieved AUC values greater than 0.7 (Figure 5), with most exceeding 0.9. These results indicate that the prediction accuracy was high and the model performance was stable and reliable [56,57].

3.1. Spatial Distribution of Different CES Types

The spatial distribution of various CES types was predicted using the MaxEnt model and visualized through the ArcGIS platform. The results reveal a clear spatial heterogeneity across all five CES categories. High-value CES areas were concentrated in the central urban area of Lhasa and extended in an east–west direction along the Lhasa River. Outside of these core areas, CES values gradually decreased in a spatial gradient pattern.
The CES types displayed distinguishable yet patterned distributions. As shown in Figure 6, the five CES types can be grouped into two clusters with similar spatial characteristics. The first group includes ecotourism and aesthetic (Figure 6a) and health (Figure 6d), which, despite having relatively broad spatial coverage, exhibited localized gaps in CES distribution in the northwest, southwest, and eastern areas. The second group includes cultural heritage (Figure 6b) and spirit and religion (Figure 6c), which were primarily concentrated in the old city cluster of the urban area.
It is noteworthy that CES values for cultural heritage (Figure 6b), spirit and religion (Figure 6c), and education and knowledge (Figure 6e) were relatively low in the western and eastern sections of the study area. In contrast, ecotourism and aesthetic (Figure 6a) and health (Figure 6d) showed higher spatial values in these same areas.

3.2. Contribution of Environmental Variables to CESs

The contribution of each environmental variable to CES spatial distribution was evaluated using the Jackknife test embedded in the MaxEnt model. As shown in Table 6 and Figure 7, the spatial distribution of the five CES categories was influenced by a combination of natural and social environmental variables. Each CES type exhibited a distinct response to different environmental drivers.
DTT emerged as the dominant environmental variable, contributing significantly to all five CES categories. Specifically, DTT contributed 78.3% to cultural heritage, 86.1% to spirit and religion, and 42.2% to ecotourism and aesthetic. Other variables also showed notable importance for specific CES types. For ecotourism and aesthetic, both DTN and LULC contributed 12.3%. In the case of cultural heritage, DTW and LULC contributed 3.4% and 11.7%, respectively. Similarly, DTW and LULC contributed 3.1% and 6.0% to the distribution of spirit and religion. For health, NDVI and DTW contributed 15.4% and 19.5%, respectively. LULC and DTS were the most influential variables for education and knowledge, contributing 31.7% and 15.8%, respectively. In contrast, ELEV, SLOPE, DTF, and DTR showed relatively limited influence on CES spatial distribution.

3.3. Classification of CES Types at the Community Level

In this study, the predictions generated by the MaxEnt model were mapped to the community scale. The CES community spatial distribution (Figure 8) and the CES service level classification (Figure 9) for the central urban area of Lhasa were evaluated using spatial statistical methods. All five types of CES exhibited clear gradient patterns and spatial differentiation across community units. However, distinct variations were observed among CES types in specific communities. Particularly within the old city cluster, communities along subdistricts such as Barkhor and Zhaxi consistently scored high in CES categories like ecotourism and aesthetic, and education and knowledge, but showed relatively low scores in cultural heritage, and spirit and religion, with the health scoring particularly low and revealing significant spatial heterogeneity.
Regarding to Figure 9, communities were classified into four levels: excellent service, high service, medium service, and low service. All five CES types exhibited a spatial pattern characterized by higher service levels in the central area and lower levels in the eastern and western wings. Among ecotourism and aesthetic, cultural heritage, and spirit and religion, there were significant differences in service levels across communities, with the disparity being most pronounced for ecotourism and aesthetic. Specifically, for ecotourism and aesthetic, only 1.48% of communities were classified as excellent service, while more than 60% were categorized as low service. For cultural heritage and spirit and religion, excellent service communities accounted for less than 1%, and over 80% of the area fell into the medium or low service categories (Table 7).
Notably, communities within Barkhor Subdistrict exhibited excellent service levels across four CES categories, with the exception of health, where they were classified as low service. This may be attributed to the area’s designation as a core development zone, where high building density and limited open space constrain the spatial capacity to support health-related CESs. Furthermore, the study identified several communities that consistently showed either high or low service levels across multiple CES types. For example, peripheral communities such as Deyang Village, Sangda Village, Jiamu Village, Yangda Village, Yeba Village, and Baina Village demonstrated overall low service levels, reflecting limited CES resource availability and significant potential for future development. In contrast, core communities such as Cemenlin, Raosai, and Tiebanggang performed well across all CES types, indicating the advantages of resource diversity, high spatial density, and service concentration.

