Assessing allergy risk from ornamental trees in a city: Integrating open access remote sensing data with pollen measurements

• Published in: Journal of environmental management 2024-09, Vol.367, p.122051, Article 122051
• Main Authors: Sobieraj, Kacper, Grewling, Łukasz, Bogawski, Paweł
• Format: Article
• Language: English
• Subjects: Aerobiology; LiDAR; plane tree; Random forest; Tree species classification; Urban green space
• ISSN: 0301-4797; 1095-8630; 1095-8630
• DOI: 10.1016/j.jenvman.2024.122051

Platanus sp. pl. (plane trees) are common ornamental tree in Poland that produces a large amount of wind-transported pollen, which contains proteins that induce allergy symptoms. Allergy sufferers can limit their contact with pollen by avoiding places with high pollen concentrations, which are restricted mainly to areas close to plane trees. Their location is thus important, but creating a detailed street tree inventory is expensive and time-consuming. However, high-resolution remote sensing data provide an opportunity to detect the location of specific plants. But acquiring high-resolution spatial data of good quality also incurs costs and requires regular updates. Therefore, this study explored the potential of using open access remote sensing data to detect plane trees in the highly urbanized environment of Poznań (western Poland). Airborne light detection and ranging (LiDAR) was used to detect training treetops, which were subsequently marked as young plane trees, mature plane trees, other trees or artefacts. Spectral and spatial variables were extracted from circular buffers (r = 1 m) around the treetops to minimize the influence of shadows and crown overlap. A random forest machine learning algorithm was applied to assess the importance of variables and classify the treetops within a radius of 6.2 km around the functioning pollen monitoring station. The model performed well during 10-fold cross-validation (overall accuracy ≈ 92%). The predicted Platanus sp. pl. locations, aggregated according to 16 wind directions, were significantly correlated with the hourly pollen concentrations. Based on the correlation values, we established a threshold of prediction confidence, which allowed us to reduce the fraction of false-positive predictions. We proposed the spatially continuous index of airborne pollen exposure probability, which can be useful for allergy sufferers. The results showed that open-access geodata in Poland can be applied to recognize major local sources of plane pollen. •A field campaign to create a plane tree inventory was performed for Poznań, Poland.•Plane trees were detected using open access remote sensing data and a random forest.•The predicted locations significantly correlated with the pollen influx direction.•Areas with higher Platanus pollen exposure were highlighted in Poznań.