LiDAR Forest Structure Metrics for Predicting Bat Foraging Habitat

LiDAR-derived forest structure variables are used to predict suitable foraging areas for the endangered Greater Mouse-eared Bat (Myotis myotis). The dataset supports a study that sampled bat activity near 18 maternity roosts in Switzerland and applied a generalized linear mixed model (GLMM). The research was published by ENVIDAT and the data was last updated in 2022.

Use Cases

• Predict bat activity levels using LiDAR-derived variables like canopy density and forest layering.
• Model the relationship between forest structural features, such as shrub layer presence and free flight space, and species habitat suitability.
• Create nationwide prediction maps of potential foraging habitats by applying the GLMM to LiDAR data across larger regions.

Strengths

• Data is validated through systematic field sampling of bat activity at 18 distinct maternity roost sites.
• Study methodology employs a generalized linear mixed model (GLMM) to statistically link LiDAR metrics to biological activity.

Limitations

• The specific sample size (number of data points or rows) is not provided in the description.
• Geographic scope is limited to forests in Switzerland, which may limit generalizability to other regions.

Provenance

Source

ENVIDAT via NASA Earthdata.

Collection Method

Airborne LiDAR data collection combined with systematic field sampling of bat activity, analyzed using a GLMM.

Freshness

2022-01-01

Geography

Forests near 18 maternity roosts in Switzerland.