Whole System Data Integration for Condition Assessments of Climate Change Impacts: An Example in High-Mountain Ecosystems in Rila (Bulgaria)

Abstract

To study climate impacts, data integration from heterogeneous sources is imperative for long-term monitoring in data sparse areas such as the High Mountain Ecosystems in the Rila Mountain, Bulgaria—difficult to both access and observe remotely due to frequent clouds. This task is especially challenging because discerning trends in vegetation location, condition and functioning requires observing over decades. To integrate the existing sparse data, we apply the Whole System framework adapted nationally in the Bulgarian Methodological Framework for Mapping and Assessment of ecosystem services. As the framework mainly relies on field data, we complement it with remote sensing vegetation indices (NDVI, NDWI and NDGI) for 42 years, together with Copernicus High Resolution Layer products and climate change reanalysis data for 40 years. We confirmed that the Whole System framework is extensible and semantically, ontologically and methodologically well suited for heterogeneous data fusion, co-analysis, reanalysis and joint interpretation. We found trends in ecosystem extent and functioning, in particular species composition, in line with climate change trends since around 1990 and exclusively attributable to climate change since 2015. Furthermore, we specified a data crosswalk between habitats and ecosystems at Level 3 (ecosystem subtype), and define new candidate indicators suitable for remotely monitoring climate change’s effects on the ecosystems’ extent and condition, as candidates for inclusion in the methodological framework.