**1. Introduction**

Heritage railways have played an important role in history from the first advent of steam trains in the world [1]. Today, most are in poor repair and unable to compete with road transport due to their inefficiency and slowness [2]. Through travel literature and other advertisements, people have begun to accept railways as a daily mode of transportation, and the appeal of such railways is growing. The heritage railway tourism industry has recently seen a resurgence of interest in travel by historic train [3,4]. Moreover, in many countries around the world, heritage railways are being converted into railway museums, greenways, or parks [5–7]. Tourism, culture, and ecological values are becoming increasingly important with respect to railways. Management and planning of heritage railways, however, is becoming too heritage-oriented to ignore the railway natural landscape, and there is incomplete understanding of the railway landscape. Landscape can be clearly explained when it is classified by spatial units, which is of great significance for recognition of its abundance and heterogeneity [8]. LCA combines natural and cultural landscapes with people's perceptions [9,10]; it is a method for recognizing the spatial units that provide a locality its "sense of place" and pinpointing the heterogeneity of adjacent areas [11,12]. Moreover, it can be carried out at many different scales, and provides a framework for the implementation of the European Landscape Convention (ELC) [13,14].

**Citation:** Wang, Y.; Du, J.; Kuang, J.; Chen, C.; Li, M.; Wang, J. Two-Scaled Identification of Landscape Character Types and Areas: A Case Study of the Yunnan–Vietnam Railway (Yunnan Section), China. *Sustainability* **2023**, *15*, 6173. https://doi.org/10.3390/ su15076173

Academic Editor: Derk Jan Stobbelaar

Received: 3 February 2023 Revised: 19 March 2023 Accepted: 31 March 2023 Published: 3 April 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

In recent years, landscape character research methods have proliferated. The existing research methods used in the study of landscape character are primarily divided into holistic and parametric methods. Holistic methods tend to be intuitive, descriptive, and expert-oriented [15], and exclude the quantitative indicators of visual perception proposed in recent studies [16]. Parametric methods, on the other hand, tend to overlay or combine maps of different topics into a new comprehensive map [17]. The growth of open digital resources and the advancement of statistical methods have greatly promoted the development of parametric methods. ArcGIS overlay analysis of thematic maps, statistical analysis, or a combination of these two analyses are universally utilized [18,19]. Although relatively objective, parametric methods are highly dependent on the selection of the data, and are limited by differences in data sources, time, resolution, and scale [20–22]. When using this approach, it is important to capture, sort, and combine the available data sources [23,24]. Using a single method to study landscape characteristics is not suitable for all locations, and the integration of multiple methods is an inevitable trend. Inspired by the idea of multi-scale classification for LCA, research frameworks that integrate holistic and parametric approaches are beginning to emerge [25]. Yang and Gao adopted a framework for classifying landscape character types and areas using two-step cluster analysis and the MRS tool [26]. However, this framework does not take into account the correlation of landscape character variables and the mixed attributes of data. In addition, despite its widespread use in rural areas and national parks, LCA has not yet been extended to railway research.

The main objective of this study is to provide a more efficient and flexible LCA framework that can recognize the landscape characteristics of railways. Our specific objectives are: (i) to describe two-scaled identification of landscape character types and areas for the railway by integrating holistic and parametric methods, which can provide a reference for other heritage railway or linear heritage research; and (ii) to identify the natural and cultural characteristics of a heritage railway at two scales in order to provide a basic database for future planning, management, and evaluation.

#### **2. Materials and Methods**

#### *2.1. Study Area*

The Yunnan–Vietnam Railway, connecting Haiphong (the largest port city in northern Vietnam) and Kunming (the capital city of Yunnan, China), has a long history of over 120 years. The railway traverses 854 km, of which the Vietnam section (from Haiphong to Laocai) is 389 km and the Yunnan section (from Hekou to Kunming) is 465 km [3]. The Yunnan–Vietnam Railway was the first international alpine narrow-gauge railway in China, and is an outstanding example of Asian alpine narrow-gauge railway technology at the turn of the 20th century. It played an important role in the transformation and economic development of Yunnan and Vietnam. Its name was inscribed on the first edition of the Chinese Industrial Heritage List in 2018 [4]. A rich and heterogeneous landscape, including undulating mountains, natural rivers, plateaus, valleys, and many historical sites, is distributed along the railway. This paper adopted a hierarchical classification framework, and the study area included two-scaled boundaries. At the regional scale, it was made up of 17 counties, districts, and county-level cities along or near the railway. At the corridor scale, it was determined as a 15.8 km-wide linear buffer along the railway, which covered 76.9% of the total resource points (Figure 1).
