1 Introduction
Rural ecological revitalization is key to rural revitalization, and creating a high-quality rural ecological environment serves as an effective pathway and critical measure to achieve this goal
[1]. Rural landscapes, as critical carriers of rural ecological and cultural systems, require systemic classification to support structural and functional studies. This process underpins landscape pattern analysis, planning and design, and construction management while providing a pivotal interface between landscape ecology theory and practical implementation
[2]. All these aspects are essential for comprehending rural landscapes, building ecologically livable rural areas, and advancing rural ecological revitalization.
Research on rural landscape classification initially emerged in geographical land classification and later extended to human impacts and landscape functional morphology
[3]. By natural and cultural attributes, rural landscapes were categorized into settlement and non-settlement landscapes
[4]. They can also be classified into natural, managed, and artificial landscapes by human disturbance levels
[5]. Functionally, they might be divided into landscape regions, classes, subclasses, and units
[6]. Besides, a four-tier system comprising landscape groups, regions, classes, and elements was proposed, integrating rural landscapes' physical types and subjective perceptions
[7]. In Poland, rural landscapes were categorized into geomorphological units and subunits such as hills, plains, basins, and valleys according to natural, cultural, and visual characteristics
[8].
While existing studies primarily focus on physical morphological features, rural landscapes are not only visual entities but also integral components of rural ecosystems, delivering a variety of ecosystem services
[9]. Rapid urbanization, however, has gradually weakened these critical ecosystem services
[10]. Ecological processes, material flows, and energy transfer within rural landscapes are closely linked to their services. Thus, identifying dominant ecosystem services can deepen understanding of the key roles of rural landscapes in maintaining ecological balance, delivering ecosystem services, and enhancing human well-being.
Functional dominance is defined by the spatial hierarchy of landscape functions, where one or a few dominant functions represent the overall landscape
[11]. This concept has been widely applied in landscape classification and the identification of production, living, and ecological spaces. For example, some studies identified the dominant functions of land parcels by analyzing and evaluating their natural agricultural suitability and geographical locations; based on functional differences, agricultural landscapes were further classified into production, service facility, recreational, and ecological types
[12]. Other scholars identified the dominant, secondary, and auxiliary functions of different land-use types by assessing their ecological, productive, and living functional values, thereby refining spatial classification
[13]. In a research about Beijing, the points of interest (POI) and land-use data were used to quantify ecosystem service contributions by various land-use types within village buffers, enabling dominant service identification and rural landscape resource classification
[10].
On the whole, current studies that conducted ecosystem service assessments to identify dominant ecosystem services for rural landscape classification are scarce and often lack granularity, making it difficult to provide precise guidances for rural landscape enhancement. Precise landscape classification is fundamental to conservation, development, and optimization. Focusing on Wuhan, this study evaluates its rural ecosystem services, identifies the dominant services at the administrative village level, and establishes a rural landscape classification system based on dominant ecosystem services. The findings aim to provide technical guidances for enhancing rural landscapes and theoretical support for rural revitalization.
2 Study Area and Research Methods
2.1 Study Area
Wuhan is located in eastern Hubei Province in China at the confluence of the Yangtze River and the Hanjiang River. Its rural areas encompass diverse ecosystems such as lakes, wetlands, forests, and farmlands, with rich natural and cultural resources. As of 2022, the rural population stood at 2.3052 million (24.4% of the city's total), and the gross output of agriculture, forestry, animal husbandry, and fishery reached 83.672 billion yuan
[14]. Rapid socio-economic development has driven urban expansion into peripheral zones, subjecting rural landscapes to dual pressures from urbanization and rural development. These pressures have triggered ecological degradation, such as water pollution and soil erosion. Against this backdrop, this study selected administrative villages in Wuhan's rural areas as study units (Fig.1) to explore landscape classification methods and improvement strategies based on the dominant ecosystem services.
2.2 Research Roadmap
Rural landscapes, as relatively pristine spatial systems, provide irreplaceable ecological, economic, and social functions distinct from urban areas.
[15] They play a vital role in maintaining ecological balance, supporting agricultural production, and offering recreational resources.
