English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Due to China’s rapid human activity expansion, landscapes have lost their distinctive and typical characteristics. This paper addresses this issue by proposing a landscape character management framework for the Beijing shallow relief area. The framework utilises machine learning techniques to assess and enhance landscape integrity. The process involves landscape character identification through Principal Component Analysis, Gaussian Mixture Model clustering, and Canny Edge Detection. Additionally, a comprehensive landscape sensitivity evaluation considers both landscape character and visual sensitivity. The study develops five landscape management strategies based on field surveys and employs a Transformer Matrix Process and a multi-expert decision-making mechanism. Extensive validation confirms the framework’s effectiveness in improving the recognition accuracy of Landscape Character Types. The findings reveal that over 30% of the landscape characters in the study area require improvement. Importantly, the machine learning techniques employed in this study can be transferred to other regions, facilitating landscape characterisation, evaluation, and management.

Harnessing machine learning for landscape character management in a shallow relief region of China