Spatially-explicit uncertainty assessment of ecosystem extent mapping

Ecosystems are a key component of biodiversity, providing vital services to humans and the economy. Anthropogenic pressures driving environmental change result in widespread ecosystem degradation and loss. The area and spatial distribution of ecosystem types, referred to as ecosystem extent, provide a critical entry point for assessing ecosystem condition, functioning, and associated services, and therefore require detailed and spatially explicit monitoring.

Despite advances in geospatial analysis, consistent mapping and delineation of ecosystem extent remain challenging. Map products on ecosystem extent should, therefore, be supported by uncertainty assessments, ideally in a spatially explicit manner. According to best practices in related fields, the minimum requirement for uncertainty quantification for thematic maps is the aggregated estimation of per-class accuracy and per-class area uncertainty, following a validation procedure based on independent reference data. However, the standard practice remains spatially implicit. To date, there is no established practice for spatially explicit uncertainty quantification procedures.

This study presents a spatial solution for estimating the uncertainty of maps produced using machine-learning algorithms. The approach builds on the standard map-validation procedure and extends it to pixel-wise assessments using conformal prediction. While conformal prediction can be applied to any machine learning algorithm, ecosystem extent mapping poses domain-specific challenges, including a high-dimensional multi-class setting and hierarchical class structures. This study, therefore, focuses on developing solutions to ensure robust class-specific coverage, exploring different conformal prediction implementation variants, and adapting them from flat to hierarchical mapping scenarios.

To assess the feasibility and applicability of our approach, we tested it on the Oslo-Viken municipality in Norway. In this case study, we developed an ecosystem extent map for 2024 and quantified and mapped its uncertainty at pixel-level. This analysis helped to evaluate the practical application and performance of the approach on real-world cases.