Changes in tree cover and impervious surfaces have been observed across many cities in the United States over the past 70 years. Many municipalities are implementing tree planting programs in efforts to increase tree cover. A detailed understanding of historical changes in land cover can inform urban forest management. I applied a convolutional neural network image segmentation approach to historical aerial imagery to delineate changes in land cover in 1957, 1974, and 2017 in Utica, New York, a small, postindustrial city. The model predicted tree, pavement, and building land cover in each year with overall accuracies ranging from 82-87%. From 1957 to 2017, tree cover declined in many areas and impervious surface cover (buildings and pavement) increased. Tree cover gains largely occurred in uninhabited, natural areas; whereas, the greatest declines in tree coverage occurred in many residential areas following the start of the urban renewal efforts in 1957. Current tree planting efforts targeted at homeowners could drive disparities in future tree cover since several areas of Utica with low tree have a high proportion of renter occupied homes and a low median household income. Convolutional Neural Network approaches for image segmentation of aerial imagery are a helpful tool in understanding patterns in changes in tree and impervious surfaces. A better understanding of the legacies of historical policies and neighborhood-scale changes in land cover can assist in highlighting priorities for urban forest management and justice-oriented urban forestry approaches to urban tree planting.
Keywords: Convolutional neural network; Historical aerial imagery; Impervious surface cover; Land cover; Urban tree cover.
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