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<Article>
<Journal>
<PublisherName>OICC Press</PublisherName>
<JournalTitle>Journal of Rangeland Science</JournalTitle>
<Issn>2423-642X</Issn>
<Volume>16</Volume>
<Issue>3</Issue>
<PubDate PubStatus="epublish">
<Year>2026</Year>
<Month>09</Month>
<Day>30</Day>
</PubDate>
</Journal>
<ArticleTitle>An Integrated Geospatial Assessment of Desertification Hotspots in Iran: Multi-Scale Mapping and Sustainable Management Strategies</ArticleTitle>
<VernacularTitle></VernacularTitle>
<FirstPage></FirstPage>
<LastPage></LastPage>
<ELocationID EIdType="doi">10.57647/JRS.2026.1603.20</ELocationID>
<Language>EN</Language>
<AuthorList>
<Author>
<FirstName>Mostafa</FirstName>
<LastName>Dastorani</LastName>
<Affiliation>Faculty of Geography and Environmental Sciences, Hakim Sabzevari University. Sabzevar, Iran</Affiliation>
<Identifier Source="ORCID"></Identifier>
</Author>
</AuthorList>
<PublicationType>Journal Article</PublicationType>
<History>
<PubDate PubStatus="received">
<Year>2026</Year>
<Month>09</Month>
<Day>30</Day>
</PubDate>
</History>
<Abstract>Desertification represents a critical environmental and socio-economic challenge for Iran, severely affecting food security, rural livelihoods, biodiversity, and ecological stability. As a predominantly dryland country, Iran faces escalating land degradation driven by both climatic variability and unsustainable human activities. This study develops an integrated geospatial framework to assess desertification hotspots in Iran, leveraging remote sensing datasets including MODIS, Landsat-8, and Sentinel-2, alongside GIS-based spatial analysis. Key environmental indicators, including vegetation cover (NDVI trends), land use classifications, soil texture, and climatic variables, were incorporated into the Environmentally Sensitive Areas Index (ESAI) modeling framework, complemented by a Bayesian network to better capture the probabilistic relationships among drivers. The results showed that 85.57% of Iran’s land area falls into fragile to critical desertification categories - a figure notably higher than in many other arid nations- with 60.42% classified as Medium Critical and 13.34% as High Critical. Provinces such as Sistan and Baluchestan, Kerman, and Yazd are identified as the most vulnerable due to a combination of severe aridity, shallow soils, and intense anthropogenic pressure. Correlation analyses reveal that the Management Quality Index (MQI) (r = 0.81, P≤0.01) and Vegetation Quality Index (VQI) (r = 0.50, P≤ 0.05) had high and moderate correlations with desertification risk, respectively, indicating the critical role of land use management and vegetation stability. Mitigation strategies proposed include the implementation of drip irrigation systems, agroforestry designs, biochar amendments for soil rehabilitation, watershed management projects, and participatory land-use planning. While some pilot programs on water conservation and reforestation exist, broad national-scale application remains limited due to policy barriers and funding constraints. This study underscores the urgent need for scientifically informed, region-specific interventions to enhance Iran's resilience to desertification. By integrating high-resolution remote sensing techniques and probabilistic modeling, the research provides a robust, actionable framework for sustainable land management and environmental policy reform in dryland regions. Consequently, this approach offers a practical solution to combat desertification and promote sustainable development in vulnerable areas.</Abstract>
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<Param Name="value">Desertification</Param>
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<Object Type="keyword">
<Param Name="value">Remote Sensing</Param>
</Object>
<Object Type="keyword">
<Param Name="value">GIS-Based Analysis</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Sustainable Land Management</Param>
</Object>
<Object Type="keyword">
<Param Name="value">ESAI</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Land Degradation</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Climate Resilience</Param>
</Object>
<Object Type="keyword">
<Param Name="value">Iran</Param>
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</Article>
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