Exploring Wetland Dynamics in Large River Floodplain Systems with Unsupervised Machine Learning: A Case Study of the Dongting Lake, China

Large river floodplain systems (LRFS) are among the most diverse and dynamic ecosystems. Accurately monitoring the dynamics of LRFS over long time series is fundamental and essential for their sustainable development. However, challenges remain because the spatial distribution of LRFS is never stati...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Remote sensing (Basel, Switzerland) Jg. 12; H. 18; S. 2995
Hauptverfasser:	Jing, Lei, Zhou, Yan, Zeng, Qing, Liu, Shuguang, Lei, Guangchun, Lu, Cai, Wen, Li
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	MDPI AG 01.09.2020
Schlagworte:	accuracy administrative management algorithms artificial intelligence assets case studies China cost effectiveness dynamics ecological function ecosystems environmental factors floodplains k-medoids algorithm (PAM) lakes large river floodplain system long-term dynamics meadows migratory behavior moderate resolution imaging spectroradiometer monitoring remediation remote sensing rivers spatial distribution sustainable development time series analysis unsupervised machine-learning algorithm (UMLA) vegetation index water birds wetlands wintering grounds China
ISSN:	2072-4292, 2072-4292
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!