Combining aerial photos and LiDAR data to detect canopy cover change in urban forests

The advancement and accessibility of high-resolution remotely sensed data has made it feasible to detect tree canopy cover (TCC) changes over small spatial scales. However, the short history of these high-resolution collection techniques presents challenges when assessing canopy changes over longer time scales (> 50 years). This research shows how using high-resolution LiDAR data in conjunction with historical aerial photos can overcome this limitation. We used the University of British Columbia's Point Grey campus in Vancouver, Canada, as a case study, using both historical aerial photographs from 1949 and 2015 LiDAR data. TCC was summed in 0.05 ha analysis polygons for both the LiDAR and aerial photo data, allowing for TCC comparison across the two different data types. Methods were validated using 2015 aerial photos, the means (Delta 0.24) and a TOST test indicated that the methods were statistically equivalent (+/- 5.38% TCC). This research concludes the methods outlined is suitable for small scale TCC change detection over long time frames when inconsistent data types are available between the two time periods.

Automated summary

This research demonstrates how the combination of high-resolution LiDAR data and historical aerial photos can overcome challenges in assessing long-term tree canopy cover changes. The methods outlined in this study are suitable for detecting small-scale canopy changes over long time frames when inconsistent data types are available between the two time periods.