Assessing and Mapping Forest Landscape Quality in China

Forest landscape plays a critical role in the resource management and recreational planning of forest destinations. An assessment of forest landscape quality (FLQ) could reflect the distribution of landscape resources, hence identifying the hotpots and areas with high visual quality and protection values. The objective of this study is to propose, for the first time, a methodology for assessing FLQ at the national level. Based on China’s forestry inventory database, the paper identified landform patterns and vegetative patterns as determinants (including 12 indicators) to establish an evaluation index system, and further implemented and mapped FLQ using the ArcGIS Engine platform. Results show high mountain ranges and tropical areas in China often have a high-quality forest landscape, while low FLQ scores are found in low mountains and foothills. The distribution of the four FLQ levels indicates most forest areas are featured with mediocre- or low- quality landscape values, and the differences of FLQ among different forest types are obvious. Furthermore, there is a relatively low correlation between the total forest area and the area of high-quality forest landscape. Overall, this study could contribute to enriching the existing assessment system for FLQ and to guiding the planning, policy development, and decision-making for China’s forestry administration

Estimating the Spatial Heterogeneity and Seasonal Differences of the Contribution of Tourism Industry Activities to Night Light Index by POI

The spatial distribution of tourism has a profound impact on its operational efficiency and geographical relevance. Point of interest (POI), as a kind of spatial data shared by subject and object, can reflect the spatial distribution form and function of tourism geographical objects under the all-for-one tourism policy. Continuous satellite observation and in-depth study of night lights pave the way to clarify human activities and socio-economic dynamics. The purpose of this paper is to investigate the seasonal changes of night light images and their correlation with tourism in 122 counties (cities, districts) of Hunan Province. We obtained night earth observation data (seasonality) and POI in 2019 and processed them by Geographic Information System and statistical analysis (ordinary least squares (OLS) and geographically weighted regression (GWR)). The results show that the luminous radiation intensity is highly correlated with the POI of tourism activities. The POI of different tourism activities in different regions shows obvious spatial heterogeneity and seasonal differences, which is the result of the comprehensive effect of tourism resource distribution and social environment in Hunan Province. GWR has proved to be a more effective tool. It provides a new method and perspective for tourism research and especially reveals the geographical spatial differences of tourism activities, which is helpful to study the spatial distribution and seasonality of tourism at the county level. In addition, the spatial evaluation of the contribution of tourism and luminous radiation can provide reference and suggestions for relevant departments to formulate tourism night protection measures

Carbon Footprint and Its Composition: A Comparison between Domestic and International Tourists to Chenzhou City, China

This study aims to provide a scientific basis to address the strategies for sustainable development of urban tourism industry. By using the Life Cycle Assessment method, it decomposes tourism activities into seven different functional units (different tourism activities)-transportation, catering, accommodation, sightseeing, shopping, entertainment and waste disposal-based on the expression of services provided by tourism activities, and determine the boundary range of each different functional unit in terms of the pathways and the functional orientation of the products (resources and energy) provided by the services of each functional unit. A “bottom-up” model is then constructed to measure the carbon footprint of tourism. Based on data collected from various sources for the period 2014–2019, it compares the composition and differences of domestic and international tourists’ carbon footprints in Chenzhou City, one of inland mountainous regions of central China, through several steps, including target and scope definition, inventory analysis, impact evaluation and life cycle interpretation. Results show that domestic tourists contributed more than 90% of the total annual carbon footprints to the city, ranging from 76.8809 × 106 kg to 194.6067 × 106 kg. Transportation is the dominant category, accounting for over 80% of the total carbon footprints. The study suggests that optimizing tourism resources, reducing transportation distances, and switching to low-carbon modes can effectively reduce the tourism carbon footprints in Chenzhou and similar regions. This study reveals the structural characteristics of the tourism carbon footprint and its influencing factors and provides valuable insights for policy development involving energy saving and low carbon tourism, thus enhancing the long-term sustainability of tourism development in an urban tourism destination like Chenzhou

The Spatial Pattern and Spillover Effect of the Eco-Efficiency of Regional Tourism from the Perspective of Green Development: An Empirical Study in China

Scientifically analyzing the spatial pattern and spillover effect of the eco-efficiency of regional tourism embodies the green development theory. In addition, it is also of important significance for realizing the sustainable development of regional tourism and promoting regional ecological civilization. This study incorporates energy consumption and CO2 emissions of tourism into the efficiency evaluation index system. On this basis, the slacks-based measure–data envelopment analysis (SBM-DEA) with undesirable output, the spatial autocorrelation (SAC) model and the spatial Durbin model (SDM) are used to study the spatial pattern and spillover effect of the eco-efficiency of provincial tourism in China from 2008 to 2017. Results show that the following: (1) The average eco-efficiency of national tourism is 0.534, which is at the medium development level as a whole. Among the decomposed efficiencies of eco-efficiency, the scale efficiency drives the optimal development of eco-efficiency in tourism. (2) The eco-efficiency of tourism shows a spatial differentiation pattern on the regional scale as follows: it is the highest in the central region, moderate in the western region, and lowest in the eastern region. (3) The degree of clustering of the eco-efficiency of tourism first increases and then decreases. The SAC-based cluster pattern is dominated by a low-low (LL) cluster, followed successively by a high-high (HH) cluster and a low-high (LH) outlier, while a high-low (HL) outlier is the least significant (4). Among the influencing factors, the technical level shows spatial spillover effects on both the eco-efficiency and pure technical efficiency of tourism; the economic development level and traffic accessibility mainly have spatial spillover effects on the pure technical efficiency and scale efficiency of tourism; the industrial structure and environmental regulation separately have a spatial spillover effect only on the pure technical efficiency and the scale efficiency of tourism