[16] This study first identified the ecological, economic, and social functions of rural landscapes, integrating these with Wuhan's rural characteristics to establish an ecosystem service evaluation and classification framework. Based on the evaluation results, rural landscapes were categorized according to the dominant ecosystem services identified at the village level. Finally, tailored improvement strategies were developed for different rural landscape types, focusing on functional optimization and distinctiveness, providing actionable guidances for rural revitalization (Fig.2).
Fig.2 Research framework. |
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2.3 Data and Methods
2.3.1 Data Sources and Processing
The data used in this study and their sources are detailed in Tab.1. Village boundary data were obtained from Map World and the rural areas were delineated based on the compilation regulations for statistical zoning code and urban–rural classification code by the National Bureau of Statistics of China (selecting regions where the first digit of the urban–rural classification code is "2"). The study area encompasses 1, 690 village-level administrative units, including administrative villages, forest farms, agricultural farms, tea plantations, aquaculture farms, fishery farms, and agricultural research institutes. The land use and land cover data, from China Land Cover Dataset (2000 ~ 2020), was retrieved from Zenodo and reclassified into 8 categories: farmland, grassland, forest, shrubland, wetland, water body, bare land, and built-up land. The precipitation and evapotranspiration data were spatially interpolated using Kriging methods in ArcGIS after linking tabular data to corresponding meteorological stations, generating raster datasets that represent their spatial distribution. The POI data were obtained from Amap. Those data of POIs with cultural service value were categorized into three categories based on the POI Classification Code from Amap Open Platform: tourist attraction, science/culture & education service, and sports & recreation, encompassing 22 subcategories and 76 subtypes. The POI data for Wuhan's rural areas were then clipped by the boundaries of the 1, 690 village units in ArcGIS, yielding spatial distribution data of the rural POIs within the study area. Additional datasets, such as soil composition, normalized difference vegetation index (NDVI), digital elevation model (DEM), road, water body, historical cultural village, and famous tourist village, were downloaded from corresponding open data platforms. These datasets were standardized and projected to the WGS_1984_UTM_Zone_49N coordinate system, with raster data resampled to a resolution of 30 m and analyses conducted using 2020 as the temporal benchmark.
Tab.1 Research data and sources |
| Name | Format | Source | Description |
|---|
| Village boundary in Wuhan | Vector | Map World (National Platform for Common GeoSpatial Information Services of China) | Extracted a total of 1, 690 villages |
| Land use and land cover | Raster | Zenodo | Resolution of 30 m × 30 m |
| Precipitation and evapotranspiration | — | Resource and Environmental Science Data Platform, Chinese Academy of Sciences | Daily precipitation and evapotranspiration data from national meteorological stations of China |
| Soil composition | Raster | National Tibetan Plateau Data Center of China | Resolution of 1 km × 1 km |
| NDVI | Raster | National Ecosystem Science Data Center of China | Resolution of 30 m × 30 m |
| DEM | Raster | Geospatial Data Cloud | Resolution of 30 m × 30 m |
| Road | Vector | BIGEMAP | Major roads (national, provincial, expressways), secondary roads (urban arterials), and other roads (streets, country roads) |
| Water body | Vector | BIGEMAP | Rivers and lakes, including the Yangtze River, Hanjiang River, Liangzi Lake, and Mulan Lake |
| POI geographic data | Vector | Amap Open Platform | Categories including "tourist attraction, " "science/culture & education service, " and "sports & recreation" |
| Lists of historical and cultural village and famous tourist village | Vector | Wuhan Municipal Bureau of Culture and Tourism | Including 1 national-level historical and cultural village, 9 city-level historical and cultural villages, and 6 city-level famous tourist villages |
2.3.2 Ecosystem Service Evaluation Methods
(1) Selection of ecosystem service evaluation indicators
Ecosystem services refer to the environmental conditions and benefits provided by ecosystems that are essential for human survival and development
[17]. The
Millennium EcosystemAssessment categorizes them into regulating, provisioning, cultural, and supporting services
[18].
China's rural revitalization strategies emphasize the ecological functions of rural landscapes, such as climate regulation and environment improvement, to ensure ecological security. These align with the regulating services of ecosystems. Wuhan is prone to flooding and soil erosion due to its hydrogeological conditions, though it is well known for its abundant water resources. Therefore, water retention and water and soil conservation were selected as evaluation indicators for regulating services.
