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

Abstract

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 (Δ 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.

Influence of urban forests on residential property values: A systematic review of remote sensing-based studies

“…In addition, tree parameters such as tree canopy cover and number of trees can be manually mapped over long-term periods using high-resolution remote sensing images from Google Earth as a low-budget solution compared to field surveys, especially when the geographical area is small or sampled areas are used as in a sampling-based approach, instead of the large geographical area [ 103 ]. Furthermore, it is feasible to combine high-resolution LiDAR data in conjunction with historical aerial photographs to detect tree canopy cover changes over longer time scales and over small urban forest areas when inconsistent data types are available between the two time periods [ 104 , 123 ]. Overall, we need to adopt and apply technological advancements made in the fields of precision forestry, data science, and remote sensing to improve our further understanding of the influence of urban forests and residential property values [ 43 ].…”