Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran

Authors

Neda Bihamta Toosi, Ali Reza Soffianian, Sima Fakheran, Saeied Pourmanafi, Christian Ginzler, Lars T Waser

Publication date

2019/5/11

Journal

Global Ecology and Conservation

Pages

e00662

Publisher

Elsevier

Description

Mangrove forests in Iran are highly productive and complex ecosystems since they represent the interface between land and sea. They are a unique environment for supporting biodiversity, and they provide direct and indirect benefits to humans. Investigating changes in mangrove forests is essential for ecologists and forest managers to improve the assessment and conservation of natural ecosystems. The goals of the present study include: (I) to evaluate and compare four supervised classification algorithms based on Landsat time series imagery to detect mangrove cover in southern Iran, (II) to detect changes in mangrove cover between 1985, 1998, and 2017; and (III) to compare the four different predictions resulting from the applied classification algorithms. An accuracy assessment was conducted using k-fold cross-validation and independent validation, and differences between the classification techniques …