Exploring the Long-Term Changes in Visual Attributes of Urban Green Spaces Using Point Clouds

Xiaohan Zhang, Yuhao Fang, Guanting Zhang and Shi Cheng ()
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Xiaohan Zhang: Department of Architecture, Technical University of Munich, 80333 Munich, Germany
Yuhao Fang: Department of Architecture, Southeast University, Nanjing 210096, China
Guanting Zhang: College of Architecture, Nanjing Tech University, Nanjing 211816, China
Shi Cheng: Department of Architecture, Southeast University, Nanjing 210096, China

Land, 2024, vol. 13, issue 6, 1-14

Abstract: The visual attributes of urban green spaces influence people’s perceptions, preferences, and behavioural activities. While many studies have established correlations between landscape perception and visual attributes, they often focus on specific timeframes and overlook dynamic changes in the spatial form of urban green spaces. This study aims to explore the long-term changes in the visual attributes of urban green spaces. We propose a method to quantitatively analyse changes in visual attributes using point clouds to simulate visual interfaces. Using an unmanned aerial vehicle, we conducted a five-axis tilt photography survey of Qinglvyuan Park in Nanjing, China, in August 2018 and September 2023. Point cloud models were generated for the two periods, and five visual attribute indicators, openness (OP), depth variance (DV), green view ratio (GVR), sky view ratio (SVR), and skyline complexity (SC), were analysed for long-term changes. The results indicate that OP, DV, and SVR decreased after five years, while GVR increased. The maximum increase in GVR was 26.6%, and the maximum decrease in OP was 12.8%. There is a positive correlation between GVR and its change (d_GVR). Conversely, there are negative correlations between SC and its change (d_SC), as well as between SVR and d_GVR. Tree growth emerged as a primary factor influencing changes in the visual attributes of urban green spaces. This study highlights the importance of adopting a long-term and dynamic perspective in visual landscape studies, as well as in landscape design and maintenance practices. Future research on predicting long-term changes in the visual attributes of urban green spaces should focus on understanding the relationships between tree properties and environmental conditions.

Keywords: urban green spaces; visual attributes; long-term changes; point clouds; dynamic design and maintenance (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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