Airborne data and machine learning for urban tree species mapping: Enhancing the legend design to improve the map applicability for city greenery management

Abstract

Presently remote sensing appears to be the only technology that makes it possible to conduct an inventory of tree taxa throughout the whole city. One of the main challenges during a remote sensing implementation project is the constructing of a map legend for tree taxa. The article presents the first comprehensive investigation into the construction of a legend for urban tree species mapping in the entire city, using machine learning and airborne data. The analyses were based on the CatBoost algorithm, which is relatively new in environmental research, as well as hyperspectral and LiDAR data acquired by instrument fusion. The analysis covered the entire city of Warsaw (517.24 km2). The main aim of the study was to compare three approaches to creating map legends of taxonomic diversity in city trees. This research sought to answer which of these three approaches to creating a map legend produces a result with the highest application potential for use by urban greenery managers. Two scenarios are based on different levels of taxonomic organization. The first one, based on a generic level, contained 42 classes and obtained an overall accuracy score (OA) of 0.809. The second one, on the species level, consisted of 81 classes, and the OA reached an accuracy of 0.728. The third scenario presents a mixed approach by grouping classes as different combinations of genus, species and variety. It contained 60 classes and its OA totaled to 0.798. The obtained results indicate that while in the classification process the generic scenario achieved the highest accuracy and the species scenario had the most complex legend, the mixed scenario emerged as the most useful for the greenery managers because it presented a compromise between the complexity of the legend and the high accuracy of the map. In the mixed scenario, accurate classification was achieved by separating high-accuracy individual taxa at the species level and grouping the rest at different taxonomic levels. One of the most significant novelties of our research is proving that remote sensing currently makes it possible to develop a map of tree species containing as many as 60 classes of legends and to achieve an accuracy that allows for practical application in urban greenery management. The study lists suggestions of practical strategies for enhancing map accuracy such as aggregating similar species into species groups, creating distinct groups for ornamental cultivars with unique leaf characteristics or combining species or genera with similar traits.

Keywords

• Hyperspectral
• LiDAR
• Tree inventory
• Classification
• CatBoost
• Urban Tree