4. Discussion

4.1. Spatial Distribution of CESs and the Influence of Environmental Variables

Based on the MaxEnt modeling results, the five CES categories in the central urban area of Lhasa exhibited significant spatial heterogeneity. This spatial pattern aligns with findings from Zhao et al. in cities of the southeastern United States [59], Hooftman et al. in multiple urban and regional contexts across the United Kingdom [60], and Yoshimura in Hokkaido, Japan [39]. In terms of CES spatial distribution, High-value CES areas were primarily concentrated in the central urban core, reflecting the tendency of cultural services to cluster in high-density built-up areas. The overall CES distribution showed an east–west extension pattern along the Lhasa River, further confirming the positive correlation between water bodies and CES provision [61]. The spatial variation in urban CESs is influenced by multiple factors, including landform, cultural resource distribution, and infrastructure accessibility [62]. In this study, significant CES deficiencies were observed in the northwest, southwest, and eastern parts of the central urban area, likely due to a relative lack of landscape resources, limited transportation accessibility, or underdeveloped public service infrastructure. Multiple CES types overlapped spatially in the central urban core, a phenomenon also reported in studies from Mizhi County, China [23], and case studies in Portugal [32]. This supports the notion that “a single landscape can simultaneously provide multiple cultural services,” highlighting the multifunctional nature of CESs. It is worth noting that many previous studies have suggested an inverse relationship between geographical distance and residents’ attention to CES resources [63], meaning that the farther a site is from residential areas, the less likely residents are to access its cultural services [51]. However, CES types such as Cultural Heritage and Spirit and Religion in this study did not exhibit significant distance decay effects. This can be attributed to the strong service aggregation around core cultural landmarks such as the Potala Palace and Jokhang Temple, as well as the high density of surrounding religious facilities [64]. The resulting “siphon effect” partially mitigates the spatial limitations typically imposed by geographic distance [63,65].
In terms of environmental variables, the high contribution of DTN is closely linked to the local lifestyle preference for proximity to nature. The roles of mountains and rivers in providing aesthetic, recreational, and health-related services were confirmed in this study, aligning well with empirical findings from other cities [66,67,68]. LULC, as a widely used environmental variable, also showed stable and significant effects in this study, further supporting its universality and explanatory power in urban CES spatial modeling [32,45]. However, compared to cities with more culturally homogeneous backgrounds, DTT played a more dominant role across several CES types in the central urban area of Lhasa, especially for Cultural Heritage and Spirit and Religion, where its contribution was significantly higher. This distinction can be attributed to the strong cultural specificity embedded in Lhasa’s tourism resources, particularly those closely associated with Tibetan Buddhism, such as culturally significant landmarks and religious sites. As a result, the spatial accessibility of tourism resources has become a key pathway through which residents access cultural services [69], explaining the dominant role of DTT in the CES modeling framework specific to this region.

