Dose of nature and landscape preference
Journal Artical
A Dose–response Curve Describing the Relationship Between Tree Cover Density and Landscape Preference
Landscape and Urban Planning, 2014, IF= 8.119
Does adding more and more trees to a residential street yield a reliable increase in preference? Or is there a point at which, in terms of preference, additional trees will have minimal effect, no effect, or even a negative effect? To address these questions, we selected 121 community streets in four Midwestern urban areas in the U.S. and produced a panoramic photograph of each site and then measured the density of tree cover visible at eye level (Panorama). We also collected Google Earth aerial photographs to measure
the top-down tree cover density (Google) for the sites. Then, 320 individuals provided preference ratings for a randomized subset of the panoramic photographs (15 pictures per person). Through linear and curvilinear regression analysis, we found a power line model best describes the relationship between each measure of tree cover density and preference. The power lines have a similar shape: when sites are relatively barren, a slight increase in tree density yields a steep increase in preference. After tree cover density exceeded those values, however, higher tree densities yielded smaller, but still positive increases in preference. These findings suggest that to ensure a moderate level of preference, tree cover density should be not less than 41% as measured by panoramic photographs or 20% as measured by Google Earth aerial photographs. Planting trees in barren residential areas will result in considerably more impact than if the same trees were planted in already green areas. Still, the findings here demonstrate that, for preference, every tree matters.
Effects of Seasonality on Visual Aesthetic Preference
Landscape Research, 2022, IF=2.055
Seasonality is a typical feature of landscapes in temperate regions. Seasonality’s effects on visual aesthetic quality (VAQ) are widely recognised but not well understood. To address this gap, 10 sample sites were selected to represent the diversity of urban green spaces in Xuzhou, eastern China, which has a typical temperate monsoon climate. Photographs of the 10 sites were acquired in eight typical months to capture seasonality. Online surveys were used to evaluate the VAQ of the photographs. The mean value of the coefficient of variation of 16 landscape characteristics of a site during the seasons was used to represent seasonal diversity. The results indicated that: (1) the autumn landscape was the most preferred, and the winter landscape was the least preferred; (2) there was a significantly inverted U-shaped relationship between year-round VAQ and seasonal diversity. This is the first study to define seasonal diversity and its effect on VAQ.
Analyzing the Effects of Nature Exposure on Perceived Satisfaction with Running Routes: An Activity Path-based Measure Approach
Urban Forestry & Urban Greening, 2022, IF= 5.766
Studies on the linkages between nature exposure and physical activities often focus simply on the immediate vicinity of home locations, but path-based exercises, such as running and cycling, are continuous activities and cover a broad spatial extent. Thus, the traditional home buffer approach fails to acknowledge the settings where road running actually occurs. This study employed an activity path-based measure approach using public participation GIS (PPGIS) to investigate the associations between running satisfaction and nature exposure. The mapped routes (N=545) that included an assessment of satisfaction level were collected from 249 runners resided in the Helsinki Metropolitan Area, Finland. Logistic regression analyses revealed a positive association between running satisfaction and nature exposure, including eye-level greenness, top-down greenness and blue space density. Top-down greenness was assessed by Normalized Difference Vegetation Index (NDVI) and the eye-level greenness by Green View Index (GVI), the latter one of which uses a deep learning algorithm. Running environment was more satisfying in those routes with more public transport nodes. Other traffic-related factors breaking the momentum of runners such as traffic light density were inversely related to running satisfaction. Demographic characteristics such as education background also played a significant role in the perceived satisfaction with running routes. The positive impacts of nature exposure on running satisfaction further verify the linkages between landscape and public health.
Does Density of Green Infrastructure Predict Preference?
Urban Forestry & Urban Greening, 2018, IF= 5.766
Green Infrastructure (GI) refers to the natural spaces in a city that improve urban ecology and bring social, economic, and environmental benefits to residents and communities. Although we know a good deal about people’s preference for urban forests, we know little about how people reaction to other types of GI and even less about how varying levels of vegetation density influence preference. Without this knowledge, planners and designers risk creating landscapes that people experience as insufficiently restorative. To understand people’s preference for different types and vegetation density levels of GI, we conducted three GI preference surveys and utilized a new technology called Brown Dog’s Green Index Extractor to calculate vegetation density. We found that, overall, tree density and understory vegetation density are positively associated with preference in a power-curve relationship. The nature of the relationship between bioretention density and preference remains unclear, even though it is significant and positive. The findings presented here expand our knowledge of landscape preference to the emerging field of GI. Designers and planners can use these results to create preferred landscapes that manage stormwater that also promote human well-being. Future studies might explore the relationship between GI density and preference further by investigating other aspects of GI such as planting designs and maintenance.