Accessible light detection and ranging: estimating large tree density for habitat identification

Abstract Large trees are important to a wide variety of wildlife, including many species of conservation concern, such as the California spotted owl (Strix occidentalis occidentalis). Light detection and ranging (LiDAR) has been successfully utilized to identify the density of large‐diameter trees, either by segmenting the LiDAR point cloud into individual trees, or by building regression models between variables extracted from the LiDAR point cloud and field data. Neither of these methods is easily accessible for most land managers due to the reliance on specialized software, and much available LiDAR data are being underutilized due to the steep learning curve required for advanced processing using these programs. This study derived a simple, yet effective method for estimating the density of large‐stemmed trees from the LiDAR canopy height model, a standard raster product derived from the LiDAR point cloud that is often delivered with the LiDAR and is easy to process by personnel trained in geographic information systems (GIS). Ground plots needed to be large (1 ha) to build a robust model, but the spatial accuracy of plot center was less crucial to model accuracy. We also showed that predicted large tree density is positively linked to California spotted owl nest sites.