Percent tree cover maps derived from Landsat imagery provide a useful data source for monitoring changes in tree cover over time. Urban trees are a special group of trees outside forests (TOFs) and occur often as solitary trees, in roadside alleys and in small groups, exhibiting a wide range of crown shapes. Framed by house walls and with impervious surfaces as background and in the immediate neighborhood, they are difficult to assess from Landsat imagery with a 30 m pixel size. In fact, global maps based on Landsat partly failed to detect a considerable portion of urban trees. This study presents a neural network approach applied to the urban trees in the metropolitan area of Bengaluru, India, resulting in a new map of estimated tree cover (MAE = 13.04%); this approach has the potential to also detect smaller trees within cities. Our model was trained with ground truth data from WorldView-3 very high resolution imagery, which allows to assess tree cover per pixel from 0% to 100%. The results of this study may be used to improve the accuracy of Landsat-based time series of tree cover in urban environments.
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