Modeling Forest Canopy Structure and Density by Combining Point Quadrat Sampling and Survival Analysis
Abstract: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.
Document Type: Research Article
Publication date: 2013-12-06
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- Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
Forest Science is published bimonthly in February, April, June, August, October, and December.
2015 Impact Factor: 1.702
Ranking: 16 of 66 in forestry
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