Quantification of crown changes and change uncertainty of trees in an urban environment

Local authorities require a detailed report of the state of green resources in cities to quantify the benefits of urban trees and determine urban forestry interventions. This study uses bitemporal remote sensing data to monitor changes of urban trees over time. It presents a fuzzy approach to recognize the fuzziness of tree crowns from high resolution images in urban areas. The method identifies tree crown elliptical objects after iterative fitting of a Gaussian function to crown membership images of two dates. Gradual and abrupt changes are obtained, as well as a measure of change uncertainty for the retrieved objects. The method allows a dual treatment of change both as a crisp and as a fuzzy process. This is demonstrated in two residential areas in The Netherlands using a set of Quickbird and aerial images. Results show that the proposed method is informative of the changes at the object level, recognizes the fuzzy character and mixed-pixel effect of tree crowns in images and it provides useful information to end users on change uncertainty.