The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for mapping the presence/absence of understory shrub species and different snag diameter classes important for birds (i.e. ≥ 15 cm, ≥ 25 cm and ≥ 30 cm) in a 30,000 ha mixed-conifer forest in Northern Idaho (USA). We used forest inventory plots, LiDAR-derived metrics, and the Random Forest algorithm to achieve classification accuracies of 83% for the understory shrubs and 86% to 88% for the different snag diameter classes. Second, we evaluated the use of LiDAR data for mapping wildlife habitat suitability using four avian species (one flycatcher and three woodpeckers) as case studies. For this, we integrated LiDAR-derived products of forest structure with available models of habitat suitability to derive a variety of species-habitat associations (and therefore habitat suitability patterns) across the study area. We found that the value of LiDAR resided in the ability to quantify 1) ecological variables that are known to influence the distribution of understory vegetation and snags, such as canopy cover, topography, and forest succession, and 2) direct structural metrics that indicate or suggest the presence of shrubs and snags, such as the percent of vegetation returns in the lower strata of the canopy (for the shrubs) and the vertical heterogeneity of the forest canopy (for the snags). When applied to wildlife habitat assessment, these new LiDAR-based maps refined habitat predictions in ways not previously attainable using other remote sensing technologies. This study highlights new value of LiDAR in characterizing key forest structure components important for wildlife, and warrants further applications to other forested environments and wildlife species.
Bibliographical noteFunding Information:
This work was made possible through funding by the USGS Gap Analysis Program and the USDA Forest Service International Institute of Tropical Forestry (IITF). The LiDAR acquisition, processing, and analysis were supported by the Agenda 2020 program, a research partnership between the USFS Rocky Mountain Research Station, Bennett Lumber Products, Inc., and Potlatch Forest Holdings, Inc., and the University of Idaho. We thank G. Roloff for facilitating the HSI model for the dusky flycatcher, R. Nelson for commenting on an earlier version of this manuscript, N. Crookston and R. Díaz-Uriarte for fruitful discussions about Random Forest and varSelRF, and three anonymous reviewers. Work at IITF is done in collaboration with the University of Puerto Rico.
- Forest structure
- Keystone structures
- LiDAR metrics
- Species distribution modeling
- Wildlife habitat