4.2. Adaptability and Transferability of the Assessment Framework

This framework developed a regionally adaptive assessment and mapping framework for CESs based on the MaxEnt modeling principle and the integration of multidimensional indicator data. The framework is specifically designed to address the challenges of CES mapping in cities with high geographical and cultural heterogeneity.
In terms of indicator system construction, the framework incorporates locally relevant CES categories as core indicators, such as cultural heritage and spirit and religion. These categories align closely with Lhasa’s characteristics as a multicultural hub and effectively reveal the spatial distribution patterns of CESs in the central urban area of Lhasa, particularly the high-value clusters in the old city cluster. While indicators such as “spirit and religion” reflect region-specific cultural traits, the framework allows flexible adjustment of indicator dimensions to suit different cultural settings. For instance, in cities like Kyoto, Mexico City, or Istanbul, the selected CES elements can be redefined based on local characteristics [20], such as focusing on local religious facilities and ancient architectural sites. To better reflect local cultural distinctions, the framework includes environmental variables such as DTN and DTT, which capture the city’s unique tradition of nature reverence. These variables help identify the association between dominant environmental drivers and regional characteristics, thereby enhancing the framework’s adaptability to local specificities and offering insights that are transferable to other culturally diverse urban areas.
In terms of data integration and modeling, the framework combined remote sensing imagery, POI datasets, and official statistics to ensure comprehensive and reliable input data. The MaxEnt model demonstrated strong operability and computational efficiency, facilitating rapid and reliable CES mapping even in data-limited urban contexts, and enabling effective analysis of environmental influences on CES distributions [70,71]. The integration of POI semantic annotations with multi-scale environmental variables demonstrates strong generalizability and is applicable to most global cities where baseline data are available [72,73]. These outputs offered robust scientific support for developing spatial optimization strategies.
Moreover, the model results were further evaluated and classified at the community level, a scale that aligns with governance structures in most cities. This alignment allows CES assessment results to interface directly with local administrative systems and become readily applicable to urban governance units [74]. This level enhanced the practical relevance of the findings by supporting community-scale environmental management and cultural service planning. Overall, the proposed framework provides theoretical guidance and practical reference for cities worldwide facing complex environmental, social, and cultural heterogeneity.

4.3. Strategies for Enhancing CES Sustainability at the Community Level

With the ongoing process of urbanization, Lhasa has progressively established the conservation of ecological landscapes and historical-cultural heritage as a core directive of urban development. This vision has been explicitly articulated in recent policy documents, including the 14th Five-Year Plan for Ecological and Environmental Protection in Lhasa, the Lhasa Territorial Spatial Master Plan (2021–2035), and the Protection Plan for the Lhasa Historical and Cultural City (2021–2035). Through the implementation of projects such as the afforestation initiative on the southern and northern mountains, the restoration of urban water systems, and the conservation of iconic areas like the Potala Palace and Barkhor Subdistrict, the city has significantly enhanced the attractiveness of its cultural landmarks and improved overall ecosystem quality. Nevertheless, due to the relatively late onset of urban development and the complexity of the region’s terrain, the spatial layout of cultural ecosystem services (CESs) remains underdeveloped. Particularly, culturally driven perceptions related to human well-being are still underrepresented in current urban planning frameworks. Findings from this study reveal that although CESs are spatially clustered in the central urban area of Lhasa, significant disparities exist among different CES types across communities, resulting in uneven cultural service levels.
In core areas such as Barkhor Subdistrict, where cultural heritage resources are densely concentrated, communities exhibit high CES performance in cultural heritage, spirit and religion, ecotourism and aesthetics, and education and knowledge. However, due to high building density and limited space for expansion, access to health-related services is restricted and green space remains scarce. To address this issue, it is recommended to introduce small-scale green spaces, such as culturally themed pocket parks and community gardens, thereby expanding accessible outdoor spaces and enhancing the provision of health-related CESs in high-density urban cores. Furthermore, it is advisable to harness the resource-sharing potential of high-service CES communities like Cemenlin and Raosa by promoting cross-community engagement. This could include organizing community-based cultural and spiritual activities alongside cultural heritage education programs, thereby facilitating more balanced CES service provision across neighborhoods.
In communities such as Liuwu, Naiqiong, and Bangdui, located on the eastern and western peripheries of the city, limited attention in existing urban planning has led to persistently inadequate cultural service provision, resulting in a contiguous zone with relatively low service levels. To address this issue, it is recommended that waterfront ecological parks, suburban wetland parks, and urban forest parks be developed in communities such as Deyang Village, Yeba Village, and Naiqiong Village [75]. These parks should capitalize on the natural features of the Lhasa River, surrounding mountainous terrain, and existing forest resources. By integrating landscape interactions with elements of ethnic culture, ethnic-themed tourism routes can be created, along with the establishment of wetland-based ecological education facilities, thereby enhancing the aesthetic, recreational, wellness, and educational functions of the region. Furthermore, communities situated near the tributaries of the Lhasa River and within valley areas, although endowed with abundant wetland floodplains and scenic mountain-water landscapes, have not fully capitalized on these natural advantages in current spatial planning, resulting in comparatively low levels of cultural service provision. This study recommends that communities such as Bangdui Village in the east and Sema Village in the west fully leverage their ecological and cultural assets, identify dominant CES categories, plan ecological view corridors along mountain and water features, and promote landscape development pathways rooted in local traditions of mountain-water worship.
While the strategies recommended above aim to enhance equitable access to CESs in Lhasa’s central areas, remote communities continue to face challenges such as poor transportation, limited local resources, and land tenure constraints, all of which may hinder implementation. Addressing these challenges requires prioritizing multi-sectoral collaboration through policy analysis, infrastructure improvement, and stronger community governance [76,77]. Building on this, the localized optimization of natural and cultural resource allocation should be pursued. By relying on integrated data systems and statistical evaluation, effective practices can be standardized and operationalized, thereby achieving both scalable replication across regions and context-specific adaptation [78]. Specifically, an adaptable indicator system and policy toolkit should be developed to align with local ecological and cultural conditions [79], enabling easy implementation. Furthermore, institutionalized mechanisms for data sharing and performance evaluation should be established to facilitate the rapid dissemination of successful practices among similar cities and to allow contextual refinement. Simultaneously, assessments of regional variation and adaptability should be conducted to identify and consolidate best practices, which can then be translated into actionable policies and plans [75]. These measures not only strengthen the theoretical foundation of spatial governance in diverse contexts but also provide practical insights and guidance for improving well-being, ensuring equitable resource access, and advancing sustainable urban development [80].

