Satellite remote sensing can operationalise the IUCN Global Ecosystem Typology in the biome-diverse North-East of Brazil

Accurate biome delineation is difficult where biomes occupy the same climatic space, as is the case for tropical dry forest and savanna. The resulting confusion limits our ability to understand and manage impacts of global change on these biomes. To address this, we developed an unsupervised, repeatable method to delineate biomes and their component functional ecosystems, based on landscape-level vegetation structure measured using remote sensing and an understanding of the ecology of the region. This approach contrasts with previous definitions, based on climate differences amongst savanna, dry forest and rain forest.

Using the heterogeneous north-east Brazil, where several biomes interdigitate, as a case study, a hierarchical functional ecosystem classification is proposed that aligns with both the IUCN Global Ecosystem Typology (GET) and previous work. Based on fuzzy clustering of remotely sensed vegetation attributes, seven groups were found, identified as rain forest, cerrado (savanna) and five caatinga vegetation groups. These groups broadly align with the literature, for example, sedimentary and arboreal caatinga. These groups align with three ‘Ecosystem Functional Groups’ (EFGs) described by the IUCN GET and, additionally, suggest there is a new, fourth EFG in the region: non-pyric shrublands. Random Forest models showed soil pH was the most important environmental variable distinguishing these vegetation groups.

These results suggest a remotely sensed structure-based approach is an effective method for operationalising the IUCN GET. North-East Brazil – where many EFGs are interdigitated – serves as a challenging case study and, therefore, we hope our approach will have generality for other regions globally.