LiDAR Remote Sensing of Forest Structure and GPS Telemetry Data Provide Insights on Winter Habitat Selection of European Roe Deer

Abstract

Abstract:The combination of GPS-Telemetry and resource selection functions is widely used to analyze animal habitat selection. Rapid large-scale assessment of vegetation structure allows bridging the requirements of habitat selection studies on grain size and extent, particularly in forest habitats. For roe deer, the cold period in winter forces individuals to optimize their trade off in searching for food and shelter. We analyzed the winter habitat selection of roe deer (Capreolus capreolus) in a montane forest landscape combining estimates of vegetation cover in three different height strata, derived from high resolution airborne Laser-scanning (LiDAR, Light detection and ranging), and activity data from GPS telemetry. Specifically, we tested the influence of temperature, snow height, and wind speed on site selection, differentiating between active and resting animals using mixed-effects conditional logistic regression models in a case-control design. Site selection was best explained by temperature deviations from hourly means, snow height, and activity status of the animals. Roe deer tended to use forests of high canopy cover more frequently with decreasing temperature, and when snow height exceeded 0.6 m. Active animals preferred lower canopy cover, but higher understory cover. Our approach demonstrates the OPEN ACCESSForests 2014, 5 1375 potential of LiDAR measures for studying fine scale habitat selection in complex three-dimensional habitats, such as forests.

Automated Detection of Forest Gaps in Spruce Dominated Stands Using Canopy Height Models Derived from Stereo Aerial Imagery

“…Derived metrics describing the geometry of gap have been used for example by Braunisch and Suchant [71] to define an optimal proportion of gap area for the Capercaillie-friendly habitats, or by Getzin et al [28] who used a perimeter-to-area ratio and gap shape complexity index to assess floristic diversity of the forest understory. In addition, the canopy cover-as calculated as an interim step in our method-is one of the most frequently used predictors in modeling habitat suitability for forest dwelling species, e.g., Capercaille [71][72][73], bats [74,75] or the European Roe Deer (Capreolus capreolus) [76].…”

Airborne Lidar for Woodland Habitat Quality Monitoring: Exploring the Significance of Lidar Data Characteristics when Modelling Organism-Habitat Relationships

“…For example, two recent studies have investigated forest dwelling bats and the impacts of forest structure on their foraging activities [22,23], demonstrating species-specific relationships in habitat use. In addition, the selection of specific forest structure has been demonstrated for providing sheltering habitats for roe deer (Capreolus capreolus) both from predators [24] and during low winter temperatures [25], and for moose (Alces alces) during high summer temperatures [26].…”

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

“…Latifi et al [107] divided the canopy into height layers according to phytosociological standards and found a strong relationship with different LiDAR metrics using regression models. Similar approaches were used by Vogler et al [126] and Ewald et al, [127] to represent the understory relevant for birds and deer and to detect forest regeneration [128]. A more recent study applied a 3D segmentation algorithm to estimate regeneration cover and achieved an accuracy of 70% [129].…”

“…Vertical forest structure [125][126][127][128][129]144] LiDAR data is widely used to represent vertical forest canopy complexity and forest regeneration. These variables have great potential to predict species diversity.…”