4.4. Limitations and Future Directions

This study developed a CES assessment framework specifically tailored to the geographical and cultural context of the local area. However, due to the inherent intangibility of CESs, the interpretation of certain service types inevitably involved subjective judgments, potentially leading to deviations between the assessment results and on-the-ground realities. To achieve a more comprehensive understanding of local CESs, future frameworks may benefit from incorporating preference samples from stakeholders of diverse backgrounds during field visits and surveys. Including such perspectives can enhance the contextual relevance of sample data and model outputs, thereby improving the representativeness and reliability of the CES assessment.
In terms of data dimensions, this study has integrated both natural and social variables. Future research could further introduce economic-related indicators to establish a comprehensive environmental dataset encompassing ecological, social, and economic dimensions. This would contribute to the systematic development and refinement of CES evaluation indicators, ultimately supporting more holistic urban ecosystem management.

5. Conclusions

To enhance the regional adaptability of CES mapping methods in cities characterized by high geographical and cultural heterogeneity, this study focused on the central urban area of Lhasa and developed a comprehensive CES spatial mapping framework. This framework integrated the MaxEnt model with multi-source indicator data to uncover the response relationships between CESs and both natural and human environmental variables, evaluate the spatial heterogeneity of five CES types at the community level, and propose corresponding spatial optimization strategies. The results indicated that high-value CES areas were concentrated in the old city cluster and extended along the Lhasa River in an east–west direction. Among all environmental variables, DTT emerged as the most influential factor, contributing 78.3% to cultural heritage, 86.1% to spirit and religion, and 42.2% to ecotourism and aesthetic services. In addition, LULC, DTN, DTW, NDVI, and DTS exhibited relatively strong influences on individual types of CESs, whereas the contribution rates of other variables were comparatively limited. At the community scale, the five CES types generally exhibited a spatial pattern of higher values in central areas and lower values in the eastern and western zones. For the ecotourism and aesthetic category, 61.47% of the community area was classified as having a low service level, while only 1.48% and 7.33% were identified as excellent and high, respectively. Several communities along subdistricts such as Barkhor and Zhaxi consistently demonstrated high scores in categories such as ecotourism and aesthetics, and education and knowledge, but showed notably lower scores in other CES categories, especially in the health, which revealed significant spatial heterogeneity. Notable differences were observed in the spatial extent of high and low-service communities across CES categories such as ecotourism and aesthetic, cultural heritage, and spirit and religion. Communities within Barkhor Subdistrict were identified as having excellent service levels across most CES types, but only medium or low levels in health-related CESs, which contrasted with the overall trend. The proposed framework offers a transferable spatial governance approach for cities characterized by environmental and cultural heterogeneity. It provides both theoretical foundations and practical insights to support human well-being and sustainable urban development in similar urban contexts across different regions globally.

