Abstract
Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.
Highlights
Urban trees provide ecosystem services such as air pollution removal, cooling, and storm water retention as well as ecosystem disservices, including the release of volatile organic compounds and allergenic pollen [1,2]
Nearmap imagery by itself had an overall accuracy of 68%, whereas WorldView 2 imagery had an accuracy of 58%; when LiDAR-derived metrics were included, accuracy rates rose to 70% for Nearmap imagery rose and to 63% for WorldView 2
Multi-temporal aerial images were useful for urban tree classification and performed better than commonly used WorldView 2 satellite imagery
Summary
Urban trees provide ecosystem services such as air pollution removal, cooling, and storm water retention as well as ecosystem disservices, including the release of volatile organic compounds and allergenic pollen [1,2]. Most data on urban trees is collected using plot-based sampling methods such as i-Tree Eco [11], and the United States Forest Service has initiated a plot-based survey program in over 100 urban forests [12]. These plot-based sampling approaches provide unbiased city-wide estimates of urban tree composition [11], but do not generate the comprehensive maps of trees that would be most useful for managers and researchers. There is considerable demand for more comprehensive tree mapping methods
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