Mountainous scenic spots are important tourism resources, and the study of tourists’ landscape perception and emotional preference when visiting them is beneficial to the management of scenic spots in order to improve the service quality and promote the protection, development, and utilization of scenic resources. In this paper, we use the location photo data of tourists at Huangshan Mountain to extract the visual semantic information of location photos, calculate the photo sentiment value, and mine the landscape perception and sentiment preference features of tourists using DeepSentiBank image recognition model and photo visual semantic quantification method. The results show the following: (1) Huangshan tourists mainly focus on nine types of photos, with the most attention paid to the category of mountain rock landscapes and the least attention paid to the category of animal landscapes. (2) In terms of spatial distribution, the landscape types of tourist photos show the spatial characteristics of “concentrated into a belt”, “significant nucleus”, and “fragmented distribution”. The spatial variation of the emotional value of tourists’ photos is significant, and the high values are mainly distributed at the entrances and exits, interchanges, and famous attractions. (3) On a temporal scale, the type of perception of the Huangshan location photograph landscape shows a significant imbalance. The emotional values of tourists’ photos vary significantly, with a “slowly sloping straight line” type of emotional change on the seasonal scale, a “W” type of emotional change on the monthly scale, an “N” type of emotional change on the weekly scale, and an “M” type of emotional change on the hourly scale. This study attempts to explore the landscape perceptions and emotional preferences of tourists in mountainous scenic areas with new data and methods, aiming to promote the sustainable and high-quality development of mountainous scenic areas.

Wetlands are important ecosystems but face a grim future due to the impacts of human activities, such as lost area and function. Their landscape patterns and the environment quickly respond to external disturbance. This paper selected Jiaozhou Bay as the study area and used man-machine interactive remote sensing to extract information from wetlands. Human disturbance indices and landscape transition methods were used to conduct a scenario analysis of the evolution of landscape disturbance in Jiaozhou Bay wetlands from 1973 2018. The quantitative and spatial changes in landscape disturbance types on the wetlands were also analyzed, the changes were clustered and merged, and the dynamic change types of disturbance in the study area were summarized. Among landscape disturbance types, anthropogenic renewable and non-renewable landscapes increased continuously: in particular, the anthropogenic non-renewable type had the largest area increase, a testament to continued human disturbance. After these types of changes are sorted out, a K-Means clustering algorithm was used to classify the dynamic characteristics of disturbance transition into nine types. Due to their superior physiographic conditions, the wetlands of Jiaozhou Bay have been more vulnerable to disturbance by human activities, national industrial policies, socio-economic development, and other factors have also contributed to intensified disturbance.

Worldwide solar dimming from the 1960s to the 1980s has been widely recognized, but the occurrence of solar brightening since the late 1980s is still under debate—particularly in China. This study aims to properly examine the biases of urbanization in the observed sunshine duration series from 1987 to 2016 and explore the related driving factors based on five meteorological stations around Hangzhou City, China. The results inferred a weak and insignificant decreasing trend in annual mean sunshine duration (−0.09 h/d decade⁻¹) from 1987 to 2016 in the Hangzhou region, indicating a solar dimming tendency. However, large differences in sunshine duration changes between rural, suburban, and urban stations were observed on the annual, seasonal, and monthly scales, which can be attributed to the varied urbanization effects. Using rural stations as a baseline, we found evident urbanization effects on the annual mean sunshine duration series at urban and suburban stations—particularly in the period of 2002–2016. The effects of urbanization on the annual mean sunshine duration trends during 1987−2016 were estimated to be −0.16 and −0.35 h/d decade⁻¹ at suburban and urban stations, respectively. For urban stations, the strongest urbanization effect was observed in summer (−0.46 h/d decade⁻¹) on the seasonal scale and in June (−0.63 h/d decade⁻¹) on the monthly scale. The notable negative impact of urbanization on local solar radiation changes was closely related to the changes in anthropogenic pollutions, which largely reduced the estimations of solar radiation trends in the Hangzhou region. This result highlights the necessity to carefully consider urbanization impacts when analyzing the trend in regional solar radiation and designing cities for sustainable development.