Enhancing environmental indicator trustworthiness: A framework for user-specific quality assessment of spatial input data using data-fitness-for-purpose principles
Abstract
The presentation introduces a framework for improving the trustworthiness of environmental indicators through user-specific quality assessments of spatial input data based on data-fitness-for-use (DFFU) and data-fitness-for-purpose (DFFP) principles. It distinguishes between general data suitability (DFFU) and context-dependent usability (DFFP), highlighting challenges in assessing input data quality when reference data are unavailable. Using case examples from biodiversity and extreme weather indicators, the study shows that standard accuracy metrics are insufficient for composite indicators lacking spatial accuracy information. It proposes extending geospatial quality standards like ISO 19157-1 by incorporating spatial uncertainty metrics to evaluate and communicate data usability. The framework aligns with FAIR principles, promoting data reusability and AI readiness through the structured packaging of research objects with quality metadata.
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1EGU 2025 |Session ESSI2.7 www.julius-kuehn.de Enhancing environmental indicator trustworthiness: A framework for user-specific quality assessment of spatial input data using data-fitness-for-purpose principles Markus M¨oller1, Claus Weiland2, Daniel Martini3 1Julius K¨uhn-Institut ·Digitalisation and Artificial Intelligence ·Kleinmachnow, Germany 2Senckenberg – Leibniz Institution for Biodiversity and Earth System Research, Frankfurt am Main, Germany 3Kuratorium f¨ur Technik und Bauwesen in der Landwirtschaft e.V. (KTBL), Darmstadt, Germany
2EGU 2025 |Session ESSI2.7 www.julius-kuehn.de FAIRagro (https://fairagro.net/en/) FAIRagro aims to advance agrosystem research through collaborative RDM to enable and support linkages across disciplines, scales and methods FAIR data for agrosystem research
3EGU 2025 |Session ESSI2.7 www.julius-kuehn.de Outline An use case-based evaluation procedure 1Exist domain-specific (geodata) quality standards and associated quality elements and metrics? 2Are the metrics suitable to make geodata products trustworthy? Trustworthiness and Data Quality The accuracy of [spatial] data and data sources is highly associated with trust and with having confidence that the quality of data is sufficient to serve as evidence base for critical decision making. Lokers et al. (2016) https://doi.org/10.1016/j.envsoft.2016.07.017
3EGU 2025 |Session ESSI2.7 www.julius-kuehn.de Outline An use case-based evaluation procedure 1Exist domain-specific (geodata) quality standards and associated quality elements and metrics? 2Are the metrics suitable to make geodata products trustworthy? Trustworthiness and Data Quality The accuracy of [spatial] data and data sources is highly associated with trust and with having confidence that the quality of data is sufficient to serve as evidence base for critical decision making. Lokers et al. (2016) https://doi.org/10.1016/j.envsoft.2016.07.017
4EGU 2025 |Session ESSI2.7 www.julius-kuehn.de (1) Existing domain-specific (geodata) quality standards ISO standard 19157 Data Quality: Degree to which a set of inherent characteristics of data fulfils the requirements [of a potential user]. Data Quality Elements ThematicQuality Accuracy of quantitative attributes and the correctness of non-quantitative attributes and of the classifications of features and their relationships. ISO19157-1, 2023. Geographic information: Data quality. Part 1: General requirements. International Organization for Standardization, Geneva, Switzerland.
5EGU 2025 |Session ESSI2.7 www.julius-kuehn.de (1) Existing domain-specific (geodata) quality standards Confusion Matrix-based metrics Kaur et al. (2023) https://doi.org/10.1007/s10462-023-10570-9 ThematicClassification Correctness Comparison of the classes assigned to features or their attributes to a universe of discourse. ISO19157-1, 2023. Geographic information: Data quality. Part 1: General requirements. International Organization for Standardization, Geneva, Switzerland. ISO19157-3, 202?. Geographic information: Data quality. Part 3: Data quality measures register. International Organization for Standardization, Geneva, Switzerland. Under development.
6EGU 2025 |Session ESSI2.7 www.julius-kuehn.de (1) Existing domain-specific (geodata) quality standards Numerical Metrics Kaur et al. (2023) https://doi.org/10.1007/s10462-023-10570-9 QuantitativeAttribute Accuracy Closeness of the value of a quantitative attribute to a value accepted as or known to be true. ISO19157-1, 2023. Geographic information: Data quality. Part 1: General requirements. International Organization for Standardization, Geneva, Switzerland. ISO19157-3, 202?. Geographic information: Data quality. Part 3: Data quality measures register. International Organization for Standardization, Geneva, Switzerland. Under development. -
7EGU 2025 |Session ESSI2.7 www.julius-kuehn.de (2) Use case-based evaluation Biodiversity Indicator Germany-wide Crop Type Classifications (CTC; 2017-2019) 1Blickensd¨orfer et al. (2022) https://doi.org/10.1016/j.rse.2021.112831 2Preidl et al. (2020) https://doi.org/https://doi.org/10.1016/j.rse.2020.111673 Hexagonal reference units (1 km2) Peri´c et al. (2022) https://doi.org/10.5281/zenodo.6623510 Regional validation data Land Parcel Identification System (LPIS) for 2017, 2018 and 2019 for the federal states Brandenburg (BB) und Lower Saxony (LS) Baiamonte et al. (2023)
8EGU 2025 |Session ESSI2.7 www.julius-kuehn.de (2) Use case-based evaluation Biodiversity Indicator calculation workflow DQ Data Quality metrics CTC Crop Type Classification O1Class Harmonization Spatial subsetting O2Spatial aggregation Metric calculation BDI Biodiversity Indicator
15 EGU 2025 |Session ESSI2.7 www.julius-kuehn.de Questions? DQ ⇔FDO ⇔SciWinClient Tracked workflow with data quality elements described in FDOs FDO – FAIR digital objects →RO Crates →Annotated Research Contexts (ARCs) Contact Markus M¨oller ·markus.mo[email protected]