Graphical Representation of Landscape Heterogeneity Identification through Unsupervised Acoustic Analysis

Changes in land use and global warming pose an increasing threat to global biodiversity and ecosystems, calling for the urgent development of effective conservation strategies. Recognizing landscape heterogeneity, which refers to the variation in natural features within an area, is crucial for these strategies. While remote sensing images quantify landscape heterogeneity, they might fail to detect ecological patterns in moderately disturbed areas, particularly at minor spatial scales. This is partly because satellite imagery may not effectively capture undergrowth conditions due to its resolution constraints. In contrast, soundscape analysis, which studies environmental acoustic signals, emerges as a novel tool for understanding ecological patterns, providing reliable information on habitat conditions and landscape heterogeneity in complex environments across diverse scales and serving as a complement to remote sensing methods.

We propose an unsupervised approach using passive acoustic monitoring data and network inference methods to analyze acoustic heterogeneity patterns based on biophony composition and richness. This method uses sonotypes, unique acoustic entities characterized by their specific time-frequency spaces, to establish the acoustic structure of a site through sonotype occurrences, focusing on general biophony rather than specific species and providing information on the acoustic footprint of a site. From a sonotype composition matrix, we use the Graphical Lasso method, a sparse Gaussian graphical model, to identify acoustic similarities across sites, map ecological complexity relationships through the nodes (sites) and edges (similarities) and transform acoustic data into a graphical representation of ecological interactions and landscape acoustic diversity.

We implemented the proposed method across 17 sites within an oil palm plantation in Santander, Colombia. The resulting inferred graphs visualize the acoustic similarities among sites, reflecting the biophony achieved by characterizing the landscape through its acoustic structures. Correlating our findings with ecological metrics like the Bray-Curtis dissimilarity index and satellite imagery indices reveals significant insights into landscape heterogeneity.

This unsupervised approach offers a new perspective on understanding ecological and biological interactions and advances soundscape analysis. The soundscape decomposition into sonotypes underscores the method’s advantage, offering the possibility to associate sonotypes with species and identify their contribution to the similarity proposed by the graph.