Geostatistical modeling of riparian forest microclimate and its implications for sampling
Abstract:Predictive models of microclimate under various site conditions in forested headwater stream – riparian areas are poorly developed, and sampling designs for characterizing underlying riparian microclimate gradients are sparse. We used riparian microclimate data collected at eight headwater streams in the Oregon Coast Range to compare ordinary kriging (OK), universal kriging (UK), and kriging with external drift (KED) for point prediction of mean maximum air temperature (T air). Several topographic and forest structure characteristics were considered as site-specific parameters. Height above stream and distance to stream were the most important covariates in the KED models, which outperformed OK and UK in terms of root mean square error. Sample patterns were optimized based on the kriging variance and the weighted means of shortest distance criterion using the simulated annealing algorithm. The optimized sample patterns outperformed systematic sample patterns in terms of mean kriging variance mainly for small sample sizes. These findings suggest methods for increasing efficiency of microclimate monitoring in riparian areas.
Document Type: Research Article
Affiliations: 1: Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, OR 97331-5706, USA. 2: Biology and Culture of Forest Plants Team, U.S. Department of Agriculture Forest Service, Pacific Northwest Research Station, Corvallis, Oregon, USA. 3: U.S. Geological Survey, Forest and Rangeland Ecosystem Science Center, Forestry Sciences Lab, 3200 SW Jefferson Way, Corvallis, OR 97331, USA.
Publication date: 2011-05-19
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