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

Two different sampling approaches for estimating urban tree canopy cover were applied to two medium-sized cities in the United States, in conjunction with two freely available remotely sensed imagery products. A random point-based sampling approach, which involved 1000 sample points, was compared against a plot/grid sampling (cluster sampling) approach that involved a 1.83 m square grid of points embedded within 0.04 ha circular plots. The imagery products included aerial photography from the U.S. Department of Agriculture National Agricultural Imagery Program (viewed within ArcGIS), and Google Earth imagery. For Tallahassee, Florida, the estimate of tree canopy cover was 48.649.1% using Google Earth imagery and 44.545.1% using NAIP imagery within ArcGIS. Statistical tests suggested that the two sampling approaches produced significantly different estimates using the two different imagery sources. For Tacoma, Washington, the estimated tree canopy cover was about 19.2-20.0% using Google Earth imagery and 17.3-18.1% when using NAIP imagery in ArcGIS. Here, there seemed to be no significant difference between the random point-based sampling efforts when used with the two different image sources, while the opposite was true when using the plot/grid sampling approach. However, our findings showed some similarities between the two sampling approaches; hence, the random point-based sampling approach might be preferred due to the time and effort required, and because fewer opportunities for classification problems might arise. Continuous review of urban canopy cover estimation procedures suggested by organizations such as the Climate Action Reserve and others can provide society with information on the accuracy and effectiveness resource assessment methods employed for making wise decisions about climate change and carbon management. (C) 2016 Elsevier GmbH. All rights reserved.