Bianca N. I. Eskelson, Tara M. Barrett, Hailemariam Temesgen. (2009). Imputing mean annual change to estimate current forest attributes. Silva Fennica vol. 43 no. 4 article id 185. https://doi.org/10.14214/sf.185

Keywords: forest inventory and analysis; forest monitoring; national forest inventories; nearest neighbor imputation; Pacific Northwest; paneled inventory data

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When a temporal trend in forest conditions is present, standard estimates from paneled forest inventories can be biased. Thus methods that use more recent remote sensing data to improve estimates are desired. Paneled inventory data from national forests in Oregon and Washington, U.S.A., were used to explore three nearest neighbor imputation methods to estimate mean annual change of four forest attributes (basal area/ha, stems/ha, volume/ha, biomass/ha). The randomForest imputation method outperformed the other imputation approaches in terms of root mean square error. The imputed mean annual change was used to project all panels to a common point in time by multiplying the mean annual change with the length of the growth period between measurements and adding the change estimate to the previously observed measurements of the four forest attributes. The resulting estimates of the mean of the forest attributes at the current point in time outperformed the estimates obtained from the national standard estimator.

Eskelson, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: bianca.eskelson@oregonstate.edu
Barrett, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: tmb@nn.us
Temesgen, Oregon State University, Department of Forest Engineering, Resources and Management, 204 Peavy Hall, Corvallis, Oregon 97331, USA E-mail: ht@nn.us

article id 241, category Research article

Hailemariam Temesgen, Tara M. Barrett, Greg Latta. (2008). Estimating cavity tree abundance using Nearest Neighbor Imputation methods for western Oregon and Washington forests. Silva Fennica vol. 42 no. 3 article id 241. https://doi.org/10.14214/sf.241

Keywords: stand structure; nearest neighbor imputation; snag size; snag frequency; forest landscape modeling

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Cavity trees contribute to diverse forest structure and wildlife habitat. For a given stand, the size and density of cavity trees indicate its diversity, complexity, and suitability for wildlife habitat. Size and density of cavity trees vary with stand age, density, and structure. Using Forest Inventory and Analysis (FIA) data collected in western Oregon and western Washington, we applied correlation analysis and graphical approaches to examine relationships between cavity tree abundance and stand characteristics. Cavity tree abundance was negatively correlated with site index and percent composition of conifers, but positively correlated with stand density, quadratic mean diameter, and percent composition of hardwoods. Using FIA data, we examined the performance of Most Similar Neighbor (MSN), k nearest neighbor, and weighted MSN imputation with three variable transformations (regular, square root, and logarithmic) and Classification and Regression Tree with MSN imputation to estimate cavity tree abundance from stand attributes. There was a large reduction in mean root mean square error from 20% to 50% reference sets, but very little reduction in using the 80% reference sets, corresponding to the decreases in mean distances. The MSN imputation using square root transformation provided better estimates of cavity tree abundance for western Oregon and western Washington forests. We found that cavity trees were only 0.25 percent of live trees and 13.8 percent of dead trees in the forests of western Oregon and western Washington, thus rarer and more difficult to predict than many other forest attributes. Potential applications of MSN imputation include selecting and modeling wildlife habitat to support forest planning efforts, regional inventories, and evaluation of different management scenarios.

Temesgen, Department of Forest Resources, Oregon State University, Corvallis, OR, USA E-mail: hailemariam.temesgen@oregonstate.edu
Barrett, Pacific Northwest Research Station, Anchorage, AK, USA E-mail: tmb@nn.us
Latta, Department of Forest Resources, Oregon State University, Corvallis, OR, USA E-mail: gl@nn.us

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