A comparison of two sampling approaches for assessing the urban forest canopy cover from aerial photography

Urban forests provide many benefits to citizens, such as improving air quality, providing diverse wildlife habitat, and increasing human health and well-being. These ecosystem services are often directly related to the amount of tree canopy cover. Thus, accurate estimation of canopy cover can be a critical aspect of assessing the ecosystem services potentially provided by an urban forest. This paper examines two sampling methods and two remotely sensed imagery sources to estimate urban tree canopy cover in Tallahassee, Florida and Tacoma, Washington. The two sampling approaches employed were the random point-based approach, with a sample size of 1000, and the plot/grid approach, which involved a 1.83-meter square grid of points embedded within 0.04-hectare circular plots. The authors also utilized the United States Department of Agriculture National Agriculture Imagery Program (NAIP) within ArcGIS and Google Earth imagery to determine the percentage of tree canopy cover and to verify whether estimated canopy cover levels would be comparable throughout both imagery sources. The estimated tree canopy cover in Tallahassee, Florida was 44.5-45.1% using NAIP imagery and 48.6-49.1% using Google Earth imagery. Statistical tests indicated that the two sampling approaches produced significantly different estimates when using two different imagery sources. For Tacoma, Washington, the estimated tree canopy cover was approximately 17.3-18.1% when using NAIP imagery and 19.2-20.0% using Google Earth imagery. Although there was no significant difference between the random point-based sampling approach when used with the two different imagery sources, the opposite was true when using the plot/grid sampling approach. The results demonstrate that although some of the differences are statistically significant, the estimates of tree canopy cover from remotely sensed data are similar. Based on this research, urban forest managers can use remotely sensed imagery to monitor changes in tree cover levels and to facilitate processes for sustaining desired canopy levels.