Ecosystem mapping with remote sensing images and ground observations

Natural ecosystem maps are a fundamental tool for describing natural habitats. They are used when analysing ecological networks, for studying ecological connectivity, for conservation planning or for the management of ecosystem services. While remote sensing information is commonly employed for mapping land cover, accurate ecosystem maps require going beyond the classification of the physical surface of the land and attempting to distinguish different communities of living beings. New research directions for ecosystem classification leverage species observations created by citizen scientists on social media or crowd-sourcing platforms, profiting from their extensive spatial and temporal coverage and their low acquisition cost.
In this work, we make complimentary use of crowd-sourced data with remote sensing imagery to produce ecosystem maps in alpine areas. Our study area covers the state of Valais, a territory of about 5, 000km2 in southwestern Switzerland. We retrieve nearly 3 million species observations from the Global Biodiversity Information Facility (GBIF) database that includes governmental, crowd-sourced and scientific observations. We combine the species data with high-resolution aerial remote sensing imagery provided by the Swiss Federal Office of Topography swisstopo. As reference ecosystem maps, we follow the European Nature Information System (EUNIS) at a 100m scale. To solve the task of classifying ecosystems, we propose a multi-modal data fusion approach based on a multi-modal transformer architecture. Such a model can handle redundant and complementary information coming from different data sources and provide an explicit and interpretable decision through the visualisation of its attention scores. Through our preliminary experiments, we observed that the unequal distribution of samples between the classes and also the sampling biases negatively impacted the performance of our approach. We are working towards a more informative inclusion of species observation and a more balanced learning of each ecosystem type.