Three-dimensional characterization of pine forest type and red-cockaded woodpecker habitat by small-footprint, discrete-return lidar

Accurate measurement of forest canopy structure is critical for understanding forest-wildlife habitat relationships. Although most theory and application have been based on in situ measurements, imaging technologies such as Light Detection and Ranging (lidar) provide measurements that are both vertically accurate and horizontally extensive. We use small-footprint, multiple-return lidar from a state-wide dataset (1-m footprint, 0.11 point/m2) to characterize the vertical and horizontal structure of successional loblolly pine (Pinus taeda) and mature, fire-maintained longleaf pine (Pinus palustris) forests on the coastal plain of North Carolina, USA. The relationship between these characteristics and the federally-endangered red-cockaded woodpecker’s (Picoides borealis, Vieillot) habitat preferences were assessed; as this species has a strong affinity for mature longleaf pine forests. Vertical structure was characterized by lidar-derived metrics (e.g., average and standard deviation of canopy height) and horizontal patterns of vertical structure were quantified by semivariograms and lacunarity analysis. Lidar metrics were compared with field measurements of stand structure and with woodpecker habitat use. We predicted woodpecker distribution using the Maxent species distribution modeling algorithm with elevation, landcover, and hydrography geospatial variables, with and without lidar-derived structural variables. Lidar successfully quantified canopy variation and differentiated between the structural characteristics of these two similar coniferous evergreen forest types (e.g. significant differences in maximum height, canopy cover, and size classes). Loblolly stands were found to have the tallest trees on average with a higher canopy cover. Both semivariograms and lacunarity analyses clearly differentiated between evergreen forest structural types (e.g. semivariogram range was 18.7 m for longleaf, 32.3 m for loblolly). By examining the immediate area around cavity nesting sites we found taller trees than those found across broader foraging sites. The species distribution model accurately predicted woodpecker distribution (tested with woodpecker presence, AUC > .85). The addition of lidar-derived variables improved the model’s predictive power by 8% compared to the model based only on elevation, landcover, and hydrography environmental variables. We show that relatively low density lidar data are valuable for wildlife studies by characterizing and separating similar canopy types, describing different use zones (foraging vs. nesting), and for use in species distribution models.

► We use lidar to characterize canopy structural variation in coniferous forest types.
► Lidar quantifies canopy variation and differentiates between these forest types.
► Structural characteristics in forests affect wildlife habitat preferences.
► We predict red-cockaded woodpecker distribution with and without lidar variables.
► Adding lidar to the distribution model significantly improves predictive power.