The primary productive function of rural areas, i.e. generating agricultural products, corresponds to the provisioning services. Wuhan, situated in the middle and lower Yangtze River Plain, benefits from abundant arable land and favorable agricultural conditions. Thus, grain production, the core metric for economic functions of rural landscapes, was chosen as the evaluation indicator for provisioning services.
Rural landscapes also hold significant natural and historical cultural values, corresponding to cultural services. Wuhan's rural areas feature interconnected rivers and lakes, along with rich historical and cultural heritages and a long history of traditional agricultural practices. With the rise of rural tourism, these landscapes increasingly serve as venues for leisure and cultural experiences. Cultural services are "nonmaterial benefits people obtained from ecosystems through spiritual enrichment, cognitive development, reflection, recreation, and aesthetic experiences, " including spiritual and religious, educational, and aesthetic values
[18]. Based on visual communication, spiritual conveyance, and place usage, this study refined the classification of cultural services to reflect the ecological value of rural landscapes, categorizing them into 1) natural scenic value, 2) scientific, educational, and cultural value, and 3) leisure and recreational value
[19].
Supporting services, which underpin the other three types of services to ensure ecosystem stability and health (e.g., nutrient cycling essential for life on Earth), were not a focus of this research.
(2) Regulating service evaluation
Water retention capacity, measured as water retention volume, was used as the evaluation indicator, considering factors such as river source areas, climate, land cover, and topography. The calculation formula is as follows
[20]:
where TQ is the water retention volume (m), representing the total retained water in a specific area over a given time; i represents the land use type and jrepresents the total number of land use types in the study area; Pi is for the precipitation (mm), Ri is for the runoff (mm), ETi is for the evapotranspiration (mm), and A is for the area (km2) of land use type i.
Water and soil conservation was evaluated based on the difference between potential and actual soil erosion, calculated using the Revised Universal Soil Loss Equation (RUSLE)
[20]:
where A represents the= amount×× of×soil×conserved(1−) (t/hm2·a); R is the rainfall erosivity (MJ·mm/hm2·h·a) reflecting the energy and intensity of rainfall, with higher values indicating greater soil erosion potential; K is the soil erodibility (t·hm2·h/hm2·MJ·mm), representing the susceptibility of specific soil types to erosion under given conditions; L and S are topographic factors—L represents the slope length (longer slopes are associated with higher erosion risk), while S represents the slope steepness (steeper slopes correspond to higher erosion rates); C is the vegetation cover factor, representing vegetation's ability to suppress soil erosion (lower values indicate higher vegetation cover, stronger suppression, and lower erosion rates).
(3) Provisioning service evaluation
Grain production was evaluated using cropland as the target area, with the
NDVI correction method applied to redistribute the total grain yield across grid cells within the study area
[21]. The calculation formula is as follows:
where Gi is the grain yield (kg/hm2) allocated to cropland grid i; Gsum is the total grain yield of major crops in the study area (kg); NDVIi and NDVIsum are the NDVI value of cropland grid i and the total NDVI valueof cropland within the study area, respectively.
(4) Cultural service evaluation
This study used the MaxEnt model combined with POI data to evaluate cultural services. The MaxEnt model, based on the maximum entropy theory, uses known incomplete information to infer the probability distribution of unknown information. Initially developed for predicting species distribution in ecology, it has since been applied to evaluate cultural services due to its spatial visualization capabilities
[22][23]. The model allows for the quantitative evaluation of the impacts of environmental variables on cultural services. The evaluation included the following 5 steps.
1) POI data classification. Based on the subtype standards of the POI Classification Code from Amap Open Platform, POI data related to cultural services were selected and categorized into natural scenic value, scientific, educational, and cultural value, and leisure and recreational value (Tab.2). This classification generated spatial distribution points for each value category.
