A deep learning convolutional neural network algorithm for detecting saline flow sources and mapping the environmental impacts of the Urmia Lake drought in Iran

Author:

Feizizadeh Bakhtiar,Garajeh Mohammad Kazemi,Lakes Tobia,Blaschke Thomas

Funder

Alexander von Humboldt-Stiftung

Publisher

Elsevier BV

Subject

Earth-Surface Processes

Reference118 articles.

1. Alizade Govarchin Ghale, Y.,2020. Urmia lake desiccation as a new source of dust in themiddle east: Investigation of the anthropogenic impactsand climatic factors on drying up of urmia lake, PhD thesis, stanbul Technical University, Eurasia Institute of Earth Sciences.

2. Alizade Govarchin Ghale, Y., Baykara, M., & Unal, A., 2017. Analysis of decadal land cover changes and salinization in Urmia Lake Basin using remote sensing techniques. Natural Hazards and Earth System Sciences Discussions, 1-15.

3. The minimum accuracy value as an index of classification accuracy;Aronoff;Photogramm. Eng. Remote Sens.,1985

4. Assessment of remote sensing-based classification methods for change detection of salt-affected areas (Biskra area, Algeria);Afrasinei;J. Appl. Remote Sens.,2017

5. Classification of soil aggregates: A novel approach based on deep learning;Azizi;Soil Tillage Res.,2020

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