The Ming Great Wall (M-GW) is the most representative large linear heritage in China, and faces the problem of landscape fragmentation caused by traditional monument-based protection and disorderly tourism development. We propose to utilise character-based approach for managing landscape change and preserving its integrity. This paper presents a hierarchical characterisation method for the M-GW landscape in Ji-Town in two steps: definition and delimitation of landscape area, and zoning of landscape characters. The landscape area was identified based on the landscape relevance of the space from natural, cultural, and visual aspects. The landscape character types and areas were identified by two dominant attributes using a layout method at Level I (general zoning), and by eight specific attributes using a synthetic method that combines digital and manual approaches at Level II (detailed zoning). According to the analysis results, a wide belt landscape area of about 8650.7 km² was delimited. A total of eight landscape character types, 15 sub-types, 47 landscape character areas and 359 sub-areas were obtained. Additionally, the results highlighted the key landscape characteristics that could be used for the planning and construction of the National Cultural Park. Finally, this research provides further direction for the theoretical and technical basis of future research on landscape characterization and sustainable management of the whole M-GW and other linear heritage landscapes.

The interaction of human activity in national parks and their surrounding areas with natural landscapes is an important factor in the formulation of strategies for the protection and management of protected areas. From an integrated ecological and social perspective, this study aims to develop a human–land coupling model to reveal the ecological threats of human activities to national park and their surrounding areas under rapid urbanization. We first established a four-quadrant model based on the coupling relationship between human activity intensity (HAI) and the landscape pattern index (LPI). On this basis, we analyzed the spatial coupling characteristics of these indices from 2000 to 2020, preliminarily assessed the human–land relationship in Lushan National Park and its surrounding areas (LNPSA), and then explored the driving factors of the coupling relationship. The results show the following. (1) The proportion of regions with high and extremely high human activity intensity increased from 6.02% to 16.41% over the past two decades. These regions are mainly distributed in the surroundings of Lushan National Park, showing a gradually spreading trend to the core protected area. (2) The surroundings had higher landscape fragmentation, landscape diversity, and total variation in the landscape pattern compared with the core protected area. (3) Vegetation coverage and distance to Lushan have the most significant effect on the human–land coupling relationship in LNPSA, and human activity and natural evolution together shape the characteristics of this relationship in the study area. (4) Utilizing administrative divisions as the fundamental framework, the study area is delineated into four distinct zones based on the results of the human–land coupling analysis—harmonious development zones, stable transformation zones, environmental regulation zones, and risk prevention zones—with tailored optimization strategies proposed for each zone’s characteristics. These findings can facilitate the optimized formulation of schemes for different regions and provide a comprehensive methodology to guide the planning and management of natural protected areas.

Toponyms reflect the multifaceted relationship between humans and nature, recording and transmitting important cultural information. A toponymic cultural landscape (TCL) is the comprehensive embodiment of the cultural connotations and landscape significance carried by toponyms, reflecting various factors such as regional culture, historical memory, and social values. Wuhan, as the hinterland of Jing-Chu culture, carries a profound geo-culture and brings together numerous toponymic cultural heritages. Studying the spatial distribution characteristics of Wuhan’s toponymic cultural heritage and their association with landscape elements is of great significance in promoting the inheritance of intangible cultural heritage and realizing the orderly continuation of local culture, and it also confers deeper cultural connotations and local characteristics to the process of landscape characterization. This study analyzes 3638 toponyms in Wuhan as the research objects, utilizing geospatial analysis methods, including kernel density analysis, standard deviation ellipse analysis, and average nearest neighbor analysis, to visualize the spatial distribution of Wuhan’s TCL. It further combines these methods with the optimal parameter geographic detector (OPGD) model to explore the influence of landscape elements on the spatial distribution of different types of toponyms and their interaction effects. The results show the following: (1) The TCL of Wuhan is divided into two basic types, the natural landscape (61.16%) and the humanistic landscape (41.37%), of which hydrological-related toponyms occupy a significant proportion, reaching 35.02% of the total number of toponyms in Wuhan. (2) The distribution of Wuhan’s TCL is characterized by aggregation, mostly in the core area of the city, with the Yangtze River as the main axis, and the density of the spatial distribution of humanistic landscape toponyms varies greatly. (3) The results of a single-factor analysis show that construction land (X2) is an important influencing factor in both natural and humanistic landscape toponyms, which indicates the central role of human activities in the formation of toponyms. (4) The explanatory power of the interaction effect of two factors on the spatial differentiation of Wuhan’s TCL is stronger than that of the single factors, which suggests that the spatial differentiation characteristics of the TCL of Wuhan are formed under the joint influence of the respective variables.

The East–West Mountain Region (EWMR) of Wuhan is a vital natural and cultdural asset, characterized by its scenic nature landscapes and rich historical and cultural heritage. This study aims to address the problems of landscape character degradation and weakened public preferences caused by rapid urbanization and proposes a research framework integrating landscape character assessment and public preferences. Initially, we utilize K-means cluster analysis to identify landscape character types based on six landscape elements, resulting in a landscape character map with 20 types. Subsequently, we employ emotion analysis based on Natural Language Processing (NLP) techniques to analyze user-generated content (UGC) from Weibo check-in data to establish perception characteristic indicators reflecting public preferences. Finally, we quantitatively identify the environmental factors influencing public preferences through the SoIVES model and compare and integrate the landscape character map with the public emotion value map. The results show that (1) public preferences hotspots are concentrated in three types: (a) urban construction-driven types, including areas dominated by commercial service functions and those characterized by mixed-function residential areas; (b) natural terrain-dominated types with well-developed supporting facilities; and (c) hybrid transition types predominated by educational and scientific research land uses. These areas generally feature a high degree of functional diversity and good transportation accessibility. (2) Landscapes eliciting stronger emotional responses integrate moderate slopes, multifunctional spaces, and robust public services, whereas areas with weaker responses are characterized by single-function use or excessive urbanization. (3) The emotional variations within categories could be influenced by (a) functional hybridity through enhanced environmental exploration; (b) spatial usage frequency through place attachment formation; and (c) visual harmony through cognitive overload prevention. These findings provide critical insights for formulating zoning optimization plans aimed at the refined conservation and utilization of urban landscape resources, as well as offering guidance for improving landscape planning and management in the EWMR.