Tab.2 Cultural service value types and corresponding POI subtypes from Amap |
| Cultural service value category | Number of POIs | POI subcategory from Amap |
|---|
| Natural scenic value | 314 | Tourist attraction related, scenery spot, world heritage, national view spot, provincial view spot, beach |
| Scientific, educational, and cultural value | 1, 082 | Memorial hall, Buddhist & Taoist temple, church, mosque, science & education cultural place, museum, Audi museum, Mercedes-Benzes museum, exhibition hall, convention & exhibition center, art gallery, library, science & technology museum, planetarium, cultural palace, archives hall, arts organization, media organization |
| Leisure and recreational value | 755 | Sports & recreation places, sports places, sports center, bowling hall, tennis court, basketball stadium, football field, ski field, skating rink, outdoor gym facility, public beach, natatorium, gym center, table tennis hall, pool room, squash court, horse riding club, race track, rugby court, badminton court, taekwondo venue, golf related, golf course, golf training course, recreation center, night club, KTV, disco, pub, game center, card & chess room, lottery center, internet bar, holiday & nursing resort, resort, nursing home, recreation place, amusement park, fishing spot, plucking park, camping site, water sports center, theater and cinema related, cinema, concert hall, theater, park & square, park, zoo, botanical garden, aquarium, city plaza |
2) Selection of environmental variables. Referring to previous studies, the selected environmental variables included land use and land cover, slope, elevation, distance to water bodies, distance to roads, and
NDVI[24][25]. The Shapiro-Wilk test showed that
p-values for all variables were much less than 0.05, indicating non-normal distributions. Consequently, Spearman's correlation coefficient was employed to quantify the association between the variables. Multicollinearity test results revealed that the correlation coefficients between variables were all less than 0.75, variance inflation factors (VIF) were all less than 10, and tolerances were all greater than 0.2, indicating weak correlations
[26] and no multicollinearity issues
[27]. Therefore, all variables were retained for modeling.
3) MaxEnt model analysis. The three types of cultural service value distribution points and environmental variables were input into the MaxEnt model to generate spatial distribution maps for each value category.
4) Spatial distribution result adjustment. Using the information of historical and tourist villages within the study area, the spatial distribution maps of the scientific, educational, and cultural value and leisure and recreational value were normalized. Specifically, the scientific, educational, and cultural value results of the national-level historical and cultural village and city-level historical and cultural villages (10 in total) were adjusted to the maximum value of 1. Similarly, the leisure and recreational value results of 6 city-level famous tourist villages of Wuhan with rich tourism resources and well-developed infrastructure were adjusted to the maximum value of 1.
5) Generation of cultural service evaluation results. The normalized maps of the three cultural service values were assigned equal weights and overlaid to obtain the final distribution results of rural landscape in cultural service values.
2.3.3 Classification Standards for Ecosystem Service
The classification of ecosystem services should reduce the fragmentation of the highest-level categories and facilitate comparison
[28]. Common classification methods include the natural breaks method
[29], the quantile method
[30], and the cumulative proportion method
[31]. Studies have shown that the cumulative proportion method outperforms others in terms of classification quality, distinctiveness, and applicability. This method has relatively fixed cumulative values at each level, providing strong comparability
[28]. Specifically, with this method, the values of individual ecosystem service, corresponding to each grid cell, were ranked in descending order, and cumulative service values were calculated. Following the Guidelines on Delimitation of Ecological Protection Red Line, the grid values corresponding to a cumulative service value that accounts for 50% and 80% of the total ecosystem service value were used as thresholds. Using ArcGIS reclassification tools, the importance of each ecosystem service type was divided into three levels: very important (top 50%), important (50% ~ 80%), and moderate (80% ~ 100%). For regulating services, the importance level was determined by the highest level from the classifications of water retention and water and soil conservation.
2.3.4 Classification Standards for Rural Landscapes
Classifying rural landscapes at the administrative village level helps clarify the dominant ecosystem service of individual villages, providing guidances for targeted improvement strategies and future development of rural areas. This study determined the dominance based on the area proportion of each ecosystem service
[10]. Referring to related research, an ecosystem service was considered dominant if the area of its very important zone exceeded 33% of the administrative village's total area
[10]. Given the equal importance of regulating, provisioning, and cultural services for rural landscapes, this study adopted 33% as the baseline. Based on the dominant ecosystem services, rural landscapes can be categorized into 4 major types: single function, dual functions, comprehensive functions, and no clear dominant function. These further formed 8 combinations (Tab.3).
