Monitoring Agricultural Land Use Intensity with Remote Sensing and Traits

Angela Lausch (), Jan Bumberger, András Jung, Marion Pause, Peter Selsam, Tao Zhou and Felix Herzog
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Angela Lausch: Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, D-04318 Leipzig, Germany
Jan Bumberger: Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, D-04318 Leipzig, Germany
András Jung: Faculty of Informatics, Institute of Cartography and Geoinformatics, Eötvös Loránd University, Pázmány Péter sétány 1/A, H-1117 Budapest, Hungary
Marion Pause: Department of Architecture, Facility Management and Geoinformation, Institute for Geo-Information and Land Surveying, Anhalt University of Applied Sciences, Seminarplatz 2a, D-06846 Dessau, Germany
Peter Selsam: Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research—UFZ, Permoserstr. 15, D-04318 Leipzig, Germany
Tao Zhou: Landscape Ecology Lab, Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany
Felix Herzog: Agroecology and Environment, Agroscope, 8046 Zürich, Switzerland

Agriculture, 2025, vol. 15, issue 21, 1-84

Abstract: The intensification of agricultural land use (A-LUI) is a central driver of global environmental change, affecting soil health, water quality, biodiversity, and greenhouse gas balances. Monitoring A-LUI remains challenging because it is shaped by multiple management practices, ecological processes, and spatio-temporal dynamics. This review provides a comprehensive synthesis of existing definitions and standards of A-LUI at national and international levels (FAO, OECD, World Bank, EUROSTAT) and evaluates in situ methods alongside the rapidly expanding potential of remote sensing (RS). We introduce a novel RS-based taxonomy of A-LUI indicators, structured into five complementary categories: trait, genesis, structural, taxonomic, and functional indicators. Numerous examples illustrate how traits and management practices can be translated into RS proxies and linked to intensity signals, while highlighting key challenges such as sensor limitations, cultivar variability, and confounding environmental factors. We further propose an integrative framework that connects management practices, plant and soil traits, RS observables, validation needs, and policy relevance. Emerging technologies—such as hyperspectral imaging, solar-induced fluorescence, radar, artificial intelligence, and semantic data integration—are discussed as promising pathways to advance the monitoring of A-LUI across scales. By compiling and structuring RS-derived indicators, this review establishes a conceptual and methodological foundation for transparent, standardised, and globally comparable assessments of agricultural land use intensity, thereby supporting both scientific progress and evidence-based agricultural policy.

Keywords: land-use intensity; agricultural land-use intensity; agricultural intensification; remote sensing; earth observation; traits; in situ; monitoring; indicators; policy relevance (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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