11 research outputs found
Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition
Light detection and ranging (LiDAR) technology has the potential to radically alter theway researchers and managers collect data onwildlife–habitat relationships. To date, the technology has fostered several novel approaches to characterizing avian habitat, but has been limited by the lack of detailed LiDAR-habitat attributes relevant to species across a continuum of spatial grain sizes and habitat requirements. We demonstrate a novel three-step approach for using LiDAR data to evaluate habitat based on multiple habitat attributes and accounting for their influence at multiple grain sizes using federally endangered red-cockaded woodpecker (RCW; Picoides borealis) foraging habitat data fromthe Savannah River Site (SRS) in South Carolina, USA. First,we used high density LiDAR data (10 returns/m2) to predict detailed forest attributes at 20-mresolution across the entire SRS using a complementary application of nonlinear seemingly unrelated regression andmultiple linear regressionmodels. Next,we expanded on previous applications of LiDAR by constructing 95% joint prediction confidence intervals to quantify prediction error at various spatial aggregations and habitat thresholds to determine a biologically and statistically meaningful grain size. Finally,we used aggregations of 20-m cells and associated confidence interval boundaries to demonstrate a newapproach to produce maps of RCWforaging habitat conditions based on the guidelines described in the species\u27 recovery plan. Predictive power (R2) of regression models developed to populate raster layers ranged from 0.34 to 0.81, and prediction error decreased as aggregate size increased, but minimal reductions in prediction error were observed beyond 0.64-ha (4 × 4 20-m cells) aggregates. Mapping habitat quality while accounting for prediction error provided a robust method to determine the potential range of habitat conditions and specific attributes that were limiting in terms of the amount of suitable habitat. The sequential steps of our analytical approach provide a useful framework to extract detailed and reliable habitat attributes for a forest-dwelling habitat specialist, broadening the potential to apply LiDAR in conservation and management of wildlife populations.
A zipped folder of Google maps is attached below as a related file
Quantitative analysis of woodpecker habitat using high-resolution airborne LiDAR estimates of forest structure and composition
Light detection and ranging (LiDAR) technology has the potential to radically alter theway researchers and managers collect data onwildlife–habitat relationships. To date, the technology has fostered several novel approaches to characterizing avian habitat, but has been limited by the lack of detailed LiDAR-habitat attributes relevant to species across a continuum of spatial grain sizes and habitat requirements. We demonstrate a novel three-step approach for using LiDAR data to evaluate habitat based on multiple habitat attributes and accounting for their influence at multiple grain sizes using federally endangered red-cockaded woodpecker (RCW; Picoides borealis) foraging habitat data fromthe Savannah River Site (SRS) in South Carolina, USA. First,we used high density LiDAR data (10 returns/m2) to predict detailed forest attributes at 20-mresolution across the entire SRS using a complementary application of nonlinear seemingly unrelated regression andmultiple linear regressionmodels. Next,we expanded on previous applications of LiDAR by constructing 95% joint prediction confidence intervals to quantify prediction error at various spatial aggregations and habitat thresholds to determine a biologically and statistically meaningful grain size. Finally,we used aggregations of 20-m cells and associated confidence interval boundaries to demonstrate a newapproach to produce maps of RCWforaging habitat conditions based on the guidelines described in the species\u27 recovery plan. Predictive power (R2) of regression models developed to populate raster layers ranged from 0.34 to 0.81, and prediction error decreased as aggregate size increased, but minimal reductions in prediction error were observed beyond 0.64-ha (4 × 4 20-m cells) aggregates. Mapping habitat quality while accounting for prediction error provided a robust method to determine the potential range of habitat conditions and specific attributes that were limiting in terms of the amount of suitable habitat. The sequential steps of our analytical approach provide a useful framework to extract detailed and reliable habitat attributes for a forest-dwelling habitat specialist, broadening the potential to apply LiDAR in conservation and management of wildlife populations.
A zipped folder of Google maps is attached below as a related file
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Model-Assisted Forest Yield Estimation with Light Detection and Ranging
Previous studies have demonstrated that light detection and ranging (LiDAR)-derived variables can be used to model forest yield variables, such as biomass, volume, and number of stems. However, the next step is underrepresented in the literature: estimation of forest yield with appropriate confidence intervals. It is of great importance that the procedures required for conducting forest inventory with LiDAR and the estimation precision of such procedures are sufficiently documented to enable their evaluation and implementation by land managers. In this study, we demonstrated the regression estimator, a model-assisted estimator (approximately design-unbiased), using LiDAR-derived variables for estimation of total forest yield. The LiDAR-derived variables are statistics associated with vegetation height and cover. The estimation procedure requires complete coverage of the forest with LiDAR and a random sample of precisely georeferenced field measurement plots. Regression estimation relies on sample-based ordinary least squares (OLS) regression models relating forest yield and LiDAR-derived variables. Estimation was performed using the OLS models and LiDAR-derived variables for the entire population. Regression estimates of basal area, volume, stand density, and biomass were much more precise than simple random sampling estimates (design effects were 0.25, 0.24, 0.44, and 0.27, respectively).Keywords: Model-assisted, Regression estimation, Design-based LiDAR, Forest Inventor