Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/147033
Title: Impacts of the urmia lake drought on soil salinity and degradation risk: an integrated geoinformatics analysis and monitoring approach
Author: Feizizadeh, Bakhtiar  
Omarzadeh, Davoud  
Salehi, Keyvan  
Blaschke, Thomas  
Makki, Mohsen  
Others: University of Tabriz
Humboldt-Universität zu Berlin
Universitat Oberta de Catalunya (UOC)
University of Salzburg
Keywords: Google Earth Engine
environmental impacts assessment
soil salinity monitoring
soil degradation mapping
Urmia Lake Basin
Iran
Issue Date: 15-Jul-2022
Publisher: Remote Sensing
Citation: Feizizadeh, B., Omarzadeh, D., Mohammadzadeh Alajujeh, K., Blaschke, T. & Makki, M. (2022). Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach. Remote Sensing, 14(14), 1-26. doi: 10.3390/rs14143407
Published in: 14;14
Also see: https://doi.org/10.3390/rs14143407
Abstract: Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents.
Language: English
URI: http://hdl.handle.net/10609/147033
ISSN: 2072-4292MIAR
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