Urbanization diverges residents’ landscape preferences but towards a more natural landscape: case to complement landsenses ecology from the lens of landscape perception

ABSTRACT

Analysis of the evolutionary process and the driving forces of the meliorization model (MM) for sustainable land use is the focus of landsenses ecology. Among different landsenses, the relationships between landscape preference changes and land-use dynamics have not been fully explored. Taking Fujian Province as an example, we investigated landscape preference changes during urbanization, which represents the most prominent driver and key process of land-use dynamics in China. Mix-marching data from a survey questionnaire of 2044 residents at different urbanization levels were used to reveal landscape preference changes during urbanization. The findings demonstrated that urbanization, rather than naturalness, was a better predictor of a person’s preferences for natural landscapes. The more urbanized the area that a person resides in, the more they prefer natural landscapes. Furthermore, the younger the population, the higher the preference for natural landscapes is. Teenagers always prefer landscapes with high naturalness regardless of the level of urbanization of where they live. Older people had a lower preference for natural landscapes. There was also greater variation with respect preference for different levels of urbanization: Males less preferred natural landscape compared with females. This study presents potential development patterns of landscape preferences during urbanization considering demographic differences, which may expand our understanding of the dynamic development of the MM.

KEYWORDS:

• Landscape preference
• landscape cognition
• landsenses ecology
• urbanization
• land-use change

Disclosure statement

There are no conflicts of interest to declare.

Supplementary material

Supplemental data for this article can be accessed here.

Additional information

Funding

This work was supported by the National Key R&D Program of China under grants 2018YFC0704705 and 2016YFC0502903, and the National Natural Science Foundation of China under grants 41371192 and 41871153.