Natural environments contribute to people’s perception, preference and health restoration. Many researchers have focused either on the positive effects of overall rural environments on stress recovery or concentrated on the perception and preference aspects of the rural landscape, but few have integrated perception, preference and stress recovery simultaneously. This paper developed a framework which includes 11 elements and 38 element components related to Linpan, China, and distributed it online as part of a survey. As a result, a total of 324 valid questionnaires were collected. The questionnaire included demographic details, perception and preference degree for Linpan, as well as self-estimations of stress recovery. Stepwise multiple linear regression was employed, and revealed 16 significant predictors for the perception, preference and stress recovery in rural environments. In terms of elements, atmosphere and imagery were the two elements that could be best perceived, while woodland, farmland, water, residence and road were the five most important elements for the preference. Moreover, seven elements were also identified as significant predictors for stress recovery. Among the element components, tranquility as well as road and water proximity were the two significant predictors for perception, while wide visibility as well as woodland and residence blending contributed significantly to stress recovery. The five element components of woodland interior, vegetable field, stream, courtyard space and branch road each had a significantly predictive ability for preference and stress recovery. These findings extend the understanding of the perception, preference and restorative properties of rural environments through the combination of elements and element components in Linpan of Western Sichuan, helping to improve the quality and characteristics of rural external and internal environments and create health-promoting environments. 相似文献
Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR) images always play an important role in oil spill detection. Many oil spill detection algorithms have been implemented based on these features. Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes, some oil spill detection algorithms do not consider the environmental factors. To distinguish oil spills and look-alikes more accurately based on environmental fact... 相似文献