Lausch et al. (2025) Monitoring Agricultural Land Use Intensity with Remote Sensing and Traits

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

Identification

• Journal: Agriculture
• Year: 2025
• Date: 2025-10-26
• Authors: Angela Lausch, Jan Bumberger, András Jung, Marion Pause, Peter Selsam, Tao Zhou, Félix Herzog
• DOI: 10.3390/agriculture15212233

Research Groups

Not specified in the provided text.

Short Summary

This review synthesizes existing definitions and methods for monitoring agricultural land use intensification (A-LUI), proposing a novel remote sensing (RS)-based taxonomy of indicators and an integrative framework to enable transparent, standardised, and globally comparable assessments.

Objective

• To provide a comprehensive synthesis of existing definitions and standards of agricultural land use intensification (A-LUI), evaluate in situ and remote sensing methods, and introduce a novel RS-based taxonomy and integrative framework to advance A-LUI monitoring across scales.

Study Configuration

• Spatial Scale: Local to global, aiming for globally comparable assessments.
• Temporal Scale: Continuous monitoring and assessment of spatio-temporal dynamics.

Methodology and Data

• Models used: Not applicable (review paper; discusses emerging technologies like artificial intelligence but does not apply specific models).
• Data sources: Literature review, synthesis of national and international standards (FAO, OECD, World Bank, EUROSTAT), in situ observation methods, and various remote sensing platforms and sensors.

Main Results

• A comprehensive synthesis of existing definitions and standards for agricultural land use intensification (A-LUI) at national and international levels was conducted.
• In situ methods and the rapidly expanding potential of remote sensing (RS) for A-LUI monitoring were evaluated.
• A novel RS-based taxonomy of A-LUI indicators was introduced, structured into five categories: trait, genesis, structural, taxonomic, and functional indicators.
• An integrative framework was proposed, connecting management practices, plant and soil traits, RS observables, validation needs, and policy relevance.
• Emerging technologies, including hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration, were identified as promising pathways for advancing A-LUI monitoring across scales.

Contributions

• Provides a comprehensive synthesis of A-LUI definitions and monitoring methods, addressing a critical challenge in global environmental change research.
• Introduces a novel, structured remote sensing-based taxonomy of A-LUI indicators, offering a new conceptual and methodological foundation.
• Proposes an integrative framework that bridges management practices, biophysical traits, remote sensing, and policy, enhancing the utility of A-LUI assessments.
• Establishes a foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, supporting both scientific progress and evidence-based agricultural policy.

Funding

Not specified in the provided text.

Citation

@article{Lausch2025Monitoring,
  author = {Lausch, Angela and Bumberger, Jan and Jung, András and Pause, Marion and Selsam, Peter and Zhou, Tao and Herzog, Félix},
  title = {Monitoring Agricultural Land Use Intensity with Remote Sensing and Traits},
  journal = {Agriculture},
  year = {2025},
  doi = {10.3390/agriculture15212233},
  url = {https://doi.org/10.3390/agriculture15212233}
}