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.
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.
China is the richest country in the world in terms of bamboo forest resources, with moso bamboo as the dominated landscape distribution. Analysis of its spatial distribution, landscape change, and its drivers is crucial for forest ecosystem management and sustainable development. However, investigations on the effects of multiple geographical and environmental factors on changes in the landscape of moso bamboo forests are still limited. In this study, Chinese moso bamboo forests in 2010, 2015 and 2020 were selected as the study objects, and 19 provinces (data for Hong Kong, Macao, and Taiwan are unavailable), where Chinese moso bamboo forests were actually distributed, were taken as the study areas. This paper aims to determine the spatial distribution and landscape level of moso bamboo forests in China, as well as to conduct a preliminary study on the natural and socioeconomic factors of landscape change within moso bamboo forests and their buffer zones through density analysis, landscape fragmentation analysis, and patch-generating land use simulation model. The analysis using ArcGIS kernel density analysis revealed significant variability in the spatial distribution of moso bamboo forests in China, expanding in both the north and southwest directions. China’s moso bamboo forests expanded fast between 2010 and 2020, with the landscape becoming more fragmented, landscape fragmentation increasing, aggregation diminishing, and overall landscape quality declining. Climate has the greatest influence on the shifting landscape distribution of moso bamboo forests, followed by locational factors and soil and terrain, and socioeconomic factors such as location, population density, and GDP also impact the shifting distribution and landscape of the moso bamboo forest.
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