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      <dc:title>Indicadores de degradación del suelo en el parque forestal “Monte Gibralfaro” (Málaga, España).</dc:title>
      <dc:creator>Sillero-Medina, José Antonio</dc:creator>
      <dc:creator>Menjíbar-Romero, Mario</dc:creator>
      <dc:creator>González Pérez, Javier</dc:creator>
      <dc:creator>Ruiz-Sinoga, José Damián</dc:creator>
      <dc:subject>Suelos - Conservación</dc:subject>
      <dc:subject>Cambio medioambiental global</dc:subject>
      <dc:subject>Ecosistemas - Gestión</dc:subject>
      <dc:description>Soil conservation in natural areas within urban environments is essential for promoting environmental sustainability. These zones act as vital green lungs, providing key ecosystem services. Effective soil management in urban areas enhances air quality, human well-being, and resilience to extreme climatic events, fostering a sustainable balance between urban growth and environmental conservation. In the context of climate change, particularly in Mediterranean regions, soil degradation poses a significant threat. Therefore, implementing sustainable strategies for assessing, conserving, restoring, and rewilding these spaces is crucial to promoting more resilient land management. This study focuses on an urban forest area in the city of Málaga (Spain)—Monte Gibralfaro—characterized by low biodiversity and a lack of territorial connectivity with the natural matrix, which increases its susceptibility to climate change effects. The research aims to conduct a detailed assessment of soil degradation in this site. Specifically, the study seeks to: (i) Determine soil erosion rates using the RUSLE model; (ii) Estimate organic carbon sequestration (SOC stock); and (iii) Evaluate soil quality through the relationship between organic carbon and clay content (SOC:Clay Ratio). To achieve these objectives, a total of 23 soil samples were collected, evenly distributed across the forest park, and analyzed in the laboratory to assess their main physical, chemical, and hydrological properties. Subsequently, the established indicators were calculated and mapped using innovative machine learning techniques, such as the Random Forest algorithm.</dc:description>
      <dc:date>2025-10-21T10:43:32Z</dc:date>
      <dc:date>2025-10-21T10:43:32Z</dc:date>
      <dc:date>2025</dc:date>
      <dc:type>conference output</dc:type>
      <dc:identifier>https://hdl.handle.net/10630/40370</dc:identifier>
      <dc:language>spa</dc:language>
      <dc:relation>XXIX CONGRESO DE LA ASOCIACIÓN ESPAÑOLA DE GEOGRAFÍA  "DESAFÍOS DE LA GEOGRAFÍA ANTE EL CAMBIO GLOBAL"</dc:relation>
      <dc:relation>Cáceres</dc:relation>
      <dc:relation>14-17 octubre 2025</dc:relation>
      <dc:rights>http://creativecommons.org/licenses/by-nc-nd/4.0/</dc:rights>
      <dc:rights>open access</dc:rights>
      <dc:rights>Attribution-NonCommercial-NoDerivatives 4.0 Internacional</dc:rights>
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