Mapping the understorey of deciduous woodland from leaf-on and leaf-off airborne LiDAR data: A case study in lowland Britain

This study examines the understorey information present in discrete-return LiDAR (Light Detection And Ranging) data acquired for temperate deciduous woodland in mid summer (leaf-on) and in early spring when the understorey had mostly leafed out, but the overstorey had only just begun budburst (referred to here as leaf-off). The woodland is ancient, semi-natural broadleaf and has a heterogeneous structure with a mostly closed canopy overstorey and a patchy understorey layer. In this study, the understorey was defined as suppressed trees and shrubs growing beneath an overstorey canopy. Forest mensuration data for the study site were examined to identify thresholds (taking the 95th percentile) for crown depth as a percentage of crown top height for the six overstorey tree species present. These data were used in association with a digital tree species map and leaf-on first return LiDAR data, to identify the possible depth of space available below the overstorey canopy in which an understorey layer could exist. The leaf-off last return LiDAR data were then examined to identify whether they contained information on where this space was occupied by suppressed trees or shrubs forming an understorey. Thus, understorey was mapped from the leaf-off last return data where the height was below the predicted crown depth. A height threshold of 1 m was applied to separate the ground vegetation layer from the understorey. The derived understorey model formed a discontinuous layer covering 46.4 ha (or 31% of the study site), with an average height of 2.64 m and a 77% correspondence with field data on the presence/absence of suppressed trees and shrubs (kappa 0.53). Because the first return data in leaf-on and leaf-off conditions were very similar (differing by an average of just 0.87 m), it was also possible to map the understorey layer using leaf-off data alone. The resultant understorey model covered 39.4 ha (or 26% of the study site), and had a 72% correspondence with field data on the presence/absence of suppressed trees and shrubs (kappa 0.45). This moderate reduction in the area of understorey mapped and associated accuracy came with a saving of half of all data acquisition and pre-processing costs. Whilst the understorey modelling presented here undoubtedly benefited from the specific timing of LiDAR data acquisition and from ancillary data available for the study site, the conclusions have resonance beyond this case study. Given that the understorey and overstorey canopies in lowland broadleaf woodland can merge into one another, the modelling of understorey information from discrete-return LiDAR data must consider overstorey canopy characteristics and laser penetration through the overstorey. It is not adequate in such circumstances to apply simple height thresholds to LiDAR height frequency distributions, as this is unlikely to distinguish whether a return has backscattered from the lower parts of the overstorey canopy or from near the surface of the understorey canopy.