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.

Moso bamboo (Phyllostachys edulis) forests, characterized by their rapid growth and clonal reproduction, have emerged as a significant threat to adjacent forest ecosystems. However, in China, the area, speed, and spatial distribution of moso bamboo forest expansion into other types of forests remains poorly understood. In this study, we present a case analysis of moso bamboo forests, employing a decade-long dataset from the forest second type inventory (FSTI) that utilizes transition matrices, neighboring ratio analysis, and spatio-temporal autocorrelation. This comprehensive investigation focuses on the spatio-temporal expansion of moso bamboo forests into diverse types of forests, with the aim of providing science-based recommendations for effective moso bamboo forest management. Our findings reveal that areas of moso bamboo forests have been expanding at an approximate annual rate of 2%, with an average expansion speed (including moso bamboo forests manually planted) of approximately 8 m per year. The length of moso bamboo–woodland ecotones (BWEs) increases as a consequence of moso bamboo forest expansion, indicating a sustained escalation in the extent of this expansion. Coniferous forests and evergreen broad-leaved forests are mainly invaded, accounting for around 58% of all invaded forests. The rate of moso bamboo forest expansion into different types of forests varies, although the rate remains fairly consistent within the same forest type. Moso bamboo forest expansion exhibits significant spatial heterogeneity. Furthermore, the area of moso bamboo forest intrusion into various types of forests in different provinces is notably influenced by the presence of moso bamboo forests and the proportional distribution of different forest types. The factors contributing to bamboo forest expansion encompass stand characteristics, soil attributes, light intensity, moso bamboo afforestation, forestry practices, and human disturbances.

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.

Traditional nighttime light (NTL) research has largely focused on urban areas, neglecting approximately 80% of Earth’s low-light or dark sky regions. This oversight may result in a significant underestimation of light pollution, especially in global protected areas that are often biodiversity hotspots. Our study employs a novel approach combining a residual neural network with a raster function model to tackle key challenges, including NTL restoration in high-latitude regions, long-term data continuity, and gap alignment across different sensor types. For the first time, we enable continuous calibration and temporal extension of global DVNL and DMSP/OLS data. Our dataset outperforms similar products by offering greater explanatory power for economic activities, enhanced temporal stability, and improved spatial distribution accuracy. Furthermore, it exhibits heightened sensitivity to subtle changes in low-light areas across global, national, urban, and protected scales, making it especially valuable for monitoring human activities and assessing environmental impacts in critical regions like World Heritage Sites, Dark Sky Preserves, and national parks.

Cunninghamia lanceolata (Lamb.) Hook accounts for 12% of the total forest area in southern China, second only to Masson pine forests, and is an important part of the forest landscape in this region, which has a significant impact on the overall forest structure in southern China. In this study, we used kernel density analysis, landscape index calculation, variance test, and Markov prediction to analyze and forecast the evolution trend of landscape pattern in the central area of C. lanceolata in ten years. The objective is to investigate the change trend of the spatial pattern of C. lanceolata landscape in the long time series and its possible impact on zonal vegetation, as well as the macro-succession trend of C. lanceolata under the current social and economic background, and to make a scientific and reasonable prediction of its future succession trend. The current and future forecast results show that the landscape fragmentation degree of C. lanceolata is intensified, the erosion of bamboo forest is continuously intensified, and the landscape quality is continuously low. These results provide a reference for the future development direction of C. lanceolata and emphasize the need for targeted C. lanceolata management strategies in the future development of C. lanceolata, emphasizing the strengthening of monitoring, controlling harvesting, and managing bamboo competition in order to balance wood production with landscape quality and ecosystem stability.