The visual composition and human perception are found to relate to the reuse and tourism of heritage railways. Previous studies have used either environmental audits and on-site interviews that have limitations in terms of cost, time, and measurement scale, or virtual perception base on two-dimension images but with gaps in interactivity, virtual immersion and field of view. This study developed an “objective + subjective” visual evaluation and perception framework integrating Computer Vision (CV) and Immersive Virtual Reality (IVR) to assess the visual quality of industrial heritage sites along the Yunnan-Vietnam Railway (Yunnan Section). The stepwise multiple linear regression models were carried out to investigate the relationship between objective evaluation and subjective perception. The results showed that 16 landscape elements of the heritage sites were successfully segmented. According to the visual perception score bands, the 120 industrial heritage sites were classified as 39 high-score sites, 66 medium-score sites, and 15 low-score sites. In general, although the sky and hard ground accounted for a higher proportion, they had little effect on the sum scores, while the vegetation, water, and buildings played a significant role in the perception of visual quality. The results can help researchers, planners, and government departments clarify the visual quality to scientifically specify bottom-up planning and management solutions for railway industrial heritage sites. Moreover, the simplicity, accuracy, and effectiveness of this framework make it suitable for large-scale visual evaluation of other railway industrial heritage sites and linear heritage sites.

In recent decades, the role of heritage railways has gradually shifted from transportation, economy, and trade to tourism, culture, and ecology. The heritage railway landscape is experiencing multiple changes along with a value ambiguity problem. There is a need to comprehensively recognize this landscape in order to promote the transformations and monitor the changes. Inspired by Landscape Character Assessment (LCA), this paper adopts a two-scaled identification framework of landscape character types and areas of the Yunnan–Vietnam Railway (Yunnan section) by integrating holistic and parametric methods. At the regional scale, the landscape character was divided by five natural variables: landform, vegetation, hydrology, soil, and geology. At the corridor scale, the landscape character was classified by five natural and cultural variables: altitude, slope, aspect, land use, and heritage density. At these two scales, k-prototype cluster analysis and multiresolution segmentation (MRS) tool were used to identify landscape character types and areas. The results showed that there were 11 different landscape character types and 80 landscape character areas at the regional scale, and 12 different landscape character types and 58 landscape character areas at the corridor scale. Furthermore, the composition, area, and distribution of these landscape character types and areas were described. The results of this study can form a database for planning, management, and evaluation of the railway.

Contacting forests in different ways and conducting public perception evaluations of forests are important ways to evaluate forest construction. In order to explore the differences between on-site and manual post-collection indoor evaluations in forest landscape surveys, we combined subjective evaluation and objective indicator monitoring (eye movement characteristics, physiological indicators) based on different forest observation scales. We compared and analyzed the suitability of the following four visual approaches: on-site observation, manual collection, followed by indoor viewing normal photos (NP), videos (VD), and virtual reality panoramas (VR), in terms of public preference, perception, and psychological emotion. The results showed the following: (1) Compared with the on-site evaluation, the three indoor visual approaches (NP, VD, and VR) showed no significant difference in “landscape beauty” and “spatial perception”. VD also showed no significant difference in “landscape perception”, “seeing”, and “plant color preference” and had the strongest substitution for site evaluation. (2) With the exception of small-scale landscapes, in which on-site evaluation showed no substitutability, for the rest of the landscape scales, each of the three indoor visual approaches showed substitutability for on-site evaluation to varying degrees. (3) When conducting physiological and psychological surveys, watching videos and VR are more ideal. In terms of eye tracking, VR is closest to on-site observation. Practice shows that video was closer to on-site observation in most landscape preferences and perceptions. VR was suitable for presenting public visual behavioral characteristics, and NP showed some advantages in landscape beauty and spatial perceptions. The findings of the study can provide a scientific basis for the selection of visual approaches in future landscape evaluation.

