Forests are important place for outdoor recreation and scenery appreciation. So in order to better meet the needs of the public, forest appreciation has received increasing attention from foresters in recent years. However, related research is still limited. Therefore, this paper seeks to examine the relationship between forest colors (measured by specific elements and spatial indices of color) and Scenic Beauty Estimation values. We researched Jiuzhai Valley in China by selecting 104 pictures to determine the scenic beauty estimation values of forests in a mountainous region. Quantitative color elements were extracted by programming on Matlab, and spatial indices of color patches were extracted by ArcGIS and FRAGSTATS. A total of 23 indices were obtained to explain the color characteristics of each forest picture. The results showed that the yellow and red colors were the main mutable colors of Jiuzhai Valley in autumn, but the color patches index had no significant change over time in that season. After partial correlation analysis, principal component analysis, and cluster analysis, we found that 14 color elements, eight color patch factors and six particular indices had an effect on the SBE values, which can then be used to efficiently measure and enhance the forest color beauty of Jiuzhai Valley.

Urban expansion leads to changes in the visual aesthetic quality and ecological degradation of the surrounding slope forest landscapes. Color is a crucial visual element to examine when viewing this large-scale slope forest landscape from a long distance. This is particularly true for the autumn color of slope forest, which is very attractive to the public. An exploration of the relationship between the change in color of a natural slope forest and its visual aesthetic quality enables the implementation of the configuration of superior aesthetic tree species. Therefore, it can provide aesthetic rules and a reference to configure local tree species to support their visual aesthetic quality, ecological sustainability and native biodiversity restoration in a local urban slope forest. However, such research is critically lacking. This study investigated the visual aesthetic quality of the color dynamics of a natural slope forest in Jiaozi Mountain, China in the autumn. We analyzed both the composition of tree species and the changes in color for each species of tree in nine forest sites that exhibited superior visual aesthetic quality. The results showed that the forests with superior visual aesthetic quality were more green, red, and yellow, had moderately higher saturation and value, more obvious color contrast, and diverse colors with primary and secondary contrast. Diverse and balanced color patches or a dominant color patch contrasted by many small patches with interspersed color components also highlighted the superior visual aesthetic quality of slope forest features. Different combinations of color features can result in high visual aesthetic quality. The 84 tree species in the superior visual aesthetic quality forests primarily displayed 10 types of color changes that varied as green, yellow, blue, red, withered yellow, withered red and gray.

Forest colors are important elements for public enjoying the scenery. So increasing attention has been acquired on forest color cognition. However researches on relationship between forest colors and public response are still insufficient, which cannot provide sufficient theoretical basis for the regulation of forest landscape in color. Therefore, We seek to examine the relationship between forest color and visual behavior based on eye tracking technology, and further interpret the visual indicators through the value of scenic beauty. This study researched Jiaozi Mountain in China by selecting 29 sampling points, counting up 116 photographs in 4 seasons by a mountainous region. On this basis, Matlab was performed to quantitatively extract color elements, while ArcGIS and Fragstats were applied to extract the spatial index of color patches. A total of 10 indicators were obtained to explain the color characteristics of each forest image. Through both visual behavior experiment and landscape preference evaluation, the results showed that people tend to have different visual behaviors and preference cognition when observing forest colors of different seasonal types. Based on the study of forest landscape color in all seasons, the subjects tend to judge the image in comparison to other seasonal forest landscape color photos to identify it more easily. For a single-season forest colors, diversified color information and abundant visual attention are important factors influencing the correlation between visual behavior, landscape preference, and forest color characteristics. This study aims to further reveal people’s perceptions and psychological preference to forest colors, contribute to the establishment of a more quantitative and scientific scenery evaluation system, and provide a scientific basis for forest color planning and design.

Climate change is becoming increasingly severe. Today, several studies have found that climate change substantially influences the increasing number of forest fires. Regional climate models (RCMs) are currently a vital tool for climate forecasting in researching how to combat forest fires. As China’s forest fire area, Yunnan province has frequent forest fires that generate significant losses, so it is a crucial area for forest fire prevention in China. Therefore, this study uses meteorological observational data from 25 stations in Yunnan over the period 2004–2018 to compares and evaluates the Regional Climate Forecast Model (RegCM) and Weather Research and Forecasting model (WRF) in multiple dimensions. The optimal RCM is then determined for the forest area of Yunnan. The results show that the deviations of RegCM predictions from the spatial mean of the real temperature are less than 3°C, whereas the deviations of WRF are all greater than 3°C. In addition, the RegCM correlation coefficient exceeds 0.8, whereas the WRF correlation coefficient exceeds 0.75. In terms of precipitation, the deviation of RegCM predictions for the whole territory is less than 2 mm, whereas the overall deviation of WRF predictions is great. The correlation coefficient for RegCM and WRF are both less than 0.5, but the RegCM correlation coefficient exceeds that of the WRF. We thus conclude that RegCM is more suitable for predicting the climate of the forest area of Yunnan. This study also provides references for related climate forecasting and research into forest fire dynamics in general.

This study presents groundbreaking insights into urban butterfly conservation by developing an integrated modelling framework to predict habitat suitability under climate change. The research addresses three critical gaps in current knowledge: (1) the lack of robust methodologies for assessing subtropical urban Lepidoptera habitats, (2) insufficient understanding of microclimate‐mediated edge effects in fragmented landscapes and (3) the absence of predictive tools for climate‐adaptive conservation planning. Our analytical approach combined Maxent species distribution modelling with high‐resolution GIS sensitivity analysis, incorporating 64 georeferenced occurrence records and 22 environmental variables. The optimised model achieved exceptional predictive accuracy (AUC = 0.966), identifying temperature seasonality (Bio7, 30.8% contribution) and dry‐season precipitation (Bio17, 21.5%) as dominant habitat filters. Spatial projections revealed a previously undocumented habitat paradox: while high suitability core areas may expand by 138.73 km ² under optimal scenarios, total suitable habitat could contract by 53.89 km ² due to climate‐driven edge effects. Three key innovations emerge from this work: First, we established a novel protocol for urban biodiversity assessment that integrates climatic, topographic and anthropogenic variables. Second, we demonstrated the critical role of microclimate buffering in maintaining habitat refugia, particularly in the southwestern Guandu and northwestern Chenggong districts. Third, we developed a decision‐support framework that identifies priority conservation zones based on habitat stability thresholds. These findings advance ecological theory by quantifying the impacts of urban heat islands on species distributions, providing actionable tools for city planners. The habitat stability maps and climate‐resilience indicators developed in this study are currently being implemented in Kunming's urban green space master plan, demonstrating the immediate practical relevance of this research.