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Home / Equine Research Bank / Animals (Basel) / Using Satellite Remote Sensing to Estimate Rangeland Carryin...
Animals : an open access journal from MDPI2025; 15(24); 3507; doi: 10.3390/ani15243507

Using Satellite Remote Sensing to Estimate Rangeland Carrying Capacity for Sustainable Management of the Marismeño Horse in Doñana National Park, Spain.

Abstract: Rangeland degradation poses a serious challenge for the sustainable management of free-ranging livestock in Mediterranean wetlands. In National Park, Spain, the endangered horse depends exclusively on natural forage, making it essential to monitor vegetation productivity and grazing suitability under increasing climate variability. This study presents a satellite-based assessment of rangeland carrying capacity to support the adaptive management of this iconic breed. A six-year time series (2015-2020) of 1242 images from Landsat 8 OLI/TIRS and Sentinel-2 (L1C/L2A) was processed using ILWIS and Python-based workflows to derive vegetation indices (GNDVI, NDMI) and model aboveground biomass, forage energy, and grazing pressure across five grazing units. Results revealed strong seasonal cycles, with biomass and nutritive value peaking in spring and declining sharply in summer. Ecotonal zones such as acted as crucial refuges during drought-induced resource shortages. The harmonized multi-sensor approach demonstrated high reliability for mapping forage dynamics and assessing carrying capacity at fine scales. This remote sensing framework offers an effective, scalable tool for sustainable livestock management in , directly supporting biodiversity conservation and the long-term resilience of Mediterranean rangeland ecosystems.
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Publication Date: 2025-12-05 PubMed ID: 41463791PubMed Central: PMC12729776DOI: 10.3390/ani15243507Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study uses satellite remote sensing to estimate the rangeland carrying capacity in Doñana National Park, Spain, focusing on the sustainable management of the endangered Marismeño horse that relies solely on natural forage.
  • The research integrates multi-year satellite data to monitor vegetation productivity and grazing suitability, aiding adaptive management strategies amid climate variability.

Introduction and Problem Statement

  • Rangeland degradation is a critical issue threatening the sustainable use of free-ranging livestock in Mediterranean wetland ecosystems.
  • The Marismeño horse, an endangered breed in Doñana National Park, depends exclusively on naturally available forage, making monitoring of vegetation productivity vital.
  • Climate variability exacerbates the risk of resource shortages, necessitating tools to track changes in vegetation and carrying capacity over time.

Methodology

  • The study utilized a six-year dataset (2015–2020) consisting of 1242 satellite images from two sources:
    • Landsat 8 OLI/TIRS
    • Sentinel-2 (L1C/L2A)
  • Data processing was conducted using ILWIS (Integrated Land and Water Information System) and Python-based workflows for efficient analysis.
  • Vegetation indices such as Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Moisture Index (NDMI) were derived to assess vegetation health and moisture content.
  • Models were developed to estimate aboveground biomass, forage energy content, and grazing pressure across five designated grazing units within the park.

Key Findings

  • Strong seasonal cycles in biomass and nutritive value were observed, with:
    • Peak biomass and forage quality occurring during spring.
    • Marked declines in these metrics throughout the summer season.
  • Ecotonal zones (transition areas between ecosystems) served as critical refuges, providing food resources during droughts when other areas experienced scarcity.
  • The combined use of multiple satellite sensors enhanced the reliability and resolution of forage dynamics mapping, allowing fine-scale assessment of carrying capacity.

Implications and Significance

  • The satellite-based framework offers a scalable and effective tool for ongoing monitoring of natural forage conditions.
  • It supports adaptive management of the Marismeño horse population by informing decisions based on precise, timely vegetation and grazing data.
  • By ensuring sustainable grazing, the approach contributes to biodiversity conservation and enhances the resilience of Mediterranean rangeland ecosystems to climate variability.
  • This method can potentially be applied to other free-ranging livestock management systems in similar ecological contexts.

Conclusion

  • This research successfully demonstrated the value of utilizing remote sensing technology to inform sustainable rangeland management in a complex wetland ecosystem.
  • The study’s multi-sensor, multi-year approach provides critical insights into vegetation dynamics and carrying capacity that underpin the long-term viability of both the Marismeño horse and the surrounding landscape.

Cite This Article

APA
Ramírez-Juidias E, Díaz de la Serna-Moreno Á, Delgado-Pertíñez M. (2025). Using Satellite Remote Sensing to Estimate Rangeland Carrying Capacity for Sustainable Management of the Marismeño Horse in Doñana National Park, Spain. Animals (Basel), 15(24), 3507. https://doi.org/10.3390/ani15243507

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 15
Issue: 24
PII: 3507

Researcher Affiliations

Ramírez-Juidias, Emilio
  • Instituto Universitario de Arquitectura y Ciencias de la Construcción (IUACC), Universidad de Sevilla, 2 Reina Mercedes Avenue, 41012 Seville, Spain.
Díaz de la Serna-Moreno, Ángel
  • Departamento de Agronomía, Escuela Técnica Superior de Ingeniería Agronómica, Universidad de Sevilla, Ctra. Utrera Km 1, 41013 Sevilla, Spain.
Delgado-Pertíñez, Manuel
  • Departamento de Agronomía, Escuela Técnica Superior de Ingeniería Agronómica, Universidad de Sevilla, Ctra. Utrera Km 1, 41013 Sevilla, Spain.

Conflict of Interest Statement

The authors declare no conflicts of interest.

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