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

Assessing variations in soil wind erosion (SWE) is critical for identifying key change areas and formulating desertification control strategies. Satellite images with an expansive spatial coverage and temporal repeatability make it possible to monitor the process of soil degradation and its consequences such as SWE. This research aims to model SWE in the eastern shoreline of Urmia Lake in the 2005–2017 period through multiple-criteria decision analysis (MCDA). Soil moisture, soil erodibility (SE), soil crust index, number of snow cover days, wind field intensity, and vegetation fraction were determined as critical factors affecting SWE. The analytic hierarchy process (AHP) method was applied to determine the weight of each factor. High SE and poor vegetation were the most important factors in the developed SWE model. The SE was precisely estimated (relative percent deviation (RPD)=2.01) by the support vector regression (SVR) method using Landsat-8 image. The developed SWE estimation method had an overall accuracy of 81%. Most of the eastern shoreline of Urmia Lake Region was classified in the severe SWE class. Results showed a declining erosion intensity trend from central parts with high wind erosion (47% of the region) to northern and southern parts of the region. Increasing the distance from the lake led to an increase in SWE.

Soil wind erodibility and erosion estimation using Landsat satellite imagery and multiple-criteria decision analysis in Urmia Lake Region, Iran