Tab.3 Classification of rural landscapes based on the dominant ecosystem services |
| Category | Dominant function type | Regulating service | Provisioning service | Cultural service |
|---|
| Single function | Village dominated by regulating services | A≥33% | B < 33% | C < 33% |
| Village dominated by provisioning services | A < 33% | B≥33% | C < 33% |
| Village dominated by cultural services | A < 33% | B < 33% | C≥33% |
| Dual functions | Village dominated by regulating–provisioning services | A≥33% | B≥33% | C < 33% |
| Village dominated by regulating–cultural services | A≥33% | B < 33% | C≥33% |
| Village dominated by provisioning–cultural services | A < 33% | B≥33% | C≥33% |
| Comprehensive functions | Village dominated by regulating–provisioning–cultural services | A≥33% | B≥33% | C≥33% |
| Without clear dominant function | Village with no clear dominant function | A < 33% | B < 33% | C < 33% |
3 Results
3.1 Spatial Distribution Characteristics of Regulating Service
The importance levels of water retention showed a general spatial trend of being higher in the north and south and lower in the center (Fig.3). Very important areas accounted for 26.54%, mainly distributed in the northwest, northeast, and southern parts of the study area. Important areas accounted for 43.02%, concentrated in the central region. Moderate areas accounted for 30.43%, primarily located in the northern and central regions. This spatial distribution was closely related to vegetation cover and terrain, with the northwest and eastern areas characterized by high mountains and forests and the south dominated by cropland and water bodies.
Fig.3 Importance levels of regulating service of Wuhan's rural landscapes. |
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The importance levels of water and soil conservation were generally low across the region, though with a few clusters of high-value villages (Fig.3). Very important areas accounted for 3.55%, mainly located in the northwest and northeast, overlapping with the higher-importance-level areas for water retention. Important areas accounted for 6.84%, mainly surrounding the very important areas of water and soil conservation. Moderate areas accounted for 89.61%, widely distributed across the rural areas.
The spatial distribution of regulating service importance levelsreflected the combined ecosystem services of water retention and water and soil conservation, aligning closely with the distribution of water retention (26.98%, 45.10%, and 27.91% for very important, important, and moderate areas, respectively) (Fig.3).
3.2 Spatial Distribution Characteristics of Provisioning Service
The spatial distribution of provisioning service importance levels exhibited a general pattern of being higher in the south and lower in the northwest (Fig.4). Very important areas accounted for 29.38%, concentrated in the southeast, dominated by cropland with high NDVI values and forming contiguous clusters. Important areas accounted for 24.12%, mainly in the north, also dominated by cropland. Moderate areas accounted for 46.50%, primarily in the northwest, southwest, and areas around central urban regions, dominated by forests, built-up land, and water bodies. The overall result showed that the total proportion of very important and important areas was relatively high, underscoring the significance of rural landscapes in grain production.
Fig.4 Importance levels of provisioning service of Wuhan's rural landscapes. |
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3.3 Spatial Distribution Characteristics of Cultural Service
The spatial distribution of cultural service importance levels reflected the combined value of the three categories. The MaxEnt model employed in this study used the jackknife method to measure the contribution of each environmental factor to model construction. Results showed that the natural scenic value was higher in the north and lower in the south, which was strongly correlated with elevation and slope (Fig.5). The high-value areas of scientific, educational, and cultural value were concentrated in areas around central urban regions and historical and cultural villages. Land use and land cover and NDVI were the main influencing factors, suggesting that this value was closely related to land use type and vegetation cover (Fig.5). The leisure and recreational value showed a linear spatial distribution, with distance to roads and land use and land cover as the primary influencing factors (Fig.5). Overall, very important areas of the cultural services accounted for 23.26%, mainly in the northern region and surrounding urban built-up areas, where it found abundant natural and historical cultural resources, convenient transportation, and comprehensive public services; important areas accounted for 29.17%, primarily located around very important areas; and moderate areas accounted for 47.58%, distributed in the southern region, dominated by cropland with a lack of distinctive natural and historical cultural resources (Fig.5-Fig.4).
