How can remote sensing support urban tree cadaster efforts and diversity analysis of dominant genera?

Kurzfassung

The study of urban trees is often limited by the incomplete coverage of official tree inventories, which generally exclude data from private lands. This limitation also restricts comprehensive analyses of urban forest composition. To address the lack of tree data, we developed a hierarchical approach for genus-level classification of urban trees using bi-temporal WorldView imagery, which was tested in Munich, Germany. We performed a diversity analysis of the dominant tree genera in the study area, comparing diversity metrics estimated with the remote-sensing-based classification with those derived from the official tree cadaster. We found that remote sensing can capture vegetation and tree patterns with an accuracy higher than 90% and identify dominant tree genera with a balanced accuracy of up to 87.7% depending on land use. We showed that remote sensing based diversity analysis of dominant genera can capture general spatial patterns despite certain limitations and can be comparable to cadastral-based analysis, with about 60% of the study area having a difference of less than 10% on the Simpson's index. These results confirm the potential of remote sensing for complementing official tree cadaster efforts as it provides data on trees in private areas, which is necessary for a more complete understanding of urban forest composition.