Author Contributions

Conceptualization, Y.L. (Yuqi Li) and S.Z.; Methodology, Y.L. (Yuqi Li), S.Z. and Z.N.; Software, Y.L. (Yuqi Li) and S.Z.; Validation, Y.L. (Yuqi Li) and S.Z.; Formal analysis, Y.L. (Yuqi Li), S.Z. and Z.N.; Investigation, Y.L. (Yuqi Li) and S.Z.; Resources, Y.L. (Yuqi Li) and Y.L. (Yunyuan Li); Data curation, A.J. and Y.L. (Yunyuan Li); Writing—original draft, Y.L. (Yuqi Li), S.Z. and Y.L. (Yunyuan Li); Writing—review and editing, S.Z., A.J. and Y.L. (Yunyuan Li); Visualization, A.J. and Z.N.; Supervision, Y.L. (Yunyuan Li); Project administration, Y.L. (Yuqi Li) and Y.L. (Yunyuan Li); Funding acquisition, Y.L. (Yunyuan Li). All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Key Research and Development Program of China (Project No. 2024YFD2200902).

Data Availability Statement

Data and materials will be provided after contact and consent has been obtained.

Acknowledgments

The authors thank the anonymous reviewers for their valuable comments and suggestions on this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Location of the central urban area of Lhasa. (a) The location of Lhasa’s central urban area within Lhasa City and the Tibet Autonomous Region. (b) Communities in the map are labeled with numerical codes (ID); detailed names are provided in Table 1.
Figure 1. Location of the central urban area of Lhasa. (a) The location of Lhasa’s central urban area within Lhasa City and the Tibet Autonomous Region. (b) Communities in the map are labeled with numerical codes (ID); detailed names are provided in Table 1.
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Figure 2. CES assessment and mapping framework for the central urban area of Lhasa. Arrows indicate the logical workflow and data processing sequence within the CES assessment and mapping framework.
Figure 2. CES assessment and mapping framework for the central urban area of Lhasa. Arrows indicate the logical workflow and data processing sequence within the CES assessment and mapping framework.
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Figure 3. Distribution of CES-related POIs in the central urban area of Lhasa.
Figure 3. Distribution of CES-related POIs in the central urban area of Lhasa.
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Figure 4. Correlation analysis of environmental variables (based on Origin software).
Figure 4. Correlation analysis of environmental variables (based on Origin software).
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Figure 5. AUC test results of the MaxEnt model for the five CES types. The model exhibits high predictive performance across all CES types, with mean AUC values of 0.922 for Ecotourism and Aesthetic, 0.981 for Cultural Heritage, 0.984 for Spirit and Religion, 0.959 for Health, and 0.938 for Education and Knowledge.
Figure 5. AUC test results of the MaxEnt model for the five CES types. The model exhibits high predictive performance across all CES types, with mean AUC values of 0.922 for Ecotourism and Aesthetic, 0.981 for Cultural Heritage, 0.984 for Spirit and Religion, 0.959 for Health, and 0.938 for Education and Knowledge.
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Figure 6. Spatial distribution of CESs in the central urban area of Lhasa. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
Figure 6. Spatial distribution of CESs in the central urban area of Lhasa. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
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Figure 7. The gain of environmental variables to the spatial distribution of CESs based on the MaxEnt model. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
Figure 7. The gain of environmental variables to the spatial distribution of CESs based on the MaxEnt model. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
