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Modeling Forest Canopy Structure and Density by Combining Point Quadrat Sampling and Survival Analysis

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Point quadrat sampling has been used relatively infrequently for modeling canopy structure and density, primarily because of the large number of sample points needed to obtain accurate estimates. We address these limitations by showing how point quadrat data are a form of time-to-event data, analogous to what are commonly observed in biomedical studies. This equivalence allows for point quadrat data to be analyzed using existing survival analysis methods. We illustrate the usefulness of this relationship by analyzing data from a field study conducted in northeast Oregon. Within each of 60 forest plots, we obtained canopy-height measurements using a handheld laser rangefinder, and we used a survival-based regression model to estimate canopy profiles and leaf area indices via the Weibull hazard function. The resulting survival-based estimates of canopy density and structure appeared robust to sample size limitations, whereas the relatively small number of samples per plot led to an apparent underestimation of canopy density by the traditional point quadrat estimator. Overall, the incorporation of survival analysis methods and point quadrat sampling greatly increases the usefulness of this sampling method, resulting in an efficient tool for quickly assessing the structure of forest canopies.

Keywords: forest sampling; leaf area index; regression; variance estimation; vertical structure

Document Type: Research Article


Publication date: December 29, 2013

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