Fig.5 Importance levels of cultural services of Wuhan's rural landscapes. |
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3.4 Rural Landscape Classification Results
The classification results for rural landscapes in Wuhan based on the dominant ecosystem services are shown in Fig.6. Single-function villages (46.80%) consisted of 202 villages dominated by regulating services, 362 by provisioning services, and 227 by cultural services. Dual-function villages (19.70%) included 142 villages dominated by regulating–provisioning services, 56 by regulating–cultural services, and 135 by provisioning–cultural services. The number of comprehensive-function villages was 20 (1.18%). Villages with no clear dominant function numbered 546 (32.31%).
Fig.6 Classification results of Wuhan's rural landscape. |
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Different types of rural landscapes within the study area showed evident spatial clustering. Villages dominated by regulating services were distributed around high mountain forest areas and water-dense regions; those dominated by provisioning services were concentrated in the north and south, dominated by cropland in flat terrains; and villages dominated by cultural services were located in areas with abundant natural and historical cultural resources, convenient transportation, and comprehensive public services in the north. Moreover, villages dominated by regulating–provisioning services were found in the south, leveraging cropland and water resources; those dominated by regulating–cultural services were mainly in areas around high mountain forests and water bodies, characterized by distinctive natural scenery and ecological conditions; and those dominated by provisioning–cultural services were located around villages dominated by provisioning and cultural services, enjoying agricultural and cultural tourism resources. In addition, villages with dominant regulating–provisioning–cultural services were distributed in the northeast, where natural, ecological, and cultural resources are prominent; while villages with no clear dominant function were primarily around urban built-up areas, impacted by development and lacking distinct ecosystem service.
4 Strategies for Improving Rural Landscapes
Rural landscapes, as vital components of rural ecosystems, form a complex ecological–economic–social system
[32]. This study evaluated rural landscapes based on regulating, provisioning, and cultural services of ecosystems, taking administrative villages as units and classifying landscapes based on dominant ecosystem services. Improvement strategies from the perspectives of function and landscape distinctiveness to improve rural landscapes were proposed.
4.1 Strengthening Functions to Balance Protection and Development
The capacity, vitality, and attraction of rural landscapes
[33] reflect their ecological, economic, and social functions. Enhancing rural landscapes requires addressing these three functions to achieve integrated development of rural ecosystems. Generally, based on ecological functions, it is essential to strengthen the maintenance and management of rural landscape resources to enhance their carrying capacity; driven by economic functions, wise planning of rural industrial layouts should be implemented to foster sustainable economic development and vitality; highlighting by social functions, the creation of regional rural cultural landscapes should be prioritized to enhance attractiveness
[33]. According to the classification results of rural landscapes in Wuhan, the functional guidance strategies are proposed as follows.
Regarding single-function villages, those dominated by regulating services are critical for climate regulation and water retention, where ecological protection should be prioritized. For northern mountain forest areas, measures of protecting forest ecosystems, restoring water retention functions, and implementing water quality management should be taken. In the southern water-rich regions, emphasis should be placed on preserving wetland ecosystems and enhancing flood management and water resource utilization. Villages dominated by provisioning or cultural services should focus on development. Villages dominated by provisioning services, often with flat terrain and rich cropland resources, should optimize industrial structures, develop green agriculture, and promote specialized industries to enhance economic benefits while maintaining food security. Villages dominated by cultural services, the second most abundant in quantity, should strengthen historical site and natural heritage protection while avoiding over-commercialization. Establishing regional cultural corridors and enhancing linkages among cultural landscapes are essential.
Dual-function and comprehensive-function villages should balance ecological protection and agricultural development. For instance, villages dominated by regulating–provisioning services should promote efficient water-saving agriculture and protect aquatic ecosystems. Villages dominated by provisioning–cultural services should integrate agricultural production with cultural heritage to develop diversified tourism industries such as agritourism, farming experiences, and rural retreats.
Villages with no clear dominant function require on-site investigations and market research to understand resource endowments and market demands better. Development should be guided towards establishing a dominant function while avoiding resource wastage by aligning with locational and market conditions.
4.2 Differentiated Strategies to Create Distinctive Rural Landscape Features
This study further selected representative villages from various rural landscape types, and based on their landscape characteristics, explores how to leverage their resource endowments to create rural landscape features.