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Figure 8. Community spatial distribution of CESs in the central urban area of Lhasa. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
Figure 8. Community spatial distribution of CESs in the central urban area of Lhasa. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
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Figure 9. Community classification of CES service levels in the central urban area of Lhasa. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
Figure 9. Community classification of CES service levels in the central urban area of Lhasa. (a) Ecotourism and Aesthetic; (b) Cultural Heritage; (c) Spirit and Religion; (d) Health; (e) Education and Knowledge.
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Table 1. List of communities in the central urban area of Lhasa.
Table 1. List of communities in the central urban area of Lhasa.
IDCommunity NameIDCommunity Name
1Barkhor Subdistrict, Barkhor Community39Najin Subdistrict, Najin Village
2Barkhor Subdistrict, Bailin Community40Najin Subdistrict, Naryu Community
3Barkhor Subdistrict, Chongsaikang Community41Najin Subdistrict, Tama Village
4Barkhor Subdistrict, Dangjeling Community42Nyange Subdistrict, Abalinka Community
5Barkhor Subdistrict, Lugu Community43Nyange Subdistrict, Cisongtang Community
6Barkhor Subdistrict, Raosai Community44Nyange Subdistrict, Jisu Village
7Barkhor Subdistrict, Xia Sasu Community45Nyange Subdistrict, Galsi Village
8Caigongtang Subdistrict, Bai Ding Village46Nyange Subdistrict, Rinchen Cai Village
9Caigongtang Subdistrict, Cai Village47Zhaxi Subdistrict, Nizhulin Community
10Caigongtang Subdistrict, Ciguoling Village48Zhaxi Subdistrict, Unity New Village Community
11Raidi Subdistrict, Luo’o Village49Zhaxi Subdistrict, Xiongha Community
12Raidi Subdistrict, Sangyi Community50Zhaxi Subdistrict, Zhaxi Community
13Jiedi Subdistrict, Weiba Village51Zhaxi Subdistrict, Zhaxi Xincun Community
14Gamagongsang Subdistrict, Ojetang Community52Bangdui Township, Bangdui Village
15Gamagongsang Subdistrict, Gamagongsang Community53Bangdui Township, Lin’a Village
16Gamagongsang Subdistrict, Najin Road North Community54Bangdui Township, Yeba Village
17Garma Gonsang Subdistrict, Tujian Community55Deqing Township, Baina Village
18Gongdeling Subdistrict, Gatso Community56Deqing Township, Deqing Village
19Gongdeling Subdistrict, Lalu Community57Deqing Township, Sangzhulin Village
20Gongdeling Subdistrict, Happiness Community58Deqing Township, Xincang Village
21Gongdeling Subdistrict, Xue Community59Dongga Subdistrict, Dongga Village
22Gongdeling Subdistrict, Tsemenlin Community60Dongga Subdistrict, Nanga Village
23Jibungang Subdistrict, Jibungangang Neighborhood61Dongga Subdistrict, Sangmu Village
24Jibungang Subdistrict, Mulu Neighborhood62Gurong Town, Baje Village
25Jibungang Subdistrict, Jiemuqi Neighborhood63Gurong Town, Join Village
26Jibungang Subdistrict, Palangxue Neighborhood64Liwu Subdistrict, Deyang Village
27Jiri Subdistrict, Hebalin Neighborhood65Liwu Subdistrict, Liwu Village
28Jiri Subdistrict, Jiri Neighborhood66Liuwu Subdistrict, Sangda Village
29Jiri Subdistrict, Tiebanggang Community67Naiqiong Subdistrict, Boma Village
30Jinzhu West Road, Bayi Community68Naiqiong Subdistrict, Gangdelin Village
31Jinzhu West Road, Dangba Community69Naiqiong Subdistrict, Jiamu Village
32Jinzhu West Road, Jinzhu West Road Community70Naiqiong Subdistrict, Jiage Village
33Jinzhu West Road, Lodu Community71Naiqiong Subdistrict, Naiqiong Village
34Liangdao Subdistrict, Karmalingka Community72Naiqiong Subdistrict, Sema Village
35Liangdao Subdistrict, Xianzudao Community73Yangda Subdistrict, Gangpu Village
36Najin Subdistrict, Zangge Community74Yangda Subdistrict, Yangda Village
37Najin Subdistrict, Garba Village75Yangda Subdistrict, Tongga Village
38Najin Subdistrict, Garong Community
Table 2. Data sources used in the study.
