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.

<p>Abstract: Chinese Traditional Villages (TV) were selected from millions of villages based on their important historical and cultural heritage value. The distribution of TV characterized by spatial differentiation is subject to complex and diverse influencing factors. This study takes 6819 TV in China (as of the end of 2019) as research objects to analyse the distribution density of TV in different provinces; the spatial autocorrelation module in ArcGIS' spatial statistical tool was used to analyse the distribution characteristics; a total of 9 factors were selected from the three indicator groups of climate, geography and humanities, and introduced into the clustering and outlier analysis (Anselin Local Moran's I) module to analyse their spatial relationships with TV distribution. The results show that: 1. The spatial distribution of Chinese TV presents an obvious uneven aggregation state. Among them, the highest distribution density was 10.18 per 10,000 km² in Zhejiang province, while less than 0.5 per 10,000 km² in Inner Mongolia, Heilongjiang, Tibet and Xinjiang. The Global Moran's I index of TV distribution is 0.352, and the z-value of normal statistic is 949.76, which has a strong spatial autocorrelation. 2. The distribution of TV is mainly interpreted by humidity index, annual average temperature, elevation, slope, cultural relics, and population. 3. The results of clustering and outlier show that there are significant differences in the effect of the influencing factors on the distribution of TV in different regions. This paper aims to understand the influencing factors that affect the spatial distribution of TV in China and provide more comprehensive research content. This study indicates the importance of further cross-regional analysis of the TV distribution and provides a reference for its environmental management and protective measures and policies.</p>