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

Biological invasions are a global environmental problem. They have resulted in enormous ecological and economic consequences worldwide, and they are among the greatest threats to biodiversity and ecosystem function. Spartina alterniflora, an invasive plant, has caused great ecological influence since it was introduced to China over 30 years ago. According to supervised classification and visual interpretation of three remote-sensing images (2002, 2006 and 2010), we attained the spatial population distribution of S. alterniflora in Xiangshan Bay, Ningbo, Zhejiang province. To explore the topic further, dynamic change processes were also analyzed using the IDRISI software and their spatial distributions in 2010 and 2014 predicted using a CA-Markov model. It was found that compared with remote-sensing image interpretation maps in 2010 and 2013, accuracy verification yielded overall Kappa coefficients of 81.99% and 85.57% respectively, indicating a good predictive result from the CA-Markov model. Therefore, the model can be used for long-term forecasting such as determining the dynamic change processes and evolution trends of S. alterniflora in the Xiangshan area over the next 20 years. This can in turn provide guidance for effective management and control of invasive plant populations such as S. alterniflora.

Dynamic simulation of Spartina alterniflora based on CA-Markov model—a case study of Xiangshan bay of Ningbo City, China