Table 2. Data sources used in the study.
Data NameResolutionYearSources
Administrative Boundary Vector Data--National Geographic Information Resources Catalog Service System (https://www.webmap.cn/) (accessed on 20 May 2025)
Land Use and Land Cover (LULC) Data30 m2022Earth Resource Data Cloud (www.gis5g.com) (accessed on 20 May 2025)
Normalized Difference Vegetation Index (NDVI)30 m2022Chinese Academy of Sciences Ecological Data Center (https://www.nesdc.org.cn) (accessed on 20 May 2025)
Digital Elevation Model (DEM)30 m2022NASA Earthdata (https://search.earthdata.nasa.gov/search) (accessed on 20 May 2025)
Point of Interest (POI) Data-2022Open platform of the GAODE map
Transportation Network Data-2022OpenStreetMap website (https://www.openstreetmap.org) (accessed on 20 May 2025)
Table 3. List of CES indicator types for the central urban area of Lhasa.
Table 3. List of CES indicator types for the central urban area of Lhasa.
TypeDescriptionTags and Keywords
Ecotourism and AestheticProvides locations for outdoor ecotourism and scenic areas with aesthetic value.Yaowang Mountain, Thousand-Buddha Cliff, Hongya Cave, Lhasa River Water Conservancy Scenic Area, ethnic villages, nature reserves, etc.
Cultural HeritageLocations of immovable cultural properties that hold historical and cultural significance and are officially recognized and registered.Potala Palace, Lukhang (palace), Buddhist chapel, corner tower, white pagoda, Jokhang Temple, Guandi Temple, Norbulingka (royal garden), Qugong Historical Site, Dazhalugong Commemorative Stele, Longxia Residence, Imperial Stele for the Pacification of Tibet, etc.
Spirit and ReligionPlaces for spiritual reflection, meditation, religious worship, and religious practice that influence personal well-being.Lakhang (palace), Podrang (shrine), sacred temple, Pingkang courtyard, Mani stone cairn, prayer flags, debating courtyard, monastic, pilgrimage square, Tibetan culture, Tibetan Buddhism, ancient and notable trees, etc.
HealthSites that provide regular and sustained access to green spaces and promote physical and mental well-being.Linka (garden), Tax Bureau Linka (garden), Bo Linka (garden), Gesang Linka Community Park, Nanshan Park, Nuoba Garden, Potala Palace Front Green Space, Zhaji Garden, Copper Bull Park, Princess Wencheng Cultural Tourism Theme Park, etc.
Education and KnowledgeLocations for ecological research and public education.Lalu Wetland, Plateau Research Institute, Snow Mountain Museum, Grassland Education Institute, Thangka Art Gallery, Tibetan Culture Art Museum, etc.
Note: Some of the site names listed under “Tags and Keywords” are transliterations of local terms or specific cultural and religious landmarks in Lhasa (e.g., Hongya Cave, Lukhang). Others refer to preserved traditional royal or noble gardens and dwellings (e.g., Linka, Longxia Residence). These terms reflect indigenous naming and cultural significance, helping preserve the cultural and geographical context of the CES assessment in Lhasa.
Table 4. List of environmental variables affecting CESs.
Table 4. List of environmental variables affecting CESs.
VariableSelection StatusVariableSelection Status
NDVISelectedElevationSelected
LULCSelectedSlopeSelected
Distance to religious facilitiesNot SelectedDistance to tourist spotSelected
Distance to cultural heritageNot SelectedDistance to forestSelected
Distance to natural featuresSelectedDistance to roadSelected
Distance to waterSelectedDistance to settlementSelected
Table 5. List of environmental variables for the central urban area of Lhasa.
Table 5. List of environmental variables for the central urban area of Lhasa.
TypeDescriptionTags and Keywords
NaturalNormalized difference vegetation
index (NDVI)		Reflects vegetation coverage conditions, which influence people’s perception of biodiversity and landscape.
Elevation (ELEV)Reflects the basic terrain elevation.