Bamboo is a plant that may replace plastics and wood as a new green environmental protection material that can be developed. Ferrocalamus strictus is a very special and rare bamboo which is a plant species with extremely small populations (PSESP) endemic to Yunnan, China, and it is listed as a China key national first-class protected wild plant. To explore the effects of extreme climate and environmental changes on this PSESP, we conducted a case study on the response of F. strictus to climate under two extreme future emission scenarios. We combined 29 environmental variables, including the temperature, precipitation, altitude, slope, aspect, soil pH, soil organic carbon content, and mean UV-B radiation of the highest month, to analyze the important environmental factors limiting the distribution of F. strictus and identified the priority conservation areas (PCAs) of F. strictus. Under the two future extreme climate scenarios, the core potential suitable distribution area of F. strictus would decrease by 26%–34% in 2040, 44%–46% in 2060, and 23%–58% in 2080, respectively. Bio18 (precipitation in the warmest quarter) and Bio3 (isotherm) were the two key factors limiting the geographical distribution of F. strictus (the contribution rates were 40.1% and 18.45%, respectively). The results of this study show that F. strictus is in urgent need of protection and PSESPs require more attention.

A key means of promoting the high-quality development of karst areas is the maintenance of the area’s ecological security. A full recognition of the special ecological function of karst areas, as well as their significance to the surrounding region’s ecological, economic, and social development, is crucial in strengthening the overall strategic deployment of the national ecological construction and the protection and sustainable development of karst landscapes around the globe. In this study, the karst landscape of Puzhehei, Qiubei County, Wenshan Prefecture, Yunnan Province, China, was used as the research object. This study identified ecological source sites through a combination of morphological spatial pattern analysis and landscape connectivity assessment. As a result, 10 factors were selected to construct a comprehensive ecological resistance surface from the natural environment and socio-economic perspective; the resistance surface was corrected by combining the sensitivity of rocky desertification. An ecological corridor and ecological nodes were identified to construct the ecological security pattern based on the minimum cumulative resistance model and circuit theory. The results show that (1) the source areas of the Puzhehei karst landscape ecological protection comprised 11 core area patches with the landscape connectivity index of (dPC) ≥ 10, with a total area of 166.6572 km², which constituted 46.06% of the total study area, and the ecological source area totaled 77.275 km², or 21.36% of the total study area; (2) there were 78 potential ecological corridors in the Puzhehei karst region, with a total length of 545.186 km, including 12 key corridors and 66 general corridors; (3) a total of 51 ecological nodes were identified, including 11 “source-type ecological nodes”, 30 “ecological pinch points”, and 10 “ecological obstacles”, including 16 key ecological nodes. This study provides a theoretical basis for the integration of Puzhehei Nature Reserve, as well as a reference for the ecologically sustainable development of similar karst areas.

This study focuses on the Kunming municipal area, located in the low-latitude plateau of southwestern China, utilizing remote sensing image data from four distinct periods between 2005 and 2020 to analyze changes in its green landscape patterns. Net primary productivity (NPP) was employed as a metric for carbon sequestration analysis to assess variations in NPP within the Kunming municipal area. Based on Pearson correlation analysis and the XGBoost-SHAP model, the correlations, important indicators, and responses of changes in the green space patterns of the Kunming municipal area to changes in carbon sequestration were analyzed and combined with policy and human factors. The findings indicate the following: (1) From 2005 to 2020, the area proportions of various green space types within the Kunming municipal area were ranked as follows: forest land > grassland > cultivated land > water bodies. (2) Between 2005 and 2015, the patch shapes of green spaces became increasingly complex, with heightened fragmentation among patches. After 2015, this complexity was reduced while connectivity continued to decline alongside an increase in the landscape heterogeneity and richness. (3) Over the period from 2005 to 2020, NPP values for cultivated land, forest land, and grassland exhibited a trend of decreasing and then increasing, reaching their lowest point in 2010. High NPP areas were predominantly found in regions characterized by a hilly topography, elevated altitudes, and substantial natural vegetation cover. (4) There was a significant correlation between green space pattern indices and NPP (p < 0.01), with SHDI, CONTAG, and DIVISION identified as three critical indices influencing NPP. The relationship between landscape patterns and carbon sequestration was most pronounced during the period from 2015 to 2020, followed by that from 2005 to 2010.