Larrea et al. (2026) Scale-dependent biases in systematic land cover maps undermine freshwater ecological assessment

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Environmental Monitoring and Assessment
• Year: 2026
• Date: 2026-09-14
• Authors: Iñaki Fernández de Larrea, Jon Gonzalez-Ibarzabal, Aitor Bastarrika Izaguirre, Aitor Larrañaga
• DOI: 10.1007/s10661-026-15882-1

Research Groups

• University of Barcelona (Department of Ecology)
• Spanish National Research Council (CSIC) (Institute for Water and Environmental Studies)

Short Summary

This study evaluates the reliability of systematic land cover maps and compares their accuracy with Landsat-based supervised classification in representing landscape patterns and dynamics at ecologically relevant scales.

Objective

• Investigate the extent to which systematic land cover maps accurately represent landscape patterns and long-term dynamics at ecologically relevant spatial and temporal scales.

Study Configuration

• Spatial Scale: Five headwater catchments (< 70 km²) within Natura 2000 sites in northern Spain.
• Temporal Scale: Four decades (1984–2023)

Methodology and Data

• Models used: Random Forest classification implemented in Google Earth Engine
• Data sources: Landsat satellite imagery, CORINE Land Cover, SIOSE, Spanish National Forest Inventory

Main Results

• Supervised classification achieved consistently high accuracy across all periods (mean overall accuracy = 87.8%).
• Discrepancies between supervised classification and systematic maps increased at finer spatial scales and varied with landscape context.
• Systematic maps showed limited temporal consistency, frequently misrepresenting land cover trends relative to supervised classification.

Contributions

• This study provides original insights into the limitations of systematic land cover maps in representing landscape patterns and dynamics at ecologically relevant scales.
• The findings highlight the importance of integrating reproducible, remote sensing–based classifications for improving the accuracy and interpretability of land cover indicators in landscape ecological research.

Funding

• This research was funded by the Spanish Ministry of Science and Innovation (project code: RTI2018-102143-B-I00) and the European Union's Horizon 2020 research and innovation programme (grant agreement No. 869449).

Citation

@article{Larrea2026Scaledependent,
  author = {Larrea, Iñaki Fernández de and Gonzalez-Ibarzabal, Jon and Izaguirre, Aitor Bastarrika and Larrañaga, Aitor},
  title = {Scale-dependent biases in systematic land cover maps undermine freshwater ecological assessment},
  journal = {Environmental Monitoring and Assessment},
  year = {2026},
  doi = {10.1007/s10661-026-15882-1},
  url = {https://doi.org/10.1007/s10661-026-15882-1}
}