Long-term vegetation dynamics in Spain's National Park Network: insights from remote sensing data

Area of Science:

• Ecology and Environmental Science
• Remote Sensing and Geospatial Analysis
• Conservation Biology

Background:

• Long-term vegetation dynamics are critical for understanding ecosystem resilience and guiding conservation efforts.
• Protected areas are vital for biodiversity but face pressures from climate change and human activities.
• Assessing vegetation health over time is essential for effective management of these valuable ecosystems.

Purpose of the Study:

• To analyze vegetation trends across Spain's National Park Network from 1984 to 2023.
• To identify spatial and seasonal variations in vegetation activity and their drivers.
• To evaluate the effectiveness of current conservation strategies and inform adaptive management.

Main Methods:

• Utilized Landsat satellite imagery processed via Google Earth Engine for comprehensive coverage.
• Calculated multiple vegetation indices (NDVI, SAVI, kNDVI, NDMI) to assess vegetation status.
• Applied Mann-Kendall and Theil-Sen statistical tests to detect and quantify vegetation trends.

Main Results:

• Predominant increase in vegetation activity observed across most Spanish national parks over four decades.
• Significant spatial and seasonal variations in trends linked to topography, climate, and vegetation type.
• Negative trends identified in wetland parks (e.g., Las Tablas de Daimiel) indicating hydrological stress and vulnerability.

Conclusions:

• High-resolution remote sensing is crucial for long-term ecological monitoring in protected areas.
• Vegetation trends vary significantly, necessitating tailored management approaches for different park types.
• Integrating remote sensing with field data is vital for evidence-based conservation under climate change.