Slope (SLOPE)Reflects topographic conditions and biological habitat suitability.
Distance to water (DTW)Reflects the spatial distance to water bodies.
Distance to forest (DTF)Reflects the spatial distance to forested areas.
Distance to natural features (DTN)Reflects the distribution of sacred natural sites in Lhasa. These are often associated with culturally or spiritually significant landmarks (e.g., Mabri Mountain, Xiannudao–Duoxionglang), and the distance influences the perceived strength of CESs.
SocialLand use and land cover (LULC)Reflects land use patterns and resource status, which impact land management.
Distance to tourist spot (DTT)Reflects the spatial distribution and accessibility of tourist attractions in Lhasa, influencing ease of access to CESs.
Distance to road (DTR)Reflects accessibility to transportation infrastructure, which affects urban development.
Distance to settlement (DTS)Reflects proximity to residential areas in Lhasa, influencing accessibility to local services and infrastructure.
Table 6. Contribution rate of environmental variables for the central urban area of Lhasa. NDVI (Normalized Difference Vegetation Index); ELEV (Elevation); SLOPE (Slope); DTW (Distance to Water); DTF (Distance to Forest); DTN (Distance to Natural Features); LULC (Land Use and Land Cover); DTT (Distance to Tourist Spot); DTR (Distance to Road); DTS (Distance to Settlement).
Table 6. Contribution rate of environmental variables for the central urban area of Lhasa. NDVI (Normalized Difference Vegetation Index); ELEV (Elevation); SLOPE (Slope); DTW (Distance to Water); DTF (Distance to Forest); DTN (Distance to Natural Features); LULC (Land Use and Land Cover); DTT (Distance to Tourist Spot); DTR (Distance to Road); DTS (Distance to Settlement).
Environmental
Variables		Contribution Rate (%)
Ecotourism and AestheticCultural HeritageSpirit and ReligionHealthEducation and Knowledge
NDVI2.80.70.515.41.5
ELEV0.71.00.61.21.4
SLOPE0.40.70.81.20.7
DTW2.13.43.119.54.7
DTF0.91.11.33.54.9
DTN12.31.70.77.22.2
LULC12.311.76.010.931.7 *
DTT42.2 *78.3 *86.1 *23.0 *28.6
DTR4.10.50.48.38.4
DTS7.51.00.59.815.8
The asterisk (*) highlights the most influential rate for the corresponding CES category.
Table 7. Area and percentage distribution of CES service levels across communities in the central urban area of Lhasa.
Table 7. Area and percentage distribution of CES service levels across communities in the central urban area of Lhasa.
CES TypesExcellentHighMediumLow
Area
(km2)		Percentage (%)Area
(km2)		Percentage (%)Area
(km2)		Percentage (%)Area
(km2)		Percentage (%)
Ecotourism and Aesthetic5.311.4826.257.33105.7529.51220.3061.47
Cultural Heritage3.611.0138.1910.66172.2948.08144.2640.26
Spirit and Religion3.621.0149.3213.76164.0245.77141.4039.46
Health22.986.4148.4613.52131.2336.62155.6943.45
Education and Knowledge13.723.8341.5111.58164.2845.84138.8638.75
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Li, Y.; Zhao, S.; Jin, A.; Nie, Z.; Li, Y. Assessment and Spatial Optimization of Cultural Ecosystem Services in the Central Urban Area of Lhasa. Land 2025, 14, 1722. https://doi.org/10.3390/land14091722

AMA Style

Li Y, Zhao S, Jin A, Nie Z, Li Y. Assessment and Spatial Optimization of Cultural Ecosystem Services in the Central Urban Area of Lhasa. Land. 2025; 14(9):1722. https://doi.org/10.3390/land14091722

Chicago/Turabian Style

Li, Yuqi, Shouhang Zhao, Aibo Jin, Ziqian Nie, and Yunyuan Li. 2025. "Assessment and Spatial Optimization of Cultural Ecosystem Services in the Central Urban Area of Lhasa" Land 14, no. 9: 1722. https://doi.org/10.3390/land14091722

APA Style

Li, Y., Zhao, S., Jin, A., Nie, Z., & Li, Y. (2025). Assessment and Spatial Optimization of Cultural Ecosystem Services in the Central Urban Area of Lhasa. Land, 14(9), 1722. https://doi.org/10.3390/land14091722

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