Forest stands and LiDAR derived model estimates of marbled murrelet (Brachyramphus marmoratus) occupancy in the Coos Bay BLM District, Southwestern Oregon
February 28, 2023
We used murrelet occupancy data collected by the Bureau of Land Management Coos Bay District and canopy metrics calculated from discrete return airborne LiDAR data to fit a logistic regression model predicting the probability of occupancy. Our final model for stand-level occupancy included distance to coast and 5 LiDAR-derived variables describing canopy structure. This dataset is a shapefile of forest stands in the Coos Bay district representing the model results.
Citation Information
Publication Year | 2023 |
---|---|
Title | Forest stands and LiDAR derived model estimates of marbled murrelet (Brachyramphus marmoratus) occupancy in the Coos Bay BLM District, Southwestern Oregon |
DOI | 10.5066/P9ONYT0B |
Authors | Joan Hagar, Bianca N Eskelson, Patricia Haggerty, S. Kim Nelson, David G Vesely |
Product Type | Data Release |
Record Source | USGS Asset Identifier Service (AIS) |
USGS Organization | Forest and Rangeland Ecosystem Science Center (FRESC) Headquarters |
Rights | This work is marked with CC0 1.0 Universal |
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Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR-derived canopy data
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Modeling marbled murrelet (Brachyramphus marmoratus) habitat using LiDAR-derived canopy data
LiDAR (Light Detection And Ranging) is an emerging remote-sensing tool that can provide fine-scale data describing vertical complexity of vegetation relevant to species that are responsive to forest structure. We used LiDAR data to estimate occupancy probability for the federally threatened marbled murrelet (Brachyramphus marmoratus) in the Oregon Coast Range of the United States. Our goal was to
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