首页|The influence of 4D landscape visualisation on attitudes to reservoir renaturalisation
The influence of 4D landscape visualisation on attitudes to reservoir renaturalisation
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NSTL
Elsevier
? 2022 The Author(s)Landscapes change due to natural processes and anthropogenic influences such as farming, forestry, urban development and civil engineering. Sustainable land and water management is needed to meet human needs while responding to the climate and biodiversity crises. Landscape visualisation may facilitate this by improving stakeholder engagement and changing environmental baselines. We developed a 4D landscape visualisation of the Crummock Water catchment in England's Lake District National Park. It showed 14,000 years of landscape evolution, including 140 years of reservoir engineering and future renaturalisation scenarios (weir removal and river remeandering). We used a cognitive model novel to landscape visualisation research to understand stakeholder values, beliefs, and attitudes. We tested the hypotheses that presenting extended landscape evolution information changes stakeholder beliefs (H1) and attitudes (H2) towards the renaturalisation proposals. The experiment comprised online visualisation workshops with 45 participants in two treatments (‘long’ extended and ‘short’ control). We analysed pre- and post-workshop surveys using statistical tests and qualitative coding. Results show the workshop changed beliefs around landscape naturalness, and made attitudes towards renaturalisation more supportive. There was no significant difference in belief between treatments, therefore we reject our belief hypothesis (H1). However, participants who saw the extended information were more likely to support weir removal, supporting our attitude hypothesis (H2). We discuss the validity of the cognitive model and the utility of the extended landscape evolution information. We conclude that 4D landscape visualisation can change beliefs about landscapes and attitudes towards environmental management. This effect may be enhanced by extended landscape evolution information.
NSTL
Elsevier
