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Data from: Remote sensing data predict indicators of soil functioning in semi-arid steppes, central Spain


García-Gómez, Miguel; Maestre, Fernando T. (2013), Data from: Remote sensing data predict indicators of soil functioning in semi-arid steppes, central Spain, Dryad, Dataset, https://doi.org/10.5061/dryad.nd93j


A substantial part of current research efforts on desertification are devoted to establish monitoring systems to evaluate the status of natural resources and the onset of desertification processes. Methodologies based on ground-collected soil and plant indicators are being increasingly used for this aim because they are affordable yet do not compromise accuracy. Despite their inherent value, these methods have limitations regarding the extent of the area that can be monitored using them. Such limitations can be overcome combining field-based approaches with remote sensing data, which allow the establishment of monitoring programs over large areas. In this article we tested the relationship between a field methodology based on indicators of ecosystem functioning, the landscape function analysis (LFA), and a vegetation index (NDVI) obtained from satellite images of the ASTER sensor using data gathered in Stipa tenacissima steppes from central Spain. LFA uses soil surface indicators to assess the condition of a given ecosystem by producing three numerical indices (stability, infiltration and nutrient cycling) reflecting the status of basic soil functions. We found a significant positive linear relationship between the NDVI, the three LFA indices and some key structural attributes of vegetation related to the cover of perennial plants. Our results indicate that NDVI can be used as a surrogate of ecosystem functioning in semi-arid Mediterranean steppes, and thus can be a helpful index to monitor the functional status of large areas in these ecosystems, and the possible onset of desertification processes.

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San Martín de la Vega
Central Spain
Arganda del Rey