Quantifying the carbon storage, distribution, and change of urban trees is vital to understanding the role of vegetation in the urban environment. At present, this is mostly achieved through ground study. This paper presents a method based on the satellite image time series, which can save time and money and greatly speed the process of urban forest carbon storage mapping, and possibly of regional forest mapping. Satellite imagery collected in different decades was used to develop a regression equation to predict the urban forest carbon storage from the Normalized Difference Vegetation Index (NDVI) computed from a time sequence (1985 -1999) of Landsat image data. This regression was developed from the 1999 field-based model estimates of carbon storage in Syracuse, NY. The total carbon storage estimates based on the NDVI data agree closely with the field-based model estimates. Changes in total carbon storage by trees in Syracuse were estimated using the image data from 1985, 1992, and 1999. Radiometric correction was accomplished by normalizing the imagery to the 1999 image data. After the radiometric image correction, the carbon storage by urban trees in Syracuse was estimated to be 146,800 tons, 149,430 tons, and 148,660 tons of carbon for 1985, 1992, and 1999, respectively. The results demonstrate the rapid and cost-effective capability of remote sensing-based quantitative change detection in monitoring the carbon storage change and the impact of urban forest management over wide areas.