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    Forest understory communities are important components in forest ecosystems providing wildlife habitat and influencing nutrient cycling, fuel loadings, fire behavior and tree species composition over time. One of the most widely utilized understory component metrics is understory vegetation cover, often used as a measure of vegetation abundance. To date, understory vegetation cover estimation and prediction has proven to be inherently difficult using traditional explanatory variables such as: leaf area index, basal area, slope, and aspect. We introduce airborne lidar-derived metrics into the modeling framework for understory vegetation cover. A new airborne lidar metric, understory lidar cover density, created by filtering understory lidar points using intensity values increased traditional explanatory power from non-lidar understory vegetation cover estimation models (non-lidar R2-values: 0.2-0.45 vs. lidar R2-values: 0.7-0.8). Beta regression, a relatively new modeling technique for this type of data, was compared with a traditional weighted linear regression model using a leave-one-out cross-validation procedure. Both models provided similar understory vegetation cover accuracies (±22%) and biases (~0%) using 40.5 m2 circular plots (n=154). The method presented in this paper provides the ability to accurately obtain census understory vegetation cover information at fine spatial resolutions over a broad range of stand conditions for the interior ponderosa pine forest type. Additional model enhancement and the extension of the method into other forest types warrant further investigation. []

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    Wing, Brian M.; Ritchie, Martin W.; Boston, Kevin; Cohen, Warren B.; Gitelman, Alix; Olsen, Michael J. 2012. Prediction of understory vegetation cover with airborne lidar in an interior ponderosa pine forest. Remote Sensing of Environment. 124: 730-741.


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    Understory vegetation cover, Lidar, Intensity, Beta regression, Weighted regression

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