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Articles containing the keyword 'imputation'

Category : Research article

article id 1414, category Research article
Rami Saad, Jörgen Wallerman, Johan Holmgren, Tomas Lämås. (2016). Local pivotal method sampling design combined with micro stands utilizing airborne laser scanning data in a long term forest management planning setting. Silva Fennica vol. 50 no. 2 article id 1414. https://doi.org/10.14214/sf.1414
Keywords: LIDAR; forest management planning; local pivotal method (LPM); segmentation; most similar neighbor (MSN) imputation; suboptimal loss; Heureka; decision support system
Highlights: Most similar neighbor imputation was used to estimate forest variables using airborne laser scanning data as auxiliary data; For selecting field reference plots the local pivotal method (LPM) was compared to systematic sampling design; The LPM sampling design combined with a micro stand approach showed potential for improvement and has the potential to be a competitive method when considering cost efficiency.
Abstract | Full text in HTML | Full text in PDF | Author Info

A new sampling design, the local pivotal method (LPM), was combined with the micro stand approach and compared with the traditional systematic sampling design for estimation of forest stand variables. The LPM uses the distance between units in an auxiliary space – in this case airborne laser scanning (ALS) data – to obtain a well-spread sample. Two sets of reference plots were acquired by the two sampling designs and used for imputing data to evaluation plots. The first set of reference plots, acquired by LPM, made up four imputation alternatives (varying number of reference plots) and the second set of reference plots, acquired by systematic sampling design, made up two alternatives (varying plot radius). The forest variables in these alternatives were estimated using the nonparametric method of most similar neighbor imputation, with the ALS data used as auxiliary data. The relative root mean square error (RelRMSE), stem diameter distribution error index and suboptimal loss were calculated for each alternative, but the results showed that neither sampling design, i.e. LPM vs. systematic, offered clear advantages over the other. It is likely that the obtained results were a consequence of the small evaluation dataset used in the study (n = 30). Nevertheless, the LPM sampling design combined with the micro stand approach showed potential for improvement and might be a competitive method when considering the cost efficiency.

  • Saad, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: rami.saad@slu.se (email)
  • Wallerman, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: jorgen.wallerman@slu.se
  • Holmgren, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: johan.holmgren@slu.se
  • Lämås, Swedish University of Agricultural Sciences (SLU), Department of Forest Resource Management, Skogsmarksgränd, SE-901 83 Umeå, Sweden E-mail: tomas.lamas@slu.se
article id 185, category Research article
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
Abstract | View details | Full text in PDF | Author Info
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 (email)
  • 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
Abstract | View details | Full text in PDF | Author Info
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 (email)
  • 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
article id 496, category Research article
Hampus Holmström, Hans Kallur, Göran Ståhl. (2003). Cost-plus-loss analyses of forest inventory strategies based on kNN-assigned reference sample plot data. Silva Fennica vol. 37 no. 3 article id 496. https://doi.org/10.14214/sf.496
Keywords: uncertainty; data acquisition; imputation; forestry planning
Abstract | View details | Full text in PDF | Author Info
The usefulness of kNN (k Nearest Neighbour)-assigned reference sample plot data as a basis for forest management planning was studied. Cost-plus-loss analysis was applied, whereby the inventory cost for a specific method is added to the expected loss due to non-optimal forestry activities caused by erroneous descriptions of the forest state. Four different strategies for data acquisition were evaluated: 1) kNN imputation of sample plots based on traditional stand record information, 2) imputation based on plot-wise aerial photograph interpretation in combination with stand record information, 3) sample plot inventory in the field with 5 plots per stand, and 4) sample plot inventory with 10 plots per stand. Expected losses were derived as mean values of differences between the maximum net present value and the corresponding value obtained when the treatment schedule believed to be optimal (based on data simulated according to method 1–4) was selected. The optimal choice of method was found to depend on factors such as stand maturity, stand area, and time to next treatment (thinning or clearcutting). In general, the field sample plot methods were competitive in large mature stands, especially when the time to the next (optimal) treatment was short. By in each stand (within an estate) employing the method with the lowest cost-plus-loss rather than choosing the method that performed best on average for the entire estate, the total cost for inventory at the estate level could be decreased by 15–50%. However, it was found difficult to identify what method should optimally be employed in a stand based on general stand descriptions.
  • Holmström, Regional Board of Forestry of Västra Götaland, P.O. Box 20008, SE-50420 Borås, Sweden E-mail: hampus.holmstrom@svsvg.svo.se (email)
  • Kallur, ÖKA Skogsplan, Kopparvägen 45 O, SE-90750 Umeå, Sweden E-mail: hk@nn.se
  • Ståhl, Swedish Univ. of Agricultural Sciences, Dept. of Forest Resource Management and Geomatics, SE-90183 Umeå, Sweden E-mail: gs@nn.se

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