Doctoral Dissertation Research: Remote Sensing of Urban Tree Species and Tree Stress

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This doctoral dissertation research project will develop a robust methodology to map the species and stress symptom level of individual broadleaf deciduous trees in North American cities. The project will provide new insights regarding ways to integrate information about spectral-temporal variability from remotely sensed images with field inventories of tree species and stress symptom levels. By furthering the potential to reduce labor-intensive fieldwork and instead use new, high-resolution remote sensing imagery to assess tree species and stress symptom levels, this project can help advance research on the interplay of different factors on the health of urban forests. The project will help advance the practice of urban forest management, which has significant societal value because healthy urban forests improve urban air quality, enhance ground water maintenance, and moderate urban air temperatures. Although this project will focus on a case study in Washington, D.C., the development of an accurate, robust, and repeatable methodology for using widely available, state-of-the-art remote sensing data to update forest inventories of tree species and tree stress will have utility in a much broader set of urban and rural settings. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.More than half of the world's population now lives in cities, and the biodiversity and health of trees has been widely recognized as central to the sustainability of urban environments. The doctoral student conducting this project will develop a robust and accurate method to identify tree species and stress symptom level to improve urban sustainability using optimized suite of remote sensing data and machine-learning algorithms. She will seek answers to two questions related to phenological variations in the foliage of different kinds of trees at different times of the year: (1) With the leaf pigment-induced changes in the visible-light spectral bands, will spring leaf emergence and fall senescence phenology periods be most informative to predict tree species? (2) Will the magnitude of decline in near infrared reflectance spectral bands during late summer be the most predictive component to predict changes in stress symptom level within a certain tree species? This project will pair large field inventories from the District of Columbia Department of Transportation with a suite of WorldView-3 satellite images to map the tree species and stress symptom levels of more than 7,000 trees along Washington streets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.