This study visualizes and quantifies extant publications of rural landscape research (RLR) in Web of Science using CiteSpace for a wide range of research topics, from a multi-angle analysis of the overall research profile, while providing a method and approach for quantitative analysis of massive literature data. First, it presents the number of papers published, subject distribution, author network, the fundamental condition of countries, and research organizations involved in RLR through network analysis. Second, it identifies the high-frequency and high betweenness-centrality values of the basic research content of RLR through keyword co-occurrence analysis and keyword time zones. Finally, it identifies research fronts and trending topics of RLR in the decade from 2009 to 2018 by using co-citation clustering, and noun-term burst detection. The results show that basic research content involves protection, management, biodiversity, and land use. Five clearer research frontier pathways and top 20 research trending topics are extracted to show diversified research branch development. All this provides the reader with a general preliminary grasp of RLR, showing that cooperation and analysis involving multiple disciplines, specialties, and angles will become a dominant trend in the field.

Landscape character assessment (LCA) is a widely used tool that integrates natural, cultural, and perceptual attributes to identify and portray landscape. In this study, we used the LCA method to identify the landscape characteristics of China at the national scale. Furthermore, we applied cultural and landscape structural factors along with spatial transmission to improve the identification system. First, we incorporated all the parameters in the assessment. We selected 15 landscape character factors from four factor types including nature, culture, spatial geographic co-ordinates, and landscape structure. These parameters were analysed using multilevel overlay and spatial connection tools in ArcGis 10.2, which resulted in 2307 landscape description units (LDUs). Second, the spatial structure properties of the LDUs were determined using a semivariogram and the moving window method in ArcGis 10.2 and Fragstats 4.2 software, respectively. Third, for visualisation, we applied the principal component analysis (PCA) using the SPSS software and elbow and k-means clustering methods using MATLAB to determine 110 landscape character types (LCTs) of China’s entire terrestrial landscape. Finally, we determined 1483 landscape character areas through semiautomatic segmentation and manual visual correction using eCognition. Based on the unique characteristics of the entire terrestrial landscape of China, a set of ideas and methods for the overall identification of LCTs was proposed. Our findings can be used to optimise territorial spatial planning and landscape protection and management, and promote multiscale land-use studies in China.

Traditional Villages (TVs) are typical and representative of the agricultural civilization in millions of Chinese villages. The distribution of TVs shows spatial heterogeneity, based on the complexity and diversity of several influencing factors. In this study, 6,819 Chinese TVs were identified and the influencing factors that affect their distribution were screened in terms of three indicator groups: climatic, geographic, and humanity-related factors. Additionally, the K-means clustering algorithm clustered the TVs into different distribution regions. The quantitative relationships between the dominant influencing factors of different distribution regions were revealed to ensure a lucid understanding of the regional distribution of TVs. The results indicated that 1) climatic factors have the greatest impact on the spatial distribution of TVs, followed by geographic factors, particularly the elevation, and then by human factors, of which ethnic distribution played a relatively important role. 2) Twenty-one TV clustering distributions were obtained, which were classified into eight regions of TV distribution with different dominant influencing factors. Management and protective strategies were formulated based on the attribute analysis of influencing factors in each region. The obtained results delineated homogeneous TV distribution regions via the clustering method to achieve an accurate statistical analysis of the influencing factors. This study proposes a new perspective and reference for managing and protecting the diversity, continuity, and integrity of TVs across administrative regions.

Rural landscape character assessment (LCA) is significant for identifying and understanding rural landscapes and maintaining the cultural connotations of the rural vernacular. Taking the rural area of Songzi City as an example, this study identifies the landscape character (LC) and analyzes the coupling between the current state of its LC and a construction project based on the depth of rural landscape planning in the county and combining the ecology, arable land, and water body protection boundary as constraints. Thus, we obtain the “point, line, and surface” site selection suggestions for the construction activities of leisure agriculture, power grid, and energy facilities, and the zoning classification and layout control strategies for LC are subsequently proposed. The results show the following: (1) The county LC factor is a combination of natural and human factors used to obtain 165 LC areas in Songzi City. (2) The current state of rural LC is used to determine LCs from shallow to deep and to provide the basis for index selection and judgment for evaluation. (3) The coupling relationship between rural LC and construction projects varies and must be judged using subjective and objective methods, desktop research combined with field analyses, and multi-stakeholder participation. Based on the perspective of coupling and coordinating human and landscape, this study applies local-scale LCA to practice, strengthens the interface with rural construction planning, and provides research ideas and methodological references for the sustainable control of rural LC.