Villages dominated by regulating services can leverage abundant forests, waterways, lakes, and wetlands to create wetland parks or nature reserves. For example, Pingfeng Village in the northwest of the study area has strong water retention, water and soil conservation, and climate regulation functions. Key measures include preserving natural mountain and water features by designating critical ecological areas (water retention forests, wildlife habitats, etc.) to restrict human activities and incorporating ecological principles into rural landscape planning and design (green roofs, rain gardens, eco-friendly parking lots, etc.) to minimize environmental impacts.
Villages dominated by provisioning services can showcase traditional agricultural landscapes like rice paddies, rapeseed flower fields, and tea gardens, combined with modern agricultural modes such as greenhouses and agricultural science parks. For instance, Leiling Village in the southern study area has favorable farming and irrigation conditions. Local agricultural enterprises have introduced advanced farming techniques and management practices, enhancing production efficiency and yield. Strategies for the village include creating large-scale agricultural landscapes aligned with natural topography and promoting high-value crop cultivation and eco-friendly farming to improve economic benefits while maintaining the traditional rural aesthetic.
Villages dominated by cultural services should emphasize the creation of landscapes that reflect the local cultural heritage. For example, Qiupi Village in the northern study area, a historical and cultural village of Wuhan, should restore and maintain local traditional residences as a priority, and adapt and reinterpret architectural styles and elements, such as those from vernacular dwellings, for public spaces like roads, bridges, squares, and parks, creating a distinctive architectural and cultural landscape.
Dual-function and comprehensive-function villages can integrate ecological conservation, agricultural production, and tourism development to form multifunctional landscapes. For example, Lamei Village in the northern low hills region, characterized by cropland, forest land, and proximity to a reservoir, enjoys abundant natural resources and numerous tourist attractions. Future strategies include strengthening ecological conservation efforts, developing organic agriculture and specialty products, and integrating regional cultural and tourism resources to boost economic growth by leveraging the village's landscape appeal.
Villages with no clear dominant function have weaker ecosystem services and should avoid homogenized urban development. Instead, they can retain and protect unique rural landscape features, leveraging locational advantages to create a new countryside charm with urban civilization. For example, Yueming Village in the northwest of the study area could develop into an eco-education base and a family-friendly farm or multifunctional recreational space appealing to urban residents, acting as a "backyard" complementing the city.
5 Conclusions
Existing rural landscape classification methods are often based on land classification, human impact levels, and landscape functional forms. Taking Wuhan as a case study, this research explores a rural landscape classification method grounded in dominant ecosystem services. Six representative ecosystem services—water retention, water and soil conservation (regulating services); grain production (provisioning services); and natural scenic value, scientific, educational, and cultural value, and leisure and recreational value (cultural services)—were evaluated. The results revealed significant spatial differences in the distribution of these functions within Wuhan's rural landscapes. Specifically, regulating service showed higher importance levels in the southern and northern parts of the study area, with lower values in the central region. Provisioning services were more prominent in the south. Cultural services were particularly concentrated in the north. Based on these findings, a cumulative proportion method was employed to classify the evaluation results, identifying dominant ecosystem services by analyzing the proportion of the very important areas within each administrative village unit. The classification resulted in 8 rural landscape types—three for single-function villages, three for dual-function villages, as well as comprehensive-function villages, and villages without clear dominant function—each exhibiting notable spatial clustering patterns. Strategies for improving rural landscapes were then proposed from the perspectives of function and landscape distinctiveness based on the spatial distribution of ecosystem services and the classification results.
The proposed classification method for rural landscapes, first, via quantitative assessment, addresses the lack of fine-grained ecosystem service evaluations in previous classifications. Second, it enhances the precision of results in guiding rural landscape development through administrative village-level analysis. Third, by using the MaxEnt model combined with POI data and adjusting the results with listed historical and tourist villages, a more applicable approach for assessing cultural services in rural areas is explored.
While this study introduces methodological and strategic innovations, there were certain limitations. It examines rural landscapes from ecological, economic, and social dimensions, focusing on regulating, provisioning, and cultural services, while only evaluating 6 representative ecosystem services. Future research can expand the range of indicators to measure ecosystem services more comprehensively, enabling more detailed and nuanced classifications of rural landscapes. This would facilitate deeper analyses and provide more targeted strategies for rural landscape development.
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Fig.1 Study area.
Tab.1 Research data and sources
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