Evaluating the Use of Lidar to Discern Snag Characteristics Important for Wildlife

Jessica M Stitt,Lee A Vierling,Andrew T Hudak,Kerri T Vierling,Carlos A Silva

doi:10.3390/rs14030720

Abstract

Standing dead trees (known as snags) are historically difficult to map and model using airborne laser scanning (ALS), or lidar. Specific snag characteristics are important for wildlife; for instance, a larger snag with a broken top can serve as a nesting platform for raptors. The objective of this study was to evaluate whether characteristics such as top intactness could be inferred from discrete-return ALS data. We collected structural information for 198 snags in closed-canopy conifer forest plots in Idaho. We selected 13 lidar metrics within 5 m diameter point clouds to serve as predictor variables in random forest (RF) models to classify snags into four groups by size (small (<40 cm diameter) or large (≥40 cm diameter)) and intactness (intact or broken top) across multiple iterations. We conducted these models first with all snags combined, and then ran the same models with only small or large snags. Overall accuracies were highest in RF models with large snags only (77%), but kappa statistics for all models were low (0.29–0.49). ALS data alone were not sufficient to identify top intactness for large snags; future studies combining ALS data with other remotely sensed data to improve classification of snag characteristics important for wildlife is encouraged.

Highlights

As keystone structures [1], standing dead trees serve essential roles for wildlife species, due in part to how structurally distinct snags are compared to live trees.Snag and live tree characteristics differ across multiple dimensions, including the degree of open space occupied, the unique physical structures provided, and the composition and decay status of wood
Across the 20 iterations of random forest (RF) models classifying all snags into four classes by diameter and intactness, the top model (RFALL ) had an overall accuracy of 50%, with a kappa statistic of 0.29 (Table 4a)
Performance accuracies were highly variable by snag class, with large broken top snags having the highest producer’s accuracy, small intact snags having the highest user’s accuracy, and large intact snags having the lowest accuracy for both (Table 4a)

Summary

Introduction

Snag and live tree characteristics differ across multiple dimensions, including the degree of open space occupied, the unique physical structures provided, and the composition and decay status of wood. These combined characteristics contribute to a wide range of forest microhabitats relevant to wildlife, with many microhabitats associated primarily with snags [2,3]. Snags had higher variability in gap distributions across all canopy heights [6] This greater variability in openness around snags contributes to greater forest structural complexity and may be correlated with greater foliar height diversity and species richness [7]

Objectives

Results

Discussion

Conclusion