Comparison of airborne lidar, aerial photography, and field surveys to model the habitat suitability of a cryptic forest species – the hazel grouse

Abstract

Light detection and ranging (lidar) is a useful tool for measuring three-dimensional habitat structure; hence, its use in habitat suitability models has been explored, both as a single resource and in combination with other remote-sensing techniques. Here, we evaluated the suitability of airborne lidar data in comparison with aerial photographs and field surveys for modelling the distribution of an endangered and cryptic forest species, the hazel grouse (Bonasa bonasia). The study was conducted in the Bavarian Forest National Park of southeast Germany. Subsequently, a prediction map for conservation planning was generated for a large area, which encompassed the National Park. We examined the utility of lidar data for generating a hazel grouse distribution model by using machine learning (boosted regression trees), and then compared the results to variables derived from field surveys and aerial photographs, both separately and in combination. The cross-validated discrimination ability of the model was slightly higher when using lidar data (area under the receiver operator characteristic curve (AUC), 0.79) compared to models using aerial photographs (AUC, 0.75) or field survey data (AUC, 0.78). The predictive performance consistently increased when combining the predictors from different sources, with an AUC of 0.86 being produced in the model combining all three data sources. The three data sources complemented one another, with each data source probably having an advantage at deriving one of three key aspects of the hazel grouse habitat, namely, vertically well-structured forest stands, horizontally mixed successional vegetation stages, and certain deciduous trees as food resources such as mountain ash (Sorbus aucuparia). In addition, the diverse lidar metrics might be applied to simultaneously characterize vertically and horizontally well-structured forest stands. We conclude that public available airborne lidar data are a viable source for creating habitat suitability maps for large areas and may have increased utility for detecting forest characteristics and valuable wildlife habitats.