Using remote-sensing and citizen-science data to assess urban biodiversity for sustainable cityscapes

Abstract

Abstract Urban biodiversity is an important and growing research area as cities continue to expand and human populations concentrate in urban centers. In order to effectively conserve urban biodiversity and inform future urban planning, a thorough understanding of the patterns and underlying factors affecting biodiversity is essential. However, a methodology of assessing urban biodiversity that would be replicable to different cities has been challenging, primarily due to data limitation on habitats and species in urban areas. In response to these challenges, our study implements a biodiversity analysis framework, adapted for the municipality of Athens, Greece, a city that is facing its own unique challenges in preserving biodiversity while accommodating urban growth. The methodology is implemented at a zipcode level. By clustering the case study on different features that explain biodiversity and validating our results using citizen science data from the Global Biodiversity Information Facility (GBIF), we aim to provide a comprehensive understanding of urban biodiversity patterns in the city of Athens. This study incorporates a k-means clustering scheme that leads to the classification of habitats in the urban setting and utilizes citizen science-generated species occurrence data from the GBIF platform. This integrative approach allows us to account for fine-scale environmental variation, which plays a crucial role in species' abundances and distributions in urban settings. This work highlights the significance of zipcode-level analyses in understanding the complex interplay between urbanization and biodiversity. Our findings offer valuable insights for urban planning and biodiversity management in Athens and beyond, emphasizing the importance of incorporating both environmental and species information in the quest to protect and enhance urban biodiversity.