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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Vegetation is the natural linkage connecting soil, atmosphere and water. Itcan represent the change of land cover to a certain extent and serve as anindicator for global change research. Methods for measuring coverage can bedivided into two types: surface measurement and remote sensing. Becausevegetation cover has significant spatial and temporal differentiationcharacteristics, remote sensing has become an important technical means toestimate vegetation coverage. This paper firstly uses U-net to perform remotesensing image semantic segmentation training, then uses the result of semanticsegmentation, and then uses the integral progressive method to calculate theforestland change rate, and finally realizes automated valuation of woodlandchange rate.

U-net super-neural segmentation and similarity calculation to realize vegetation change assessment in satellite imagery