Hailemariam Temesgen 
,
Tara M. Barrett,
Greg Latta
Estimating cavity tree abundance using Nearest Neighbor Imputation methods for western Oregon and Washington forests
Temesgen H., Barrett T. M., Latta G. (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
Abstract
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.
Keywords
stand structure;
nearest neighbor imputation;
snag size;
snag frequency;
forest landscape modeling
-
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
Received 20 August 2007 Accepted 31 January 2008 Published 31 December 2008
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