Potential of Internet street-view images for measuring tree sizes in roadside forests

Abstract Tree size censusing is essential for evaluations of trees and forests, but traditional field surveys are both time- and labor-intensive. Here, we discuss the use of panoramic 360-degree street views available on the Internet for censusing of roadside trees in urban regions. Use of scale-independent, fixed-sized street objects as recalibrating meters in tandem with imagery software enabled street-view images to be used effectively in the remote measurement of diameter at breast height (DBH), tree height, underbranch height, and canopy projection size. Comparison of four independent meters determined that stem limewhite-related meters (used for tree disease and bark-freeze injury control; usually 1.3 in height throughout China) had greater precision than road curb height, lane width, and traffic line width meters. The limewhite meter’s precision was slightly lower than those of the meters in combination (i.e., when at least three of the abovementioned meters were used for the same tree measurement), but no statistically significant differences were detected between the limewhite and combined meters ( p  > 0.05). In contrast, the road curb height, traffic line width, and lane width meters all had significantly lower precision. The highest levels of precision were 92%, 87%, and 80% for DBH, height (tree height and underbranch height), and tree canopy size measurements, respectively. Empirical recalibration of the image-based measurements did not improve data precision with reference to field surveys ( p  > 0.05). Moreover, similar results were obtained regardless of individual users, and repeatability for DBH measurements (r 2  > 0.92), and maximum differences among individual users were 0.6–1.9 cm for DBH (averaged at 22 cm) and 8–50 cm for underbranch height (mean value at 8 m). Labor costs and time needed for this approach were one-thirtieth to one-tenth those required for field surveys. Thus, the use of street-view images represents a more resourceful approach to assess forest ecological services.