Analysis of Forest Landscape Preferences and Emotional Features of Chinese Forest Recreationists Based on Deep Learning of Geotagged Photos

Forest landscape preference studies have an important role and significance for forest landscape conservation, quality improvement and utilization. However, there are few studies on objective forest landscape preferences from the perspective of plants and using photos. This study relies on Deep Learning technology to select six case sites in China and uses geotagged photos of forest landscapes posted by the forest recreationists on the “2BULU” app as research objects. The preferences of eight forest landscape scenes, including look down landscape, look forward landscape, look up landscape, single-tree-composed landscape, detailed landscape, overall landscape, forest trail landscape and intra-forest landscape, were explored. It also uses Deepsentibank to perform sentiment analysis on forest landscape photos to better understand Chinese forest recreationists’ forest landscape preferences. The research results show that: (1) From the aesthetic spatial angle, people prefer the flat view, while the attention of the elevated view is relatively low. (2) From the perspective of forest scale and level, forest trail landscape has a high preference, implying that trail landscape plays an important role in forest landscape recreation. The landscape within the forest has a certain preference, while the preference of individual, detailed and overall landscape is low. (3) Although forest landscape photographs are extremely high in positive emotions and emotional states, there are also negative emotions, thus, illustrating that people’s preferences can be both positive and negative

Landscape Pattern and Succession of Chinese Fir Plantations in Jiangle County, China

Since the early 1980s, in southern China, evergreen broad-leaved forests have been replaced by Chinese fir plantations on a large scale. By analyzing the dynamic change characteristics of the landscape pattern of Chinese fir plantations in the case study, the paper explored the current status and development trend of the landscape pattern of Chinese fir plantations after 40 years of manual intervention and natural succession. The paper, based on the three-period survey data on forest resources in 2010, 2015, and 2020, analyzed the dynamic changes of the landscape pattern of Chinese fir plantations from 2010 to 2020 and, by using a transition matrix and landscape index, simulated and predicted the landscape pattern of Chinese fir plantations in Jiangle County in 2025 by constructing a CA–Markov model with Jiangle County, Fujian Province, China, as the study area. The results showed that the landscape of Chinese fir plantations is the main component of the forest landscape in southern China, accounting for 12%. The landscape quality of Chinese fir plantations degraded, mainly shown in the facts that the Chinese fir plantations were juvenile from 2010 to 2020, and that the young and middle-aged forests became the main part of the landscape of Chinese fir plantations, accounting for 54.8%. The landscape area of Chinese fir plantations showed an increasing trend, which mainly came from other coniferous forests, other woodlands, non-woodlands and non-wood forests, and the replaced Chinese fir plantations were mainly eroded by bamboo forests. The evergreen broad-leaved forests, a kind of zonal vegetation, have been effectively protected in the past 10 years. In the future, the total area of Chinese fir plantations will continue to expand, and a small part of them will continue to be eroded by bamboo forests. In order to improve the landscape quality of Chinese fir plantations, it is necessary to adjust the age group structure of Chinese fir plantations, expand the proportion of mature forests, and, meanwhile, continue to protect evergreen broad-leaved forests and curb the expansion of bamboo forests

Visual preference of plant features in different living environments using eye tracking and EEG.

Plants play a very important role in landscape construction. In order to explore whether different living environment will affect people's preference for the structural features of plant organs, this study examined 26 villagers and 33 college students as the participants, and pictures of leaves, flowers and fruits of plants as the stimulus to conduct eye-tracking and EEG detection experiments. We found that eye movement indicators can explain people's visual preferences, but they are unable to find differences in preferences between groups. EEG indicators can make up for this deficiency, which further reveals the difference in psychological and physiological responses between the two groups when viewing stimuli. The final results show that the villagers and the students liked leaves best, preferring aciculiform and leathery leaves; solitary, purple and capitulum flowers; and medium-sized, spathulate, black and pear fruits. In addition, it was found that the overall attention of the villagers when watching stimuli was far lower than that of the students, but the degree of meditation was higher. With regard to eye movement and EEG, the total duration of fixations is highly positively correlated with the number of fixations, and the average pupil size has a weak negative correlation with attention. On the contrary, the average duration of fixations has a weak positive correlation with meditation. Generally speaking, we believe that Photinia×fraseri, Metasequoia glyptostroboides, Photinia serratifolia, Koelreuteria bipinnata and Cunninghamia lanceolata are superior landscape building plants in rural areas and on campuses; Pinus thunbergii, Myrica rubra, Camellia japonica and other plants with obvious features and bright colours are also the first choice in rural landscapes; and Yulania biondii, Cercis chinensis, Hibiscus mutabilis and other plants with simple structures are the first choice in campus landscapes. This study is of great significance for selecting plants for landscape construction and management according to different environments and local conditions

Drivers of carbon emissions in China’s tourism industry

This manuscript examines the driving forces of carbon emissions in China’s tourism industry. Tourism carbon emissions are estimated by constructing China’s Economic-Environmental Accounts (EEA). Analysis is divided into five-time intervals and specifically examines intensity, scale, structure, and technology. Following index and structural decomposition methods, changes in tourism carbon emissions were segmented into sixteen economy-wide and tourism-specific driving forces. Results demonstrate that direct and total tourism carbon emissions compose 0.7% and 2.7% of total carbon emissions in China. Analysis revealed the positive driver of tourism emissions was domestic tourists, representing 140.4% increase in direct and 263.4% increase in total tourism carbon emissions. Modelling identified energy intensity as the main negative driver in total and direct tourism carbon emissions, especially for national economic sectors (−208.6%) and non-transport tourism sectors (−33.8%). Future research should focus on the measurement and implementation of mitigation policies for domestic tourism emissions.</p